code: 
  
  
  '''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:
  														__lowercase:		List[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)]
  																												)
  																												__lowercase:		Optional[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  UpperCAmelCase :
  	_lowerCamelCase :					Any    					=					None
  	_lowerCamelCase :					str    					=					()
  	_lowerCamelCase :					List[Any]    					=					()
  	_lowerCamelCase :					List[Any]    					=					True
  	_lowerCamelCase :					Optional[int]    					=					True
  	_lowerCamelCase :					Any    					=					True
  	_lowerCamelCase :					List[str]    					=					False
  
  	def 		lowercase_						( self :	Optional[Any],					a_ :	Union[str, Any],					a_ :	Union[str, Any],					a_ :	Dict=False					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						copy.deepcopy(a_					)
  
  				if model_class in get_values(a_					):
  							UpperCamelCase__ 						=						{
  							    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__ 						=						tf.ones(self.model_tester.batch_size,					dtype=tf.intaa					)
  							elif model_class in get_values(a_					):
  										UpperCamelCase__ 						=						tf.zeros(self.model_tester.batch_size,					dtype=tf.intaa					)
  										UpperCamelCase__ 						=						tf.zeros(self.model_tester.batch_size,					dtype=tf.intaa					)
  							elif model_class in [
  							    *get_values(a_					),
  							    *get_values(a_					),
  							]:
  										UpperCamelCase__ 						=						tf.zeros(self.model_tester.batch_size,					dtype=tf.intaa					)
  							elif model_class in get_values(a_					):
  										UpperCamelCase__ 						=						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__ 						=						tf.zeros(
  										    (self.model_tester.batch_size, self.model_tester.seq_length),					dtype=tf.intaa					)
  				return inputs_dict
  
  	@slow
  	def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__						,		UpperCamelCase__ 						=						self.model_tester.prepare_config_and_inputs_for_common()
  				for model_class in self.all_model_classes[:2]:
  							UpperCamelCase__ 						=						self._prepare_for_class(a_,					a_					)
  							UpperCamelCase__ 						=						model_class(a_					)
  
  							@tf.function
  							def run_in_graph_mode():
  										return model(a_					)
  
  							UpperCamelCase__ 						=						run_in_graph_mode()
  							self.assertIsNotNone(a_					)
  
  	@slow
  	def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__						,		UpperCamelCase__ 						=						self.model_tester.prepare_config_and_inputs_for_common()
  				for model_class in self.all_model_classes[:2]:
  							UpperCamelCase__ 						=						self._prepare_for_class(a_,					a_					)
  							UpperCamelCase__ 						=						model_class(a_					)
  
  							@tf.function(experimental_compile=a_					)
  							def run_in_graph_mode():
  										return model(a_					)
  
  							UpperCamelCase__ 						=						run_in_graph_mode()
  							self.assertIsNotNone(a_					)
  
  	@slow
  	def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__						,		UpperCamelCase__ 						=						self.model_tester.prepare_config_and_inputs_for_common()
  				for model_class in self.all_model_classes[:2]:
  							UpperCamelCase__ 						=						model_class(a_					)
  							if getattr(a_,					"hf_compute_loss",					a_					):
  										# Test that model correctly compute the loss with kwargs
  										UpperCamelCase__ 						=						self._prepare_for_class(inputs_dict.copy(),					a_,					return_labels=a_					)
  										# Is there a better way to remove these decoder inputs?
  										UpperCamelCase__ 						=						{
  										    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__ 						=						{
  										    "labels",
  										    "label",
  										    "label_ids",
  										    "start_positions",
  										    "start_position",
  										    "end_positions",
  										    "end_position",
  										    "next_sentence_label",
  										}
  										UpperCamelCase__ 						=						possible_label_cols.intersection(set(a_					)					)
  										self.assertGreater(len(a_					),					0,					msg="No matching label names found!"					)
  										UpperCamelCase__ 						=						{key: val for key, val in prepared_for_class.items() if key in label_names}
  										UpperCamelCase__ 						=						{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__ 						=						model.fit(
  										    a_,					validation_data=a_,					steps_per_epoch=1,					validation_steps=1,					shuffle=a_,					verbose=0,					)
  										UpperCamelCase__ 						=						history.history["loss"][0]
  										self.assertTrue(not isnan(a_					)					)
  										UpperCamelCase__ 						=						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__ 						=						model_class(a_					)
  										model.compile(optimizer=tf.keras.optimizers.SGD(0.0					),					jit_compile=a_					)
  										UpperCamelCase__ 						=						model.fit(
  										    a_,					a_,					validation_data=(inputs_minus_labels, labels),					steps_per_epoch=1,					validation_steps=1,					shuffle=a_,					verbose=0,					)
  
