




"""simple docstring"""





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers.models.swin.modeling_tf_swin import (
							    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFSwinForImageClassification,
							    TFSwinForMaskedImageModeling,
							    TFSwinModel,
							)


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class  __lowercase    :


				'''simple docstring'''


				def __init__(				self				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       Optional[Any]=1_3 ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=3_2 ,SCREAMING_SNAKE_CASE_				:       Optional[int]=2 ,SCREAMING_SNAKE_CASE_				:       List[Any]=3 ,SCREAMING_SNAKE_CASE_				:       List[Any]=1_6 ,SCREAMING_SNAKE_CASE_				:       Any=[1, 2, 1] ,SCREAMING_SNAKE_CASE_				:       Optional[int]=[2, 2, 4] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=2 ,SCREAMING_SNAKE_CASE_				:       int=2.0 ,SCREAMING_SNAKE_CASE_				:       Dict=True ,SCREAMING_SNAKE_CASE_				:       Dict=0.0 ,SCREAMING_SNAKE_CASE_				:       int=0.0 ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=0.1 ,SCREAMING_SNAKE_CASE_				:       Dict="gelu" ,SCREAMING_SNAKE_CASE_				:       List[str]=False ,SCREAMING_SNAKE_CASE_				:       str=True ,SCREAMING_SNAKE_CASE_				:       Optional[int]=0.02 ,SCREAMING_SNAKE_CASE_				:       List[str]=1E-5 ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=True ,SCREAMING_SNAKE_CASE_				:       Dict=None ,SCREAMING_SNAKE_CASE_				:       Tuple=True ,SCREAMING_SNAKE_CASE_				:       Optional[Any]=1_0 ,SCREAMING_SNAKE_CASE_				:       int=8 ,):
										snake_case__    :   Tuple							     =   parent
										snake_case__    :   List[Any]							     =   batch_size
										snake_case__    :   Tuple							     =   image_size
										snake_case__    :   int							     =   patch_size
										snake_case__    :   str							     =   num_channels
										snake_case__    :   Tuple							     =   embed_dim
										snake_case__    :   Tuple							     =   depths
										snake_case__    :   List[Any]							     =   num_heads
										snake_case__    :   Union[str, Any]							     =   window_size
										snake_case__    :   List[Any]							     =   mlp_ratio
										snake_case__    :   List[Any]							     =   qkv_bias
										snake_case__    :   Tuple							     =   hidden_dropout_prob
										snake_case__    :   List[Any]							     =   attention_probs_dropout_prob
										snake_case__    :   List[str]							     =   drop_path_rate
										snake_case__    :   str							     =   hidden_act
										snake_case__    :   str							     =   use_absolute_embeddings
										snake_case__    :   Tuple							     =   patch_norm
										snake_case__    :   Dict							     =   layer_norm_eps
										snake_case__    :   List[str]							     =   initializer_range
										snake_case__    :   Optional[Any]							     =   is_training
										snake_case__    :   Optional[Any]							     =   scope
										snake_case__    :   Dict							     =   use_labels
										snake_case__    :   Union[str, Any]							     =   type_sequence_label_size
										snake_case__    :   Dict							     =   encoder_stride

				def  _lowerCamelCase   (				self				:       Optional[int] ):
										snake_case__    :   Optional[Any]							     =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

										snake_case__    :   List[str]							     =   None
										if self.use_labels:
																snake_case__    :   Optional[int]							     =   ids_tensor([self.batch_size] ,self.type_sequence_label_size )

										snake_case__    :   Optional[int]							     =   self.get_config()

										return config, pixel_values, labels

				def  _lowerCamelCase   (				self				:       Optional[Any] ):
										return SwinConfig(
										    image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,embed_dim=self.embed_dim ,depths=self.depths ,num_heads=self.num_heads ,window_size=self.window_size ,mlp_ratio=self.mlp_ratio ,qkv_bias=self.qkv_bias ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,drop_path_rate=self.drop_path_rate ,hidden_act=self.hidden_act ,use_absolute_embeddings=self.use_absolute_embeddings ,path_norm=self.patch_norm ,layer_norm_eps=self.layer_norm_eps ,initializer_range=self.initializer_range ,encoder_stride=self.encoder_stride ,)

