





"""simple docstring"""


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:
				__magic_name__					:				Optional[Any]	= 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)]
								)
								__magic_name__					:				Optional[int]	= 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        lowercase__ :

			"""simple docstring"""
			__lowerCAmelCase    :							str								=     None
			__lowerCAmelCase    :							Dict								=     ()
			__lowerCAmelCase    :							Dict								=     ()
			__lowerCAmelCase    :							int								=     True
			__lowerCAmelCase    :							Union[str, Any]								=     True
			__lowerCAmelCase    :							List[Any]								=     True
			__lowerCAmelCase    :							Any								=     False







			def      _a			(			self	,      _A	,      _A	,      _A=False						):






									'''simple docstring'''







									UpperCamelCase							:		Union[str, Any]							      = copy.deepcopy(_A						)

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

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







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		Any							      = self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
															UpperCamelCase							:		List[str]							      = self._prepare_for_class(_A	,      _A						)
															UpperCamelCase							:		List[Any]							      = model_class(_A						)

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

															UpperCamelCase							:		str							      = run_in_graph_mode()
															self.assertIsNotNone(_A						)







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		Tuple							      = self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
															UpperCamelCase							:		str							      = self._prepare_for_class(_A	,      _A						)
															UpperCamelCase							:		List[Any]							      = model_class(_A						)

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

															UpperCamelCase							:		Dict							      = run_in_graph_mode()
															self.assertIsNotNone(_A						)







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		List[Any]							      = self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
															UpperCamelCase							:		Optional[Any]							      = model_class(_A						)
															if getattr(_A	,      """hf_compute_loss"""	,      _A						):
																					# Test that model correctly compute the loss with kwargs
																					UpperCamelCase							:		Optional[int]							      = self._prepare_for_class(inputs_dict.copy()	,      _A	,      return_labels=_A						)
																					# Is there a better way to remove these decoder inputs?
																					UpperCamelCase							:		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""")
																					}

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

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

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

																					UpperCamelCase							:		Dict							      = history.history["""loss"""][0]
																					self.assertTrue(not isnan(_A						)						)
																					UpperCamelCase							:		Optional[int]							      = history.history["""val_loss"""][0]
																					self.assertTrue(not isnan(_A						)						)







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		int							      = self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase							:		Dict							      = True
									UpperCamelCase							:		List[Any]							      = True

									if hasattr(_A	,      """use_cache"""						):
															UpperCamelCase							:		str							      = True

									UpperCamelCase							:		Optional[int]							      = getattr(self.model_tester	,      """encoder_seq_length"""	,      self.model_tester.seq_length						)
									UpperCamelCase							:		str							      = getattr(self.model_tester	,      """key_length"""	,      _A						)

									for model_class in self.all_model_classes[:2]:
															UpperCamelCase							:		List[str]							      = self._prepare_for_class(_A	,      _A						)
															UpperCamelCase							:		Any							      = model_class(_A						)
															model.build()
															UpperCamelCase							:		Optional[int]							      = len(model(_A						)						)

															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							:		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(_A	,      saved_model=_A						)
																					UpperCamelCase							:		List[str]							      = os.path.join(_A	,      """saved_model"""	,      """1"""						)
																					UpperCamelCase							:		Dict							      = tf.keras.models.load_model(_A						)
																					UpperCamelCase							:		Dict							      = model(_A						)

																					if self.is_encoder_decoder:
																											UpperCamelCase							:		str							      = outputs["""encoder_hidden_states"""]
																											UpperCamelCase							:		int							      = outputs["""encoder_attentions"""]
																					else:
																											UpperCamelCase							:		List[str]							      = outputs["""hidden_states"""]
																											UpperCamelCase							:		Any							      = outputs["""attentions"""]

																					self.assertEqual(len(_A						)	,      _A						)

																					UpperCamelCase							:		Union[str, Any]							      = getattr(
																					    self.model_tester	,      """expected_num_hidden_layers"""	,      self.model_tester.num_hidden_layers + 1						)

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

																					self.assertEqual(len(_A						)	,      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						):






									'''simple docstring'''







