




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


UpperCamelCase__			,	UpperCamelCase__			,	UpperCamelCase__    			=			False, False, False
@dataclass
class 			a__    :
						_a							:    Optional[int]					=    None
						_a							:    bool					=    True
						_a							:    bool					=    True
						_a							:    Optional[str]					=    None
						# Automatically constructed
						_a							:    ClassVar[str]					=    "dict"
						_a							:    ClassVar[Any]					=    pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}      )
						_a							:    str					=    field(default="""Audio""" ,				init=snake_case__ ,				repr=snake_case__      )






						def __call__(   self			):




											"""simple docstring"""



											return self.pa_type






						def 				__SCREAMING_SNAKE_CASE(   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				   =       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				   =       np.frombuffer(value["bytes"]		,			dtype=np.intaa			).astype(np.floataa			) / 3_2_7_6_7
																					else:
																										__lowerCAmelCase				   =       np.memmap(value["path"]		,			dtype="h"		,			mode="r"			).astype(np.floataa			) / 3_2_7_6_7

																					__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   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				   =       (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				   =       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				   =       token_per_repo_id or {}
																__lowerCAmelCase				   =       path.split("::"			)[-1]
																try:
																					__lowerCAmelCase				   =       string_to_dict(_A		,			config.HUB_DATASETS_URL			)["repo_id"]
																					__lowerCAmelCase				   =       token_per_repo_id[repo_id]
																except (ValueError, KeyError):
																					__lowerCAmelCase				   =       None

																with xopen(_A		,			"rb"		,			use_auth_token=_A			) as f:
																					__lowerCAmelCase   ,							__lowerCAmelCase				   =       sf.read(_A			)

											else:
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       sf.read(_A			)

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

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






						def 				__SCREAMING_SNAKE_CASE(   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 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											if pa.types.is_string(storage.type			):
																__lowerCAmelCase				   =       pa.array([None] * len(_A			)		,			type=pa.binary()			)
																__lowerCAmelCase				   =       pa.StructArray.from_arrays([bytes_array, storage]		,			["bytes", "path"]		,			mask=storage.is_null()			)
											elif pa.types.is_binary(storage.type			):
																__lowerCAmelCase				   =       pa.array([None] * len(_A			)		,			type=pa.string()			)
																__lowerCAmelCase				   =       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				   =       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				   =       storage.field("bytes"			)
																else:
																					__lowerCAmelCase				   =       pa.array([None] * len(_A			)		,			type=pa.binary()			)
																if storage.type.get_field_index("path"			) >= 0:
																					__lowerCAmelCase				   =       storage.field("path"			)
																else:
																					__lowerCAmelCase				   =       pa.array([None] * len(_A			)		,			type=pa.string()			)
																__lowerCAmelCase				   =       pa.StructArray.from_arrays([bytes_array, path_array]		,			["bytes", "path"]		,			mask=storage.is_null()			)
											return array_cast(_A		,			self.pa_type			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											@no_op_if_value_is_null
											def path_to_bytes(_A			):
																with xopen(_A		,			"rb"			) as f:
																					__lowerCAmelCase				   =       f.read()
																return bytes_

											__lowerCAmelCase				   =       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				   =       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				   =       pa.StructArray.from_arrays([bytes_array, path_array]		,			["bytes", "path"]		,			mask=bytes_array.is_null()			)
											return array_cast(_A		,			self.pa_type			)





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class   snake_case_       (		__A       ):
   __A	:		Optional[Any]         =				["image_processor", "tokenizer"]
   __A	:		Tuple         =				"LayoutLMv3ImageProcessor"
   __A	:		List[Any]         =				("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     int=None	,						lowercase_				:     str=None	,						**lowercase_				:     Optional[Any]  )					->						Optional[int]:
       lowercase__ :   Union[str, Any]    					=				None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."	,						lowercase_	,						)
           lowercase__ :   Optional[int]    					=				kwargs.pop("feature_extractor"  )

       lowercase__ :   int    					=				image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."  )
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."  )

       super().__init__(lowercase_	,						lowercase_  )






   def __call__(      self				:     Dict	,						lowercase_				:     Optional[Any]	,						lowercase_				:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,						lowercase_				:     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,						lowercase_				:     Union[List[List[int]], List[List[List[int]]]] = None	,						lowercase_				:     Optional[Union[List[int], List[List[int]]]] = None	,						lowercase_				:     bool = True	,						lowercase_				:     Union[bool, str, PaddingStrategy] = False	,						lowercase_				:     Union[bool, str, TruncationStrategy] = None	,						lowercase_				:     Optional[int] = None	,						lowercase_				:     int = 0	,						lowercase_				:     Optional[int] = None	,						lowercase_				:     Optional[bool] = None	,						lowercase_				:     Optional[bool] = None	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = True	,						lowercase_				:     Optional[Union[str, TensorType]] = None	,						**lowercase_				:     Dict	,						)					->						BatchEncoding:
       # verify input
       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."  )

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."  )

       # first, apply the image processor
       lowercase__ :   Union[str, Any]    					=				self.image_processor(images=lowercase_	,						return_tensors=lowercase_  )

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowercase_	,						lowercase_  ):
               lowercase__ :   Optional[Any]    					=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase__ :   Any    					=				features["words"]

       lowercase__ :   Tuple    					=				self.tokenizer(
           text=text if text is not None else features["words"]	,						text_pair=text_pair if text_pair is not None else None	,						boxes=boxes if boxes is not None else features["boxes"]	,						word_labels=lowercase_	,						add_special_tokens=lowercase_	,						padding=lowercase_	,						truncation=lowercase_	,						max_length=lowercase_	,						stride=lowercase_	,						pad_to_multiple_of=lowercase_	,						return_token_type_ids=lowercase_	,						return_attention_mask=lowercase_	,						return_overflowing_tokens=lowercase_	,						return_special_tokens_mask=lowercase_	,						return_offsets_mapping=lowercase_	,						return_length=lowercase_	,						verbose=lowercase_	,						return_tensors=lowercase_	,						**lowercase_	,						)

       # add pixel values
       lowercase__ :   Optional[int]    					=				features.pop("pixel_values"  )
       if return_overflowing_tokens is True:
           lowercase__ :   Dict    					=				self.get_overflowing_images(lowercase_	,						encoded_inputs["overflow_to_sample_mapping"]  )
       lowercase__ :   str    					=				images

       return encoded_inputs






   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     Tuple	,						lowercase_				:     List[Any]  )					->						Dict:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowercase__ :   Tuple    					=				[]
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]  )

       if len(lowercase_  ) != len(lowercase_  ):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F''' {len(lowercase_  )} and {len(lowercase_  )}'''  )

       return images_with_overflow






   def       __UpperCamelCase     (      self				:     int	,						*lowercase_				:     Union[str, Any]	,						**lowercase_				:     List[str]  )					->						Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						*lowercase_				:     str	,						**lowercase_				:     int  )					->						Dict:
       return self.tokenizer.decode(*lowercase_	,						**lowercase_  )






   @property
   def       __UpperCamelCase     (      self				:     Any  )					->						Any:
       return ["input_ids", "bbox", "attention_mask", "pixel_values"]






   @property
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Optional[Any]:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,						lowercase_	,						)
       return self.image_processor_class







   @property
   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Tuple:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,						lowercase_	,						)
       return self.image_processor



