






"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


_lowerCAmelCase       =				logging.get_logger(__name__)






def       __UpperCamelCase						(		snake_case__       ):
	if isinstance(snake_case__ , (list, tuple)       ) and isinstance(videos[0] , (list, tuple)       ) and is_valid_image(videos[0][0]       ):
		return videos

	elif isinstance(snake_case__ , (list, tuple)       ) and is_valid_image(videos[0]       ):
		return [videos]

	elif is_valid_image(snake_case__       ):
		return [[videos]]

	raise ValueError(F"""Could not make batched video from {videos}"""       )




class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Any 			=					["""pixel_values"""]





	def __init__(self  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = PILImageResampling.BILINEAR  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = 1 / 255  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  **lowerCAmelCase_  ,  ):
		super().__init__(**lowerCAmelCase_				)
		A_		:    Any						     = size if size is not None else {"""shortest_edge""": 224}
		A_		:    Optional[Any]						     = get_size_dict(lowerCAmelCase_  ,  default_to_square=lowerCAmelCase_				)
		A_		:    List[str]						     = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
		A_		:    List[Any]						     = get_size_dict(lowerCAmelCase_  ,  param_name="""crop_size"""				)

		A_		:    int						     = do_resize
		A_		:    int						     = size
		A_		:    Any						     = do_center_crop
		A_		:    int						     = crop_size
		A_		:    Tuple						     = resample
		A_		:    str						     = do_rescale
		A_		:    str						     = rescale_factor
		A_		:    int						     = do_normalize
		A_		:    Union[str, Any]						     = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		A_		:    List[Any]						     = image_std if image_std is not None else IMAGENET_STANDARD_STD





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = PILImageResampling.BILINEAR  ,  lowerCAmelCase_ = None  ,  **lowerCAmelCase_  ,  ):
		A_		:    Tuple						     = get_size_dict(lowerCAmelCase_  ,  default_to_square=lowerCAmelCase_				)
		if "shortest_edge" in size:
			A_		:    Union[str, Any]						     = get_resize_output_image_size(lowerCAmelCase_  ,  size["""shortest_edge"""]  ,  default_to_square=lowerCAmelCase_				)
		elif "height" in size and "width" in size:
			A_		:    List[Any]						     = (size["""height"""], size["""width"""])
		else:
			raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
		return resize(lowerCAmelCase_  ,  size=lowerCAmelCase_  ,  resample=lowerCAmelCase_  ,  data_format=lowerCAmelCase_  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None  ,  **lowerCAmelCase_  ,  ):
		A_		:    Dict						     = get_size_dict(lowerCAmelCase_				)
		if "height" not in size or "width" not in size:
			raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""				)
		return center_crop(lowerCAmelCase_  ,  size=(size["""height"""], size["""width"""])  ,  data_format=lowerCAmelCase_  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None  ,  **lowerCAmelCase_  ,  ):
		return rescale(lowerCAmelCase_  ,  scale=lowerCAmelCase_  ,  data_format=lowerCAmelCase_  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None  ,  **lowerCAmelCase_  ,  ):
		return normalize(lowerCAmelCase_  ,  mean=lowerCAmelCase_  ,  std=lowerCAmelCase_  ,  data_format=lowerCAmelCase_  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = ChannelDimension.FIRST  ,  ):
		if do_resize and size is None or resample is None:
			raise ValueError("""Size and resample must be specified if do_resize is True."""				)

		if do_center_crop and crop_size is None:
			raise ValueError("""Crop size must be specified if do_center_crop 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("""Image mean and std must be specified if do_normalize is True."""				)

		# All transformations expect numpy arrays.
		A_		:    Optional[Any]						     = to_numpy_array(lowerCAmelCase_				)

		if do_resize:
			A_		:    List[Any]						     = self.resize(image=lowerCAmelCase_  ,  size=lowerCAmelCase_  ,  resample=lowerCAmelCase_				)

		if do_center_crop:
			A_		:    Optional[Any]						     = self.center_crop(lowerCAmelCase_  ,  size=lowerCAmelCase_				)

		if do_rescale:
			A_		:    List[str]						     = self.rescale(image=lowerCAmelCase_  ,  scale=lowerCAmelCase_				)

		if do_normalize:
			A_		:    List[str]						     = self.normalize(image=lowerCAmelCase_  ,  mean=lowerCAmelCase_  ,  std=lowerCAmelCase_				)

		A_		:    Dict						     = to_channel_dimension_format(lowerCAmelCase_  ,  lowerCAmelCase_				)
		return image