				def  _lowerCamelCase   (				self				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Dict ):
										snake_case__    :   List[Any]							     =   TFSwinModel(config=SCREAMING_SNAKE_CASE_ )
										snake_case__    :   str							     =   model(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Tuple							     =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
										snake_case__    :   List[str]							     =   int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

										self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, expected_seq_len, expected_dim) )

				def  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ):
										snake_case__    :   Optional[int]							     =   TFSwinForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_ )
										snake_case__    :   Tuple							     =   model(SCREAMING_SNAKE_CASE_ )
										self.parent.assertEqual(
										    result.logits.shape ,(self.batch_size, self.num_channels, self.image_size, self.image_size) )

										# test greyscale images
										snake_case__    :   Tuple							     =   1
										snake_case__    :   List[Any]							     =   TFSwinForMaskedImageModeling(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Dict							     =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
										snake_case__    :   Tuple							     =   model(SCREAMING_SNAKE_CASE_ )
										self.parent.assertEqual(result.logits.shape ,(self.batch_size, 1, self.image_size, self.image_size) )

				def  _lowerCamelCase   (				self				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ):
										snake_case__    :   str							     =   self.type_sequence_label_size
										snake_case__    :   Union[str, Any]							     =   TFSwinForImageClassification(SCREAMING_SNAKE_CASE_ )
										snake_case__    :   List[str]							     =   model(SCREAMING_SNAKE_CASE_ ,labels=SCREAMING_SNAKE_CASE_ )
										self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )

										# test greyscale images
										snake_case__    :   Dict							     =   1
										snake_case__    :   Union[str, Any]							     =   TFSwinForImageClassification(SCREAMING_SNAKE_CASE_ )
										snake_case__    :   Optional[int]							     =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
										snake_case__    :   Union[str, Any]							     =   model(SCREAMING_SNAKE_CASE_ )
										self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )





				def  _lowerCamelCase   (				self				:       str ):
										snake_case__    :   Any							     =   self.prepare_config_and_inputs()
										snake_case__     ,		snake_case__     ,		snake_case__    :   List[Any]							     =   config_and_inputs
										snake_case__    :   List[str]							     =   {'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_tf
class  __lowercase    (     snake_case_  ,				snake_case_  ,				unittest.TestCase		):


				'''simple docstring'''


				_lowerCAmelCase		     =(
				    (
				        TFSwinModel,
				        TFSwinForImageClassification,
				        TFSwinForMaskedImageModeling,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowerCAmelCase		     =(
				    {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
				    if is_tf_available()
				    else {}
				)

				_lowerCAmelCase		     =False
				_lowerCAmelCase		     =False
				_lowerCAmelCase		     =False
				_lowerCAmelCase		     =False

				def  _lowerCamelCase   (				self				:       int ):
										snake_case__    :   Dict							     =   TFSwinModelTester(self )
										snake_case__    :   Union[str, Any]							     =   ConfigTester(self ,config_class=SCREAMING_SNAKE_CASE_ ,embed_dim=3_7 )

				def  _lowerCamelCase   (				self				:       Dict ):
										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()

				def  _lowerCamelCase   (				self				:       List[Any] ):
										return

				def  _lowerCamelCase   (				self				:       str ):
										snake_case__    :   List[Any]							     =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       int ):
										snake_case__    :   List[Any]							     =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       Any ):
										snake_case__    :   Tuple							     =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )

