'''simple docstring'''




from typing import Dict, List, Optional, Tuple, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
 import torch


_A								=   logging.get_logger(__name__)


class       UpperCAmelCase__  (       _snake_case			):
   A							:       Any     =    ['''pixel_values''']
   def __init__(self       ,				_a = True       ,				_a = None       ,				_a = PILImageResampling.BILINEAR       ,				_a = True       ,				_a = None       ,				_a = True       ,				_a = 1 / 255       ,				_a = True       ,				_a = None       ,				_a = None       ,				**_a       ,				)    ->     None:
        super().__init__(**_a				)
        lowercase_					:       Any     				=					size if size is not None else {'shortest_edge': 256}
        lowercase_					:       Dict     				=					get_size_dict(_a       ,				default_to_square=_a				)
        lowercase_					:       List[Any]     				=					crop_size if crop_size is not None else {'height': 224, 'width': 224}
        lowercase_					:       Optional[Any]     				=					get_size_dict(_a       ,				param_name='crop_size'				)
        lowercase_					:       int     				=					do_resize
        lowercase_					:       Dict     				=					size
        lowercase_					:       List[Any]     				=					resample
        lowercase_					:       Optional[Any]     				=					do_center_crop
        lowercase_					:       List[str]     				=					crop_size
        lowercase_					:       List[Any]     				=					do_rescale
        lowercase_					:       Tuple     				=					rescale_factor
        lowercase_					:       Dict     				=					do_normalize
        lowercase_					:       Any     				=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowercase_					:       Union[str, Any]     				=					image_std if image_std is not None else IMAGENET_STANDARD_STD
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a = PILImageResampling.BICUBIC       ,				_a = None       ,				**_a       ,				)    ->     np.ndarray:
        lowercase_					:       Optional[Any]     				=					get_size_dict(_a       ,				default_to_square=_a				)
        if "shortest_edge" not in size:
             raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''				)
        lowercase_					:       Tuple     				=					get_resize_output_image_size(_a       ,				size=size['shortest_edge']       ,				default_to_square=_a				)
        return resize(_a       ,				size=_a       ,				resample=_a       ,				data_format=_a       ,				**_a				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a = None       ,				**_a       ,				)    ->     np.ndarray:
        lowercase_					:       Optional[Any]     				=					get_size_dict(_a				)
        if "height" not in size or "width" not in size:
             raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''				)
        return center_crop(_a       ,				size=(size['height'], size['width'])       ,				data_format=_a       ,				**_a				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a = None       ,				**_a				)    ->     np.ndarray:
        return rescale(_a       ,				scale=_a       ,				data_format=_a       ,				**_a				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a       ,				_a = None       ,				**_a       ,				)    ->     np.ndarray:
        return normalize(_a       ,				mean=_a       ,				std=_a       ,				data_format=_a       ,				**_a				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a = None       ,				_a = None       ,				_a = None       ,				_a = None       ,				_a = None       ,				_a = None       ,				_a = None       ,				_a = None       ,				_a = None       ,				_a = None       ,				_a = None       ,				_a = ChannelDimension.FIRST       ,				**_a       ,				)    ->     Union[str, Any]:
        lowercase_					:       str     				=					do_resize if do_resize is not None else self.do_resize
        lowercase_					:       Union[str, Any]     				=					size if size is not None else self.size
        lowercase_					:       List[str]     				=					get_size_dict(_a       ,				default_to_square=_a				)
        lowercase_					:       List[Any]     				=					resample if resample is not None else self.resample
        lowercase_					:       str     				=					do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_					:       Union[str, Any]     				=					crop_size if crop_size is not None else self.crop_size
        lowercase_					:       str     				=					get_size_dict(_a       ,				param_name='crop_size'				)
        lowercase_					:       Optional[int]     				=					do_rescale if do_rescale is not None else self.do_rescale
        lowercase_					:       Optional[int]     				=					rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_					:       int     				=					do_normalize if do_normalize is not None else self.do_normalize
        lowercase_					:       int     				=					image_mean if image_mean is not None else self.image_mean
        lowercase_					:       Dict     				=					image_std if image_std is not None else self.image_std

