




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__lowerCAmelCase		:				Dict       			=		{
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				str       			=		['SpeechT5Tokenizer']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				int       			=		[
		    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'SpeechT5ForSpeechToText',
		    'SpeechT5ForSpeechToSpeech',
		    'SpeechT5ForTextToSpeech',
		    'SpeechT5Model',
		    'SpeechT5PreTrainedModel',
		    'SpeechT5HifiGan',
		]

if TYPE_CHECKING:
		from .configuration_speechta import (
		    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
		    SpeechTaConfig,
		    SpeechTaHifiGanConfig,
		)
		from .feature_extraction_speechta import SpeechTaFeatureExtractor
		from .processing_speechta import SpeechTaProcessor

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_speechta import SpeechTaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speechta import (
				    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechTaForSpeechToSpeech,
				    SpeechTaForSpeechToText,
				    SpeechTaForTextToSpeech,
				    SpeechTaHifiGan,
				    SpeechTaModel,
				    SpeechTaPreTrainedModel,
				)

else:
		import sys

		__lowerCAmelCase		:				Dict       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel




def 			__magic_name__				( A		:					Union[str, Any],  A		:					str,  A		:					Optional[int]=None,  A		:					List[str]=None   ):






			'''simple docstring'''







			if attention_mask is None:
						a    =			tf.cast(tf.math.not_equal(A,  config.pad_token_id   ),  tf.inta   )
			return {"input_ids": input_ids, "attention_mask": attention_mask}




@require_tf
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							int							 =					OPTConfig
				SCREAMING_SNAKE_CASE_       :							Dict							 =					{}
				SCREAMING_SNAKE_CASE_       :							List[str]							 =					"""gelu"""





				def __init__(      self				:     List[str]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]=13					,      __lowerCamelCase				:     int=7					,      __lowerCamelCase				:     Tuple=True					,      __lowerCamelCase				:     Union[str, Any]=False					,      __lowerCamelCase				:     Optional[Any]=99					,      __lowerCamelCase				:     List[Any]=16					,      __lowerCamelCase				:     List[str]=2					,      __lowerCamelCase				:     int=4					,      __lowerCamelCase				:     Any=4					,      __lowerCamelCase				:     Union[str, Any]="gelu"					,      __lowerCamelCase				:     Any=0.1					,      __lowerCamelCase				:     List[Any]=0.1					,      __lowerCamelCase				:     Dict=20					,      __lowerCamelCase				:     List[str]=2					,      __lowerCamelCase				:     Any=1					,      __lowerCamelCase				:     Any=0					,      __lowerCamelCase				:     Optional[Any]=16					,      __lowerCamelCase				:     Optional[Any]=16					,      )					->     Any:
							a    =			parent
							a    =			batch_size
							a    =			seq_length
							a    =			is_training
							a    =			use_labels
							a    =			vocab_size
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			num_attention_heads
							a    =			intermediate_size
							a    =			hidden_act
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			max_position_embeddings
							a    =			eos_token_id
							a    =			pad_token_id
							a    =			bos_token_id
							a    =			embed_dim
							a    =			word_embed_proj_dim
							a    =			False





				def 							__UpperCAmelCase							(      self				:     str							)					->     int:
							a    =			ids_tensor([self.batch_size, self.seq_length - 1]					,      self.vocab_size							)
							a    =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)					,      1							)
							a    =			tf.concat([input_ids, eos_tensor]					,      axis=1							)

							a    =			self.config_cls(
							    vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      ffn_dim=self.intermediate_size					,      dropout=self.hidden_dropout_prob					,      attention_dropout=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      eos_token_id=self.eos_token_id					,      bos_token_id=self.bos_token_id					,      pad_token_id=self.pad_token_id					,      embed_dim=self.embed_dim					,      word_embed_proj_dim=self.word_embed_proj_dim					,      is_encoder_decoder=__lowerCamelCase					,      **self.config_updates					,      )
							a    =			prepare_opt_inputs_dict(__lowerCamelCase					,      __lowerCamelCase							)
							return config, inputs_dict





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Dict							)					->     List[str]:
							a    =			TFOPTModel(config=__lowerCamelCase							)
							a    =			inputs_dict["input_ids"]

							a    =			input_ids[:1, :]
							a    =			inputs_dict["attention_mask"][:1, :]
							a    =			1

							# first forward pass
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      use_cache=__lowerCamelCase							)

							a    ,		a    =			outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							a    =			ids_tensor((self.batch_size, 3)					,      config.vocab_size							)
							a    =			tf.cast(ids_tensor((self.batch_size, 3)					,      2							)					,      tf.inta							)

							# append to next input_ids and
							a    =			tf.concat([input_ids, next_tokens]					,      axis=-1							)
							a    =			tf.concat([attention_mask, next_attn_mask]					,      axis=-1							)