				@unittest.skip(reason='''Swin does not use inputs_embeds''' )
				def  _lowerCamelCase   (				self				:       Tuple ):
										pass

				def  _lowerCamelCase   (				self				:       int ):
										snake_case__     ,		snake_case__    :   int							     =   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case__    :   int							     =   model_class(SCREAMING_SNAKE_CASE_ )
																self.assertIsInstance(model.get_input_embeddings() ,tf.keras.layers.Layer )
																snake_case__    :   List[Any]							     =   model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_ ,tf.keras.layers.Dense ) )

				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
										snake_case__     ,		snake_case__    :   Dict							     =   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case__    :   Union[str, Any]							     =   model_class(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Dict							     =   inspect.signature(model.call )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																snake_case__    :   Optional[int]							     =   [*signature.parameters.keys()]

																snake_case__    :   List[Any]							     =   ['''pixel_values''']
																self.assertListEqual(arg_names[:1] ,SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
										snake_case__     ,		snake_case__    :   List[Any]							     =   self.model_tester.prepare_config_and_inputs_for_common()
										snake_case__    :   List[Any]							     =   True

										for model_class in self.all_model_classes:
																snake_case__    :   Tuple							     =   True
																snake_case__    :   List[str]							     =   False
																snake_case__    :   str							     =   True
																snake_case__    :   Optional[int]							     =   model_class(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Optional[Any]							     =   model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
																snake_case__    :   Tuple							     =   outputs.attentions
																snake_case__    :   Optional[int]							     =   len(self.model_tester.depths )
																self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )

																# check that output_attentions also work using config
																del inputs_dict["output_attentions"]
																snake_case__    :   Union[str, Any]							     =   True
																snake_case__    :   List[Any]							     =   config.window_size**2
																snake_case__    :   Optional[int]							     =   model_class(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   str							     =   model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
																snake_case__    :   Union[str, Any]							     =   outputs.attentions
																self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )

																self.assertListEqual(
																    list(attentions[0].shape[-3:] ) ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,)
																snake_case__    :   int							     =   len(SCREAMING_SNAKE_CASE_ )

																# Check attention is always last and order is fine
																snake_case__    :   Union[str, Any]							     =   True
																snake_case__    :   str							     =   True
																snake_case__    :   List[Any]							     =   model_class(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Any							     =   model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )

																if hasattr(self.model_tester ,'''num_hidden_states_types''' ):
																						snake_case__    :   List[Any]							     =   self.model_tester.num_hidden_states_types
																else:
																						# also another +1 for reshaped_hidden_states
																						snake_case__    :   Optional[Any]							     =   2
																self.assertEqual(out_len + added_hidden_states ,len(SCREAMING_SNAKE_CASE_ ) )

																snake_case__    :   str							     =   outputs.attentions

																self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )

																self.assertListEqual(
																    list(self_attentions[0].shape[-3:] ) ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,)

				def  _lowerCamelCase   (				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Tuple ):
										snake_case__    :   List[str]							     =   model_class(SCREAMING_SNAKE_CASE_ )
										snake_case__    :   str							     =   model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) )
										snake_case__    :   str							     =   outputs.hidden_states

										snake_case__    :   List[str]							     =   getattr(
										    self.model_tester ,'''expected_num_hidden_layers''' ,len(self.model_tester.depths ) + 1 )
										self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )

										# Swin has a different seq_length
										snake_case__    :   Dict							     =   to_atuple(config.patch_size )

										snake_case__    :   Tuple							     =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

										self.assertListEqual(
										    list(hidden_states[0].shape[-2:] ) ,[num_patches, self.model_tester.embed_dim] ,)

										snake_case__    :   Union[str, Any]							     =   outputs.reshaped_hidden_states
										self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) ,SCREAMING_SNAKE_CASE_ )

										snake_case__     ,		snake_case__     ,		snake_case__     ,		snake_case__    :   Any							     =   reshaped_hidden_states[0].shape

