
from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
						import numpy as np
						import tensorflow as tf

						from transformers import (
						    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
						    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_MASKED_LM_MAPPING,
						    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
						    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
						    TF_MODEL_FOR_PRETRAINING_MAPPING,
						    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
						    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    TFSharedEmbeddings,
						)

						if _tf_gpu_memory_limit is not None:
												lowercase__							:							Optional[int]													=     tf.config.list_physical_devices("GPU")
												for gpu in gpus:
																		# Restrict TensorFlow to only allocate x GB of memory on the GPUs
																		try:
																								tf.config.set_logical_device_configuration(
																								    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
																								)
																								lowercase__							:							Any													=     tf.config.list_logical_devices("GPU")
																								print("Logical GPUs", logical_gpus)
																		except RuntimeError as e:
																								# Virtual devices must be set before GPUs have been initialized
																								print(e)






@require_tf
class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							_snake_case       						=					None
							_snake_case       						=					()
							_snake_case       						=					()
							_snake_case       						=					True
							_snake_case       						=					True
							_snake_case       						=					True
							_snake_case       						=					False



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=False     )->				dict:





													'''simple docstring'''






													__UpperCamelCase       					=     copy.deepcopy(UpperCamelCase__     )

													if model_class in get_values(UpperCamelCase__     ):
																			__UpperCamelCase       					=     {
																			    k: tf.tile(tf.expand_dims(UpperCamelCase__       ,		1     )       ,		(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)     )
																			    if isinstance(UpperCamelCase__       ,		tf.Tensor     ) and v.ndim > 0
																			    else v
																			    for k, v in inputs_dict.items()
																			}

													if return_labels:
																			if model_class in get_values(UpperCamelCase__     ):
																									__UpperCamelCase       					=     tf.ones(self.model_tester.batch_size       ,		dtype=tf.intaa     )
																			elif model_class in get_values(UpperCamelCase__     ):
																									__UpperCamelCase       					=     tf.zeros(self.model_tester.batch_size       ,		dtype=tf.intaa     )
																									__UpperCamelCase       					=     tf.zeros(self.model_tester.batch_size       ,		dtype=tf.intaa     )
																			elif model_class in [
																			    *get_values(UpperCamelCase__     ),
																			    *get_values(UpperCamelCase__     ),
																			]:
																									__UpperCamelCase       					=     tf.zeros(self.model_tester.batch_size       ,		dtype=tf.intaa     )
																			elif model_class in get_values(UpperCamelCase__     ):
																									__UpperCamelCase       					=     tf.zeros(self.model_tester.batch_size       ,		dtype=tf.intaa     )
																			elif model_class in [
																			    *get_values(UpperCamelCase__     ),
																			    *get_values(UpperCamelCase__     ),
																			    *get_values(UpperCamelCase__     ),
																			    *get_values(UpperCamelCase__     ),
																			    *get_values(UpperCamelCase__     ),
																			]:
																									__UpperCamelCase       					=     tf.zeros(
																									    (self.model_tester.batch_size, self.model_tester.seq_length)       ,		dtype=tf.intaa     )
													return inputs_dict



							@slow
							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase					,							__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																			__UpperCamelCase       					=     self._prepare_for_class(UpperCamelCase__       ,		UpperCamelCase__     )
																			__UpperCamelCase       					=     model_class(UpperCamelCase__     )

																			@tf.function
																			def run_in_graph_mode():
																									return model(UpperCamelCase__     )

																			__UpperCamelCase       					=     run_in_graph_mode()
																			self.assertIsNotNone(UpperCamelCase__     )



							@slow
							def       A__						(					self     )->				Any:





													'''simple docstring'''






													__UpperCamelCase					,							__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																			__UpperCamelCase       					=     self._prepare_for_class(UpperCamelCase__       ,		UpperCamelCase__     )
																			__UpperCamelCase       					=     model_class(UpperCamelCase__     )

																			@tf.function(experimental_compile=UpperCamelCase__     )
																			def run_in_graph_mode():
																									return model(UpperCamelCase__     )

																			__UpperCamelCase       					=     run_in_graph_mode()
																			self.assertIsNotNone(UpperCamelCase__     )



							@slow
							def       A__						(					self     )->				str:





