


'''simple docstring'''
from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__magic_name__ : Dict					=logging.get_logger(__name__)






class 		UpperCamelCase_		(       UpperCAmelCase__ ):





						"""simple docstring"""




						UpperCAmelCase__						:							Dict       =  ['''audio_values''', '''audio_mask''']
						def __init__(			self					:       Optional[int]						,					_lowerCamelCase					:       List[Any]=20_48						,					_lowerCamelCase					:       List[Any]=1						,					_lowerCamelCase					:       List[Any]=[16, 16]						,					_lowerCamelCase					:       int=1_28						,					_lowerCamelCase					:       Union[str, Any]=4_41_00						,					_lowerCamelCase					:       Union[str, Any]=86						,					_lowerCamelCase					:       List[Any]=20_48						,					_lowerCamelCase					:       str=0.0						,					**_lowerCamelCase					:       List[Any]						,					)				->       Any:
										super().__init__(
										    feature_size=__lowerCAmelCase						,					sampling_rate=__lowerCAmelCase						,					padding_value=__lowerCAmelCase						,					**__lowerCAmelCase						,					)

										__magic_name__							=			spectrogram_length
										__magic_name__							=			num_channels
										__magic_name__							=			patch_size
										__magic_name__							=			feature_size // self.patch_size[1]
										__magic_name__							=			n_fft
										__magic_name__							=			sampling_rate // hop_length_to_sampling_rate
										__magic_name__							=			sampling_rate
										__magic_name__							=			padding_value
										__magic_name__							=			mel_filter_bank(
										    num_frequency_bins=1 + n_fft // 2						,					num_mel_filters=__lowerCAmelCase						,					min_frequency=0.0						,					max_frequency=22_050.0						,					sampling_rate=__lowerCAmelCase						,					norm="slaney"						,					mel_scale="slaney"						,					).T
						def        __A       (			self					:       Dict						,					_lowerCamelCase					:       np.array	)				->       List[Any]:
										__magic_name__							=			spectrogram(
										    __lowerCAmelCase						,					window_function(self.n_fft						,					"hann"	)						,					frame_length=self.n_fft						,					hop_length=self.hop_length						,					power=2.0						,					mel_filters=self.mel_filters.T						,					log_mel="dB"						,					db_range=80.0						,					)
										__magic_name__							=			log_spec[:, :-1]
										__magic_name__							=			log_spec - 20.0
										__magic_name__							=			np.clip(log_spec / 40.0						,					-2.0						,					0.0	) + 1.0
										return log_spec

						def __call__(			self					:       List[str]						,					_lowerCamelCase					:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,					_lowerCamelCase					:       Optional[Union[str, TensorType]] = None						,					_lowerCamelCase					:       Optional[bool] = True						,					_lowerCamelCase					:       Optional[int] = None						,					_lowerCamelCase					:       bool = False						,					_lowerCamelCase					:       bool = False						,					**_lowerCamelCase					:       Tuple						,					)				->       Dict:

										if sampling_rate is not None:
														if sampling_rate != self.sampling_rate:
																		raise ValueError(
																		    "This feature extractor is set to support sampling rate"
																		    f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
																		    f' with {self.sampling_rate} and not {sampling_rate}.'	)
										else:
														logger.warning(
														    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
														    "Failing to do so can result in silent errors that might be hard to debug."	)

										__magic_name__							=			isinstance(__lowerCAmelCase						,					np.ndarray	) and len(raw_speech.shape	) > 1
										if is_batched_numpy and len(raw_speech.shape	) > 2:
														raise ValueError(f'Only mono-channel audio is supported for input to {self}'	)
										__magic_name__							=			is_batched_numpy or (
										    isinstance(__lowerCAmelCase						,					(list, tuple)	) and (isinstance(raw_speech[0]						,					(np.ndarray, tuple, list)	))
										)
										if is_batched:
														__magic_name__							=			[np.asarray([speech]						,					dtype=np.floataa	).T for speech in raw_speech]
										elif not is_batched and not isinstance(__lowerCAmelCase						,					np.ndarray	):
														__magic_name__							=			np.asarray(__lowerCAmelCase						,					dtype=np.floataa	)
										elif isinstance(__lowerCAmelCase						,					np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
														__magic_name__							=			raw_speech.astype(np.floataa	)
										# always return batch
										if not is_batched:
														__magic_name__							=			[np.asarray([raw_speech]	).T]

										# Convert audio signals to log mel spectrograms, truncate by time axis
										__magic_name__							=			[
										    self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
										]
										if isinstance(audio_features[0]						,					__lowerCAmelCase	):
														__magic_name__							=			[np.asarray(__lowerCAmelCase						,					dtype=np.floataa	) for feature in audio_features]

										# Create audio attention mask
										__magic_name__							=			max(
										    [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
										if return_attention_mask:
														__magic_name__							=			[
														    (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
														    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
														    for feature in audio_features
														]
														__magic_name__							=			np.array(__lowerCAmelCase	).astype(np.floataa	)

