"""simple docstring"""







import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_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_speech_available():
						from transformers import WhisperFeatureExtractor

if is_torch_available():
						import torch

SCREAMING_SNAKE_CASE									=						random.Random()


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase=1.0     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None			):






	"""simple docstring"""





	if rng is None:
		_lowercase :    List[str]							=							global_rng

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

	return values



@require_torch
@require_torchaudio
class  _lowerCamelCase			(unittest.TestCase     ):







				def __init__( self    :       Dict			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       List[Any]=7			,					lowerCamelCase_    :       Any=4_0_0			,					lowerCamelCase_    :       int=2_0_0_0			,					lowerCamelCase_    :       str=1_0			,					lowerCamelCase_    :       List[str]=1_6_0			,					lowerCamelCase_    :       Optional[Any]=8			,					lowerCamelCase_    :       Optional[int]=0.0			,					lowerCamelCase_    :       Union[str, Any]=4_0_0_0			,					lowerCamelCase_    :       Union[str, Any]=False			,					lowerCamelCase_    :       Optional[Any]=True			,					):

					"""simple docstring"""






					_lowercase :    str							=							parent
					_lowercase :    List[Any]							=							batch_size
					_lowercase :    Union[str, Any]							=							min_seq_length
					_lowercase :    Dict							=							max_seq_length
					_lowercase :    List[str]							=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					_lowercase :    str							=							padding_value
					_lowercase :    Dict							=							sampling_rate
					_lowercase :    Any							=							return_attention_mask
					_lowercase :    Union[str, Any]							=							do_normalize
					_lowercase :    Optional[Any]							=							feature_size
					_lowercase :    Optional[Any]							=							chunk_length
					_lowercase :    List[Any]							=							hop_length







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       Any=False			,					lowerCamelCase_    :       Union[str, Any]=False						):

					"""simple docstring"""






					def _flatten(lowerCamelCase_    :       str						):
						return list(itertools.chain(*lowerCamelCase_						)						)

					if equal_length:
						_lowercase :    Optional[Any]							=							[floats_list((self.max_seq_length, self.feature_size)						) for _ in range(self.batch_size						)]
					else:
						# make sure that inputs increase in size
						_lowercase :    Optional[int]							=							[
						    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 :    List[str]							=							[np.asarray(lowerCamelCase_						) for x in speech_inputs]
					return speech_inputs



@require_torch
@require_torchaudio
class  _lowerCamelCase			(__lowerCamelCase						,							unittest.TestCase     ):
				_snake_case     =  WhisperFeatureExtractor if is_speech_available() else None







				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					_lowercase :    str							=							WhisperFeatureExtractionTester(self						)







				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






					_lowercase :    List[Any]							=							self.feature_extraction_class(**self.feat_extract_dict						)

					with tempfile.TemporaryDirectory() as tmpdirname:
						_lowercase :    Optional[Any]							=							feat_extract_first.save_pretrained(lowerCamelCase_						)[0]
						check_json_file_has_correct_format(lowerCamelCase_						)
						_lowercase :    Optional[int]							=							self.feature_extraction_class.from_pretrained(lowerCamelCase_						)

					_lowercase :    str							=							feat_extract_first.to_dict()
					_lowercase :    Optional[int]							=							feat_extract_second.to_dict()
					_lowercase :    Tuple							=							feat_extract_first.mel_filters
					_lowercase :    int							=							feat_extract_second.mel_filters
					self.assertTrue(np.allclose(lowerCamelCase_			,					lowerCamelCase_						)						)
					self.assertEqual(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase :    Any							=							self.feature_extraction_class(**self.feat_extract_dict						)

					with tempfile.TemporaryDirectory() as tmpdirname:
						_lowercase :    Dict							=							os.path.join(lowerCamelCase_			,					'feat_extract.json'						)
						feat_extract_first.to_json_file(lowerCamelCase_						)
						_lowercase :    Tuple							=							self.feature_extraction_class.from_json_file(lowerCamelCase_						)

					_lowercase :    Tuple							=							feat_extract_first.to_dict()
					_lowercase :    List[str]							=							feat_extract_second.to_dict()
					_lowercase :    List[str]							=							feat_extract_first.mel_filters
					_lowercase :    Tuple							=							feat_extract_second.mel_filters
					self.assertTrue(np.allclose(lowerCamelCase_			,					lowerCamelCase_						)						)
					self.assertEqual(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase :    Any							=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					# create three inputs of length 800, 1000, and 1200
					_lowercase :    Union[str, Any]							=							[floats_list((1, x)						)[0] for x in range(8_0_0			,					1_4_0_0			,					2_0_0						)]
					_lowercase :    Optional[Any]							=							[np.asarray(lowerCamelCase_						) for speech_input in speech_inputs]

					# Test feature size
					_lowercase :    List[Any]							=							feature_extractor(lowerCamelCase_			,					padding='max_length'			,					return_tensors='np'						).input_features
					self.assertTrue(input_features.ndim == 3						)
					self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames						)
					self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size						)

