
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				f'''{sampling_rate}'''
      UpperCamelCase__    :   Optional[int]					=				'''1'''
      UpperCamelCase__    :   List[str]					=				'''f32le'''
      UpperCamelCase__    :   List[Any]					=				[
          '''ffmpeg''',
          '''-i''',
          '''pipe:0''',
          '''-ac''',
          ac,
          '''-ar''',
          ar,
          '''-f''',
          format_for_conversion,
          '''-hide_banner''',
          '''-loglevel''',
          '''quiet''',
          '''pipe:1''',
      ]

      try:
            with subprocess.Popen(lowercase__ ,		stdin=subprocess.PIPE ,		stdout=subprocess.PIPE		) as ffmpeg_process:
                  UpperCamelCase__    :   List[str]					=				ffmpeg_process.communicate(lowercase__		)
      except FileNotFoundError as error:
            raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''		) from error
      UpperCamelCase__    :   Dict					=				output_stream[0]
      UpperCamelCase__    :   List[Any]					=				np.frombuffer(lowercase__ ,		np.floataa		)
      if audio.shape[0] == 0:
            raise ValueError('''Malformed soundfile'''		)
      return audio


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = "f32le" ,		):
      UpperCamelCase__    :   List[Any]					=				f'''{sampling_rate}'''
      UpperCamelCase__    :   Optional[int]					=				'''1'''
      if format_for_conversion == "s16le":
            UpperCamelCase__    :   List[str]					=				2
      elif format_for_conversion == "f32le":
            UpperCamelCase__    :   Tuple					=				4
      else:
            raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''		)

      UpperCamelCase__    :   Union[str, Any]					=				platform.system()
      if system == "Linux":
            UpperCamelCase__    :   List[Any]					=				'''alsa'''
            UpperCamelCase__    :   Optional[int]					=				'''default'''
      elif system == "Darwin":
            UpperCamelCase__    :   Tuple					=				'''avfoundation'''
            UpperCamelCase__    :   int					=				''':0'''
      elif system == "Windows":
            UpperCamelCase__    :   Dict					=				'''dshow'''
            UpperCamelCase__    :   Optional[Any]					=				'''default'''

      UpperCamelCase__    :   Union[str, Any]					=				[
          '''ffmpeg''',
          '''-f''',
          format_,
          '''-i''',
          input_,
          '''-ac''',
          ac,
          '''-ar''',
          ar,
          '''-f''',
          format_for_conversion,
          '''-fflags''',
          '''nobuffer''',
          '''-hide_banner''',
          '''-loglevel''',
          '''quiet''',
          '''pipe:1''',
      ]
      UpperCamelCase__    :   Optional[int]					=				int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
      UpperCamelCase__    :   Union[str, Any]					=				_ffmpeg_stream(lowercase__ ,		lowercase__		)
      for item in iterator:
            yield item


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = None ,		UpperCamelCase__ = None ,		UpperCamelCase__ = "f32le" ,		):







      if stream_chunk_s is not None:
            UpperCamelCase__    :   List[Any]					=				stream_chunk_s
      else:
            UpperCamelCase__    :   Any					=				chunk_length_s

      UpperCamelCase__    :   Any					=				ffmpeg_microphone(lowercase__ ,		lowercase__ ,		format_for_conversion=lowercase__		)
      if format_for_conversion == "s16le":
            UpperCamelCase__    :   Union[str, Any]					=				np.intaa
            UpperCamelCase__    :   List[str]					=				2
      elif format_for_conversion == "f32le":
            UpperCamelCase__    :   Any					=				np.floataa
            UpperCamelCase__    :   List[Any]					=				4
      else:
            raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''		)

      if stride_length_s is None:
            UpperCamelCase__    :   Union[str, Any]					=				chunk_length_s / 6
      UpperCamelCase__    :   Any					=				int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
      if isinstance(lowercase__ ,		(int, float)		):
            UpperCamelCase__    :   Dict					=				[stride_length_s, stride_length_s]

