




import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
				import os

				import jax.numpy as jnp
				from jax import jit

				from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
				from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

				a_		:						Union[str, Any]             =    '0.12'  # assumed parallelism: 8


if is_torch_available():
				import torch






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=None):





			if rng is None:
						SCREAMING_SNAKE_CASE						= random.Random()

			SCREAMING_SNAKE_CASE						= 1
			for dim in shape:
						total_dims *= dim

			SCREAMING_SNAKE_CASE						= []
			for _ in range(SCREAMING_SNAKE_CASE__):
						values.append(rng.randint(0							,       vocab_size - 1))

			SCREAMING_SNAKE_CASE						= np.array(SCREAMING_SNAKE_CASE__							,       dtype=jnp.intaa).reshape(SCREAMING_SNAKE_CASE__)

			return output






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase=None):
			SCREAMING_SNAKE_CASE						= ids_tensor(SCREAMING_SNAKE_CASE__							,       vocab_size=2							,       rng=SCREAMING_SNAKE_CASE__)
			# make sure that at least one token is attended to for each batch
			SCREAMING_SNAKE_CASE						= 1
			return attn_mask

@require_flax
class 		_snake_case  :
			_lowercase		:    Optional[int]			     =							None
			_lowercase		:    Tuple			     =							()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

						# cut to half length & take max batch_size 3
						SCREAMING_SNAKE_CASE						= 2
						SCREAMING_SNAKE_CASE						= inputs["""input_ids"""].shape[-1] // 2
						SCREAMING_SNAKE_CASE						= inputs["""input_ids"""][:max_batch_size, :sequence_length]

						SCREAMING_SNAKE_CASE						= jnp.ones_like(lowercase__)
						SCREAMING_SNAKE_CASE						= attention_mask[:max_batch_size, :sequence_length]

						# generate max 5 tokens
						SCREAMING_SNAKE_CASE						= input_ids.shape[-1] + 5
						if config.eos_token_id is not None and config.pad_token_id is None:
									# hack to allow generate for models such as GPT2 as is done in `generate()`
									SCREAMING_SNAKE_CASE						= config.eos_token_id
						return config, input_ids, attention_mask, max_length






			@is_pt_flax_cross_test
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= max_length
						SCREAMING_SNAKE_CASE						= 0

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(lowercase__)

									SCREAMING_SNAKE_CASE						= model_class.__name__[4:]  # Skip the "Flax" at the beginning
									SCREAMING_SNAKE_CASE						= getattr(lowercase__		,					lowercase__)
									SCREAMING_SNAKE_CASE						= pt_model_class(lowercase__).eval()
									SCREAMING_SNAKE_CASE						= load_flax_weights_in_pytorch_model(lowercase__		,					flax_model.params)

									SCREAMING_SNAKE_CASE						= flax_model.generate(lowercase__).sequences
									SCREAMING_SNAKE_CASE						= pt_model.generate(torch.tensor(lowercase__		,					dtype=torch.long))

									if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
												SCREAMING_SNAKE_CASE						= flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

									self.assertListEqual(pt_generation_outputs.numpy().tolist()		,					flax_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= max_length

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(lowercase__)

									SCREAMING_SNAKE_CASE						= model.generate(lowercase__).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					lowercase__)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(lowercase__).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= max_length

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(lowercase__)

									SCREAMING_SNAKE_CASE						= model.generate(lowercase__).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					lowercase__)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(lowercase__).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= max_length
						SCREAMING_SNAKE_CASE						= 2

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(lowercase__)

									SCREAMING_SNAKE_CASE						= model.generate(lowercase__).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					lowercase__)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(lowercase__).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= max_length
						SCREAMING_SNAKE_CASE						= 2
						SCREAMING_SNAKE_CASE						= 2

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(lowercase__)

									SCREAMING_SNAKE_CASE						= model.generate(lowercase__).sequences
									self.assertEqual(generation_outputs.shape[0]		,					input_ids.shape[0] * config.num_return_sequences)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= max_length
						SCREAMING_SNAKE_CASE						= 0.8
						SCREAMING_SNAKE_CASE						= 10
						SCREAMING_SNAKE_CASE						= 0.3
						SCREAMING_SNAKE_CASE						= 1
						SCREAMING_SNAKE_CASE						= 8
						SCREAMING_SNAKE_CASE						= 9

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(lowercase__)

									SCREAMING_SNAKE_CASE						= model.generate(lowercase__).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					lowercase__)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(lowercase__).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= max_length
						SCREAMING_SNAKE_CASE						= 1
						SCREAMING_SNAKE_CASE						= 8
						SCREAMING_SNAKE_CASE						= 9

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(lowercase__)

									SCREAMING_SNAKE_CASE						= model.generate(lowercase__).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					lowercase__)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(lowercase__).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= max_length
						SCREAMING_SNAKE_CASE						= 2
						SCREAMING_SNAKE_CASE						= 1
						SCREAMING_SNAKE_CASE						= 8
						SCREAMING_SNAKE_CASE						= 9

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(lowercase__)

									SCREAMING_SNAKE_CASE						= model.generate(lowercase__).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					lowercase__)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(lowercase__).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()

						# pad attention mask on the left
						SCREAMING_SNAKE_CASE						= attention_mask.at[(0, 0)].set(0)

						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= max_length

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(lowercase__)

									SCREAMING_SNAKE_CASE						= model.generate(lowercase__		,					attention_mask=lowercase__).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					lowercase__)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(lowercase__		,					attention_mask=lowercase__).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()

						# pad attention mask on the left
						SCREAMING_SNAKE_CASE						= attention_mask.at[(0, 0)].set(0)

						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= max_length

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(lowercase__)

									SCREAMING_SNAKE_CASE						= model.generate(lowercase__		,					attention_mask=lowercase__).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					lowercase__)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(lowercase__		,					attention_mask=lowercase__).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()

						# pad attention mask on the left
						SCREAMING_SNAKE_CASE						= attention_mask.at[(0, 0)].set(0)

						SCREAMING_SNAKE_CASE						= 2
						SCREAMING_SNAKE_CASE						= max_length

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(lowercase__)

									SCREAMING_SNAKE_CASE						= model.generate(lowercase__		,					attention_mask=lowercase__).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					lowercase__)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(lowercase__		,					attention_mask=lowercase__).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())



@require_flax
class 		_snake_case  (  unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert')
						SCREAMING_SNAKE_CASE						= FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only')

						SCREAMING_SNAKE_CASE						= """Hello world"""
						SCREAMING_SNAKE_CASE						= tokenizer(lowercase__		,					return_tensors='np').input_ids

						# typos are quickly detected (the correct argument is `do_sample`)
						with self.assertRaisesRegex(lowercase__		,					'do_samples'):
									model.generate(lowercase__		,					do_samples=lowercase__)

						# arbitrary arguments that will not be used anywhere are also not accepted
						with self.assertRaisesRegex(lowercase__		,					'foo'):
									SCREAMING_SNAKE_CASE						= {"""foo""": """bar"""}
									model.generate(lowercase__		,					**lowercase__)












from typing import TYPE_CHECKING

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


a_		:						int             =    {
    '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:
				a_		:						Optional[Any]             =    ['SpeechT5Tokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[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

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