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

									# try/finally block to ensure subsequent tests run in float32
									try:
															UpperCamelCase      ,	UpperCamelCase							:		Tuple							      = self.model_tester.prepare_config_and_inputs_for_common()
															for model_class in self.all_model_classes[:2]:
																					UpperCamelCase							:		Optional[Any]							      = self._prepare_for_class(_A	,      _A						)
																					UpperCamelCase							:		str							      = model_class(_A						)
																					UpperCamelCase							:		List[str]							      = model(_A						)

																					self.assertIsNotNone(_A						)
									finally:
															tf.keras.mixed_precision.set_global_policy("""float32"""						)







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		Dict							      = 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							:		int							      = {
									    module_member
									    for model_class in self.all_model_classes
									    for module in (import_module(model_class.__module__						),)
									    for module_member_name in dir(_A						)
									    if module_member_name.endswith("""MainLayer"""						)
									    for module_member in (getattr(_A	,      _A						),)
									    if isinstance(_A	,      _A						)
									    and tf.keras.layers.Layer in module_member.__bases__
									    and getattr(_A	,      """_keras_serializable"""	,      _A						)
									}

									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							:		Optional[int]							      = TFSharedEmbeddings(self.model_tester.vocab_size	,      self.model_tester.hidden_size	,      name="""shared"""						)
																					UpperCamelCase							:		Optional[int]							      = False
																					UpperCamelCase							:		Union[str, Any]							      = main_layer_class(_A	,      embed_tokens=_A						)
															else:
																					UpperCamelCase							:		List[str]							      = main_layer_class(_A						)

															UpperCamelCase							:		str							      = {
															    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							:		Any							      = self.model_tester.num_labels
															else:
																					UpperCamelCase							:		List[str]							      = 2

															UpperCamelCase							:		Tuple							      = 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							:		List[Any]							      = main_layer(_A						)[0]
															UpperCamelCase							:		Union[str, Any]							      = tf.keras.layers.Dense(_A	,      activation="""softmax"""	,      name="""outputs"""						)(_A						)
															UpperCamelCase							:		List[str]							      = tf.keras.models.Model(inputs=_A	,      outputs=[outputs]						)

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

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







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		int							      = self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes[:2]:
															UpperCamelCase							:		Any							      = model_class(_A						)

															UpperCamelCase							:		Optional[int]							      = copy.deepcopy(_A						)

															if not self.is_encoder_decoder:
																					UpperCamelCase							:		Any							      = inputs["""input_ids"""]
																					del inputs["input_ids"]
															else:
																					UpperCamelCase							:		int							      = inputs["""input_ids"""]
																					UpperCamelCase							:		List[str]							      = inputs.get("""decoder_input_ids"""	,      _A						)
																					del inputs["input_ids"]
																					inputs.pop("""decoder_input_ids"""	,      _A						)

															if not self.is_encoder_decoder:
																					UpperCamelCase							:		Any							      = model.get_input_embeddings()(_A						)
															else:
																					UpperCamelCase							:		Dict							      = model.get_input_embeddings()(_A						)
																					UpperCamelCase							:		str							      = model.get_input_embeddings()(_A						)

															UpperCamelCase							:		Tuple							      = self._prepare_for_class(_A	,      _A						)

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

															UpperCamelCase							:		int							      = run_in_graph_mode()
															self.assertIsNotNone(_A						)







			def      _a			(			self	,      _A	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		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
									UpperCamelCase							:		Any							      = []
									while len(_A						) < num_bad_tokens:
															UpperCamelCase							:		Any							      = tf.squeeze(ids_tensor((1, 1)	,      self.model_tester.vocab_size						)	,      0						).numpy()[0]
															if token not in special_tokens:
																					bad_tokens.append(_A						)
									return bad_tokens







			def      _a			(			self	,      _A						):






									'''simple docstring'''







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







			def      _a			(			self	,      _A	,      _A						):






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










"""simple docstring"""


def     UpperCamelCase       (SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE    ):
						UpperCamelCase							:		int							      = len(SCREAMING_SNAKE_CASE    )
						UpperCamelCase							:		int							      = len(SCREAMING_SNAKE_CASE    )
						UpperCamelCase							:		int							      = (
						    first_input_list_length
						    if first_input_list_length > second_input_list_length
						    else second_input_list_length
						)
						UpperCamelCase							:		list							      = []
						for char_count in range(SCREAMING_SNAKE_CASE    ):
												if char_count < first_input_list_length:
																		output_result_list.append(first_input_list[char_count]    )
												if char_count < second_input_list_length:
																		output_result_list.append(second_input_list[char_count]    )

						return output_result_list


if __name__ == "__main__":
		print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)



