
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:
												_UpperCAmelCase     :					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)]
																								)
																								_UpperCAmelCase     :					str       =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 			snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					Dict   =							None
						SCREAMING_SNAKE_CASE__ :					Optional[Any]   =							()
						SCREAMING_SNAKE_CASE__ :					List[str]   =							()
						SCREAMING_SNAKE_CASE__ :					Optional[Any]   =							True
						SCREAMING_SNAKE_CASE__ :					Dict   =							True
						SCREAMING_SNAKE_CASE__ :					int   =							True
						SCREAMING_SNAKE_CASE__ :					Optional[Any]   =							False




						def   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase=False							)   ->      dict:
										lowerCAmelCase_	: Optional[Any]      			=			copy.deepcopy(lowerCamelCase__							)

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

										if return_labels:
														if model_class in get_values(lowerCamelCase__							):
																		lowerCAmelCase_	: int      			=			tf.ones(self.model_tester.batch_size				,  dtype=tf.intaa							)
														elif model_class in get_values(lowerCamelCase__							):
																		lowerCAmelCase_	: Optional[Any]      			=			tf.zeros(self.model_tester.batch_size				,  dtype=tf.intaa							)
																		lowerCAmelCase_	: int      			=			tf.zeros(self.model_tester.batch_size				,  dtype=tf.intaa							)
														elif model_class in [
														    *get_values(lowerCamelCase__							),
														    *get_values(lowerCamelCase__							),
														]:
																		lowerCAmelCase_	: List[Any]      			=			tf.zeros(self.model_tester.batch_size				,  dtype=tf.intaa							)
														elif model_class in get_values(lowerCamelCase__							):
																		lowerCAmelCase_	: str      			=			tf.zeros(self.model_tester.batch_size				,  dtype=tf.intaa							)
														elif model_class in [
														    *get_values(lowerCamelCase__							),
														    *get_values(lowerCamelCase__							),
														    *get_values(lowerCamelCase__							),
														    *get_values(lowerCamelCase__							),
														    *get_values(lowerCamelCase__							),
														]:
																		lowerCAmelCase_	: str      			=			tf.zeros(
																		    (self.model_tester.batch_size, self.model_tester.seq_length)				,  dtype=tf.intaa							)
										return inputs_dict




						@slow
						def   lowercase_	(			self							)   ->      Optional[Any]:
										lowerCAmelCase_			,					lowerCAmelCase_	: List[Any]      			=			self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
														lowerCAmelCase_	: Optional[int]      			=			self._prepare_for_class(lowerCamelCase__				,  lowerCamelCase__							)
														lowerCAmelCase_	: Optional[int]      			=			model_class(lowerCamelCase__							)

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

														lowerCAmelCase_	: Any      			=			run_in_graph_mode()
														self.assertIsNotNone(lowerCamelCase__							)




						@slow
						def   lowercase_	(			self							)   ->      Dict:
										lowerCAmelCase_			,					lowerCAmelCase_	: Dict      			=			self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
														lowerCAmelCase_	: List[Any]      			=			self._prepare_for_class(lowerCamelCase__				,  lowerCamelCase__							)
														lowerCAmelCase_	: List[Any]      			=			model_class(lowerCamelCase__							)

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

														lowerCAmelCase_	: Optional[Any]      			=			run_in_graph_mode()
														self.assertIsNotNone(lowerCamelCase__							)




						@slow
						def   lowercase_	(			self							)   ->      List[str]:
										lowerCAmelCase_			,					lowerCAmelCase_	: int      			=			self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
														lowerCAmelCase_	: Optional[Any]      			=			model_class(lowerCamelCase__							)
														if getattr(lowerCamelCase__				,  '''hf_compute_loss'''				,  lowerCamelCase__							):
																		# Test that model correctly compute the loss with kwargs
																		lowerCAmelCase_	: Any      			=			self._prepare_for_class(inputs_dict.copy()				,  lowerCamelCase__				,  return_labels=lowerCamelCase__							)
																		# Is there a better way to remove these decoder inputs?
																		lowerCAmelCase_	: Optional[Any]      			=			{
																		    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''')
																		}