													'''simple docstring'''






													__UpperCamelCase					,							__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																			__UpperCamelCase       					=     model_class(UpperCamelCase__     )
																			if getattr(UpperCamelCase__       ,		'''hf_compute_loss'''       ,		UpperCamelCase__     ):
																									# Test that model correctly compute the loss with kwargs
																									__UpperCamelCase       					=     self._prepare_for_class(inputs_dict.copy()       ,		UpperCamelCase__       ,		return_labels=UpperCamelCase__     )
																									# Is there a better way to remove these decoder inputs?
																									__UpperCamelCase       					=     {
																									    key: val
																									    for key, val in prepared_for_class.items()
																									    if key not in ('''head_mask''', '''decoder_head_mask''', '''cross_attn_head_mask''', '''decoder_input_ids''')
																									}

																									__UpperCamelCase       					=     {
																									    '''labels''',
																									    '''label''',
																									    '''label_ids''',
																									    '''start_positions''',
																									    '''start_position''',
																									    '''end_positions''',
																									    '''end_position''',
																									    '''next_sentence_label''',
																									}
																									__UpperCamelCase       					=     possible_label_cols.intersection(set(UpperCamelCase__     )     )
																									self.assertGreater(len(UpperCamelCase__     )       ,		0       ,		msg='''No matching label names found!'''     )
																									__UpperCamelCase       					=     {key: val for key, val in prepared_for_class.items() if key in label_names}
																									__UpperCamelCase       					=     {key: val for key, val in prepared_for_class.items() if key not in label_names}
																									self.assertGreater(len(UpperCamelCase__     )       ,		0     )

																									# Make sure it works with XLA!
																									model.compile(optimizer=tf.keras.optimizers.SGD(0.0     )       ,		jit_compile=UpperCamelCase__     )
																									# Make sure the model fits without crashing regardless of where we pass the labels
																									__UpperCamelCase       					=     model.fit(
																									    UpperCamelCase__       ,		validation_data=UpperCamelCase__       ,		steps_per_epoch=1       ,		validation_steps=1       ,		shuffle=UpperCamelCase__       ,		verbose=0       ,		)
																									__UpperCamelCase       					=     history.history['''loss'''][0]
																									self.assertTrue(not isnan(UpperCamelCase__     )     )
																									__UpperCamelCase       					=     history.history['''val_loss'''][0]
																									self.assertTrue(not isnan(UpperCamelCase__     )     )

																									# Now test it with separate labels, to make sure that path works in XLA too.
																									__UpperCamelCase       					=     model_class(UpperCamelCase__     )
																									model.compile(optimizer=tf.keras.optimizers.SGD(0.0     )       ,		jit_compile=UpperCamelCase__     )
																									__UpperCamelCase       					=     model.fit(
																									    UpperCamelCase__       ,		UpperCamelCase__       ,		validation_data=(inputs_minus_labels, labels)       ,		steps_per_epoch=1       ,		validation_steps=1       ,		shuffle=UpperCamelCase__       ,		verbose=0       ,		)

																									__UpperCamelCase       					=     history.history['''loss'''][0]
																									self.assertTrue(not isnan(UpperCamelCase__     )     )
																									__UpperCamelCase       					=     history.history['''val_loss'''][0]
																									self.assertTrue(not isnan(UpperCamelCase__     )     )



							@slow
							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase					,							__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()
													__UpperCamelCase       					=     True
													__UpperCamelCase       					=     True

													if hasattr(UpperCamelCase__       ,		'''use_cache'''     ):
																			__UpperCamelCase       					=     True

