

"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
				from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

				from .test_feature_extraction_speechta import floats_list


_SCREAMING_SNAKE_CASE       :  List[str]        = get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")

@require_torch
class     __a   (      unittest.TestCase    ):

			"""simple docstring"""

			def    _lowerCAmelCase			(							self     :					Union[str, Any]						):
								UpperCamelCase__		:    Optional[int]         =tempfile.mkdtemp()

								UpperCamelCase__		:    int         =SpeechTaTokenizer(lowercase_						)
								tokenizer.save_pretrained(self.tmpdirname						)

								UpperCamelCase__		:    str         ={
								    '''feature_size''': 1,
								    '''padding_value''': 0.0,
								    '''sampling_rate''': 1_6000,
								    '''do_normalize''': False,
								    '''num_mel_bins''': 80,
								    '''hop_length''': 16,
								    '''win_length''': 64,
								    '''win_function''': '''hann_window''',
								    '''fmin''': 80,
								    '''fmax''': 7600,
								    '''mel_floor''': 1e-10,
								    '''reduction_factor''': 2,
								    '''return_attention_mask''': True,
								}

								UpperCamelCase__		:    Optional[Any]         =os.path.join(self.tmpdirname						,  lowercase_						)
								with open(self.feature_extraction_file						,  '''w'''						,  encoding='''utf-8'''						) as fp:
													fp.write(json.dumps(lowercase_						) + '''\n'''						)






			def    _lowerCAmelCase			(							self     :					Tuple						,  **lowercase_     :					List[Any]						):
								return SpeechTaTokenizer.from_pretrained(self.tmpdirname						,  **lowercase_						)






			def    _lowerCAmelCase			(							self     :					Tuple						,  **lowercase_     :					List[Any]						):
								return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname						,  **lowercase_						)






			def    _lowerCAmelCase			(							self     :					Any						):
								shutil.rmtree(self.tmpdirname						)






			def    _lowerCAmelCase			(							self     :					int						):
								UpperCamelCase__		:    str         =self.get_tokenizer()
								UpperCamelCase__		:    List[str]         =self.get_feature_extractor()

								UpperCamelCase__		:    List[Any]         =SpeechTaProcessor(tokenizer=lowercase_						,  feature_extractor=lowercase_						)

								processor.save_pretrained(self.tmpdirname						)
								UpperCamelCase__		:    Dict         =SpeechTaProcessor.from_pretrained(self.tmpdirname						)

								self.assertEqual(processor.tokenizer.get_vocab()						,  tokenizer.get_vocab()						)
								self.assertIsInstance(processor.tokenizer						,  lowercase_						)

								self.assertEqual(processor.feature_extractor.to_json_string()						,  feature_extractor.to_json_string()						)
								self.assertIsInstance(processor.feature_extractor						,  lowercase_						)






			def    _lowerCAmelCase			(							self     :					Optional[Any]						):
								UpperCamelCase__		:    int         =SpeechTaProcessor(tokenizer=self.get_tokenizer()						,  feature_extractor=self.get_feature_extractor()						)
								processor.save_pretrained(self.tmpdirname						)

								UpperCamelCase__		:    Tuple         =self.get_tokenizer(bos_token='''(BOS)'''						,  eos_token='''(EOS)'''						)
								UpperCamelCase__		:    Optional[Any]         =self.get_feature_extractor(do_normalize=lowercase_						,  padding_value=1.0						)

								UpperCamelCase__		:    List[str]         =SpeechTaProcessor.from_pretrained(
								    self.tmpdirname						,  bos_token='''(BOS)'''						,  eos_token='''(EOS)'''						,  do_normalize=lowercase_						,  padding_value=1.0						)

								self.assertEqual(processor.tokenizer.get_vocab()						,  tokenizer_add_kwargs.get_vocab()						)
								self.assertIsInstance(processor.tokenizer						,  lowercase_						)

								self.assertEqual(processor.feature_extractor.to_json_string()						,  feature_extractor_add_kwargs.to_json_string()						)
								self.assertIsInstance(processor.feature_extractor						,  lowercase_						)






			def    _lowerCAmelCase			(							self     :					List[Any]						):
								UpperCamelCase__		:    Tuple         =self.get_feature_extractor()
								UpperCamelCase__		:    Tuple         =self.get_tokenizer()