  										UpperCamelCase__ 						=						history.history["loss"][0]
  										self.assertTrue(not isnan(a_					)					)
  										UpperCamelCase__ 						=						history.history["val_loss"][0]
  										self.assertTrue(not isnan(a_					)					)
  
  	@slow
  	def 		lowercase_						( self :	Tuple					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__						,		UpperCamelCase__ 						=						self.model_tester.prepare_config_and_inputs_for_common()
  				UpperCamelCase__ 						=						True
  				UpperCamelCase__ 						=						True
  
  				if hasattr(a_,					"use_cache"					):
  							UpperCamelCase__ 						=						True
  
  				UpperCamelCase__ 						=						getattr(self.model_tester,					"encoder_seq_length",					self.model_tester.seq_length					)
  				UpperCamelCase__ 						=						getattr(self.model_tester,					"key_length",					a_					)
  
  				for model_class in self.all_model_classes[:2]:
  							UpperCamelCase__ 						=						self._prepare_for_class(a_,					a_					)
  							UpperCamelCase__ 						=						model_class(a_					)
  							model.build()
  							UpperCamelCase__ 						=						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__ 						=						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__ 						=						os.path.join(a_,					"saved_model",					"1"					)
  										UpperCamelCase__ 						=						tf.keras.models.load_model(a_					)
  										UpperCamelCase__ 						=						model(a_					)
  
  										if self.is_encoder_decoder:
  													UpperCamelCase__ 						=						outputs["encoder_hidden_states"]
  													UpperCamelCase__ 						=						outputs["encoder_attentions"]
  										else:
  													UpperCamelCase__ 						=						outputs["hidden_states"]
  													UpperCamelCase__ 						=						outputs["attentions"]
  
  										self.assertEqual(len(a_					),					a_					)
  
  										UpperCamelCase__ 						=						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 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				tf.keras.mixed_precision.set_global_policy("mixed_float16"					)
  
  				# try/finally block to ensure subsequent tests run in float32
  				try:
  							UpperCamelCase__						,		UpperCamelCase__ 						=						self.model_tester.prepare_config_and_inputs_for_common()
  							for model_class in self.all_model_classes[:2]:
  										UpperCamelCase__ 						=						self._prepare_for_class(a_,					a_					)
  										UpperCamelCase__ 						=						model_class(a_					)
  										UpperCamelCase__ 						=						model(a_					)
  
  										self.assertIsNotNone(a_					)
  				finally:
  							tf.keras.mixed_precision.set_global_policy("float32"					)
  
  	@slow
  	def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__						,		UpperCamelCase__ 						=						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__ 						=						{
  				    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__ 						=						TFSharedEmbeddings(self.model_tester.vocab_size,					self.model_tester.hidden_size,					name="shared"					)
  										UpperCamelCase__ 						=						False
  										UpperCamelCase__ 						=						main_layer_class(a_,					embed_tokens=a_					)
  							else:
  										UpperCamelCase__ 						=						main_layer_class(a_					)
  
  							UpperCamelCase__ 						=						{
  							    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__ 						=						self.model_tester.num_labels
  							else:
  										UpperCamelCase__ 						=						2
  
  							UpperCamelCase__ 						=						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__ 						=						main_layer(a_					)[0]
  							UpperCamelCase__ 						=						tf.keras.layers.Dense(a_,					activation="softmax",					name="outputs"					)(a_					)
  							UpperCamelCase__ 						=						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__ 						=						os.path.join(a_,					"keras_model.h5"					)
  										model.save(a_					)
  										if "T5" in main_layer_class.__name__:
  													UpperCamelCase__ 						=						tf.keras.models.load_model(
  													    a_,					custom_objects={
  													        main_layer_class.__name__: main_layer_class,
  													        "TFSharedEmbeddings": TFSharedEmbeddings,
  													    },					)
  										else:
  													UpperCamelCase__ 						=						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 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__						,		UpperCamelCase__ 						=						self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes[:2]:
  							UpperCamelCase__ 						=						model_class(a_					)
  
  							UpperCamelCase__ 						=						copy.deepcopy(a_					)
  
  							if not self.is_encoder_decoder:
  										UpperCamelCase__ 						=						inputs["input_ids"]
  										del inputs["input_ids"]
  							else:
  										UpperCamelCase__ 						=						inputs["input_ids"]
  										UpperCamelCase__ 						=						inputs.get("decoder_input_ids",					a_					)
  										del inputs["input_ids"]
  										inputs.pop("decoder_input_ids",					a_					)
  
  							if not self.is_encoder_decoder:
  										UpperCamelCase__ 						=						model.get_input_embeddings()(a_					)
  							else:
  										UpperCamelCase__ 						=						model.get_input_embeddings()(a_					)
  										UpperCamelCase__ 						=						model.get_input_embeddings()(a_					)
  
