


'''simple docstring'''



import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset

A__						:   Any				=    random.Random()



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							Dict=1.0 ,       UpperCAmelCase_       :							str=None ,       UpperCAmelCase_       :							List[Any]=None				)	->						Union[str, Any]:
     if rng is None:
          __lowerCamelCase  :      Any														=						global_rng

     __lowerCamelCase  :      List[Any]														=						[]
     for batch_idx in range(shape[0]				):
          values.append([]				)
          for _ in range(shape[1]				):
               values[-1].append(rng.random() * scale				)

     return values





class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=4_00     ,	SCREAMING_SNAKE_CASE_=20_00     ,	SCREAMING_SNAKE_CASE_=20_48     ,	SCREAMING_SNAKE_CASE_=1_28     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5_12     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=4_41_00     ,	)							->	str:
         __lowerCamelCase  :      Dict														=						parent
         __lowerCamelCase  :      Union[str, Any]														=						batch_size
         __lowerCamelCase  :      Any														=						min_seq_length
         __lowerCamelCase  :      List[Any]														=						max_seq_length
         __lowerCamelCase  :      List[Any]														=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         __lowerCamelCase  :      Any														=						spectrogram_length
         __lowerCamelCase  :      Any														=						feature_size
         __lowerCamelCase  :      List[str]														=						num_audio_channels
         __lowerCamelCase  :      Dict														=						hop_length
         __lowerCamelCase  :      Optional[Any]														=						chunk_length
         __lowerCamelCase  :      Optional[Any]														=						sampling_rate







    def  lowercase_							(				self      )							->	List[str]:
         return {
             "spectrogram_length": self.spectrogram_length,
             "feature_size": self.feature_size,
             "num_audio_channels": self.num_audio_channels,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "sampling_rate": self.sampling_rate,
         }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False      )							->	Dict:
         def _flatten(SCREAMING_SNAKE_CASE_      ):
              return list(itertools.chain(*SCREAMING_SNAKE_CASE_      )      )

         if equal_length:
              __lowerCamelCase  :      Tuple														=						[floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
         else:
              # make sure that inputs increase in size
              __lowerCamelCase  :      Dict														=						[
                  floats_list((x, self.feature_size)      )
                  for x in range(self.min_seq_length     ,	self.max_seq_length     ,	self.seq_length_diff      )
              ]
         if numpify:
              __lowerCamelCase  :      Union[str, Any]														=						[np.asarray(SCREAMING_SNAKE_CASE_      ) for x in speech_inputs]
         return speech_inputs







@require_torch
@require_torchaudio
class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Dict					=     TvltFeatureExtractor







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Any														=						TvltFeatureExtractionTester(self      )







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      Tuple														=						self.feature_extraction_class(**self.feat_extract_dict      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'spectrogram_length'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'feature_size'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'num_audio_channels'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'hop_length'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'chunk_length'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'sampling_rate'      )      )







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      int														=						self.feature_extraction_class(**self.feat_extract_dict      )

         with tempfile.TemporaryDirectory() as tmpdirname:
              __lowerCamelCase  :      List[str]														=						feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE_      )[0]
              check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[Any]														=						self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Tuple														=						feat_extract_first.to_dict()
         __lowerCamelCase  :      Optional[int]														=						feat_extract_second.to_dict()
         __lowerCamelCase  :      Union[str, Any]														=						dict_first.pop('mel_filters'      )
         __lowerCamelCase  :      List[Any]														=						dict_second.pop('mel_filters'      )
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )      )
         self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      int														=						self.feature_extraction_class(**self.feat_extract_dict      )

         with tempfile.TemporaryDirectory() as tmpdirname:
              __lowerCamelCase  :      int														=						os.path.join(SCREAMING_SNAKE_CASE_     ,	'feat_extract.json'      )
              feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Any														=						self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Dict														=						feat_extract_first.to_dict()
         __lowerCamelCase  :      Optional[Any]														=						feat_extract_second.to_dict()
         __lowerCamelCase  :      Union[str, Any]														=						dict_first.pop('mel_filters'      )
         __lowerCamelCase  :      str														=						dict_second.pop('mel_filters'      )
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )      )
         self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	List[str]:
         # Initialize feature_extractor
         __lowerCamelCase  :      Dict														=						self.feature_extraction_class(**self.feat_extract_dict      )

