


import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
       __lowerCamelCase   	=  True
       __lowerCamelCase   	=  '''p3dn.24xlarge'''
       import smdistributed.dataparallel.tensorflow as sdp
else:
       __lowerCamelCase   	=  False



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase=None			):
     a_     :  int				      =     tqdm(__UpperCamelCase			)
     for i, batch in enumerate(__UpperCamelCase			):
          with tf.GradientTape() as tape:
               a_      ,      a_     :  List[Any]				      =     batch
               a_     :  List[str]				      =     model(__UpperCamelCase			)
               a_     :  Dict				      =     loss(__UpperCamelCase	,	outputs.logits			)

          if SDP_ENABLED:
               a_     :  Dict				      =     sdp.DistributedGradientTape(__UpperCamelCase	,	sparse_as_dense=__UpperCamelCase			)

          a_     :  Optional[Any]				      =     tape.gradient(__UpperCamelCase	,	model.trainable_variables			)
          opt.apply_gradients(zip(__UpperCamelCase	,	model.trainable_variables			)			)

          pbar.set_description(F'''Loss: {loss_value:.4f}'''			)

          if SDP_ENABLED and i == 0:
               sdp.broadcast_variables(model.variables	,	root_rank=0			)
               sdp.broadcast_variables(opt.variables()	,	root_rank=0			)

          if max_steps and i >= max_steps:
               break

     a_     :  List[str]				      =     {"""loss""": loss_value.numpy()}
     return train_results



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     # Load dataset
     a_      ,      a_     :  int				      =     load_dataset("""imdb"""	,	split=["""train""", """test"""]			)

     # Preprocess train dataset
     a_     :  Optional[Any]				      =     train_dataset.map(
         lambda __UpperCamelCase			: tokenizer(e["""text"""]	,	truncation=__UpperCamelCase	,	padding="""max_length"""			)	,	batched=__UpperCamelCase			)
     train_dataset.set_format(type="""tensorflow"""	,	columns=["""input_ids""", """attention_mask""", """label"""]			)

     a_     :  Optional[Any]				      =     {
         x: train_dataset[x].to_tensor(default_value=0	,	shape=[None, tokenizer.model_max_length]			)
         for x in ["""input_ids""", """attention_mask"""]
     }
     a_     :  int				      =     tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])			)

     # Preprocess test dataset
     a_     :  Any				      =     test_dataset.map(
         lambda __UpperCamelCase			: tokenizer(e["""text"""]	,	truncation=__UpperCamelCase	,	padding="""max_length"""			)	,	batched=__UpperCamelCase			)
     test_dataset.set_format(type="""tensorflow"""	,	columns=["""input_ids""", """attention_mask""", """label"""]			)

     a_     :  int				      =     {
         x: test_dataset[x].to_tensor(default_value=0	,	shape=[None, tokenizer.model_max_length]			)
         for x in ["""input_ids""", """attention_mask"""]
     }
     a_     :  Optional[int]				      =     tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])			)

     if SDP_ENABLED:
          a_     :  int				      =     tf_train_dataset.shard(sdp.size()	,	sdp.rank()			)
          a_     :  Optional[Any]				      =     tf_test_dataset.shard(sdp.size()	,	sdp.rank()			)
     a_     :  str				      =     tf_train_dataset.batch(__UpperCamelCase	,	drop_remainder=__UpperCamelCase			)
     a_     :  List[str]				      =     tf_test_dataset.batch(__UpperCamelCase	,	drop_remainder=__UpperCamelCase			)

     return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
       __lowerCamelCase   	=  argparse.ArgumentParser()

       # Hyperparameters sent by the client are passed as command-line arguments to the script.
       parser.add_argument('''--epochs''', type=int, default=3)
       parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
       parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
       parser.add_argument('''--model_name_or_path''', type=str)
       parser.add_argument('''--learning_rate''', type=str, default=5e-5)
       parser.add_argument('''--do_train''', type=bool, default=True)
       parser.add_argument('''--do_eval''', type=bool, default=True)
       parser.add_argument('''--output_dir''', type=str)
       parser.add_argument('''--max_steps''', type=int, default=None)

       # Data, model, and output directories
       parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
       parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
       parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

       __lowerCamelCase	,							__lowerCamelCase   	=  parser.parse_known_args()

       # Set up logging
       __lowerCamelCase   	=  logging.getLogger(__name__)

       logging.basicConfig(
           level=logging.getLevelName('''INFO'''),
           handlers=[logging.StreamHandler(sys.stdout)],
           format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
       )

       if SDP_ENABLED:
              sdp.init()

