



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A__			   = {
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RetriBertModel""",
	    """RetriBertPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
	from .tokenization_retribert import RetriBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_retribert_fast import RetriBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_retribert import (
		    RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RetriBertModel,
		    RetriBertPreTrainedModel,
		)

else:
	import sys

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








import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


A__			   = random.Random()


def 	_UpperCAmelCase       (   snake_case			,		snake_case=1.0			,		snake_case=None			,		snake_case=None    ):





					"""simple docstring"""

					if rng is None:
										_lowerCAmelCase      			=					global_rng

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

					return values





@require_torch
class       __lowerCAmelCase				(  unittest.TestCase ):




				def __init__(							self		,       _snake_case		,       _snake_case=7		,       _snake_case=400		,       _snake_case=2000		,       _snake_case=1		,       _snake_case=0.0		,       _snake_case=24000		,       _snake_case=True		,       ):



									"""simple docstring"""





									_lowerCAmelCase      			=					parent
									_lowerCAmelCase      			=					batch_size
									_lowerCAmelCase      			=					min_seq_length
									_lowerCAmelCase      			=					max_seq_length
									_lowerCAmelCase      			=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									_lowerCAmelCase      			=					feature_size
									_lowerCAmelCase      			=					padding_value
									_lowerCAmelCase      			=					sampling_rate
									_lowerCAmelCase      			=					return_attention_mask




				def 							snake_case     (							self	):



									"""simple docstring"""





									return {
									    "feature_size": self.feature_size,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "return_attention_mask": self.return_attention_mask,
									}




				def 							snake_case     (							self		,       _snake_case=False		,       _snake_case=False	):



									"""simple docstring"""





									def _flatten(_snake_case	):
														return list(itertools.chain(*_snake_case	)	)

									if equal_length:
														_lowerCAmelCase      			=					floats_list((self.batch_size, self.max_seq_length)	)
									else:
														# make sure that inputs increase in size
														_lowerCAmelCase      			=					[
														    _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:
														_lowerCAmelCase      			=					[np.asarray(_snake_case	) for x in audio_inputs]

									return audio_inputs





@require_torch
class       __lowerCAmelCase				(  lowerCamelCase__ ,		unittest.TestCase ):
				__lowerCamelCase				=       EncodecFeatureExtractor




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					EnCodecFeatureExtractionTester(self	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
									# create three inputs of length 800, 1000, and 1200
									_lowerCAmelCase      			=					[floats_list((1, x)	)[0] for x in range(800		,       1400		,       200	)]
									_lowerCAmelCase      			=					[np.asarray(_snake_case	) for audio_input in audio_inputs]

									# Test not batched input
									_lowerCAmelCase      			=					feat_extract(audio_inputs[0]		,       return_tensors="""np"""	).input_values
									_lowerCAmelCase      			=					feat_extract(np_audio_inputs[0]		,       return_tensors="""np"""	).input_values
									self.assertTrue(np.allclose(_snake_case		,       _snake_case		,       atol=1e-3	)	)

									# Test batched
									_lowerCAmelCase      			=					feat_extract(_snake_case		,       padding=_snake_case		,       return_tensors="""np"""	).input_values
									_lowerCAmelCase      			=					feat_extract(_snake_case		,       padding=_snake_case		,       return_tensors="""np"""	).input_values
									for enc_seq_a, enc_seq_a in zip(_snake_case		,       _snake_case	):
														self.assertTrue(np.allclose(_snake_case		,       _snake_case		,       atol=1e-3	)	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
									_lowerCAmelCase      			=					np.random.rand(100	).astype(np.floataa	)
									_lowerCAmelCase      			=					np_audio_inputs.tolist()

									for inputs in [py_audio_inputs, np_audio_inputs]:
														_lowerCAmelCase      			=					feature_extractor.pad([{"""input_values""": inputs}]		,       return_tensors="""np"""	)
														self.assertTrue(np_processed.input_values.dtype == np.floataa	)
														_lowerCAmelCase      			=					feature_extractor.pad([{"""input_values""": inputs}]		,       return_tensors="""pt"""	)
														self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)




				def 							snake_case     (							self		,       _snake_case	):