  							UpperCamelCase__ 						=						self._prepare_for_class(a_,					a_					)
  
  							@tf.function
  							def run_in_graph_mode():
  										return model(a_					)
  
  							UpperCamelCase__ 						=						run_in_graph_mode()
  							self.assertIsNotNone(a_					)
  
  	def 		lowercase_						( self :	Optional[Any],					a_ :	str,					a_ :	Optional[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						[]
  				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__ 						=						[]
  				while len(a_					) < num_bad_tokens:
  							UpperCamelCase__ 						=						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 		lowercase_						( self :	List[Any],					a_ :	List[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				for token_id in output_ids[0].numpy().tolist():
  							self.assertGreaterEqual(a_,					0					)
  							self.assertLess(a_,					self.model_tester.vocab_size					)
  
  	def 		lowercase_						( self :	Optional[Any],					a_ :	List[Any],					a_ :	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(a_					),					len(a_					)					):
  													# if tokens match
  													if generated_ids_slice[i - len(a_					) : i] == bad_word_ids:
  																return True
  				return False
style_context: 
  
  
  '''simple docstring'''
  
  from dataclasses import dataclass
  
  __lowercase:		List[str]							=     "0.1"
  __lowercase:		Union[str, Any]							=     "Lucia Harcekova"
  
  
  
  
  
  
  
  @dataclass
  class  UpperCAmelCase :
   _lowerCamelCase :					int
   _lowerCamelCase :					int
   _lowerCamelCase :					str
  
   def __repr__( self :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return f'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
   def __init__( self :	Optional[int],					a_ :	int = 13,					a_ :	int = 6					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						window_size
      UpperCamelCase__ 						=						lookahead_buffer_size
      UpperCamelCase__ 						=						self.window_size - self.lookahead_buffer_size
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[]
      UpperCamelCase__ 						=						""
  
      # while there are still characters in text to compress
      while text:
         # find the next encoding phrase
         # - triplet with offset, length, indicator (the next encoding character)
         UpperCamelCase__ 						=						self._find_encoding_token(a_,					a_					)
  
         # update the search buffer:
         # - add new characters from text into it
         # - check if size exceed the max search buffer size, if so, drop the
         #   oldest elements
         search_buffer += text[: token.length + 1]
         if len(a_					) > self.search_buffer_size:
            UpperCamelCase__ 						=						search_buffer[-self.search_buffer_size :]
  
         # update the text
         UpperCamelCase__ 						=						text[token.length + 1 :]
  
         # append the token to output
         output.append(a_					)
  
      return output
  
   def 		lowercase_						( self :	List[Any],					a_ :	list[Token]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						""
  
      for token in tokens:
         for _ in range(token.length					):
            output += output[-token.offset]
         output += token.indicator
  
      return output
  
   def 		lowercase_						( self :	Any,					a_ :	str,					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text:
         raise ValueError("We need some text to work with."					)
  
      # Initialise result parameters to default values
      UpperCamelCase__						,		UpperCamelCase__ 						=						0, 0
  
      if not search_buffer:
         return Token(a_,					a_,					text[length]					)
  
      for i, character in enumerate(a_					):
         UpperCamelCase__ 						=						len(a_					) - i
         if character == text[0]:
            UpperCamelCase__ 						=						self._match_length_from_index(a_,					a_,					0,					a_					)
            # if the found length is bigger than the current or if it's equal,
            # which means it's offset is smaller: update offset and length
            if found_length >= length:
               UpperCamelCase__						,		UpperCamelCase__ 						=						found_offset, found_length
  
      return Token(a_,					a_,					text[length]					)
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str,					a_ :	str,					a_ :	int,					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text or text[text_index] != window[window_index]:
         return 0
      return 1 + self._match_length_from_index(
          a_,					window + text[text_index],					text_index + 1,					window_index + 1					)
  
  
  if __name__ == "__main__":
         from doctest import testmod
  
         testmod()
         # Initialize compressor class
         __lowercase:		Optional[Any]							=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
         # Example
         __lowercase:		Union[str, Any]							=     "cabracadabrarrarrad"
         __lowercase:		Optional[int]							=     lzaa_compressor.compress(TEXT)
         print(lzaa_compressor.compress("ababcbababaa"))
         __lowercase:		Optional[int]							=     lzaa_compressor.decompress(compressed_text)
         assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."