													__UpperCamelCase       					=     getattr(self.model_tester       ,		'''encoder_seq_length'''       ,		self.model_tester.seq_length     )
													__UpperCamelCase       					=     getattr(self.model_tester       ,		'''key_length'''       ,		UpperCamelCase__     )

													for model_class in self.all_model_classes[:2]:
																			__UpperCamelCase       					=     self._prepare_for_class(UpperCamelCase__       ,		UpperCamelCase__     )
																			__UpperCamelCase       					=     model_class(UpperCamelCase__     )
																			model.build()
																			__UpperCamelCase       					=     len(model(UpperCamelCase__     )     )

																			for key in list(class_inputs_dict.keys()     ):
																									# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
																									if key not in model.input_signature:
																															del class_inputs_dict[key]
																									# Check it's a tensor, in case the inputs dict has some bools in it too
																									elif isinstance(class_inputs_dict[key]       ,		tf.Tensor     ) and class_inputs_dict[key].dtype.is_integer:
																															__UpperCamelCase       					=     tf.cast(class_inputs_dict[key]       ,		tf.intaa     )

																			if set(class_inputs_dict.keys()     ) != set(model.input_signature.keys()     ):
																									continue  # Some models have inputs that the preparation functions don't create, we skip those

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									model.save_pretrained(UpperCamelCase__       ,		saved_model=UpperCamelCase__     )
																									__UpperCamelCase       					=     os.path.join(UpperCamelCase__       ,		'''saved_model'''       ,		'''1'''     )
																									__UpperCamelCase       					=     tf.keras.models.load_model(UpperCamelCase__     )
																									__UpperCamelCase       					=     model(UpperCamelCase__     )

																									if self.is_encoder_decoder:
																															__UpperCamelCase       					=     outputs['''encoder_hidden_states''']
																															__UpperCamelCase       					=     outputs['''encoder_attentions''']
																									else:
																															__UpperCamelCase       					=     outputs['''hidden_states''']
																															__UpperCamelCase       					=     outputs['''attentions''']

																									self.assertEqual(len(UpperCamelCase__     )       ,		UpperCamelCase__     )

																									__UpperCamelCase       					=     getattr(
																									    self.model_tester       ,		'''expected_num_hidden_layers'''       ,		self.model_tester.num_hidden_layers + 1     )

																									self.assertEqual(len(UpperCamelCase__     )       ,		UpperCamelCase__     )
																									self.assertListEqual(
																									    list(output_hidden_states[0].shape[-2:]     )       ,		[self.model_tester.seq_length, self.model_tester.hidden_size]       ,		)

																									self.assertEqual(len(UpperCamelCase__     )       ,		self.model_tester.num_hidden_layers     )
																									self.assertListEqual(
																									    list(output_attentions[0].shape[-3:]     )       ,		[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]       ,		)



							@slow
							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													tf.keras.mixed_precision.set_global_policy('''mixed_float16'''     )

													# try/finally block to ensure subsequent tests run in float32
													try:
																			__UpperCamelCase					,							__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()
																			for model_class in self.all_model_classes[:2]:
																									__UpperCamelCase       					=     self._prepare_for_class(UpperCamelCase__       ,		UpperCamelCase__     )
																									__UpperCamelCase       					=     model_class(UpperCamelCase__     )
																									__UpperCamelCase       					=     model(UpperCamelCase__     )

																									self.assertIsNotNone(UpperCamelCase__     )
													finally:
																			tf.keras.mixed_precision.set_global_policy('''float32'''     )



							@slow
							def       A__						(					self     )->				Any:





													'''simple docstring'''






													__UpperCamelCase					,							__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()
													# head_mask and decoder_head_mask has different shapes than other input args
													if "head_mask" in inputs_dict:
																			del inputs_dict["head_mask"]
													if "decoder_head_mask" in inputs_dict:
																			del inputs_dict["decoder_head_mask"]
													if "cross_attn_head_mask" in inputs_dict:
																			del inputs_dict["cross_attn_head_mask"]
													__UpperCamelCase       					=     {
													    module_member
													    for model_class in self.all_model_classes
													    for module in (import_module(model_class.__module__     ),)
													    for module_member_name in dir(UpperCamelCase__     )
													    if module_member_name.endswith('''MainLayer'''     )
													    for module_member in (getattr(UpperCamelCase__       ,		UpperCamelCase__     ),)
													    if isinstance(UpperCamelCase__       ,		UpperCamelCase__     )
													    and tf.keras.layers.Layer in module_member.__bases__
													    and getattr(UpperCamelCase__       ,		'''_keras_serializable'''       ,		UpperCamelCase__     )
													}

													for main_layer_class in tf_main_layer_classes:
																			# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
																			if "T5" in main_layer_class.__name__:
																									# Take the same values than in TFT5ModelTester for this shared layer
																									__UpperCamelCase       					=     TFSharedEmbeddings(self.model_tester.vocab_size       ,		self.model_tester.hidden_size       ,		name='''shared'''     )
																									__UpperCamelCase       					=     False
																									__UpperCamelCase       					=     main_layer_class(UpperCamelCase__       ,		embed_tokens=UpperCamelCase__     )
																			else:
																									__UpperCamelCase       					=     main_layer_class(UpperCamelCase__     )