																		lowerCAmelCase_	: int      			=			{
																		    '''labels''',
																		    '''label''',
																		    '''label_ids''',
																		    '''start_positions''',
																		    '''start_position''',
																		    '''end_positions''',
																		    '''end_position''',
																		    '''next_sentence_label''',
																		}
																		lowerCAmelCase_	: List[str]      			=			possible_label_cols.intersection(set(lowerCamelCase__							)							)
																		self.assertGreater(len(lowerCamelCase__							)				,  0				,  msg='''No matching label names found!'''							)
																		lowerCAmelCase_	: Dict      			=			{key: val for key, val in prepared_for_class.items() if key in label_names}
																		lowerCAmelCase_	: Any      			=			{key: val for key, val in prepared_for_class.items() if key not in label_names}
																		self.assertGreater(len(lowerCamelCase__							)				,  0							)

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

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

																		lowerCAmelCase_	: Optional[int]      			=			history.history['''loss'''][0]
																		self.assertTrue(not isnan(lowerCamelCase__							)							)
																		lowerCAmelCase_	: List[Any]      			=			history.history['''val_loss'''][0]
																		self.assertTrue(not isnan(lowerCamelCase__							)							)




						@slow
						def   lowercase_	(			self							)   ->      Dict:
										lowerCAmelCase_			,					lowerCAmelCase_	: Tuple      			=			self.model_tester.prepare_config_and_inputs_for_common()
										lowerCAmelCase_	: Optional[int]      			=			True
										lowerCAmelCase_	: int      			=			True

										if hasattr(lowerCamelCase__				,  '''use_cache'''							):
														lowerCAmelCase_	: Union[str, Any]      			=			True

										lowerCAmelCase_	: Tuple      			=			getattr(self.model_tester				,  '''encoder_seq_length'''				,  self.model_tester.seq_length							)
										lowerCAmelCase_	: Any      			=			getattr(self.model_tester				,  '''key_length'''				,  lowerCamelCase__							)

										for model_class in self.all_model_classes[:2]:
														lowerCAmelCase_	: Tuple      			=			self._prepare_for_class(lowerCamelCase__				,  lowerCamelCase__							)
														lowerCAmelCase_	: List[Any]      			=			model_class(lowerCamelCase__							)
														model.build()
														lowerCAmelCase_	: List[str]      			=			len(model(lowerCamelCase__							)							)

														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:
																						lowerCAmelCase_	: List[str]      			=			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(lowerCamelCase__				,  saved_model=lowerCamelCase__							)
																		lowerCAmelCase_	: Any      			=			os.path.join(lowerCamelCase__				,  '''saved_model'''				,  '''1'''							)
																		lowerCAmelCase_	: Optional[int]      			=			tf.keras.models.load_model(lowerCamelCase__							)
																		lowerCAmelCase_	: str      			=			model(lowerCamelCase__							)

																		if self.is_encoder_decoder:
																						lowerCAmelCase_	: int      			=			outputs['''encoder_hidden_states''']
																						lowerCAmelCase_	: str      			=			outputs['''encoder_attentions''']
																		else:
																						lowerCAmelCase_	: List[Any]      			=			outputs['''hidden_states''']
																						lowerCAmelCase_	: Dict      			=			outputs['''attentions''']

																		self.assertEqual(len(lowerCamelCase__							)				,  lowerCamelCase__							)

																		lowerCAmelCase_	: Any      			=			getattr(
																		    self.model_tester				,  '''expected_num_hidden_layers'''				,  self.model_tester.num_hidden_layers + 1							)

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

																		self.assertEqual(len(lowerCamelCase__							)				,  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   lowercase_	(			self							)   ->      int:
										tf.keras.mixed_precision.set_global_policy('''mixed_float16'''							)

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

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




						@slow
						def   lowercase_	(			self							)   ->      Union[str, Any]:
										lowerCAmelCase_			,					lowerCAmelCase_	: int      			=			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"]
										lowerCAmelCase_	: List[Any]      			=			{
										    module_member
										    for model_class in self.all_model_classes
										    for module in (import_module(model_class.__module__							),)
										    for module_member_name in dir(lowerCamelCase__							)
										    if module_member_name.endswith('''MainLayer'''							)
										    for module_member in (getattr(lowerCamelCase__				,  lowerCamelCase__							),)
										    if isinstance(lowerCamelCase__				,  lowerCamelCase__							)
										    and tf.keras.layers.Layer in module_member.__bases__
										    and getattr(lowerCamelCase__				,  '''_keras_serializable'''				,  lowerCamelCase__							)
										}

