






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_		:						Optional[Any]             =    False, False, False

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






   def __call__(       self)      ->       Union[str, Any]:
      return self.pa_type






   def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       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(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
         SCREAMING_SNAKE_CASE						= 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!)
               SCREAMING_SNAKE_CASE						= np.frombuffer(value['bytes']		,					dtype=np.intaa).astype(np.floataa) / 3_2767
            else:
               SCREAMING_SNAKE_CASE						= np.memmap(value['path']		,					dtype='h'		,					mode='r').astype(np.floataa) / 3_2767

            SCREAMING_SNAKE_CASE						= 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)      ->       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(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:
         SCREAMING_SNAKE_CASE						= token_per_repo_id or {}
         SCREAMING_SNAKE_CASE						= path.split('::')[-1]
         try:
            SCREAMING_SNAKE_CASE						= string_to_dict(a		,					config.HUB_DATASETS_URL)['repo_id']
            SCREAMING_SNAKE_CASE						= token_per_repo_id[repo_id]
         except (ValueError, KeyError):
            SCREAMING_SNAKE_CASE						= None

         with xopen(a		,					'rb'		,					use_auth_token=a) as f:
            SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= sf.read(a)

      else:
         SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= sf.read(a)

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

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






   def    SCREAMING_SNAKE_CASE__    (       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    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       pa.StructArray:
      if pa.types.is_string(storage.type):
         SCREAMING_SNAKE_CASE						= pa.array([None] * len(a)		,					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(a)		,					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(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:
            SCREAMING_SNAKE_CASE						= storage.field('bytes')
         else:
            SCREAMING_SNAKE_CASE						= pa.array([None] * len(a)		,					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(a)		,					type=pa.string())
         SCREAMING_SNAKE_CASE						= 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)      ->       pa.StructArray:

      @no_op_if_value_is_null
      def path_to_bytes(a):
         with xopen(a		,					'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(a) 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(a		,					self.pa_type)












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__					):
			_lowercase		:    Union[str, Any]			     =							['''image_processor''', '''tokenizer''']
			_lowercase		:    Optional[int]			     =							'''Pix2StructImageProcessor'''
			_lowercase		:    str			     =							('''T5Tokenizer''', '''T5TokenizerFast''')






			def __init__(       self		,					a		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= False
						super().__init__(a		,					a)






			def __call__(       self		,					a=None		,					a = None		,					a = True		,					a = False		,					a = None		,					a = None		,					a = 2048		,					a = 0		,					a = None		,					a = None		,					a = False		,					a = False		,					a = False		,					a = False		,					a = False		,					a = True		,					a = None		,					**a		,					)      ->       BatchEncoding:
						if images is None and text is None:
									raise ValueError('You have to specify either images or text.')

						# Get only text
						if images is None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)
									return text_encoding

						if not self.image_processor.is_vqa:
									# add pixel_values
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					**a)
						else:
									# add pixel_values and bbox
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					header_text=a		,					**a)

						if text is not None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)

									if "attention_mask" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('attention_mask')
									if "input_ids" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('input_ids')
						else:
									SCREAMING_SNAKE_CASE						= None

						if text_encoding is not None:
									encoding_image_processor.update(a)

						return encoding_image_processor






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       List[Any]:
						return self.tokenizer.batch_decode(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       Optional[int]:
						return self.tokenizer.decode(*a		,					**a)







			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.tokenizer.model_input_names
						SCREAMING_SNAKE_CASE						= self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