																			__UpperCamelCase       					=     {
																			    name: tf.keras.Input(tensor.shape[1:]       ,		dtype=tensor.dtype     ) for name, tensor in inputs_dict.items()
																			}

																			if hasattr(self.model_tester       ,		'''num_labels'''     ):
																									__UpperCamelCase       					=     self.model_tester.num_labels
																			else:
																									__UpperCamelCase       					=     2

																			__UpperCamelCase       					=     tf.data.Dataset.from_tensor_slices(
																			    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)     ))     ).batch(1     )

																			__UpperCamelCase       					=     main_layer(UpperCamelCase__     )[0]
																			__UpperCamelCase       					=     tf.keras.layers.Dense(UpperCamelCase__       ,		activation='''softmax'''       ,		name='''outputs'''     )(UpperCamelCase__     )
																			__UpperCamelCase       					=     tf.keras.models.Model(inputs=UpperCamelCase__       ,		outputs=[outputs]     )

																			model.compile(loss='''binary_crossentropy'''       ,		optimizer='''adam'''       ,		metrics=['''binary_accuracy''']     )
																			model.fit(UpperCamelCase__       ,		epochs=1     )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									__UpperCamelCase       					=     os.path.join(UpperCamelCase__       ,		'''keras_model.h5'''     )
																									model.save(UpperCamelCase__     )
																									if "T5" in main_layer_class.__name__:
																															__UpperCamelCase       					=     tf.keras.models.load_model(
																															    UpperCamelCase__       ,		custom_objects={
																															        main_layer_class.__name__: main_layer_class,
																															        '''TFSharedEmbeddings''': TFSharedEmbeddings,
																															    }       ,		)
																									else:
																															__UpperCamelCase       					=     tf.keras.models.load_model(
																															    UpperCamelCase__       ,		custom_objects={main_layer_class.__name__: main_layer_class}     )
																									assert isinstance(UpperCamelCase__       ,		tf.keras.Model     )
																									model(UpperCamelCase__     )



							@slow
							def       A__						(					self     )->				Any:





													'''simple docstring'''






													__UpperCamelCase					,							__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes[:2]:
																			__UpperCamelCase       					=     model_class(UpperCamelCase__     )

																			__UpperCamelCase       					=     copy.deepcopy(UpperCamelCase__     )

																			if not self.is_encoder_decoder:
																									__UpperCamelCase       					=     inputs['''input_ids''']
																									del inputs["input_ids"]
																			else:
																									__UpperCamelCase       					=     inputs['''input_ids''']
																									__UpperCamelCase       					=     inputs.get('''decoder_input_ids'''       ,		UpperCamelCase__     )
																									del inputs["input_ids"]
																									inputs.pop('''decoder_input_ids'''       ,		UpperCamelCase__     )

																			if not self.is_encoder_decoder:
																									__UpperCamelCase       					=     model.get_input_embeddings()(UpperCamelCase__     )
																			else:
																									__UpperCamelCase       					=     model.get_input_embeddings()(UpperCamelCase__     )
																									__UpperCamelCase       					=     model.get_input_embeddings()(UpperCamelCase__     )

																			__UpperCamelCase       					=     self._prepare_for_class(UpperCamelCase__       ,		UpperCamelCase__     )

																			@tf.function
																			def run_in_graph_mode():
																									return model(UpperCamelCase__     )

																			__UpperCamelCase       					=     run_in_graph_mode()
																			self.assertIsNotNone(UpperCamelCase__     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Any:





													'''simple docstring'''






													__UpperCamelCase       					=     []
													if model.config.bos_token_id is not None:
																			special_tokens.append(model.config.bos_token_id     )
													if model.config.pad_token_id is not None:
																			special_tokens.append(model.config.pad_token_id     )
													if model.config.eos_token_id is not None:
																			special_tokens.append(model.config.eos_token_id     )