										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
																		lowerCAmelCase_	: Optional[Any]      			=			TFSharedEmbeddings(self.model_tester.vocab_size				,  self.model_tester.hidden_size				,  name='''shared'''							)
																		lowerCAmelCase_	: int      			=			False
																		lowerCAmelCase_	: Tuple      			=			main_layer_class(lowerCamelCase__				,  embed_tokens=lowerCamelCase__							)
														else:
																		lowerCAmelCase_	: str      			=			main_layer_class(lowerCamelCase__							)

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

														if hasattr(self.model_tester				,  '''num_labels'''							):
																		lowerCAmelCase_	: Dict      			=			self.model_tester.num_labels
														else:
																		lowerCAmelCase_	: Union[str, Any]      			=			2

														lowerCAmelCase_	: Dict      			=			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							)

														lowerCAmelCase_	: Dict      			=			main_layer(lowerCamelCase__							)[0]
														lowerCAmelCase_	: Optional[Any]      			=			tf.keras.layers.Dense(lowerCamelCase__				,  activation='''softmax'''				,  name='''outputs'''							)(lowerCamelCase__							)
														lowerCAmelCase_	: str      			=			tf.keras.models.Model(inputs=lowerCamelCase__				,  outputs=[outputs]							)

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

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




						@slow
						def   lowercase_	(			self							)   ->      int:
										lowerCAmelCase_			,					lowerCAmelCase_	: str      			=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes[:2]:
														lowerCAmelCase_	: str      			=			model_class(lowerCamelCase__							)

														lowerCAmelCase_	: int      			=			copy.deepcopy(lowerCamelCase__							)

														if not self.is_encoder_decoder:
																		lowerCAmelCase_	: str      			=			inputs['''input_ids''']
																		del inputs["input_ids"]
														else:
																		lowerCAmelCase_	: Optional[Any]      			=			inputs['''input_ids''']
																		lowerCAmelCase_	: Optional[int]      			=			inputs.get('''decoder_input_ids'''				,  lowerCamelCase__							)
																		del inputs["input_ids"]
																		inputs.pop('''decoder_input_ids'''				,  lowerCamelCase__							)

														if not self.is_encoder_decoder:
																		lowerCAmelCase_	: Union[str, Any]      			=			model.get_input_embeddings()(lowerCamelCase__							)
														else:
																		lowerCAmelCase_	: Any      			=			model.get_input_embeddings()(lowerCamelCase__							)
																		lowerCAmelCase_	: str      			=			model.get_input_embeddings()(lowerCamelCase__							)

														lowerCAmelCase_	: Optional[int]      			=			self._prepare_for_class(lowerCamelCase__				,  lowerCamelCase__							)

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

														lowerCAmelCase_	: int      			=			run_in_graph_mode()
														self.assertIsNotNone(lowerCamelCase__							)




						def   lowercase_	(			self				,  __lowercase				,  __lowercase							)   ->      Union[str, Any]:
										lowerCAmelCase_	: Union[str, Any]      			=			[]
										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
										lowerCAmelCase_	: Tuple      			=			[]
										while len(lowerCamelCase__							) < num_bad_tokens:
														lowerCAmelCase_	: Dict      			=			tf.squeeze(ids_tensor((1, 1)				,  self.model_tester.vocab_size							)				,  0							).numpy()[0]
														if token not in special_tokens:
																		bad_tokens.append(lowerCamelCase__							)
										return bad_tokens




						def   lowercase_	(			self				,  __lowercase							)   ->      List[str]:



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




						def   lowercase_	(			self				,  __lowercase				,  __lowercase							)   ->      Optional[int]:



										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(lowerCamelCase__							)				,  len(lowerCamelCase__							)							):
																						# if tokens match
																						if generated_ids_slice[i - len(lowerCamelCase__							) : i] == bad_word_ids:
																										return True
										return False



import math







class 			snake_case__:







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      Tuple:
										lowerCAmelCase_	: List[str]      			=			x
										lowerCAmelCase_	: Dict      			=			y
										lowerCAmelCase_	: List[Any]      			=			z




						def __repr__(			self							)   ->      str:
										return f"""Point({self.x}, {self.y}, {self.z})"""






def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	float:
				return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2	)	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()