							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase							)[0]
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      past_key_values=__lowerCamelCase							)[0]

							self.parent.assertEqual(next_tokens.shape[1]					,      output_from_past.shape[1]							)

							# select random slice
							a    =			int(ids_tensor((1,)					,      output_from_past.shape[-1]							)							)
							a    =			output_from_no_past[:, -3:, random_slice_idx]
							a    =			output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__lowerCamelCase					,      __lowerCamelCase					,      rtol=1e-3							)




@require_tf
class     snake_case__							(_UpperCamelCase    ,       _UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					(TFOPTForCausalLM,) if is_tf_available() else ()
				SCREAMING_SNAKE_CASE_       :							Tuple							 =					(
				    {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
				)
				SCREAMING_SNAKE_CASE_       :							int							 =					False
				SCREAMING_SNAKE_CASE_       :							List[str]							 =					False
				SCREAMING_SNAKE_CASE_       :							int							 =					False
				SCREAMING_SNAKE_CASE_       :							List[str]							 =					10





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     List[str]:
							a    =			TFOPTModelTester(self							)
							a    =			ConfigTester(self					,      config_class=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     List[str]:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Tuple:
							a    =			self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Union[str, Any]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

							def _get_word_embedding_weight(__lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     int							):
										if hasattr(__lowerCamelCase					,      "weight"							):
													return embedding_layer.weight
										else:
													# Here we build the word embeddings weights if not exists.
													# And then we retry to get the attribute once built.
													model.build()
													if hasattr(__lowerCamelCase					,      "weight"							):
																return embedding_layer.weight
													else:
																return None

							for model_class in self.all_model_classes:
										for size in [config.vocab_size - 10, config.vocab_size + 10]:
													# build the embeddings
													a    =			model_class(config=__lowerCamelCase							)
													a    =			_get_word_embedding_weight(__lowerCamelCase					,      model.get_input_embeddings()							)
													a    =			_get_word_embedding_weight(__lowerCamelCase					,      model.get_output_embeddings()							)

													# reshape the embeddings
													model.resize_token_embeddings(__lowerCamelCase							)
													a    =			_get_word_embedding_weight(__lowerCamelCase					,      model.get_input_embeddings()							)
													a    =			_get_word_embedding_weight(__lowerCamelCase					,      model.get_output_embeddings()							)

													# check that the resized embeddings size matches the desired size.
													a    =			size if size is not None else config.vocab_size

													self.assertEqual(new_input_embeddings.shape[0]					,      __lowerCamelCase							)

													# check that weights remain the same after resizing
													a    =			True
													for pa, pa in zip(old_input_embeddings.value()					,      new_input_embeddings.value()							):
																if tf.math.reduce_sum(tf.math.abs(pa - pa							)							) > 0:
																			a    =			False
													self.assertTrue(__lowerCamelCase							)

													if old_output_embeddings is not None and new_output_embeddings is not None:
																self.assertEqual(new_output_embeddings.shape[0]					,      __lowerCamelCase							)

																a    =			True
																for pa, pa in zip(old_output_embeddings.value()					,      new_output_embeddings.value()							):
																			if tf.math.reduce_sum(tf.math.abs(pa - pa							)							) > 0:
																						a    =			False
																self.assertTrue(__lowerCamelCase							)




def 			__magic_name__				( A		:					List[Any]   ):






			'''simple docstring'''







			return tf.constant(A,  dtype=tf.intaa   )




@require_tf
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					99





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Optional[Any]:
							a    =			tf.ones((4, 1)					,      dtype=tf.intaa							) * 2
							a    =			tf.concat([ids_tensor((4, 6)					,      self.vocab_size - 3							) + 3, eos_column_vector]					,      axis=1							)
							a    =			input_ids.shape[0]
							a    =			OPTConfig(
							    vocab_size=self.vocab_size					,      hidden_size=24					,      num_hidden_layers=2					,      num_attention_heads=2					,      ffn_dim=32					,      max_position_embeddings=48					,      eos_token_id=2					,      pad_token_id=1					,      bos_token_id=0					,      )
							return config, input_ids, batch_size




@require_sentencepiece
@require_tf
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Optional[Any]:
							a    =			TFOPTModel.from_pretrained("facebook/opt-350m"							)
							a    =			_long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]							)
							a    =			tf.not_equal(__lowerCamelCase					,      model.config.pad_token_id							)
							with tf.GradientTape():
										a    =			model(input_ids=__lowerCamelCase					,      attention_mask=__lowerCamelCase							).last_hidden_state
							a    =			(1, 11, 5_12)
							self.assertEqual(output.shape					,      __lowerCamelCase							)
							a    =			tf.constant(
							    [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]							)
							self.assertTrue(np.allclose(output[:, :3, :3]					,      __lowerCamelCase					,      atol=4e-3							)							)

							a    =			tf.function(__lowerCamelCase					,      jit_compile=__lowerCamelCase							)
							a    =			xla_generate(__lowerCamelCase					,      __lowerCamelCase							)[0]
							self.assertTrue(np.allclose(output[:, :3, :3]					,      __lowerCamelCase					,      atol=4e-2							)							)