										snake_case__    :   Tuple							     =   tf.reshape(reshaped_hidden_states[0] ,(batch_size, num_channels, height * width) )
										snake_case__    :   Tuple							     =   tf.transpose(SCREAMING_SNAKE_CASE_ ,(0, 2, 1) )

										self.assertListEqual(
										    list(reshaped_hidden_states.shape[-2:] ) ,[num_patches, self.model_tester.embed_dim] ,)

				def  _lowerCamelCase   (				self				:       Tuple ):
										snake_case__     ,		snake_case__    :   int							     =   self.model_tester.prepare_config_and_inputs_for_common()

										snake_case__    :   Dict							     =   to_atuple(self.model_tester.image_size )

										for model_class in self.all_model_classes:
																snake_case__    :   Union[str, Any]							     =   True
																self.check_hidden_states_output(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																snake_case__    :   int							     =   True

																self.check_hidden_states_output(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       Dict ):
										snake_case__     ,		snake_case__    :   List[Any]							     =   self.model_tester.prepare_config_and_inputs_for_common()
										snake_case__    :   Tuple							     =   3

										snake_case__    :   str							     =   to_atuple(self.model_tester.image_size )
										snake_case__    :   Dict							     =   to_atuple(config.patch_size )

										snake_case__    :   Tuple							     =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
										snake_case__    :   str							     =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

										for model_class in self.all_model_classes:
																snake_case__    :   Dict							     =   True
																self.check_hidden_states_output(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,(padded_height, padded_width) )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																snake_case__    :   Any							     =   True
																self.check_hidden_states_output(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,(padded_height, padded_width) )





				@slow
				def  _lowerCamelCase   (				self				:       Optional[int] ):
										for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case__    :   List[Any]							     =   TFSwinModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
																self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )







@require_vision
@require_tf
class  __lowercase    (     unittest.TestCase		):


				'''simple docstring'''


				@cached_property
				def  _lowerCamelCase   (				self				:       int ):
										return (
										    AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224''' )
										    if is_vision_available()
										    else None
										)





				@slow
				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
										snake_case__    :   Dict							     =   TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224''' )
										snake_case__    :   Tuple							     =   self.default_image_processor

										snake_case__    :   str							     =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
										snake_case__    :   Any							     =   image_processor(images=SCREAMING_SNAKE_CASE_ ,return_tensors='''tf''' )

										# forward pass
										snake_case__    :   Any							     =   model(SCREAMING_SNAKE_CASE_ )

										# verify the logits
										snake_case__    :   Tuple							     =   tf.TensorShape((1, 1_0_0_0) )
										self.assertEqual(outputs.logits.shape ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   Dict							     =   tf.constant([-0.0948, -0.6454, -0.0921] )
										self.assertTrue(np.allclose(outputs.logits[0, :3] ,SCREAMING_SNAKE_CASE_ ,atol=1E-4 ) )








"""simple docstring"""





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_lowercase		 =      16
_lowercase		 =      32

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase = 16						):
						'''simple docstring'''


						snake_case__    :   int							     =   AutoTokenizer.from_pretrained('''bert-base-cased'''						)
						snake_case__    :   Dict							     =   load_dataset('''glue''' ,     '''mrpc'''						)

						def tokenize_function(lowerCamelCase						):
												# max_length=None => use the model max length (it's actually the default)
												snake_case__    :   Optional[int]							     =   tokenizer(examples['''sentence1'''] ,     examples['''sentence2'''] ,     truncation=lowerCamelCase ,     max_length=lowerCamelCase						)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												snake_case__    :   Tuple							     =   datasets.map(
												    lowerCamelCase ,     batched=lowerCamelCase ,     remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,     )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						snake_case__    :   str							     =   tokenized_datasets.rename_column('''label''' ,     '''labels'''						)

						def collate_fn(lowerCamelCase						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												snake_case__    :   Tuple							     =   128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		snake_case__    :   Optional[Any]							     =   16
												elif accelerator.mixed_precision != "no":
																		snake_case__    :   Dict							     =   8
												else:
																		snake_case__    :   str							     =   None

												return tokenizer.pad(
												    lowerCamelCase ,     padding='''longest''' ,     max_length=lowerCamelCase ,     pad_to_multiple_of=lowerCamelCase ,     return_tensors='''pt''' ,     )

