



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


lowerCAmelCase__	    =    False, False, False

@dataclass
class  snake_case__:

					"""simple docstring"""






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







					def __call__(				self   :      Dict	):
									return self.pa_type







					def 					snake_case	(				self   :      List[str] ,  SCREAMING_SNAKE_CASE   :      List[str]	):
									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
													lowercase__	:       str						=	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!)
																					lowercase__	:       Tuple						=	np.frombuffer(value["bytes"] ,  dtype=np.intaa	).astype(np.floataa	) / 32_767
																	else:
																					lowercase__	:       List[str]						=	np.memmap(value["path"] ,  dtype="h" ,  mode="r"	).astype(np.floataa	) / 32_767

																	lowercase__	:       Dict						=	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 					snake_case	(				self   :      Tuple ,  SCREAMING_SNAKE_CASE   :      Tuple ,  SCREAMING_SNAKE_CASE   :      List[str] = None	):
									if not self.decode:
													raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead."	)

									lowercase__				,				lowercase__	:       List[Any]						=	(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

									lowercase__	:       Dict						=	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:
													lowercase__	:       List[Any]						=	token_per_repo_id or {}
													lowercase__	:       str						=	path.split("::"	)[-1]
													try:
																	lowercase__	:       Dict						=	string_to_dict(_lowercase ,  config.HUB_DATASETS_URL	)["repo_id"]
																	lowercase__	:       Optional[int]						=	token_per_repo_id[repo_id]
													except (ValueError, KeyError):
																	lowercase__	:       List[Any]						=	None

													with xopen(_lowercase ,  "rb" ,  use_auth_token=_lowercase	) as f:
																	lowercase__				,				lowercase__	:       List[str]						=	sf.read(_lowercase	)

									else:
													lowercase__				,				lowercase__	:       Optional[int]						=	sf.read(_lowercase	)

									lowercase__	:       Dict						=	array.T
									if self.mono:
													lowercase__	:       Any						=	librosa.to_mono(_lowercase	)
									if self.sampling_rate and self.sampling_rate != sampling_rate:
													lowercase__	:       List[str]						=	librosa.resample(_lowercase ,  orig_sr=_lowercase ,  target_sr=self.sampling_rate	)
													lowercase__	:       List[str]						=	self.sampling_rate

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







					def 					snake_case	(				self   :      Union[str, Any]	):
									from .features import Value

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







					def 					snake_case	(				self   :      Dict ,  SCREAMING_SNAKE_CASE   :      Dict	):
									if pa.types.is_string(storage.type	):
													lowercase__	:       Tuple						=	pa.array([None] * len(_lowercase	) ,  type=pa.binary()	)
													lowercase__	:       Optional[int]						=	pa.StructArray.from_arrays([bytes_array, storage] ,  ["bytes", "path"] ,  mask=storage.is_null()	)
									elif pa.types.is_binary(storage.type	):
													lowercase__	:       List[Any]						=	pa.array([None] * len(_lowercase	) ,  type=pa.string()	)
													lowercase__	:       int						=	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"	):
													lowercase__	:       List[Any]						=	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:
																	lowercase__	:       List[Any]						=	storage.field("bytes"	)
													else:
																	lowercase__	:       List[str]						=	pa.array([None] * len(_lowercase	) ,  type=pa.binary()	)
													if storage.type.get_field_index("path"	) >= 0:
																	lowercase__	:       Optional[int]						=	storage.field("path"	)
													else:
																	lowercase__	:       List[str]						=	pa.array([None] * len(_lowercase	) ,  type=pa.string()	)
													lowercase__	:       Optional[int]						=	pa.StructArray.from_arrays([bytes_array, path_array] ,  ["bytes", "path"] ,  mask=storage.is_null()	)
									return array_cast(_lowercase ,  self.pa_type	)







					def 					snake_case	(				self   :      str ,  SCREAMING_SNAKE_CASE   :      List[str]	):

									@no_op_if_value_is_null
									def path_to_bytes(SCREAMING_SNAKE_CASE   :      int	):
													with xopen(_lowercase ,  "rb"	) as f:
																	lowercase__	:       int						=	f.read()
													return bytes_

									lowercase__	:       Optional[int]						=	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() ,  )
									lowercase__	:       Optional[Any]						=	pa.array(
									    [os.path.basename(_lowercase	) if path is not None else None for path in storage.field("path"	).to_pylist()] ,  type=pa.string() ,  )
									lowercase__	:       Optional[int]						=	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       )