													# create random bad tokens that are not special tokens
													__UpperCamelCase       					=     []
													while len(UpperCamelCase__     ) < num_bad_tokens:
																			__UpperCamelCase       					=     tf.squeeze(ids_tensor((1, 1)       ,		self.model_tester.vocab_size     )       ,		0     ).numpy()[0]
																			if token not in special_tokens:
																									bad_tokens.append(UpperCamelCase__     )
													return bad_tokens



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													for token_id in output_ids[0].numpy().tolist():
																			self.assertGreaterEqual(UpperCamelCase__       ,		0     )
																			self.assertLess(UpperCamelCase__       ,		self.model_tester.vocab_size     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													for bad_word_ids in bad_words_ids:
																			# for all slices in batch
																			for generated_ids_slice in generated_ids:
																									# for all word idx
																									for i in range(len(UpperCamelCase__     )       ,		len(UpperCamelCase__     )     ):
																															# if tokens match
																															if generated_ids_slice[i - len(UpperCamelCase__     ) : i] == bad_word_ids:
																																					return True
													return False






import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
				from comet_ml import OfflineExperiment

if is_tensorboard_available():
				import struct

				import tensorboard.compat.proto.event_pba as event_pba

lowercase__							:							Optional[int]													=     logging.getLogger(__name__)






@require_tensorboard
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													__UpperCamelCase       					=     '''test_project_with_config'''
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			accelerator.end_training()
																			for child in Path(F"{dirpath}/{project_name}"     ).glob('''*/**'''     ):
																									__UpperCamelCase       					=     list(filter(lambda SCREAMING_SNAKE_CASE_     : x.is_file()       ,		child.iterdir()     )     )[0]
																			self.assertNotEqual(str(SCREAMING_SNAKE_CASE_     )       ,		''''''     )



							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     '''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(SCREAMING_SNAKE_CASE_       ,		step=0     )
																			accelerator.end_training()
																			# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
																			# Names are randomly generated each time
																			__UpperCamelCase       					=     list(filter(lambda SCREAMING_SNAKE_CASE_     : x.is_file()       ,		Path(F"{dirpath}/{project_name}"     ).iterdir()     )     )[0]
																			self.assertNotEqual(str(SCREAMING_SNAKE_CASE_     )       ,		''''''     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''tensor''': torch.tensor(1     )}
																			accelerator.log(SCREAMING_SNAKE_CASE_       ,		step=0     )
																			accelerator.end_training()
																			# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
																			# Names are randomly generated each time
																			__UpperCamelCase       					=     list(filter(lambda SCREAMING_SNAKE_CASE_     : x.is_file()       ,		Path(F"{dirpath}/{project_name}"     ).iterdir()     )     )[0]
																			# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
																			with open(SCREAMING_SNAKE_CASE_       ,		'''rb'''     ) as f:
																									__UpperCamelCase       					=     f.read()
																			__UpperCamelCase       					=     False
																			while data:
																									__UpperCamelCase       					=     struct.unpack('''Q'''       ,		data[:8]     )

																									__UpperCamelCase       					=     data[12 : 12 + int(header[0]     )]  # 8+4
																									__UpperCamelCase       					=     data[12 + int(header[0]     ) + 4 :]
																									__UpperCamelCase       					=     event_pba.Event()

																									event.ParseFromString(SCREAMING_SNAKE_CASE_     )
																									if event.HasField('''summary'''     ):
																															for value in event.summary.value:
																																					if value.simple_value == 1.0 and value.tag == "tensor":
																																											__UpperCamelCase       					=     True
																			self.assertTrue(SCREAMING_SNAKE_CASE_       ,		'''Converted tensor was not found in the log file!'''     )



							def       A__						(					self     )->				Any:





													'''simple docstring'''






													with self.assertRaisesRegex(SCREAMING_SNAKE_CASE_       ,		'''Logging with `tensorboard` requires a `logging_dir`'''     ):
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''     )
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     ProjectConfiguration(total_limit=30     )
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_       ,		project_config=SCREAMING_SNAKE_CASE_     )






