
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

_lowerCamelCase			:							int         =     random.Random()






def 			a_						(			__lowercase  :      Optional[Any]			,   __lowercase  :      Optional[int]=1.0			,   __lowercase  :      Dict=None			,   __lowercase  :      Tuple=None					)					->    Optional[Any]:
			if rng is None:
						_snake_case            =     global_rng

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

			return values




class   SCREAMING_SNAKE_CASE__	(      unittest.TestCase   ):





	'''simple docstring'''



	def __init__(    self      :       Any     ,    lowercase      :       int     ,    lowercase      :       List[Any]=7     ,    lowercase      :       List[str]=400     ,    lowercase      :       List[str]=2_000     ,    lowercase      :       List[Any]=2_048     ,    lowercase      :       Optional[int]=128     ,    lowercase      :       Optional[Any]=1     ,    lowercase      :       int=512     ,    lowercase      :       str=30     ,    lowercase      :       str=44_100     ,    ):


				'''simple docstring'''


				_snake_case            =     parent
				_snake_case            =     batch_size
				_snake_case            =     min_seq_length
				_snake_case            =     max_seq_length
				_snake_case            =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				_snake_case            =     spectrogram_length
				_snake_case            =     feature_size
				_snake_case            =     num_audio_channels
				_snake_case            =     hop_length
				_snake_case            =     chunk_length
				_snake_case            =     sampling_rate



	def 					A				(    self      :       List[Any]      ):


				'''simple docstring'''


				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 					A				(    self      :       List[Any]     ,    lowercase      :       str=False     ,    lowercase      :       str=False      ):


				'''simple docstring'''


				def _flatten(lowercase      :       Tuple      ):
							return list(itertools.chain(*lowercase      )      )

				if equal_length:
							_snake_case            =     [floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
				else:
							# make sure that inputs increase in size
							_snake_case            =     [
							    floats_list((x, self.feature_size)      )
							    for x in range(self.min_seq_length     ,    self.max_seq_length     ,    self.seq_length_diff      )
							]
				if numpify:
							_snake_case            =     [np.asarray(lowercase      ) for x in speech_inputs]
				return speech_inputs




@require_torch
@require_torchaudio
class   SCREAMING_SNAKE_CASE__	(      UpperCAmelCase				,unittest.TestCase   ):





	'''simple docstring'''



	_UpperCAmelCase			:						Optional[Any]         =   TvltFeatureExtractor



	def 					A				(    self      :       List[Any]      ):


				'''simple docstring'''


				_snake_case            =     TvltFeatureExtractionTester(self      )



	def 					A				(    self      :       Tuple      ):


				'''simple docstring'''


				_snake_case            =     self.feature_extraction_class(**self.feat_extract_dict      )
				self.assertTrue(hasattr(lowercase     ,    'spectrogram_length'      )      )
				self.assertTrue(hasattr(lowercase     ,    'feature_size'      )      )
				self.assertTrue(hasattr(lowercase     ,    'num_audio_channels'      )      )
				self.assertTrue(hasattr(lowercase     ,    'hop_length'      )      )
				self.assertTrue(hasattr(lowercase     ,    'chunk_length'      )      )
				self.assertTrue(hasattr(lowercase     ,    'sampling_rate'      )      )



	def 					A				(    self      :       List[str]      ):


				'''simple docstring'''


				_snake_case            =     self.feature_extraction_class(**self.feat_extract_dict      )

				with tempfile.TemporaryDirectory() as tmpdirname:
							_snake_case            =     feat_extract_first.save_pretrained(lowercase      )[0]
							check_json_file_has_correct_format(lowercase      )
							_snake_case            =     self.feature_extraction_class.from_pretrained(lowercase      )

				_snake_case            =     feat_extract_first.to_dict()
				_snake_case            =     feat_extract_second.to_dict()
				_snake_case            =     dict_first.pop('mel_filters'      )
				_snake_case            =     dict_second.pop('mel_filters'      )
				self.assertTrue(np.allclose(lowercase     ,    lowercase      )      )
				self.assertEqual(lowercase     ,    lowercase      )



	def 					A				(    self      :       Dict      ):


				'''simple docstring'''


				_snake_case            =     self.feature_extraction_class(**self.feat_extract_dict      )

				with tempfile.TemporaryDirectory() as tmpdirname:
							_snake_case            =     os.path.join(lowercase     ,    'feat_extract.json'      )
							feat_extract_first.to_json_file(lowercase      )
							_snake_case            =     self.feature_extraction_class.from_json_file(lowercase      )

				_snake_case            =     feat_extract_first.to_dict()
				_snake_case            =     feat_extract_second.to_dict()
				_snake_case            =     dict_first.pop('mel_filters'      )
				_snake_case            =     dict_second.pop('mel_filters'      )
				self.assertTrue(np.allclose(lowercase     ,    lowercase      )      )
				self.assertEqual(lowercase     ,    lowercase      )



	def 					A				(    self      :       Optional[int]      ):


				'''simple docstring'''


				_snake_case            =     self.feature_extraction_class(**self.feat_extract_dict      )

				# create three inputs of length 800, 1000, and 1200
				_snake_case            =     [floats_list((1, x)      )[0] for x in range(800     ,    1_400     ,    200      )]
				_snake_case            =     [np.asarray(lowercase      ) for speech_input in speech_inputs]

				# Test not batched input
				_snake_case            =     feature_extractor(np_speech_inputs[0]     ,    return_tensors='np'     ,    sampling_rate=44_100      ).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
				_snake_case            =     feature_extractor(lowercase     ,    return_tensors='np'     ,    sampling_rate=44_100      ).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
				_snake_case            =     feature_extractor(
				    lowercase     ,    return_tensors='np'     ,    sampling_rate=44_100     ,    mask_audio=lowercase      ).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.
				_snake_case            =     [floats_list((1, x)      )[0] for x in (800, 800, 800)]
				_snake_case            =     np.asarray(lowercase      )
				_snake_case            =     feature_extractor(lowercase     ,    return_tensors='np'     ,    sampling_rate=44_100      ).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 					A				(    self      :       Dict     ,    lowercase      :       List[str]      ):


				'''simple docstring'''


				_snake_case            =     load_dataset('hf-internal-testing/librispeech_asr_dummy'     ,    'clean'     ,    split='validation'      )
				# automatic decoding with librispeech
				_snake_case            =     ds.sort('id'      ).select(range(lowercase      )      )[:num_samples]["audio"]

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





	def 					A				(    self      :       int      ):


				'''simple docstring'''


				_snake_case            =     self._load_datasamples(1      )
				_snake_case            =     TvltFeatureExtractor()
				_snake_case            =     feature_extractor(lowercase     ,    return_tensors='pt'      ).audio_values

				self.assertEquals(audio_values.shape     ,    (1, 1, 192, 128)      )

				_snake_case            =     torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]      )
				self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]     ,    lowercase     ,    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

_UpperCamelCase		     =    TaTokenizer

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

_UpperCamelCase		     =    TaTokenizerFast

_UpperCamelCase		     =    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

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

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

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    ["""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

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






