


'''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



'''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."