



from typing import TYPE_CHECKING

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


lowerCamelCase__			= {
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= ["""LongformerTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """LongformerForMaskedLM""",
						    """LongformerForMultipleChoice""",
						    """LongformerForQuestionAnswering""",
						    """LongformerForSequenceClassification""",
						    """LongformerForTokenClassification""",
						    """LongformerModel""",
						    """LongformerPreTrainedModel""",
						    """LongformerSelfAttention""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLongformerForMaskedLM""",
						    """TFLongformerForMultipleChoice""",
						    """TFLongformerForQuestionAnswering""",
						    """TFLongformerForSequenceClassification""",
						    """TFLongformerForTokenClassification""",
						    """TFLongformerModel""",
						    """TFLongformerPreTrainedModel""",
						    """TFLongformerSelfAttention""",
						]


if TYPE_CHECKING:
						from .configuration_longformer import (
						    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    LongformerConfig,
						    LongformerOnnxConfig,
						)
						from .tokenization_longformer import LongformerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_longformer_fast import LongformerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_longformer import (
												    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LongformerForMaskedLM,
												    LongformerForMultipleChoice,
												    LongformerForQuestionAnswering,
												    LongformerForSequenceClassification,
												    LongformerForTokenClassification,
												    LongformerModel,
												    LongformerPreTrainedModel,
												    LongformerSelfAttention,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_longformer import (
												    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLongformerForMaskedLM,
												    TFLongformerForMultipleChoice,
												    TFLongformerForQuestionAnswering,
												    TFLongformerForSequenceClassification,
												    TFLongformerForTokenClassification,
												    TFLongformerModel,
												    TFLongformerPreTrainedModel,
												    TFLongformerSelfAttention,
												)

else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





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__			= random.Random()

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			int=1.0				,     _SCREAMING_SNAKE_CASE       :			Any=None				,     _SCREAMING_SNAKE_CASE       :			Any=None    ):
				"""simple docstring"""







				if rng is None:
								__a       =		global_rng

				__a       =		[]
				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					):





							def __init__(   self		:		Dict      ,				__lowercase		:		Tuple      ,				__lowercase		:		Tuple=7      ,				__lowercase		:		Optional[int]=400      ,				__lowercase		:		int=2000      ,				__lowercase		:		List[Any]=2048      ,				__lowercase		:		List[str]=128      ,				__lowercase		:		Union[str, Any]=1      ,				__lowercase		:		str=512      ,				__lowercase		:		List[str]=30      ,				__lowercase		:		Tuple=44100      ,				):

											'''simple docstring'''
											__a       =		parent
											__a       =		batch_size
											__a       =		min_seq_length
											__a       =		max_seq_length
											__a       =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											__a       =		spectrogram_length
											__a       =		feature_size
											__a       =		num_audio_channels
											__a       =		hop_length
											__a       =		chunk_length
											__a       =		sampling_rate





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''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 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Optional[int]=False      ,				__lowercase		:		Any=False	):

											'''simple docstring'''
											def _flatten(__lowercase		:		Tuple	):
															return list(itertools.chain(*__lowercase	)	)

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






@require_torch
@require_torchaudio
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Dict    =TvltFeatureExtractor





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		TvltFeatureExtractionTester(self	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		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 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)

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

											__a       =		feat_extract_first.to_dict()
											__a       =		feat_extract_second.to_dict()
											__a       =		dict_first.pop("""mel_filters"""	)
											__a       =		dict_second.pop("""mel_filters"""	)
											self.assertTrue(np.allclose(__lowercase      ,				__lowercase	)	)
											self.assertEqual(__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)

											with tempfile.TemporaryDirectory() as tmpdirname:
															__a       =		os.path.join(__lowercase      ,				"""feat_extract.json"""	)
															feat_extract_first.to_json_file(__lowercase	)
															__a       =		self.feature_extraction_class.from_json_file(__lowercase	)

											__a       =		feat_extract_first.to_dict()
											__a       =		feat_extract_second.to_dict()
											__a       =		dict_first.pop("""mel_filters"""	)
											__a       =		dict_second.pop("""mel_filters"""	)
											self.assertTrue(np.allclose(__lowercase      ,				__lowercase	)	)
											self.assertEqual(__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											# Initialize feature_extractor
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)

											# create three inputs of length 800, 1000, and 1200
											__a       =		[floats_list((1, x)	)[0] for x in range(800      ,				1400      ,				200	)]
											__a       =		[np.asarray(__lowercase	) for speech_input in speech_inputs]

											# Test not batched input
											__a       =		feature_extractor(np_speech_inputs[0]      ,				return_tensors="""np"""      ,				sampling_rate=44100	).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
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""np"""      ,				sampling_rate=44100	).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
											__a       =		feature_extractor(
											    __lowercase      ,				return_tensors="""np"""      ,				sampling_rate=44100      ,				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.
											__a       =		[floats_list((1, x)	)[0] for x in (800, 800, 800)]
											__a       =		np.asarray(__lowercase	)
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""np"""      ,				sampling_rate=44100	).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 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		str	):

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

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







							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self._load_datasamples(1	)
											__a       =		TvltFeatureExtractor()
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""pt"""	).audio_values

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

											__a       =		torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]	)
											self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      ,				__lowercase      ,				atol=1E-4	)	)