@require_wandb
@mock.patch.dict(os.environ				,						{'WANDB_MODE': 'offline'}      )
class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													super().setUp()
													# wandb let's us override where logs are stored to via the WANDB_DIR env var
													self.add_mocks(mock.patch.dict(os.environ       ,		{'''WANDB_DIR''': self.tmpdir}     )     )



							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = True     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     rF"{section} ([\S\s]*?)\n\n"
													if record:
																			__UpperCamelCase       					=     rF"Record: {pattern}"
													__UpperCamelCase       					=     re.findall(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )[0]
													# A config
													if section == "config" or section == "history":
																			__UpperCamelCase       					=     re.findall(r'''"([a-zA-Z0-9_.,]+)'''       ,		SCREAMING_SNAKE_CASE_     )
																			return {key: val for key, val in zip(cleaned_record[0::2]       ,		cleaned_record[1::2]     )}
													# Everything else
													else:
																			return dict(re.findall(r'''(\w+): "([^\s]+)"'''       ,		SCREAMING_SNAKE_CASE_     )     )



							@skip
							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''test_project_with_config'''
													__UpperCamelCase       					=     Accelerator(log_with='''wandb'''     )
													__UpperCamelCase       					=     {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
													__UpperCamelCase       					=     {'''wandb''': {'''tags''': ['''my_tag''']}}
													accelerator.init_trackers(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
													accelerator.log(SCREAMING_SNAKE_CASE_       ,		step=0     )
													accelerator.end_training()
													# The latest offline log is stored at wandb/latest-run/*.wandb
													for child in Path(F"{self.tmpdir}/wandb/latest-run"     ).glob('''*'''     ):
																			if child.is_file() and child.suffix == ".wandb":
																									__UpperCamelCase       					=     subprocess.check_output(
																									    ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(SCREAMING_SNAKE_CASE_     )]       ,		env=os.environ.copy()     ).decode('''utf8'''       ,		'''ignore'''     )
																									break

        # Check HPS through careful parsing and cleaning
													__UpperCamelCase       					=     self.parse_log(SCREAMING_SNAKE_CASE_       ,		'''config'''     )
													self.assertEqual(logged_items['''num_iterations''']       ,		'''12'''     )
													self.assertEqual(logged_items['''learning_rate''']       ,		'''0.01'''     )
													self.assertEqual(logged_items['''some_boolean''']       ,		'''false'''     )
													self.assertEqual(logged_items['''some_string''']       ,		'''some_value'''     )
													self.assertEqual(logged_items['''some_string''']       ,		'''some_value'''     )

													# Run tags
													__UpperCamelCase       					=     self.parse_log(SCREAMING_SNAKE_CASE_       ,		'''run'''       ,		SCREAMING_SNAKE_CASE_     )
													self.assertEqual(logged_items['''tags''']       ,		'''my_tag'''     )

													# Actual logging
													__UpperCamelCase       					=     self.parse_log(SCREAMING_SNAKE_CASE_       ,		'''history'''     )
													self.assertEqual(logged_items['''total_loss''']       ,		'''0.1'''     )
													self.assertEqual(logged_items['''iteration''']       ,		'''1'''     )
													self.assertEqual(logged_items['''my_text''']       ,		'''some_value'''     )
													self.assertEqual(logged_items['''_step''']       ,		'''0'''     )





def 		A_	(							self							:				str	, snake_case							:				str	, snake_case							:				str	) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     run_name
						__UpperCamelCase       					=     OfflineExperiment(project_name=snake_case	, offline_directory=snake_case	)
						logger.info(f"Initialized offline CometML project {self.run_name}"	)
						logger.info('''Make sure to log any initial configurations with `self.store_init_configuration` before training!'''	)






@require_comet_ml
@mock.patch.object(SCREAMING_SNAKE_CASE_				,						'__init__'				,						SCREAMING_SNAKE_CASE_      )
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = False     )->				List[str]:





													'''simple docstring'''






													for log in log_list:
																			__UpperCamelCase       					=     json.loads(SCREAMING_SNAKE_CASE_     )['''payload''']
																			if is_param and "param" in j.keys():
																									if j["param"]["paramName"] == key:
																															return j["param"]["paramValue"]
																			if "log_other" in j.keys():
																									if j["log_other"]["key"] == key:
																															return j["log_other"]["val"]
																			if "metric" in j.keys():
																									if j["metric"]["metricName"] == key:
																															return j["metric"]["metricValue"]