									"""simple docstring"""





									from datasets import load_dataset

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

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




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					torch.tensor(
									    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
									     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
									     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
									     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
									     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
									     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]	)
									# fmt: on
									_lowerCAmelCase      			=					self._load_datasamples(1	)
									_lowerCAmelCase      			=					EncodecFeatureExtractor()
									_lowerCAmelCase      			=					feature_extractor(_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(input_values.shape		,       (1, 1, 93680)	)
									self.assertTrue(torch.allclose(input_values[0, 0, :30]		,       _snake_case		,       atol=1e-6	)	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					torch.tensor(
									    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
									     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
									     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
									     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
									     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
									     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]	)
									# fmt: on
									_lowerCAmelCase      			=					self._load_datasamples(1	)
									_lowerCAmelCase      			=					[np.tile(input_audio[0][None]		,       reps=(2, 1)	)]
									input_audio[0][1] *= 0.5
									_lowerCAmelCase      			=					EncodecFeatureExtractor(feature_size=2	)
									_lowerCAmelCase      			=					feature_extractor(_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(input_values.shape		,       (1, 2, 93680)	)
									self.assertTrue(torch.allclose(input_values[0, 0, :30]		,       _snake_case		,       atol=1e-6	)	)
									self.assertTrue(torch.allclose(input_values[0, 1, :30]		,       EXPECTED_INPUT_VALUES * 0.5		,       atol=1e-6	)	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self._load_datasamples(2	)
									# would be easier if the stride was like
									_lowerCAmelCase      			=					EncodecFeatureExtractor(feature_size=1		,       chunk_length_s=1		,       overlap=0.01	)

									# pad and trunc raise an error ?
									with self.assertRaisesRegex(
									    _snake_case		,       """^Both padding and truncation were set. Make sure you only set one.$"""		,       ):
														_lowerCAmelCase      			=					feature_extractor(
														    _snake_case		,       padding="""max_length"""		,       truncation=_snake_case		,       return_tensors="""pt"""	).input_values

									# truncate to chunk
									_lowerCAmelCase      			=					feature_extractor(_snake_case		,       truncation=_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (2, 1, 71520)	)  # 2 chunks

									# force truncate to max_length
									_lowerCAmelCase      			=					feature_extractor(
									    _snake_case		,       truncation=_snake_case		,       max_length=48000		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (2, 1, 48000)	)

									# pad to chunk
									_lowerCAmelCase      			=					feature_extractor(_snake_case		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(padded_outputs.shape		,       (2, 1, 95280)	)

									# pad to chunk
									_lowerCAmelCase      			=					feature_extractor(_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (2, 1, 95280)	)

									# force pad to max length
									_lowerCAmelCase      			=					feature_extractor(
									    _snake_case		,       padding="""max_length"""		,       max_length=100000		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (2, 1, 100000)	)

									# force no pad
									with self.assertRaisesRegex(
									    _snake_case		,       """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""		,       ):
														_lowerCAmelCase      			=					feature_extractor(_snake_case		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values

									_lowerCAmelCase      			=					feature_extractor(input_audio[0]		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (1, 1, 93680)	)

									# no pad if no chunk_length_s
									_lowerCAmelCase      			=					None
									with self.assertRaisesRegex(
									    _snake_case		,       """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""		,       ):
														_lowerCAmelCase      			=					feature_extractor(_snake_case		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values

									_lowerCAmelCase      			=					feature_extractor(input_audio[0]		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (1, 1, 93680)	)

									# no pad if no overlap
									_lowerCAmelCase      			=					2
									_lowerCAmelCase      			=					None
									with self.assertRaisesRegex(
									    _snake_case		,       """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""		,       ):
														_lowerCAmelCase      			=					feature_extractor(_snake_case		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values

									_lowerCAmelCase      			=					feature_extractor(input_audio[0]		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (1, 1, 93680)	)