         # create three inputs of length 800, 1000, and 1200
         __lowerCamelCase  :      int														=						[floats_list((1, x)      )[0] for x in range(8_00     ,	14_00     ,	2_00      )]
         __lowerCamelCase  :      Union[str, Any]														=						[np.asarray(SCREAMING_SNAKE_CASE_      ) for speech_input in speech_inputs]

         # Test not batched input
         __lowerCamelCase  :      Union[str, Any]														=						feature_extractor(np_speech_inputs[0]     ,	return_tensors='np'     ,	sampling_rate=4_41_00      ).audio_values

         self.assertTrue(encoded_audios.ndim == 4      )
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

         # Test batched
         __lowerCamelCase  :      Any														=						feature_extractor(SCREAMING_SNAKE_CASE_     ,	return_tensors='np'     ,	sampling_rate=4_41_00      ).audio_values

         self.assertTrue(encoded_audios.ndim == 4      )
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

         # Test audio masking
         __lowerCamelCase  :      Dict														=						feature_extractor(
             SCREAMING_SNAKE_CASE_     ,	return_tensors='np'     ,	sampling_rate=4_41_00     ,	mask_audio=SCREAMING_SNAKE_CASE_      ).audio_values

         self.assertTrue(encoded_audios.ndim == 4      )
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

         # Test 2-D numpy arrays are batched.
         __lowerCamelCase  :      Optional[Any]														=						[floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
         __lowerCamelCase  :      int														=						np.asarray(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Dict														=						feature_extractor(SCREAMING_SNAKE_CASE_     ,	return_tensors='np'     ,	sampling_rate=4_41_00      ).audio_values
         self.assertTrue(encoded_audios.ndim == 4      )
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         __lowerCamelCase  :      Union[str, Any]														=						load_dataset('hf-internal-testing/librispeech_asr_dummy'     ,	'clean'     ,	split='validation'      )
         # automatic decoding with librispeech
         __lowerCamelCase  :      Union[str, Any]														=						ds.sort('id'      ).select(range(SCREAMING_SNAKE_CASE_      )      )[:num_samples]['audio']

         return [x["array"] for x in speech_samples]







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Tuple														=						self._load_datasamples(1      )
         __lowerCamelCase  :      List[Any]														=						TvltFeatureExtractor()
         __lowerCamelCase  :      List[str]														=						feature_extractor(SCREAMING_SNAKE_CASE_     ,	return_tensors='pt'      ).audio_values

         self.assertEquals(audio_values.shape     ,	(1, 1, 1_92, 1_28)      )

         __lowerCamelCase  :      str														=						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]      )
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-4      )      )










'''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,
)


if is_sentencepiece_available():
      from ..ta.tokenization_ta import TaTokenizer
else:
      from ...utils.dummy_sentencepiece_objects import TaTokenizer

A__						:   int				=    TaTokenizer

if is_tokenizers_available():
      from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
      from ...utils.dummy_tokenizers_objects import TaTokenizerFast

A__						:   Dict				=    TaTokenizerFast

A__						:   Dict				=    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   Any				=    [
          """MT5EncoderModel""",
          """MT5ForConditionalGeneration""",
          """MT5ForQuestionAnswering""",
          """MT5Model""",
          """MT5PreTrainedModel""",
          """MT5Stack""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
      from .configuration_mta import MTaConfig, MTaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mta import (
                MTaEncoderModel,
                MTaForConditionalGeneration,
                MTaForQuestionAnswering,
                MTaModel,
                MTaPreTrainedModel,
                MTaStack,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
      import sys

      A__						:   Union[str, Any]				=    _LazyModule(
          __name__,
          globals()["""__file__"""],
          _import_structure,
          extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
          module_spec=__spec__,
      )