							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     CometMLTracker('''test_project_with_config'''       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			accelerator.end_training()
																			__UpperCamelCase       					=     os.listdir(SCREAMING_SNAKE_CASE_     )[0]  # Comet is nice, it's just a zip file here
																			# We parse the raw logs
																			__UpperCamelCase       					=     os.path.join(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     zipfile.ZipFile(SCREAMING_SNAKE_CASE_       ,		'''r'''     )
																			__UpperCamelCase       					=     archive.open('''messages.json'''     ).read().decode('''utf-8'''     )
													__UpperCamelCase       					=     log.split('''\n'''     )[:-1]
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''num_iterations'''       ,		SCREAMING_SNAKE_CASE_     )       ,		12     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''learning_rate'''       ,		SCREAMING_SNAKE_CASE_     )       ,		0.0_1     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''some_boolean'''       ,		SCREAMING_SNAKE_CASE_     )       ,		SCREAMING_SNAKE_CASE_     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''some_string'''       ,		SCREAMING_SNAKE_CASE_     )       ,		'''some_value'''     )



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     CometMLTracker('''test_project_with_config'''       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(SCREAMING_SNAKE_CASE_       ,		step=0     )
																			accelerator.end_training()
																			__UpperCamelCase       					=     os.listdir(SCREAMING_SNAKE_CASE_     )[0]  # Comet is nice, it's just a zip file here
																			# We parse the raw logs
																			__UpperCamelCase       					=     os.path.join(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     zipfile.ZipFile(SCREAMING_SNAKE_CASE_       ,		'''r'''     )
																			__UpperCamelCase       					=     archive.open('''messages.json'''     ).read().decode('''utf-8'''     )
													__UpperCamelCase       					=     log.split('''\n'''     )[:-1]
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''curr_step'''       ,		SCREAMING_SNAKE_CASE_     )       ,		0     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''total_loss'''     )       ,		0.1     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''iteration'''     )       ,		1     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''my_text'''     )       ,		'''some_value'''     )






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					[
							    'total_loss',
							    'iteration',
							    'my_text',
							    'learning_rate',
							    'num_iterations',
							    'some_boolean',
							    'some_string',
							]

							_snake_case       						=					'my_custom_tracker'
							_snake_case       						=					False



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     open(F"{dir}/log.csv"       ,		'''w+'''     )
													__UpperCamelCase       					=     csv.DictWriter(self.f       ,		fieldnames=self._col_names     )
													self.writer.writeheader()



							@property
							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													return self.writer



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													logger.info('''Call init'''     )
													self.writer.writerow(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													logger.info('''Call log'''     )
													self.writer.writerow(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													self.f.close()






class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     MyCustomTracker(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers('''Some name'''       ,		SCREAMING_SNAKE_CASE_     )
																			accelerator.end_training()
																			with open(F"{d}/log.csv"       ,		'''r'''     ) as f:
																									__UpperCamelCase       					=     csv.DictReader(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     next(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     {
																									    '''total_loss''': '''''',
																									    '''iteration''': '''''',
																									    '''my_text''': '''''',
																									    '''learning_rate''': '''0.01''',
																									    '''num_iterations''': '''12''',
																									    '''some_boolean''': '''False''',
																									    '''some_string''': '''some_value''',
																									}
																									self.assertDictEqual(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     MyCustomTracker(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers('''Some name'''     )
																			__UpperCamelCase       					=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(SCREAMING_SNAKE_CASE_       ,		step=0     )
																			accelerator.end_training()
																			with open(F"{d}/log.csv"       ,		'''r'''     ) as f:
																									__UpperCamelCase       					=     csv.DictReader(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     next(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     {
																									    '''total_loss''': '''0.1''',
																									    '''iteration''': '''1''',
																									    '''my_text''': '''some_value''',
																									    '''learning_rate''': '''''',
																									    '''num_iterations''': '''''',
																									    '''some_boolean''': '''''',
																									    '''some_string''': '''''',
																									}
																									self.assertDictEqual(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )




