

'''simple docstring'''

import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
							from .features import FeatureType


SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE							:				Tuple     	=			False, False, False






@dataclass
class    snake_case     :






				"""simple docstring"""
				_a    	=     None
				_a    	=     True
				_a    	=     True
				_a    	=     None
				# Automatically constructed
				_a    	=     "dict"
				_a    	=     pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}		)
				_a    	=     field(default="""Audio""",						init=lowercase_,						repr=lowercase_		)



				def __call__(    self       )     ->							Optional[Any]:
											return self.pa_type



				def   a__					(    self,     _lowercase       )     ->							dict:
											try:
																		import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
											except ImportError as err:
																		raise ImportError('To support encoding audio data, please install \'soundfile\'.'       ) from err
											if isinstance(_lowercase,     _lowercase       ):
																		return {"bytes": None, "path": value}
											elif isinstance(_lowercase,     _lowercase       ):
																		return {"bytes": value, "path": None}
											elif "array" in value:
																		# convert the audio array to wav bytes
																		SCREAMING_SNAKE_CASE_				=				BytesIO()
																		sf.write(_lowercase,     value['array'],     value['sampling_rate'],     format='wav'       )
																		return {"bytes": buffer.getvalue(), "path": None}
											elif value.get('path'       ) is not None and os.path.isfile(value['path']       ):
																		# we set "bytes": None to not duplicate the data if they're already available locally
																		if value["path"].endswith('pcm'       ):
																									# "PCM" only has raw audio bytes
																									if value.get('sampling_rate'       ) is None:
																																# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																																raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object'       )
																									if value.get('bytes'       ):
																																# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																																SCREAMING_SNAKE_CASE_				=				np.frombuffer(value['bytes'],     dtype=np.intaa       ).astype(np.floataa       ) / 32767
																									else:
																																SCREAMING_SNAKE_CASE_				=				np.memmap(value['path'],     dtype='h',     mode='r'       ).astype(np.floataa       ) / 32767

																									SCREAMING_SNAKE_CASE_				=				BytesIO(bytes()       )
																									sf.write(_lowercase,     _lowercase,     value['sampling_rate'],     format='wav'       )
																									return {"bytes": buffer.getvalue(), "path": None}
																		else:
																									return {"bytes": None, "path": value.get('path'       )}
											elif value.get('bytes'       ) is not None or value.get('path'       ) is not None:
																		# store the audio bytes, and path is used to infer the audio format using the file extension
																		return {"bytes": value.get('bytes'       ), "path": value.get('path'       )}
											else:
																		raise ValueError(
																		    f"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""       )



				def   a__					(    self,     _lowercase,     _lowercase = None       )     ->							dict:
											if not self.decode:
																		raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.'       )

											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				(value['path'], BytesIO(value['bytes']       )) if value['bytes'] is not None else (value['path'], None)
											if path is None and file is None:
																		raise ValueError(f"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}."""       )

											try:
																		import librosa
																		import soundfile as sf
											except ImportError as err:
																		raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.'       ) from err

											SCREAMING_SNAKE_CASE_				=				xsplitext(_lowercase       )[1][1:].lower() if path is not None else None
											if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
																		raise RuntimeError(
																		    'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '
																		    'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '       )
											elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
																		raise RuntimeError(
																		    'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '
																		    'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '       )

											if file is None:
																		SCREAMING_SNAKE_CASE_				=				token_per_repo_id or {}
																		SCREAMING_SNAKE_CASE_				=				path.split('::'       )[-1]
																		try:
																									SCREAMING_SNAKE_CASE_				=				string_to_dict(_lowercase,     config.HUB_DATASETS_URL       )['repo_id']
																									SCREAMING_SNAKE_CASE_				=				token_per_repo_id[repo_id]
																		except (ValueError, KeyError):
																									SCREAMING_SNAKE_CASE_				=				None

																		with xopen(_lowercase,     'rb',     use_auth_token=_lowercase       ) as f:
																									SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				sf.read(_lowercase       )

											else:
																		SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				sf.read(_lowercase       )

											SCREAMING_SNAKE_CASE_				=				array.T
											if self.mono:
																		SCREAMING_SNAKE_CASE_				=				librosa.to_mono(_lowercase       )
											if self.sampling_rate and self.sampling_rate != sampling_rate:
																		SCREAMING_SNAKE_CASE_				=				librosa.resample(_lowercase,     orig_sr=_lowercase,     target_sr=self.sampling_rate       )
																		SCREAMING_SNAKE_CASE_				=				self.sampling_rate

											return {"path": path, "array": array, "sampling_rate": sampling_rate}



				def   a__					(    self       )     ->							Union["FeatureType", Dict[str, "FeatureType"]]:
											from .features import Value

											if self.decode:
																		raise ValueError('Cannot flatten a decoded Audio feature.'       )
											return {
											    "bytes": Value('binary'       ),
											    "path": Value('string'       ),
											}



				def   a__					(    self,     _lowercase       )     ->							pa.StructArray:
											if pa.types.is_string(storage.type       ):
																		SCREAMING_SNAKE_CASE_				=				pa.array([None] * len(_lowercase       ),     type=pa.binary()       )
																		SCREAMING_SNAKE_CASE_				=				pa.StructArray.from_arrays([bytes_array, storage],     ['bytes', 'path'],     mask=storage.is_null()       )
											elif pa.types.is_binary(storage.type       ):
																		SCREAMING_SNAKE_CASE_				=				pa.array([None] * len(_lowercase       ),     type=pa.string()       )
																		SCREAMING_SNAKE_CASE_				=				pa.StructArray.from_arrays([storage, path_array],     ['bytes', 'path'],     mask=storage.is_null()       )
											elif pa.types.is_struct(storage.type       ) and storage.type.get_all_field_indices('array'       ):
																		SCREAMING_SNAKE_CASE_				=				pa.array([Audio().encode_example(_lowercase       ) if x is not None else None for x in storage.to_pylist()]       )
											elif pa.types.is_struct(storage.type       ):
																		if storage.type.get_field_index('bytes'       ) >= 0:
																									SCREAMING_SNAKE_CASE_				=				storage.field('bytes'       )
																		else:
																									SCREAMING_SNAKE_CASE_				=				pa.array([None] * len(_lowercase       ),     type=pa.binary()       )
																		if storage.type.get_field_index('path'       ) >= 0:
																									SCREAMING_SNAKE_CASE_				=				storage.field('path'       )
																		else:
																									SCREAMING_SNAKE_CASE_				=				pa.array([None] * len(_lowercase       ),     type=pa.string()       )
																		SCREAMING_SNAKE_CASE_				=				pa.StructArray.from_arrays([bytes_array, path_array],     ['bytes', 'path'],     mask=storage.is_null()       )
											return array_cast(_lowercase,     self.pa_type       )





				def   a__					(    self,     _lowercase       )     ->							pa.StructArray:

											@no_op_if_value_is_null
											def path_to_bytes(_lowercase       ):
																		with xopen(_lowercase,     'rb'       ) as f:
																									SCREAMING_SNAKE_CASE_				=				f.read()
																		return bytes_

											SCREAMING_SNAKE_CASE_				=				pa.array(
											    [
											        (path_to_bytes(x['path']       ) if x['bytes'] is None else x['bytes']) if x is not None else None
											        for x in storage.to_pylist()
											    ],     type=pa.binary(),     )
											SCREAMING_SNAKE_CASE_				=				pa.array(
											    [os.path.basename(_lowercase       ) if path is not None else None for path in storage.field('path'       ).to_pylist()],     type=pa.string(),     )
											SCREAMING_SNAKE_CASE_				=				pa.StructArray.from_arrays([bytes_array, path_array],     ['bytes', 'path'],     mask=bytes_array.is_null()       )
											return array_cast(_lowercase,     self.pa_type       )








'''simple docstring'''

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

import black


SCREAMING_SNAKE_CASE							:				List[str]     	=			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.
SCREAMING_SNAKE_CASE							:				List[str]     	=			"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				tempfile.mkdtemp()
											os.makedirs(os.path.join(self.transformer_dir,     'models/bert/'       )       )
											SCREAMING_SNAKE_CASE_				=				self.transformer_dir
											shutil.copy(
											    os.path.join(_lowercase,     'src/transformers/models/bert/modeling_bert.py'       ),     os.path.join(self.transformer_dir,     'models/bert/modeling_bert.py'       ),     )



				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				'src/transformers'
											shutil.rmtree(self.transformer_dir       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase=None       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
											if overwrite_result is not None:
																		SCREAMING_SNAKE_CASE_				=				comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
											SCREAMING_SNAKE_CASE_				=				black.Mode(target_versions={black.TargetVersion.PYaa},     line_length=119       )
											SCREAMING_SNAKE_CASE_				=				black.format_str(_lowercase,     mode=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				os.path.join(self.transformer_dir,     'new_code.py'       )
											with open(_lowercase,     'w',     newline='\n'       ) as f:
																		f.write(_lowercase       )
											if overwrite_result is None:
																		self.assertTrue(len(check_copies.is_copy_consistent(_lowercase       )       ) == 0       )
											else:
																		check_copies.is_copy_consistent(f.name,     overwrite=_lowercase       )
																		with open(_lowercase,     'r'       ) as f:
																									self.assertTrue(f.read(),     _lowercase       )



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'       )
											self.assertEqual(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							Union[str, Any]:
											# 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',     _lowercase,     )

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

											# Copy consistency with a really long name
											SCREAMING_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',     _lowercase,     _lowercase       ),     )

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





				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				check_copies.LOCALIZED_READMES['README_zh-hans.md']

											SCREAMING_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.'
											)
											SCREAMING_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'
											)
											SCREAMING_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'
											)

											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				check_copies.convert_to_localized_md(
											    _lowercase,     _lowercase,     localized_readme['format_model_list']       )

											self.assertFalse(_lowercase       )
											self.assertEqual(_lowercase,     _lowercase       )

											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				check_copies.convert_to_localized_md(
											    _lowercase,     _lowercase,     localized_readme['format_model_list']       )

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

											SCREAMING_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.'
											)
											SCREAMING_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'
											)
											SCREAMING_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'
											)

											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				check_copies.convert_to_localized_md(
											    _lowercase,     _lowercase,     localized_readme['format_model_list']       )

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