						# Instantiate dataloaders.
						snake_case__    :   List[Any]							     =   DataLoader(
						    tokenized_datasets['''train'''] ,     shuffle=lowerCamelCase ,     collate_fn=lowerCamelCase ,     batch_size=lowerCamelCase						)
						snake_case__    :   Tuple							     =   DataLoader(
						    tokenized_datasets['''validation'''] ,     shuffle=lowerCamelCase ,     collate_fn=lowerCamelCase ,     batch_size=lowerCamelCase						)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							_lowercase		 =      mocked_dataloaders  # noqa: F811

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,     lowerCamelCase						) == "1":
												snake_case__    :   Any							     =   2
						# Initialize accelerator
						snake_case__    :   List[Any]							     =   Accelerator(cpu=args.cpu ,     mixed_precision=args.mixed_precision						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case__    :   Any							     =   config['''lr''']
						snake_case__    :   int							     =   int(config['''num_epochs''']						)
						snake_case__    :   List[str]							     =   int(config['''seed''']						)
						snake_case__    :   Dict							     =   int(config['''batch_size''']						)

						# New Code #
						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps ,     '''isdigit'''						):
												if args.checkpointing_steps == "epoch":
																		snake_case__    :   Optional[Any]							     =   args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		snake_case__    :   Union[str, Any]							     =   int(args.checkpointing_steps						)
												else:
																		raise ValueError(
																		    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''						)
						else:
												snake_case__    :   Union[str, Any]							     =   None

						set_seed(lowerCamelCase						)

						snake_case__     ,		snake_case__    :   int							     =   get_dataloaders(lowerCamelCase ,     lowerCamelCase						)
						snake_case__    :   Optional[Any]							     =   evaluate.load('''glue''' ,     '''mrpc'''						)

						# If the batch size is too big we use gradient accumulation
						snake_case__    :   Optional[int]							     =   1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												snake_case__    :   Any							     =   batch_size // MAX_GPU_BATCH_SIZE
												snake_case__    :   Union[str, Any]							     =   MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case__    :   Tuple							     =   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,     return_dict=lowerCamelCase						)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						snake_case__    :   Dict							     =   model.to(accelerator.device						)

						# Instantiate optimizer
						snake_case__    :   Optional[int]							     =   AdamW(params=model.parameters() ,     lr=lowerCamelCase						)

						# Instantiate scheduler
						snake_case__    :   Optional[Any]							     =   get_linear_schedule_with_warmup(
						    optimizer=lowerCamelCase ,     num_warmup_steps=100 ,     num_training_steps=(len(lowerCamelCase						) * num_epochs) // gradient_accumulation_steps ,     )

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						snake_case__     ,		snake_case__     ,		snake_case__     ,		snake_case__     ,		snake_case__    :   Optional[Any]							     =   accelerator.prepare(
						    lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)

						# New Code #
						# We need to keep track of how many total steps we have iterated over
						snake_case__    :   Any							     =   0
						# We also need to keep track of the stating epoch so files are named properly
						snake_case__    :   List[str]							     =   0