										# convert into correct format for padding
										__magic_name__							=			max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
										__magic_name__							=			np.ones([len(__lowerCAmelCase	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
										__magic_name__							=			padded_audio_features * self.padding_value
										for i in range(len(__lowerCAmelCase	)	):
														__magic_name__							=			audio_features[i]
														__magic_name__							=			feature

										# return as BatchFeature
										if return_attention_mask:
														__magic_name__							=			{"audio_values": padded_audio_features, "audio_mask": audio_mask}
										else:
														__magic_name__							=			{"audio_values": padded_audio_features}

										__magic_name__							=			BatchFeature(data=__lowerCAmelCase						,					tensor_type=__lowerCAmelCase	)
										return encoded_inputs









'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowercase  =			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
_lowercase  =			"""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""
class 						a_				(			unittest.TestCase						):




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir       ,		'models/bert/'      )      )
							__snake_case						 =   self.transformer_dir
							shutil.copy(
							    os.path.join(__lowerCAmelCase       ,		'src/transformers/models/bert/modeling_bert.py'      )       ,		os.path.join(self.transformer_dir       ,		'models/bert/modeling_bert.py'      )       ,		)




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   'src/transformers'
							shutil.rmtree(self.transformer_dir      )




	def   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  List[str]=None      ):
							__snake_case						 =   comment + F'\nclass {class_name}(nn.Module):\n' + class_code
							if overwrite_result is not None:
													__snake_case						 =   comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
							__snake_case						 =   black.Mode(target_versions={black.TargetVersion.PYaa}       ,		line_length=1_1_9      )
							__snake_case						 =   black.format_str(__lowerCAmelCase       ,		mode=__lowerCAmelCase      )
							__snake_case						 =   os.path.join(self.transformer_dir       ,		'new_code.py'      )
							with open(__lowerCAmelCase       ,		'w'       ,		newline='\n'      ) as f:
													f.write(__lowerCAmelCase      )
							if overwrite_result is None:
													self.assertTrue(len(check_copies.is_copy_consistent(__lowerCAmelCase      )      ) == 0      )
							else:
													check_copies.is_copy_consistent(f.name       ,		overwrite=__lowerCAmelCase      )
													with open(__lowerCAmelCase       ,		'r'      ) as f:
																			self.assertTrue(f.read()       ,		__lowerCAmelCase      )




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'      )
							self.assertEqual(__lowerCAmelCase       ,		__lowerCAmelCase      )




	def   lowercase__   (       self     :  int      ):
							# Base copy consistency
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'       ,		'BertLMPredictionHead'       ,		REFERENCE_CODE + '\n'       ,		)

							# With no empty line at the end
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'       ,		'BertLMPredictionHead'       ,		__lowerCAmelCase       ,		)

							# Copy consistency with rename
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'       ,		'TestModelLMPredictionHead'       ,		re.sub('Bert'       ,		'TestModel'       ,		__lowerCAmelCase      )       ,		)

							# Copy consistency with a really long name
							__snake_case						 =   'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
							self.check_copy_consistency(
							    F'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'       ,		F'{long_class_name}LMPredictionHead'       ,		re.sub('Bert'       ,		__lowerCAmelCase       ,		__lowerCAmelCase      )       ,		)

							# Copy consistency with overwrite
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'       ,		'TestModelLMPredictionHead'       ,		__lowerCAmelCase       ,		overwrite_result=re.sub('Bert'       ,		'TestModel'       ,		__lowerCAmelCase      )       ,		)




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   check_copies.LOCALIZED_READMES['README_zh-hans.md']

							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
							    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
							    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
							    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
							    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
							    ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
							    ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
							    ' method has been applied to compress GPT2 into'
							    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
							    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
							    ' Multilingual BERT into'
							    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
							    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
							    ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
							    ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
							    ' Luong, Quoc V. Le, Christopher D. Manning.'
							)
							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
							    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
							)
							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
							    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
							    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
							    ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
							    ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
							    ' method has been applied to compress GPT2 into'
							    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
							    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
							    ' Multilingual BERT into'
							    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
							    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
							    ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
							    ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
							    ' Christopher D. Manning 发布。\n'
							)

							__snake_case ,       __snake_case						 =   check_copies.convert_to_localized_md(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		localized_readme['format_model_list']      )

							self.assertFalse(__lowerCAmelCase      )
							self.assertEqual(__lowerCAmelCase       ,		__lowerCAmelCase      )

							__snake_case ,       __snake_case						 =   check_copies.convert_to_localized_md(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		localized_readme['format_model_list']      )

							# Check whether the number of models is equal to README.md after conversion.
							self.assertTrue(__lowerCAmelCase      )

							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
							    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
							    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
							    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
							)
							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
							    ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
							)
							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
							    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
							)

							__snake_case ,       __snake_case						 =   check_copies.convert_to_localized_md(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		localized_readme['format_model_list']      )

							# Check if the model link is synchronized.
							self.assertEqual(__lowerCAmelCase       ,		__lowerCAmelCase      )