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = ChannelDimension.FIRST  ,  **lowerCAmelCase_  ,  ):
		A_		:    Dict						     = do_resize if do_resize is not None else self.do_resize
		A_		:    Dict						     = resample if resample is not None else self.resample
		A_		:    str						     = do_center_crop if do_center_crop is not None else self.do_center_crop
		A_		:    str						     = do_rescale if do_rescale is not None else self.do_rescale
		A_		:    str						     = rescale_factor if rescale_factor is not None else self.rescale_factor
		A_		:    str						     = do_normalize if do_normalize is not None else self.do_normalize
		A_		:    Any						     = image_mean if image_mean is not None else self.image_mean
		A_		:    List[str]						     = image_std if image_std is not None else self.image_std

		A_		:    int						     = size if size is not None else self.size
		A_		:    str						     = get_size_dict(lowerCAmelCase_  ,  default_to_square=lowerCAmelCase_				)
		A_		:    int						     = crop_size if crop_size is not None else self.crop_size
		A_		:    Union[str, Any]						     = get_size_dict(lowerCAmelCase_  ,  param_name="""crop_size"""				)

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

		A_		:    str						     = make_batched(lowerCAmelCase_				)

		A_		:    int						     = [
		    [
		        self._preprocess_image(
		            image=lowerCAmelCase_  ,  do_resize=lowerCAmelCase_  ,  size=lowerCAmelCase_  ,  resample=lowerCAmelCase_  ,  do_center_crop=lowerCAmelCase_  ,  crop_size=lowerCAmelCase_  ,  do_rescale=lowerCAmelCase_  ,  rescale_factor=lowerCAmelCase_  ,  do_normalize=lowerCAmelCase_  ,  image_mean=lowerCAmelCase_  ,  image_std=lowerCAmelCase_  ,  data_format=lowerCAmelCase_  ,  )
		        for img in video
		    ]
		    for video in videos
		]

		A_		:    Any						     = {"""pixel_values""": videos}
		return BatchFeature(data=lowerCAmelCase_  ,  tensor_type=lowerCAmelCase_				)









"""simple docstring"""
import math
import os
import sys






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    Optional[Any]						     = """"""
	try:
		with open(snake_case__ , """rb"""       ) as binary_file:
			A_		:    Union[str, Any]						     = binary_file.read()
		for dat in data:
			A_		:    Dict						     = F"""{dat:08b}"""
			result += curr_byte
		return result
	except OSError:
		print("""File not accessible"""       )
		sys.exit()






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	lexicon.pop(snake_case__       )
	A_		:    List[str]						     = last_match_id

	if math.loga(snake_case__       ).is_integer():
		for curr_key in lexicon:
			A_		:    Dict						     = """0""" + lexicon[curr_key]

	A_		:    int						     = bin(snake_case__       )[2:]






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    Dict						     = {"""0""": """0""", """1""": """1"""}
	A_     ,  A_		:    Optional[int]						     = """""", """"""
	A_		:    Tuple						     = len(snake_case__       )

	for i in range(len(snake_case__       )       ):
		curr_string += data_bits[i]
		if curr_string not in lexicon:
			continue

		A_		:    List[str]						     = lexicon[curr_string]
		result += last_match_id
		add_key_to_lexicon(snake_case__ , snake_case__ , snake_case__ , snake_case__       )
		index += 1
		A_		:    int						     = """"""

	while curr_string != "" and curr_string not in lexicon:
		curr_string += "0"

	if curr_string != "":
		A_		:    Any						     = lexicon[curr_string]
		result += last_match_id

	return result






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Optional[int]						     = os.path.getsize(snake_case__       )
	A_		:    Dict						     = bin(snake_case__       )[2:]
	A_		:    Optional[Any]						     = len(snake_case__       )

	return "0" * (length_length - 1) + file_length_binary + compressed






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Tuple						     = 8
	try:
		with open(snake_case__ , """wb"""       ) as opened_file:
			A_		:    Dict						     = [
			    to_write[i : i + byte_length]
			    for i in range(0 , len(snake_case__       ) , snake_case__       )
			]

			if len(result_byte_array[-1]       ) % byte_length == 0:
				result_byte_array.append("""10000000"""       )
			else:
				result_byte_array[-1] += "1" + "0" * (
				    byte_length - len(result_byte_array[-1]       ) - 1
				)

			for elem in result_byte_array:
				opened_file.write(int(snake_case__ , 2       ).to_bytes(1 , byteorder="""big"""       )       )
	except OSError:
		print("""File not accessible"""       )
		sys.exit()






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    List[str]						     = read_file_binary(snake_case__       )
	A_		:    str						     = compress_data(snake_case__       )
	A_		:    int						     = add_file_length(snake_case__ , snake_case__       )
	write_file_binary(snake_case__ , snake_case__       )


if __name__ == "__main__":
							compress(sys.argv[1], sys.argv[2])

