
'''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


A      ,			A      ,			A			      =      False, False, False




@dataclass
class     __snake_case		:
					_lowerCAmelCase			     =	None
					_lowerCAmelCase			     =	True
					_lowerCAmelCase			     =	True
					_lowerCAmelCase			     =	None
					# Automatically constructed
					_lowerCAmelCase			     =	"dict"
					_lowerCAmelCase			     =	pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()})
					_lowerCAmelCase			     =	field(default='''Audio'''    ,   init=a__    ,   repr=a__)
					def __call__(     self      ):

									"""simple docstring"""


									return self.pa_type
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									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(A,    A      ):
													return {"bytes": None, "path": value}
									elif isinstance(A,    A      ):
													return {"bytes": value, "path": None}
									elif "array" in value:
													# convert the audio array to wav bytes
													lowerCamelCase					:			Union[str, Any]									= BytesIO()
													sf.write(A,    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!)
																					lowerCamelCase					:			Tuple									= np.frombuffer(value['bytes'],    dtype=np.intaa      ).astype(np.floataa      ) / 3_2767
																	else:
																					lowerCamelCase					:			str									= np.memmap(value['path'],    dtype='h',    mode='r'      ).astype(np.floataa      ) / 3_2767

																	lowerCamelCase					:			Union[str, Any]									= BytesIO(bytes()      )
																	sf.write(A,    A,    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     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									if not self.decode:
													raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.'      )

									lowerCamelCase			, lowerCamelCase					:			Union[str, 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

									lowerCamelCase					:			Dict									= xsplitext(A      )[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:
													lowerCamelCase					:			Tuple									= token_per_repo_id or {}
													lowerCamelCase					:			Tuple									= path.split('::'      )[-1]
													try:
																	lowerCamelCase					:			Tuple									= string_to_dict(A,    config.HUB_DATASETS_URL      )['repo_id']
																	lowerCamelCase					:			Tuple									= token_per_repo_id[repo_id]
													except (ValueError, KeyError):
																	lowerCamelCase					:			Tuple									= None

													with xopen(A,    'rb',    use_auth_token=A      ) as f:
																	lowerCamelCase			, lowerCamelCase					:			List[Any]									= sf.read(A      )

									else:
													lowerCamelCase			, lowerCamelCase					:			Union[str, Any]									= sf.read(A      )

									lowerCamelCase					:			Dict									= array.T
									if self.mono:
													lowerCamelCase					:			Optional[int]									= librosa.to_mono(A      )
									if self.sampling_rate and self.sampling_rate != sampling_rate:
													lowerCamelCase					:			Optional[int]									= librosa.resample(A,    orig_sr=A,    target_sr=self.sampling_rate      )
													lowerCamelCase					:			Dict									= self.sampling_rate

									return {"path": path, "array": array, "sampling_rate": sampling_rate}
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									from .features import Value

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

									"""simple docstring"""


									if pa.types.is_string(storage.type      ):
													lowerCamelCase					:			List[Any]									= pa.array([None] * len(A      ),    type=pa.binary()      )
													lowerCamelCase					:			List[Any]									= pa.StructArray.from_arrays([bytes_array, storage],    ['bytes', 'path'],    mask=storage.is_null()      )
									elif pa.types.is_binary(storage.type      ):
													lowerCamelCase					:			Tuple									= pa.array([None] * len(A      ),    type=pa.string()      )
													lowerCamelCase					:			Tuple									= 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'      ):
													lowerCamelCase					:			Any									= pa.array([Audio().encode_example(A      ) 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:
																	lowerCamelCase					:			List[str]									= storage.field('bytes'      )
													else:
																	lowerCamelCase					:			int									= pa.array([None] * len(A      ),    type=pa.binary()      )
													if storage.type.get_field_index('path'      ) >= 0:
																	lowerCamelCase					:			Dict									= storage.field('path'      )
													else:
																	lowerCamelCase					:			Union[str, Any]									= pa.array([None] * len(A      ),    type=pa.string()      )
													lowerCamelCase					:			Union[str, Any]									= pa.StructArray.from_arrays([bytes_array, path_array],    ['bytes', 'path'],    mask=storage.is_null()      )
									return array_cast(A,    self.pa_type      )




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									@no_op_if_value_is_null
									def path_to_bytes(A      ):
													with xopen(A,    'rb'      ) as f:
																	lowerCamelCase					:			Union[str, Any]									= f.read()
													return bytes_

									lowerCamelCase					:			List[Any]									= 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(),    )
									lowerCamelCase					:			List[str]									= pa.array(
									    [os.path.basename(A      ) if path is not None else None for path in storage.field('path'      ).to_pylist()],    type=pa.string(),    )
									lowerCamelCase					:			Dict									= pa.StructArray.from_arrays([bytes_array, path_array],    ['bytes', 'path'],    mask=bytes_array.is_null()      )
									return array_cast(A,    self.pa_type      )


'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A			      =      {
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPTokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPFeatureExtractor']
					A			      =      ['CLIPImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CLIPModel',
					    'CLIPPreTrainedModel',
					    'CLIPTextModel',
					    'CLIPTextModelWithProjection',
					    'CLIPVisionModel',
					    'CLIPVisionModelWithProjection',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCLIPModel',
					    'TFCLIPPreTrainedModel',
					    'TFCLIPTextModel',
					    'TFCLIPVisionModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'FlaxCLIPModel',
					    'FlaxCLIPPreTrainedModel',
					    'FlaxCLIPTextModel',
					    'FlaxCLIPTextPreTrainedModel',
					    'FlaxCLIPVisionModel',
					    'FlaxCLIPVisionPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_clip import (
					    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPConfig,
					    CLIPOnnxConfig,
					    CLIPTextConfig,
					    CLIPVisionConfig,
					)
					from .processing_clip import CLIPProcessor
					from .tokenization_clip import CLIPTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_clip_fast import CLIPTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clip import CLIPFeatureExtractor
										from .image_processing_clip import CLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clip import (
										    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPModel,
										    CLIPPreTrainedModel,
										    CLIPTextModel,
										    CLIPTextModelWithProjection,
										    CLIPVisionModel,
										    CLIPVisionModelWithProjection,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_clip import (
										    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCLIPModel,
										    TFCLIPPreTrainedModel,
										    TFCLIPTextModel,
										    TFCLIPVisionModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_clip import (
										    FlaxCLIPModel,
										    FlaxCLIPPreTrainedModel,
										    FlaxCLIPTextModel,
										    FlaxCLIPTextPreTrainedModel,
										    FlaxCLIPVisionModel,
										    FlaxCLIPVisionPreTrainedModel,
										)


else:
					import sys

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