					# Test not batched input
					_lowercase :    str							=							feature_extractor(speech_inputs[0]			,					return_tensors='np'						).input_features
					_lowercase :    List[str]							=							feature_extractor(np_speech_inputs[0]			,					return_tensors='np'						).input_features
					self.assertTrue(np.allclose(lowerCamelCase_			,					lowerCamelCase_			,					atol=1E-3						)						)

					# Test batched
					_lowercase :    Optional[Any]							=							feature_extractor(lowerCamelCase_			,					return_tensors='np'						).input_features
					_lowercase :    str							=							feature_extractor(lowerCamelCase_			,					return_tensors='np'						).input_features
					for enc_seq_a, enc_seq_a in zip(lowerCamelCase_			,					lowerCamelCase_						):
						self.assertTrue(np.allclose(lowerCamelCase_			,					lowerCamelCase_			,					atol=1E-3						)						)

					# Test 2-D numpy arrays are batched.
					_lowercase :    List[Any]							=							[floats_list((1, x)						)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
					_lowercase :    Optional[int]							=							np.asarray(lowerCamelCase_						)
					_lowercase :    int							=							feature_extractor(lowerCamelCase_			,					return_tensors='np'						).input_features
					_lowercase :    Optional[Any]							=							feature_extractor(lowerCamelCase_			,					return_tensors='np'						).input_features
					for enc_seq_a, enc_seq_a in zip(lowerCamelCase_			,					lowerCamelCase_						):
						self.assertTrue(np.allclose(lowerCamelCase_			,					lowerCamelCase_			,					atol=1E-3						)						)

					# Test truncation required
					_lowercase :    Optional[int]							=							[floats_list((1, x)						)[0] for x in range(2_0_0			,					(feature_extractor.n_samples + 5_0_0)			,					2_0_0						)]
					_lowercase :    int							=							[np.asarray(lowerCamelCase_						) for speech_input in speech_inputs]

					_lowercase :    List[Any]							=							[x[: feature_extractor.n_samples] for x in speech_inputs]
					_lowercase :    Optional[int]							=							[np.asarray(lowerCamelCase_						) for speech_input in speech_inputs_truncated]

					_lowercase :    Union[str, Any]							=							feature_extractor(lowerCamelCase_			,					return_tensors='np'						).input_features
					_lowercase :    Optional[int]							=							feature_extractor(lowerCamelCase_			,					return_tensors='np'						).input_features
					for enc_seq_a, enc_seq_a in zip(lowerCamelCase_			,					lowerCamelCase_						):
						self.assertTrue(np.allclose(lowerCamelCase_			,					lowerCamelCase_			,					atol=1E-3						)						)







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					import torch

					_lowercase :    Optional[int]							=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					_lowercase :    Any							=							np.random.rand(1_0_0			,					3_2						).astype(np.floataa						)
					_lowercase :    str							=							np_speech_inputs.tolist()

					for inputs in [py_speech_inputs, np_speech_inputs]:
						_lowercase :    str							=							feature_extractor.pad([{'input_features': inputs}]			,					return_tensors='np'						)
						self.assertTrue(np_processed.input_features.dtype == np.floataa						)
						_lowercase :    List[str]							=							feature_extractor.pad([{'input_features': inputs}]			,					return_tensors='pt'						)
						self.assertTrue(pt_processed.input_features.dtype == torch.floataa						)







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       Dict						):

					"""simple docstring"""






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

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







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					_lowercase :    List[str]							=							torch.tensor(
					    [
					        0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
					        0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
					        0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
					        -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
					    ]						)
					# fmt: on

					_lowercase :    Optional[Any]							=							self._load_datasamples(1						)
					_lowercase :    str							=							WhisperFeatureExtractor()
					_lowercase :    Tuple							=							feature_extractor(lowerCamelCase_			,					return_tensors='pt'						).input_features
					self.assertEqual(input_features.shape			,					(1, 8_0, 3_0_0_0)						)
					self.assertTrue(torch.allclose(input_features[0, 0, :3_0]			,					lowerCamelCase_			,					atol=1E-4						)						)







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					_lowercase :    Union[str, Any]							=							self._load_datasamples(1						)[0]
					_lowercase :    str							=							((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
					_lowercase :    List[Any]							=							feat_extract.zero_mean_unit_var_norm([audio]			,					attention_mask=lowerCamelCase_						)[0]

					self.assertTrue(np.all(np.mean(lowerCamelCase_						) < 1E-3						)						)
					self.assertTrue(np.all(np.abs(np.var(lowerCamelCase_						) - 1						) < 1E-3						)						)




"""simple docstring"""







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE									=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BioGptForCausalLM',
						    'BioGptForTokenClassification',
						    'BioGptForSequenceClassification',
						    'BioGptModel',
						    'BioGptPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
						from .tokenization_biogpt import BioGptTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_biogpt import (
												    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BioGptForCausalLM,
												    BioGptForSequenceClassification,
												    BioGptForTokenClassification,
												    BioGptModel,
												    BioGptPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE									=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



