



'''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:
										snake_case_				:			Optional[int]						 =						tf.config.list_physical_devices('GPU')
										for gpu in gpus:
															# Restrict TensorFlow to only allocate x GB of memory on the GPUs
															try:
																				tf.config.set_logical_device_configuration(
																				    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
																				)
																				snake_case_				:			Union[str, Any]						 =						tf.config.list_logical_devices('GPU')
																				print('Logical GPUs', logical_gpus)
															except RuntimeError as e:
																				# Virtual devices must be set before GPUs have been initialized
																				print(e)






@require_tf
class 							lowercase__			:
	lowercase__						       =    None
	lowercase__						       =    ()
	lowercase__						       =    ()
	lowercase__						       =    True
	lowercase__						       =    True
	lowercase__						       =    True
	lowercase__						       =    False





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       Tuple				,lowerCamelCase__      :       str				,lowerCamelCase__      :       Optional[Any]=False				):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[int]				       =     copy.deepcopy(lowerCamelCase__				)

							if model_class in get_values(lowerCamelCase__				):
													_UpperCamelCase      :	Any				       =     {
													    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__				):
																			_UpperCamelCase      :	List[str]				       =     tf.ones(self.model_tester.batch_size				,dtype=tf.intaa				)
													elif model_class in get_values(lowerCamelCase__				):
																			_UpperCamelCase      :	List[Any]				       =     tf.zeros(self.model_tester.batch_size				,dtype=tf.intaa				)
																			_UpperCamelCase      :	Tuple				       =     tf.zeros(self.model_tester.batch_size				,dtype=tf.intaa				)
													elif model_class in [
													    *get_values(lowerCamelCase__				),
													    *get_values(lowerCamelCase__				),
													]:
																			_UpperCamelCase      :	Any				       =     tf.zeros(self.model_tester.batch_size				,dtype=tf.intaa				)
													elif model_class in get_values(lowerCamelCase__				):
																			_UpperCamelCase      :	int				       =     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__				),
													]:
																			_UpperCamelCase      :	Dict				       =     tf.zeros(
																			    (self.model_tester.batch_size, self.model_tester.seq_length)				,dtype=tf.intaa				)
							return inputs_dict





	@slow
	def 			UpperCamelCase_						(				self      :       Any				):







							'''simple docstring'''
							_UpperCamelCase      :	Any				       =     self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes[:2]:
													_UpperCamelCase      :	List[Any]				       =     self._prepare_for_class(lowerCamelCase__				,lowerCamelCase__				)
													_UpperCamelCase      :	str				       =     model_class(lowerCamelCase__				)

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

													_UpperCamelCase      :	Union[str, Any]				       =     run_in_graph_mode()
													self.assertIsNotNone(lowerCamelCase__				)





	@slow
	def 			UpperCamelCase_						(				self      :       Dict				):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[int]				       =     self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes[:2]:
													_UpperCamelCase      :	int				       =     self._prepare_for_class(lowerCamelCase__				,lowerCamelCase__				)
													_UpperCamelCase      :	Optional[Any]				       =     model_class(lowerCamelCase__				)

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

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





	@slow
	def 			UpperCamelCase_						(				self      :       Dict				):







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

																			_UpperCamelCase      :	List[str]				       =     {
																			    'labels',
																			    'label',
																			    'label_ids',
																			    'start_positions',
																			    'start_position',
																			    'end_positions',
																			    'end_position',
																			    'next_sentence_label',
																			}
																			_UpperCamelCase      :	int				       =     possible_label_cols.intersection(set(lowerCamelCase__				)				)
																			self.assertGreater(len(lowerCamelCase__				)				,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      :	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
																			_UpperCamelCase      :	Dict				       =     model.fit(
																			    lowerCamelCase__				,validation_data=lowerCamelCase__				,steps_per_epoch=1				,validation_steps=1				,shuffle=lowerCamelCase__				,verbose=0				,)
																			_UpperCamelCase      :	int				       =     history.history['loss'][0]
																			self.assertTrue(not isnan(lowerCamelCase__				)				)
																			_UpperCamelCase      :	str				       =     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.
																			_UpperCamelCase      :	int				       =     model_class(lowerCamelCase__				)
																			model.compile(optimizer=tf.keras.optimizers.SGD(0.0				)				,jit_compile=lowerCamelCase__				)
																			_UpperCamelCase      :	Optional[Any]				       =     model.fit(
																			    lowerCamelCase__				,lowerCamelCase__				,validation_data=(inputs_minus_labels, labels)				,steps_per_epoch=1				,validation_steps=1				,shuffle=lowerCamelCase__				,verbose=0				,)

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





	@slow
	def 			UpperCamelCase_						(				self      :       Optional[int]				):







							'''simple docstring'''
							_UpperCamelCase      :	Union[str, Any]				       =     self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCamelCase      :	List[str]				       =     True
							_UpperCamelCase      :	Any				       =     True

							if hasattr(lowerCamelCase__				,'use_cache'				):
													_UpperCamelCase      :	Tuple				       =     True