						# We need to load the checkpoint back in before training here with `load_state`
						# The total number of epochs is adjusted based on where the state is being loaded from,
						# as we assume continuation of the same training script
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''						)
																		accelerator.load_state(args.resume_from_checkpoint						)
																		snake_case__    :   Optional[Any]							     =   os.path.basename(args.resume_from_checkpoint						)
												else:
																		# Get the most recent checkpoint
																		snake_case__    :   Any							     =   [f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
																		dirs.sort(key=os.path.getctime						)
																		snake_case__    :   str							     =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												snake_case__    :   List[Any]							     =   os.path.splitext(lowerCamelCase						)[0]

												if "epoch" in training_difference:
																		snake_case__    :   Optional[Any]							     =   int(training_difference.replace('''epoch_''' ,     ''''''						)						) + 1
																		snake_case__    :   List[str]							     =   None
												else:
																		snake_case__    :   int							     =   int(training_difference.replace('''step_''' ,     ''''''						)						)
																		snake_case__    :   Union[str, Any]							     =   resume_step // len(lowerCamelCase						)
																		resume_step -= starting_epoch * len(lowerCamelCase						)

    # Now we train the model
						for epoch in range(lowerCamelCase ,     lowerCamelCase						):
												model.train()
												# New Code #
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		snake_case__    :   int							     =   accelerator.skip_first_batches(lowerCamelCase ,     lowerCamelCase						)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		snake_case__    :   Tuple							     =   train_dataloader
												for step, batch in enumerate(lowerCamelCase						):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device						)
																		snake_case__    :   Tuple							     =   model(**lowerCamelCase						)
																		snake_case__    :   Optional[int]							     =   outputs.loss
																		snake_case__    :   Union[str, Any]							     =   loss / gradient_accumulation_steps
																		accelerator.backward(lowerCamelCase						)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()
																		# New Code #
																		overall_step += 1

																		# New Code #
																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																		# These are saved to folders named `step_{overall_step}`
																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																		# If mixed precision was used, will also save a "scalar.bin" file
																		if isinstance(lowerCamelCase ,     lowerCamelCase						):
																								snake_case__    :   Tuple							     =   f'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				snake_case__    :   Optional[int]							     =   os.path.join(args.output_dir ,     lowerCamelCase						)
																														accelerator.save_state(lowerCamelCase						)

												model.eval()
												for step, batch in enumerate(lowerCamelCase						):
																		# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
																		batch.to(accelerator.device						)
																		with torch.no_grad():
																								snake_case__    :   List[str]							     =   model(**lowerCamelCase						)
																		snake_case__    :   Optional[int]							     =   outputs.logits.argmax(dim=-1						)
																		snake_case__     ,		snake_case__    :   Dict							     =   accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
																		metric.add_batch(
																		    predictions=lowerCamelCase ,     references=lowerCamelCase ,     )

												snake_case__    :   str							     =   metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:''' ,     lowerCamelCase						)

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `epoch_{epoch}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if checkpointing_steps == "epoch":
																		snake_case__    :   str							     =   f'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								snake_case__    :   Optional[int]							     =   os.path.join(args.output_dir ,     lowerCamelCase						)
																		accelerator.save_state(lowerCamelCase						)

def 						lowerCAmelCase							(	):
						'''simple docstring'''


						snake_case__    :   Tuple							     =   argparse.ArgumentParser(description='''Simple example of training script.'''						)
						parser.add_argument(
						    '''--mixed_precision''' ,     type=lowerCamelCase ,     default=lowerCamelCase ,     choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,     help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.''' ,     )
						parser.add_argument('''--cpu''' ,     action='''store_true''' ,     help='''If passed, will train on the CPU.'''						)
						parser.add_argument(
						    '''--checkpointing_steps''' ,     type=lowerCamelCase ,     default=lowerCamelCase ,     help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.''' ,     )
						parser.add_argument(
						    '''--output_dir''' ,     type=lowerCamelCase ,     default='''.''' ,     help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' ,     )
						parser.add_argument(
						    '''--resume_from_checkpoint''' ,     type=lowerCamelCase ,     default=lowerCamelCase ,     help='''If the training should continue from a checkpoint folder.''' ,     )
						snake_case__    :   List[str]							     =   parser.parse_args()
						snake_case__    :   str							     =   {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(lowerCamelCase ,     lowerCamelCase						)


if __name__ == "__main__":
							main()