        lowercase_					:       Dict     				=					make_list_of_images(_a				)

        if not valid_images(_a				):
             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_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.
        lowercase_					:       Dict     				=					[to_numpy_array(_a				) for image in images]

        if do_resize:
             lowercase_					:       Any     				=					[self.resize(image=_a       ,				size=_a       ,				resample=_a				) for image in images]

        if do_center_crop:
             lowercase_					:       Tuple     				=					[self.center_crop(image=_a       ,				size=_a				) for image in images]

        if do_rescale:
             lowercase_					:       Dict     				=					[self.rescale(image=_a       ,				scale=_a				) for image in images]

        if do_normalize:
             lowercase_					:       Optional[int]     				=					[self.normalize(image=_a       ,				mean=_a       ,				std=_a				) for image in images]

        lowercase_					:       Optional[int]     				=					[to_channel_dimension_format(_a       ,				_a				) for image in images]

        lowercase_					:       str     				=					{'pixel_values': images}
        return BatchFeature(data=_a       ,				tensor_type=_a				)





   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     int:
        lowercase_					:       str     				=					outputs.logits

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
             if len(_a				) != len(_a				):
                  raise ValueError(
                      'Make sure that you pass in as many target sizes as the batch dimension of the logits'				)

             if is_torch_tensor(_a				):
                  lowercase_					:       Any     				=					target_sizes.numpy()

             lowercase_					:       List[Any]     				=					[]

             for idx in range(len(_a				)				):
                  lowercase_					:       str     				=					torch.nn.functional.interpolate(
                      logits[idx].unsqueeze(dim=0				)       ,				size=target_sizes[idx]       ,				mode='bilinear'       ,				align_corners=_a				)
                  lowercase_					:       Dict     				=					resized_logits[0].argmax(dim=0				)
                  semantic_segmentation.append(_a				)
        else:
             lowercase_					:       List[str]     				=					logits.argmax(dim=1				)
             lowercase_					:       Union[str, Any]     				=					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

        return semantic_segmentation





'''simple docstring'''




import math
import os
import sys

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					''
     try:
          with open(SCREAMING_SNAKE_CASE_  ,       'rb'							) as binary_file:
               lowercase_					:       Dict     				=					binary_file.read()
          for dat in data:
               lowercase_					:       Union[str, Any]     				=					f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('File not accessible'							)
          sys.exit()

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lexicon.pop(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Any     				=					last_match_id

     if math.loga(SCREAMING_SNAKE_CASE_							).is_integer():
          for curr_key in lexicon:
               lowercase_					:       Tuple     				=					'0' + lexicon[curr_key]

     lowercase_					:       Any     				=					bin(SCREAMING_SNAKE_CASE_							)[2:]

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Dict     				=					{'0': '0', '1': '1'}
     lowercase_				,lowercase_					:       Any     				=					'', ''
     lowercase_					:       List[Any]     				=					len(SCREAMING_SNAKE_CASE_							)

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

          lowercase_					:       str     				=					lexicon[curr_string]
          result += last_match_id
          add_key_to_lexicon(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
          index += 1
          lowercase_					:       List[Any]     				=					''

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

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

     return result

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					os.path.getsize(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Any     				=					bin(SCREAMING_SNAKE_CASE_							)[2:]
     lowercase_					:       Optional[Any]     				=					len(SCREAMING_SNAKE_CASE_							)

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

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					8
     try:
          with open(SCREAMING_SNAKE_CASE_  ,       'wb'							) as opened_file:
               lowercase_					:       List[Any]     				=					[
                   to_write[i : i + byte_length]
                   for i in range(0  ,       len(SCREAMING_SNAKE_CASE_							)  ,       SCREAMING_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(SCREAMING_SNAKE_CASE_  ,       2							).to_bytes(1  ,       byteorder='big'							)							)
     except OSError:
          print('File not accessible'							)
          sys.exit()

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					read_file_binary(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[str]     				=					compress_data(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Union[str, Any]     				=					add_file_length(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     write_file_binary(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)


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