							_UpperCamelCase      :	Tuple				       =     getattr(self.model_tester				,'encoder_seq_length'				,self.model_tester.seq_length				)
							_UpperCamelCase      :	int				       =     getattr(self.model_tester				,'key_length'				,lowerCamelCase__				)

							for model_class in self.all_model_classes[:2]:
													_UpperCamelCase      :	str				       =     self._prepare_for_class(lowerCamelCase__				,lowerCamelCase__				)
													_UpperCamelCase      :	Optional[Any]				       =     model_class(lowerCamelCase__				)
													model.build()
													_UpperCamelCase      :	List[Any]				       =     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:
																									_UpperCamelCase      :	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__				)
																			_UpperCamelCase      :	str				       =     os.path.join(lowerCamelCase__				,'saved_model'				,'1'				)
																			_UpperCamelCase      :	Dict				       =     tf.keras.models.load_model(lowerCamelCase__				)
																			_UpperCamelCase      :	List[str]				       =     model(lowerCamelCase__				)

																			if self.is_encoder_decoder:
																									_UpperCamelCase      :	Any				       =     outputs['encoder_hidden_states']
																									_UpperCamelCase      :	Optional[Any]				       =     outputs['encoder_attentions']
																			else:
																									_UpperCamelCase      :	str				       =     outputs['hidden_states']
																									_UpperCamelCase      :	Tuple				       =     outputs['attentions']

																			self.assertEqual(len(lowerCamelCase__				)				,lowerCamelCase__				)

																			_UpperCamelCase      :	int				       =     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 			UpperCamelCase_						(				self      :       List[Any]				):







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

							# try/finally block to ensure subsequent tests run in float32
							try:
													_UpperCamelCase      :	Optional[int]				       =     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(lowerCamelCase__				,lowerCamelCase__				)
																			_UpperCamelCase      :	str				       =     model_class(lowerCamelCase__				)
																			_UpperCamelCase      :	List[str]				       =     model(lowerCamelCase__				)

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





	@slow
	def 			UpperCamelCase_						(				self      :       Any				):







							'''simple docstring'''
							_UpperCamelCase      :	List[Any]				       =     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      :	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(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
																			_UpperCamelCase      :	int				       =     TFSharedEmbeddings(self.model_tester.vocab_size				,self.model_tester.hidden_size				,name='shared'				)
																			_UpperCamelCase      :	str				       =     False
																			_UpperCamelCase      :	Dict				       =     main_layer_class(lowerCamelCase__				,embed_tokens=lowerCamelCase__				)
													else:
																			_UpperCamelCase      :	Dict				       =     main_layer_class(lowerCamelCase__				)

													_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      :	Optional[Any]				       =     self.model_tester.num_labels
													else:
																			_UpperCamelCase      :	Optional[int]				       =     2

													_UpperCamelCase      :	Any				       =     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      :	int				       =     main_layer(lowerCamelCase__				)[0]
													_UpperCamelCase      :	List[str]				       =     tf.keras.layers.Dense(lowerCamelCase__				,activation='softmax'				,name='outputs'				)(lowerCamelCase__				)
													_UpperCamelCase      :	Dict				       =     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:
																			_UpperCamelCase      :	Union[str, Any]				       =     os.path.join(lowerCamelCase__				,'keras_model.h5'				)
																			model.save(lowerCamelCase__				)
																			if "T5" in main_layer_class.__name__:
																									_UpperCamelCase      :	int				       =     tf.keras.models.load_model(
																									    lowerCamelCase__				,custom_objects={
																									        main_layer_class.__name__: main_layer_class,
																									        'TFSharedEmbeddings': TFSharedEmbeddings,
																									    }				,)
																			else:
																									_UpperCamelCase      :	Optional[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 			UpperCamelCase_						(				self      :       Any				):







							'''simple docstring'''
							_UpperCamelCase      :	str				       =     self.model_tester.prepare_config_and_inputs_for_common()

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

													_UpperCamelCase      :	int				       =     copy.deepcopy(lowerCamelCase__				)

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

													if not self.is_encoder_decoder:
																			_UpperCamelCase      :	Any				       =     model.get_input_embeddings()(lowerCamelCase__				)
													else:
																			_UpperCamelCase      :	Optional[int]				       =     model.get_input_embeddings()(lowerCamelCase__				)
																			_UpperCamelCase      :	Optional[int]				       =     model.get_input_embeddings()(lowerCamelCase__				)

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

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

													_UpperCamelCase      :	str				       =     run_in_graph_mode()
													self.assertIsNotNone(lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       str				,lowerCamelCase__      :       Tuple				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[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      :	Union[str, Any]				       =     []
							while len(lowerCamelCase__				) < num_bad_tokens:
													_UpperCamelCase      :	List[str]				       =     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 			UpperCamelCase_						(				self      :       Optional[int]				,lowerCamelCase__      :       Optional[Any]				):







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





	def 			UpperCamelCase_						(				self      :       int				,lowerCamelCase__      :       Tuple				,lowerCamelCase__      :       Optional[int]				):







							'''simple docstring'''
							# for all bad word tokens
							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







'''simple docstring'''
def A__					(  UpperCAmelCase_							):
						_UpperCamelCase      :	Optional[Any]				       =     word.upper()
						_UpperCamelCase      :	Optional[Any]				       =     0
						_UpperCamelCase      :	Dict				       =     {}
						_UpperCamelCase      :	List[str]				       =     []

						for letter in word:
												if letter not in letter_nums:
																		_UpperCamelCase      :	int				       =     str(UpperCAmelCase_							)
																		next_num += 1
												word_pattern.append(letter_nums[letter]							)
						return ".".join(UpperCAmelCase_							)


if __name__ == "__main__":
					import pprint
					import time

					snake_case_				:			List[Any]						 =						time.time()
					with open('dictionary.txt') as in_file:
										snake_case_				:			Any						 =						in_file.read().splitlines()

					snake_case_				:			dict						 =						{}
					for word in word_list:
										snake_case_				:			Optional[int]						 =						get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															snake_case_				:			Any						 =						[word]

					with open('word_patterns.txt', 'w') as out_file:
										out_file.write(pprint.pformat(all_patterns))

					snake_case_				:			List[str]						 =						round(time.time() - start_time, 2)
					print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
					# Done!  9,581 word patterns found in 0.58 seconds.