              __lowerCamelCase   	=  tf.config.experimental.list_physical_devices('''GPU''')
              for gpu in gpus:
                     tf.config.experimental.set_memory_growth(gpu, True)
              if gpus:
                     tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
       __lowerCamelCase   	=  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       __lowerCamelCase   	=  AutoTokenizer.from_pretrained(args.model_name_or_path)

       # get datasets
       __lowerCamelCase	,							__lowerCamelCase   	=  get_datasets(
           tokenizer=tokenizer,
           train_batch_size=args.per_device_train_batch_size,
           eval_batch_size=args.per_device_eval_batch_size,
       )

       # fine optimizer and loss
       __lowerCamelCase   	=  tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
       __lowerCamelCase   	=  tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
       __lowerCamelCase   	=  [tf.keras.metrics.SparseCategoricalAccuracy()]
       model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

       # Training
       if args.do_train:
              # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
              __lowerCamelCase   	=  time.time()
              __lowerCamelCase   	=  fit(
                  model,
                  loss,
                  optimizer,
                  tf_train_dataset,
                  args.epochs,
                  args.per_device_train_batch_size,
                  max_steps=args.max_steps,
              )
              __lowerCamelCase   	=  time.time() - start_train_time
              logger.info('''*** Train ***''')
              logger.info(f'''train_runtime = {end_train_time}''')

              __lowerCamelCase   	=  os.path.join(args.output_dir, '''train_results.txt''')

              if not SDP_ENABLED or sdp.rank() == 0:
                     with open(output_eval_file, '''w''') as writer:
                            logger.info('''***** Train results *****''')
                            logger.info(train_results)
                            for key, value in train_results.items():
                                   logger.info(f'''  {key} = {value}''')
                                   writer.write(f'''{key} = {value}\n''')

    # Evaluation
       if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
              __lowerCamelCase   	=  model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
              logger.info('''*** Evaluate ***''')

              __lowerCamelCase   	=  os.path.join(args.output_dir, '''eval_results.txt''')

              with open(output_eval_file, '''w''') as writer:
                     logger.info('''***** Eval results *****''')
                     logger.info(result)
                     for key, value in result.items():
                            logger.info(f'''  {key} = {value}''')
                            writer.write(f'''{key} = {value}\n''')

    # Save result
       if SDP_ENABLED:
              if sdp.rank() == 0:
                     model.save_pretrained(args.output_dir)
                     tokenizer.save_pretrained(args.output_dir)
       else:
              model.save_pretrained(args.output_dir)
              tokenizer.save_pretrained(args.output_dir)










'''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:
				A :			Tuple							   =      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)]
								)
								A :			Tuple							   =      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       lowerCamelCase       :
					_SCREAMING_SNAKE_CASE				 =     None
					_SCREAMING_SNAKE_CASE				 =     ()
					_SCREAMING_SNAKE_CASE				 =     ()
					_SCREAMING_SNAKE_CASE				 =     True
					_SCREAMING_SNAKE_CASE				 =     True
					_SCREAMING_SNAKE_CASE				 =     True
					_SCREAMING_SNAKE_CASE				 =     False


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	int							,  __snake_case						:	Tuple=False							):





											'''simple docstring'''





											_snake_case: Tuple	       =		copy.deepcopy(__snake_case							)

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

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


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[int]	       =		self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes[:2]:
																	_snake_case: Tuple	       =		self._prepare_for_class(__snake_case							,  __snake_case							)
																	_snake_case: Dict	       =		model_class(__snake_case							)

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

																	_snake_case: Dict	       =		run_in_graph_mode()
																	self.assertIsNotNone(__snake_case							)


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[int]	       =		self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes[:2]:
																	_snake_case: Optional[Any]	       =		self._prepare_for_class(__snake_case							,  __snake_case							)
																	_snake_case: str	       =		model_class(__snake_case							)

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

																	_snake_case: str	       =		run_in_graph_mode()
																	self.assertIsNotNone(__snake_case							)


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: List[Any]	       =		self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes[:2]:
																	_snake_case: Dict	       =		model_class(__snake_case							)
																	if getattr(__snake_case							,  'hf_compute_loss'							,  __snake_case							):
																							# Test that model correctly compute the loss with kwargs
																							_snake_case: Tuple	       =		self._prepare_for_class(inputs_dict.copy()							,  __snake_case							,  return_labels=__snake_case							)
																							# Is there a better way to remove these decoder inputs?
																							_snake_case: Union[str, 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')
																							}