      UpperCamelCase__    :   Dict					=				int(round(sampling_rate * stride_length_s[0]		)		) * size_of_sample
      UpperCamelCase__    :   Optional[int]					=				int(round(sampling_rate * stride_length_s[1]		)		) * size_of_sample
      UpperCamelCase__    :   Tuple					=				datetime.datetime.now()
      UpperCamelCase__    :   Union[str, Any]					=				datetime.timedelta(seconds=lowercase__		)
      for item in chunk_bytes_iter(lowercase__ ,		lowercase__ ,		stride=(stride_left, stride_right) ,		stream=lowercase__		):
            # Put everything back in numpy scale
            UpperCamelCase__    :   List[str]					=				np.frombuffer(item['''raw'''] ,		dtype=lowercase__		)
            UpperCamelCase__    :   List[Any]					=				(
                item['''stride'''][0] // size_of_sample,
                item['''stride'''][1] // size_of_sample,
            )
            UpperCamelCase__    :   Dict					=				sampling_rate
            audio_time += delta
            if datetime.datetime.now() > audio_time + 1_0 * delta:
                  # We're late !! SKIP
                  continue
            yield item


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = False		):
      UpperCamelCase__    :   Any					=				b''''''
      UpperCamelCase__				,UpperCamelCase__    :   List[str]					=				stride
      if stride_left + stride_right >= chunk_len:
            raise ValueError(
                f'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'''		)
      UpperCamelCase__    :   List[str]					=				0
      for raw in iterator:
            acc += raw
            if stream and len(lowercase__		) < chunk_len:
                  UpperCamelCase__    :   List[str]					=				(_stride_left, 0)
                  yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
            else:
                  while len(lowercase__		) >= chunk_len:
                        # We are flushing the accumulator
                        UpperCamelCase__    :   Optional[Any]					=				(_stride_left, stride_right)
                        UpperCamelCase__    :   Optional[int]					=				{'''raw''': acc[:chunk_len], '''stride''': stride}
                        if stream:
                              UpperCamelCase__    :   Optional[Any]					=				False
                        yield item
                        UpperCamelCase__    :   str					=				stride_left
                        UpperCamelCase__    :   Optional[Any]					=				acc[chunk_len - stride_left - stride_right :]
    # Last chunk
      if len(lowercase__		) > stride_left:
            UpperCamelCase__    :   Optional[Any]					=				{'''raw''': acc, '''stride''': (_stride_left, 0)}
            if stream:
                  UpperCamelCase__    :   List[Any]					=				False
            yield item


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Optional[int]					=				2**2_4  # 16Mo
      try:
            with subprocess.Popen(lowercase__ ,		stdout=subprocess.PIPE ,		bufsize=lowercase__		) as ffmpeg_process:
                  while True:
                        UpperCamelCase__    :   Tuple					=				ffmpeg_process.stdout.read(lowercase__		)
                        if raw == b"":
                              break
                        yield raw
      except FileNotFoundError as error:
            raise ValueError('''ffmpeg was not found but is required to stream audio files from filename'''		) from error




from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
    import PIL

# soft dependency
if is_pytesseract_available():
    import pytesseract

lowerCamelCase      =logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      return [
          int(1_0_0_0 * (box[0] / width)		),
          int(1_0_0_0 * (box[1] / height)		),
          int(1_0_0_0 * (box[2] / width)		),
          int(1_0_0_0 * (box[3] / height)		),
      ]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   List[Any]					=				to_pil_image(UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__    :   Optional[int]					=				pil_image.size
      UpperCamelCase__    :   str					=				pytesseract.image_to_data(UpperCamelCase__ ,		lang=UpperCamelCase__ ,		output_type='''dict''' ,		config=UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

      # filter empty words and corresponding coordinates
      UpperCamelCase__    :   Optional[Any]					=				[idx for idx, word in enumerate(UpperCamelCase__		) if not word.strip()]
      UpperCamelCase__    :   int					=				[word for idx, word in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   int					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   List[Any]					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   List[Any]					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]
      UpperCamelCase__    :   Dict					=				[coord for idx, coord in enumerate(UpperCamelCase__		) if idx not in irrelevant_indices]

      # turn coordinates into (left, top, left+width, top+height) format
      UpperCamelCase__    :   str					=				[]
      for x, y, w, h in zip(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
            UpperCamelCase__    :   List[Any]					=				[x, y, x + w, y + h]
            actual_boxes.append(UpperCamelCase__		)