@require_tf
@slow
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[str]:
							super().setUp()
							a    =			"facebook/opt-350m"





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Tuple:
							a    =			TFOPTForCausalLM.from_pretrained(self.path_model							)
							a    =			GPTaTokenizer.from_pretrained(self.path_model							)

							a    =			[
							    "Today is a beautiful day and I want to",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							a    =			tokenizer(__lowerCamelCase					,      return_tensors="tf"					,      padding=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
							a    =			tf.math.reduce_mean(model(inputs.input_ids					,      attention_mask=inputs.attention_mask							)[0]					,      axis=-1							)
							a    =			tf.constant(
							    [
							        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
							        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
							        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
							        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
							    ]							)
							self.assertTrue(np.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1e-4							)							)

							a    =			tf.function(__lowerCamelCase					,      jit_compile=__lowerCamelCase							)
							a    =			tf.math.reduce_mean(xla_generate(inputs.input_ids					,      attention_mask=inputs.attention_mask							)[0]					,      axis=-1							)
							self.assertTrue(np.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1e-4							)							)




@require_tf
@slow
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				@property
				def 							__UpperCAmelCase							(      self				:     Any							)					->     str:
							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[str]:
							a    =			"facebook/opt-125m"

							a    =			[
							    "Today is a beautiful day and I want to",
							    "In the city of New York, the city",
							    "Paris is the capital of France and the capital",
							    "Computers and mobile phones have taken over the",
							]

							a    =			[]
							a    =			GPTaTokenizer.from_pretrained(__lowerCamelCase							)
							a    =			TFOPTForCausalLM.from_pretrained(__lowerCamelCase							)

							for prompt in self.prompts:
										a    =			tokenizer(__lowerCamelCase					,      return_tensors="tf"							).input_ids

										a    =			model.generate(__lowerCamelCase					,      max_length=10							)

										a    =			tokenizer.batch_decode(__lowerCamelCase					,      skip_special_tokens=__lowerCamelCase							)
										predicted_outputs += generated_string

							self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str							)					->     Dict:
							a    =			"facebook/opt-350m"

							a    =			GPTaTokenizer.from_pretrained(__lowerCamelCase							)
							a    =			TFOPTForCausalLM.from_pretrained(__lowerCamelCase							)

							a    =			"left"

							# use different length sentences to test batching
							a    =			[
							    "Hello, my dog is a little",
							    "Today, I",
							]

							a    =			tokenizer(__lowerCamelCase					,      return_tensors="tf"					,      padding=__lowerCamelCase							)
							a    =			inputs["input_ids"]

							a    =			model.generate(input_ids=__lowerCamelCase					,      attention_mask=inputs["attention_mask"]							)

							a    =			tokenizer(sentences[0]					,      return_tensors="tf"							).input_ids
							a    =			model.generate(input_ids=__lowerCamelCase							)

							a    =			inputs_non_padded.shape[-1] - tf.math.reduce_sum(
							    tf.cast(inputs["attention_mask"][-1]					,      tf.intaa							)							)
							a    =			tokenizer(sentences[1]					,      return_tensors="tf"							).input_ids
							a    =			model.generate(input_ids=__lowerCamelCase					,      max_length=model.config.max_length - num_paddings							)

							a    =			tokenizer.batch_decode(__lowerCamelCase					,      skip_special_tokens=__lowerCamelCase							)
							a    =			tokenizer.decode(output_non_padded[0]					,      skip_special_tokens=__lowerCamelCase							)
							a    =			tokenizer.decode(output_padded[0]					,      skip_special_tokens=__lowerCamelCase							)

							a    =			[
							    "Hello, my dog is a little bit of a dork.\nI'm a little bit",
							    "Today, I was in the middle of a conversation with a friend about the",
							]
							self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase							)
							self.assertListEqual(__lowerCamelCase					,      [non_padded_sentence, padded_sentence]							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Optional[int]:
							a    =			"facebook/opt-350m"

							a    =			[
							    "Today is a beautiful day and I want to",
							    "In the city of San Francisco, the city",
							    "Paris is the capital of France and the capital",
							    "Computers and mobile phones have taken over the",
							]

							a    =			[]
							a    =			GPTaTokenizer.from_pretrained(__lowerCamelCase							)
							a    =			TFOPTForCausalLM.from_pretrained(__lowerCamelCase							)

							for prompt in self.prompts:
										a    =			tokenizer(__lowerCamelCase					,      return_tensors="tf"							).input_ids

										a    =			model.generate(__lowerCamelCase					,      max_length=10							)

										a    =			tokenizer.batch_decode(__lowerCamelCase					,      skip_special_tokens=__lowerCamelCase							)
										predicted_outputs += generated_string

							self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase							)