								UpperCamelCase__		:    Optional[int]         =SpeechTaProcessor(tokenizer=lowercase_						,  feature_extractor=lowercase_						)

								UpperCamelCase__		:    Any         =floats_list((3, 1000)						)

								UpperCamelCase__		:    Dict         =feature_extractor(audio=lowercase_						,  return_tensors='''np'''						)
								UpperCamelCase__		:    Union[str, Any]         =processor(audio=lowercase_						,  return_tensors='''np'''						)

								for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum()						,  input_processor[key].sum()						,  delta=1e-2						)






			def    _lowerCAmelCase			(							self     :					Optional[Any]						):
								UpperCamelCase__		:    Any         =self.get_feature_extractor()
								UpperCamelCase__		:    Any         =self.get_tokenizer()

								UpperCamelCase__		:    Any         =SpeechTaProcessor(tokenizer=lowercase_						,  feature_extractor=lowercase_						)

								UpperCamelCase__		:    Dict         =floats_list((3, 1000)						)

								UpperCamelCase__		:    Optional[Any]         =feature_extractor(audio_target=lowercase_						,  return_tensors='''np'''						)
								UpperCamelCase__		:    Optional[Any]         =processor(audio_target=lowercase_						,  return_tensors='''np'''						)

								for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum()						,  input_processor[key].sum()						,  delta=1e-2						)






			def    _lowerCAmelCase			(							self     :					int						):
								UpperCamelCase__		:    List[str]         =self.get_feature_extractor()
								UpperCamelCase__		:    Optional[int]         =self.get_tokenizer()

								UpperCamelCase__		:    Dict         =SpeechTaProcessor(tokenizer=lowercase_						,  feature_extractor=lowercase_						)

								UpperCamelCase__		:    List[str]         ='''This is a test string'''

								UpperCamelCase__		:    Any         =processor(text=lowercase_						)
								UpperCamelCase__		:    Optional[Any]         =tokenizer(lowercase_						)

								for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]						,  encoded_processor[key]						)






			def    _lowerCAmelCase			(							self     :					int						):
								UpperCamelCase__		:    int         =self.get_feature_extractor()
								UpperCamelCase__		:    Dict         =self.get_tokenizer()

								UpperCamelCase__		:    Tuple         =SpeechTaProcessor(tokenizer=lowercase_						,  feature_extractor=lowercase_						)

								UpperCamelCase__		:    Dict         ='''This is a test string'''

								UpperCamelCase__		:    Union[str, Any]         =processor(text_target=lowercase_						)
								UpperCamelCase__		:    Optional[int]         =tokenizer(lowercase_						)

								for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]						,  encoded_processor[key]						)






			def    _lowerCAmelCase			(							self     :					List[str]						):
								UpperCamelCase__		:    Tuple         =self.get_feature_extractor()
								UpperCamelCase__		:    List[str]         =self.get_tokenizer()

								UpperCamelCase__		:    List[str]         =SpeechTaProcessor(tokenizer=lowercase_						,  feature_extractor=lowercase_						)

								UpperCamelCase__		:    Optional[Any]         =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								UpperCamelCase__		:    int         =processor.batch_decode(lowercase_						)
								UpperCamelCase__		:    int         =tokenizer.batch_decode(lowercase_						)

								self.assertListEqual(lowercase_						,  lowercase_						)






			def    _lowerCAmelCase			(							self     :					str						):
								UpperCamelCase__		:    Dict         =self.get_feature_extractor()
								UpperCamelCase__		:    int         =self.get_tokenizer()

								UpperCamelCase__		:    Union[str, Any]         =SpeechTaProcessor(tokenizer=lowercase_						,  feature_extractor=lowercase_						)

								self.assertListEqual(
								    processor.model_input_names						,  feature_extractor.model_input_names						,  msg='''`processor` and `feature_extractor` model input names do not match'''						,  )





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = ["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  str        = ["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = [
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

				_SCREAMING_SNAKE_CASE       :  str        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