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

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

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

																							_snake_case: List[str]	       =		history.history['loss'][0]
																							self.assertTrue(not isnan(__snake_case							)							)
																							_snake_case: Optional[Any]	       =		history.history['val_loss'][0]
																							self.assertTrue(not isnan(__snake_case							)							)


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





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

											if hasattr(__snake_case							,  'use_cache'							):
																	_snake_case: Optional[Any]	       =		True

											_snake_case: Union[str, Any]	       =		getattr(self.model_tester							,  'encoder_seq_length'							,  self.model_tester.seq_length							)
											_snake_case: int	       =		getattr(self.model_tester							,  'key_length'							,  __snake_case							)

											for model_class in self.all_model_classes[:2]:
																	_snake_case: Tuple	       =		self._prepare_for_class(__snake_case							,  __snake_case							)
																	_snake_case: int	       =		model_class(__snake_case							)
																	model.build()
																	_snake_case: str	       =		len(model(__snake_case							)							)

																	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:
																													_snake_case: Dict	       =		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(__snake_case							,  saved_model=__snake_case							)
																							_snake_case: int	       =		os.path.join(__snake_case							,  'saved_model'							,  '1'							)
																							_snake_case: Optional[Any]	       =		tf.keras.models.load_model(__snake_case							)
																							_snake_case: Any	       =		model(__snake_case							)

																							if self.is_encoder_decoder:
																													_snake_case: str	       =		outputs['encoder_hidden_states']
																													_snake_case: Union[str, Any]	       =		outputs['encoder_attentions']
																							else:
																													_snake_case: Optional[int]	       =		outputs['hidden_states']
																													_snake_case: Dict	       =		outputs['attentions']

																							self.assertEqual(len(__snake_case							)							,  __snake_case							)

																							_snake_case: int	       =		getattr(
																							    self.model_tester							,  'expected_num_hidden_layers'							,  self.model_tester.num_hidden_layers + 1							)

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

																							self.assertEqual(len(__snake_case							)							,  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  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





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

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

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


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[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"]
											_snake_case: List[str]	       =		{
											    module_member
											    for model_class in self.all_model_classes
											    for module in (import_module(model_class.__module__							),)
											    for module_member_name in dir(__snake_case							)
											    if module_member_name.endswith('MainLayer'							)
											    for module_member in (getattr(__snake_case							,  __snake_case							),)
											    if isinstance(__snake_case							,  __snake_case							)
											    and tf.keras.layers.Layer in module_member.__bases__
											    and getattr(__snake_case							,  '_keras_serializable'							,  __snake_case							)
											}

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

																	_snake_case: 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'							):
																							_snake_case: Dict	       =		self.model_tester.num_labels
																	else:
																							_snake_case: Tuple	       =		2

																	_snake_case: 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							)

																	_snake_case: List[str]	       =		main_layer(__snake_case							)[0]
																	_snake_case: List[Any]	       =		tf.keras.layers.Dense(__snake_case							,  activation='softmax'							,  name='outputs'							)(__snake_case							)
																	_snake_case: List[str]	       =		tf.keras.models.Model(inputs=__snake_case							,  outputs=[outputs]							)

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

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


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[Any]	       =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes[:2]:
																	_snake_case: List[Any]	       =		model_class(__snake_case							)

																	_snake_case: Optional[int]	       =		copy.deepcopy(__snake_case							)

																	if not self.is_encoder_decoder:
																							_snake_case: Dict	       =		inputs['input_ids']
																							del inputs["input_ids"]
																	else:
																							_snake_case: Any	       =		inputs['input_ids']
																							_snake_case: List[str]	       =		inputs.get('decoder_input_ids'							,  __snake_case							)
																							del inputs["input_ids"]
																							inputs.pop('decoder_input_ids'							,  __snake_case							)

																	if not self.is_encoder_decoder:
																							_snake_case: str	       =		model.get_input_embeddings()(__snake_case							)
																	else:
																							_snake_case: Optional[int]	       =		model.get_input_embeddings()(__snake_case							)
																							_snake_case: List[str]	       =		model.get_input_embeddings()(__snake_case							)

																	_snake_case: str	       =		self._prepare_for_class(__snake_case							,  __snake_case							)

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

																	_snake_case: Optional[Any]	       =		run_in_graph_mode()
																	self.assertIsNotNone(__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Optional[Any]							,  __snake_case						:	List[Any]							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Tuple							):





											'''simple docstring'''





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




					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	List[str]							,  __snake_case						:	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(__snake_case							)							,  len(__snake_case							)							):
																													# if tokens match
																													if generated_ids_slice[i - len(__snake_case							) : i] == bad_word_ids:
																																			return True
											return False





