




"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


a_        =       random.Random()

if is_torch_available():
			import torch







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase=1.0	,				__UpperCamelCase=None	,				__UpperCamelCase=None	):
			if rng is None:
						__lowercase			:  Optional[Any]								=       global_rng

			__lowercase			:  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		):
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_=7				,						UpperCamelCase_=4_00				,						UpperCamelCase_=20_00				,						UpperCamelCase_=1				,						UpperCamelCase_=0.0				,						UpperCamelCase_=1_60_00				,						UpperCamelCase_=True				,						UpperCamelCase_=True				,						)						->       List[Any]:
					__lowercase			:  List[Any]								=       parent
					__lowercase			:  Tuple								=       batch_size
					__lowercase			:  Tuple								=       min_seq_length
					__lowercase			:  Optional[int]								=       max_seq_length
					__lowercase			:  Optional[int]								=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					__lowercase			:  Tuple								=       feature_size
					__lowercase			:  List[Any]								=       padding_value
					__lowercase			:  int								=       sampling_rate
					__lowercase			:  Union[str, Any]								=       return_attention_mask
					__lowercase			:  Optional[int]								=       do_normalize
		def 	_lowerCamelCase							(						self       )						->       Union[str, Any]:
					return {
					    "feature_size": self.feature_size,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					    "do_normalize": self.do_normalize,
					}
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_=False				,						UpperCamelCase_=False       )						->       List[str]:
					def _flatten(UpperCamelCase_       ):
								return list(itertools.chain(*UpperCamelCase_       )       )

					if equal_length:
								__lowercase			:  str								=       floats_list((self.batch_size, self.max_seq_length)       )
					else:
								# make sure that inputs increase in size
								__lowercase			:  Optional[int]								=       [
								    _flatten(floats_list((x, self.feature_size)       )       )
								    for x in range(self.min_seq_length				,						self.max_seq_length				,						self.seq_length_diff       )
								]

					if numpify:
								__lowercase			:  Optional[int]								=       [np.asarray(UpperCamelCase_       ) for x in speech_inputs]

					return speech_inputs


@require_torch
@require_torchaudio
class   UpperCAmelCase_    (			snake_case   ,					unittest.TestCase		):
		UpperCamelCase							=ASTFeatureExtractor
		def 	_lowerCamelCase							(						self       )						->       Union[str, Any]:
					__lowercase			:  str								=       ASTFeatureExtractionTester(self       )
		def 	_lowerCamelCase							(						self       )						->       Tuple:
					# Tests that all call wrap to encode_plus and batch_encode_plus
					__lowercase			:  int								=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
					# create three inputs of length 800, 1000, and 1200
					__lowercase			:  int								=       [floats_list((1, x)       )[0] for x in range(8_00				,						14_00				,						2_00       )]
					__lowercase			:  Tuple								=       [np.asarray(UpperCamelCase_       ) for speech_input in speech_inputs]

					# Test not batched input
					__lowercase			:  List[Any]								=       feat_extract(speech_inputs[0]				,						return_tensors='''np'''       ).input_values
					__lowercase			:  List[Any]								=       feat_extract(np_speech_inputs[0]				,						return_tensors='''np'''       ).input_values
					self.assertTrue(np.allclose(UpperCamelCase_				,						UpperCamelCase_				,						atol=1E-3       )       )

					# Test batched
					__lowercase			:  Any								=       feat_extract(UpperCamelCase_				,						padding=UpperCamelCase_				,						return_tensors='''np'''       ).input_values
					__lowercase			:  List[str]								=       feat_extract(UpperCamelCase_				,						padding=UpperCamelCase_				,						return_tensors='''np'''       ).input_values
					for enc_seq_a, enc_seq_a in zip(UpperCamelCase_				,						UpperCamelCase_       ):
								self.assertTrue(np.allclose(UpperCamelCase_				,						UpperCamelCase_				,						atol=1E-3       )       )

					# Test 2-D numpy arrays are batched.
					__lowercase			:  List[str]								=       [floats_list((1, x)       )[0] for x in (8_00, 8_00, 8_00)]
					__lowercase			:  str								=       np.asarray(UpperCamelCase_       )
					__lowercase			:  Dict								=       feat_extract(UpperCamelCase_				,						return_tensors='''np'''       ).input_values
					__lowercase			:  Any								=       feat_extract(UpperCamelCase_				,						return_tensors='''np'''       ).input_values
					for enc_seq_a, enc_seq_a in zip(UpperCamelCase_				,						UpperCamelCase_       ):
								self.assertTrue(np.allclose(UpperCamelCase_				,						UpperCamelCase_				,						atol=1E-3       )       )
		@require_torch
		def 	_lowerCamelCase							(						self       )						->       Any:
					import torch

					__lowercase			:  Optional[Any]								=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
					__lowercase			:  Dict								=       np.random.rand(1_00       ).astype(np.floataa       )
					__lowercase			:  Optional[int]								=       np_speech_inputs.tolist()

					for inputs in [py_speech_inputs, np_speech_inputs]:
								__lowercase			:  Any								=       feature_extractor.pad([{'''input_values''': inputs}]				,						return_tensors='''np'''       )
								self.assertTrue(np_processed.input_values.dtype == np.floataa       )
								__lowercase			:  Optional[int]								=       feature_extractor.pad([{'''input_values''': inputs}]				,						return_tensors='''pt'''       )
								self.assertTrue(pt_processed.input_values.dtype == torch.floataa       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       List[Any]:
					from datasets import load_dataset

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

					return [x["array"] for x in speech_samples]
		@require_torch
		def 	_lowerCamelCase							(						self       )						->       Tuple:
					# fmt: off
					__lowercase			:  Union[str, Any]								=       torch.tensor(
					    [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
					     -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
					     -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
					     -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]       )
					# fmt: on

					__lowercase			:  str								=       self._load_datasamples(1       )
					__lowercase			:  Optional[int]								=       ASTFeatureExtractor()
					__lowercase			:  Optional[int]								=       feature_extractor(UpperCamelCase_				,						return_tensors='''pt'''       ).input_values
					self.assertEquals(input_values.shape				,						(1, 10_24, 1_28)       )
					self.assertTrue(torch.allclose(input_values[0, 0, :30]				,						UpperCamelCase_				,						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

__SCREAMING_SNAKE_CASE					:				str									=		TaTokenizer

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

__SCREAMING_SNAKE_CASE					:				Any									=		TaTokenizerFast

__SCREAMING_SNAKE_CASE					:				Tuple									=		{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

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

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

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				List[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

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