      # finally, normalize the bounding boxes
      UpperCamelCase__    :   str					=				[]
      for box in actual_boxes:
            normalized_boxes.append(normalize_box(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)		)

      assert len(UpperCamelCase__		) == len(UpperCamelCase__		), "Not as many words as there are bounding boxes"

      return words, normalized_boxes




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  ['''pixel_values''']






     def __init__(							self						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = 1 / 2_5_5						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ""						,						**__SCREAMING_SNAKE_CASE						,						)  ->   None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           UpperCamelCase__    :   Optional[int]					=				get_size_dict(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				do_resize
           UpperCamelCase__    :   Optional[int]					=				size
           UpperCamelCase__    :   Optional[Any]					=				resample
           UpperCamelCase__    :   Optional[Any]					=				do_rescale
           UpperCamelCase__    :   str					=				rescale_value
           UpperCamelCase__    :   Any					=				do_normalize
           UpperCamelCase__    :   List[Any]					=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           UpperCamelCase__    :   Optional[int]					=				image_std if image_std is not None else IMAGENET_STANDARD_STD
           UpperCamelCase__    :   Dict					=				apply_ocr
           UpperCamelCase__    :   List[Any]					=				ocr_lang
           UpperCamelCase__    :   str					=				tesseract_config






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				get_size_dict(__SCREAMING_SNAKE_CASE  )
           if "height" not in size or "width" not in size:
                 raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''  )
           UpperCamelCase__    :   int					=				(size['''height'''], size['''width'''])
           return resize(__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           return rescale(__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           return normalize(__SCREAMING_SNAKE_CASE						,						mean=__SCREAMING_SNAKE_CASE						,						std=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,						**__SCREAMING_SNAKE_CASE						,						)  ->   PIL.Image.Image:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__    :   List[str]					=				size if size is not None else self.size
           UpperCamelCase__    :   Dict					=				get_size_dict(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				resample if resample is not None else self.resample
           UpperCamelCase__    :   int					=				do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__    :   List[str]					=				rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase__    :   str					=				do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase__    :   Union[str, Any]					=				image_mean if image_mean is not None else self.image_mean
           UpperCamelCase__    :   Tuple					=				image_std if image_std is not None else self.image_std
           UpperCamelCase__    :   Optional[Any]					=				apply_ocr if apply_ocr is not None else self.apply_ocr
           UpperCamelCase__    :   List[str]					=				ocr_lang if ocr_lang is not None else self.ocr_lang
           UpperCamelCase__    :   List[Any]					=				tesseract_config if tesseract_config is not None else self.tesseract_config

           UpperCamelCase__    :   List[Any]					=				make_list_of_images(__SCREAMING_SNAKE_CASE  )

           if not valid_images(__SCREAMING_SNAKE_CASE  ):
                 raise ValueError(
                     '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                     '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

           if do_resize and size is None:
                 raise ValueError('''Size must be specified if do_resize is True.'''  )

           if do_rescale and rescale_factor is None:
                 raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''  )

           # All transformations expect numpy arrays.
           UpperCamelCase__    :   List[Any]					=				[to_numpy_array(__SCREAMING_SNAKE_CASE  ) for image in images]

           # Tesseract OCR to get words + normalized bounding boxes
           if apply_ocr:
                 requires_backends(self						,						'''pytesseract'''  )
                 UpperCamelCase__    :   Optional[int]					=				[]
                 UpperCamelCase__    :   List[Any]					=				[]
                 for image in images:
                       UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				apply_tesseract(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
                       words_batch.append(__SCREAMING_SNAKE_CASE  )
                       boxes_batch.append(__SCREAMING_SNAKE_CASE  )

           if do_resize:
                 UpperCamelCase__    :   str					=				[self.resize(image=__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_rescale:
                 UpperCamelCase__    :   List[Any]					=				[self.rescale(image=__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_normalize:
                 UpperCamelCase__    :   str					=				[self.normalize(image=__SCREAMING_SNAKE_CASE						,						mean=__SCREAMING_SNAKE_CASE						,						std=__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   int					=				[to_channel_dimension_format(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   List[Any]					=				BatchFeature(data={'''pixel_values''': images}						,						tensor_type=__SCREAMING_SNAKE_CASE  )

           if apply_ocr:
                 UpperCamelCase__    :   str					=				words_batch
                 UpperCamelCase__    :   Union[str, Any]					=				boxes_batch
           return data


