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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
      import PIL

if is_torch_available():
      import torch


SCREAMING_SNAKE_CASE__		:       Tuple       						=       logging.get_logger(__name__)

class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  ['pixel_values']




  def __init__(   self  :						List[str]  ,			a_  :						bool = True  ,			a_  :						Dict[str, int] = None  ,			a_  :						PILImageResampling = PILImageResampling.BILINEAR  ,			a_  :						bool = True  ,			a_  :						Union[int, float] = 1 / 255  ,			a_  :						bool = True  ,			a_  :						Dict[str, int] = None  ,			a_  :						bool = True  ,			**a_  :						str  ,			)->							None:




       """simple docstring"""
       super().__init__(**a_					)
       SCREAMING_SNAKE_CASE__					:					Any      =		size if size is not None else {'shortest_edge': 224}
       SCREAMING_SNAKE_CASE__					:					int      =		get_size_dict(a_  ,			default_to_square=a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		crop_size if crop_size is not None else {'height': 256, 'width': 256}
       SCREAMING_SNAKE_CASE__					:					str      =		get_size_dict(a_  ,			param_name='crop_size'					)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		do_resize
       SCREAMING_SNAKE_CASE__					:					str      =		size
       SCREAMING_SNAKE_CASE__					:					str      =		resample
       SCREAMING_SNAKE_CASE__					:					List[str]      =		do_rescale
       SCREAMING_SNAKE_CASE__					:					Tuple      =		rescale_factor
       SCREAMING_SNAKE_CASE__					:					List[str]      =		do_center_crop
       SCREAMING_SNAKE_CASE__					:					Tuple      =		crop_size
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		do_flip_channel_order




  def 						__lowercase(   self  :						str  ,			a_  :						np.ndarray  ,			a_  :						Dict[str, int]  ,			a_  :						PILImageResampling = PIL.Image.BILINEAR  ,			a_  :						Optional[Union[str, ChannelDimension]] = None  ,			**a_  :						List[Any]  ,			)->							np.ndarray:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		get_size_dict(a_  ,			default_to_square=a_					)
       if "shortest_edge" not in size:
            raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		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 						__lowercase(   self  :						Any  ,			a_  :						np.ndarray  ,			a_  :						Dict[str, int]  ,			a_  :						Optional[Union[str, ChannelDimension]] = None  ,			**a_  :						Dict  ,			)->							np.ndarray:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		get_size_dict(a_					)
       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()}'''					)
       return center_crop(a_  ,			size=(size['height'], size['width'])  ,			data_format=a_  ,			**a_					)




  def 						__lowercase(   self  :						Union[str, Any]  ,			a_  :						np.ndarray  ,			a_  :						Union[int, float]  ,			a_  :						Optional[Union[str, ChannelDimension]] = None  ,			**a_  :						Dict  ,			)->							Dict:




       """simple docstring"""
       return rescale(a_  ,			scale=a_  ,			data_format=a_  ,			**a_					)




  def 						__lowercase(   self  :						Optional[Any]  ,			a_  :						np.ndarray  ,			a_  :						Optional[Union[str, ChannelDimension]] = None					)->							np.ndarray:




       """simple docstring"""
       return flip_channel_order(a_  ,			data_format=a_					)




  def 						__lowercase(   self  :						int  ,			a_  :						ImageInput  ,			a_  :						bool = None  ,			a_  :						Dict[str, int] = None  ,			a_  :						PILImageResampling = None  ,			a_  :						bool = None  ,			a_  :						float = None  ,			a_  :						bool = None  ,			a_  :						Dict[str, int] = None  ,			a_  :						bool = None  ,			a_  :						Optional[Union[str, TensorType]] = None  ,			a_  :						ChannelDimension = ChannelDimension.FIRST  ,			**a_  :						List[Any]  ,			)->							PIL.Image.Image:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__					:					List[str]      =		resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__					:					str      =		do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__					:					Any      =		(
           do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
       )

       SCREAMING_SNAKE_CASE__					:					Dict      =		size if size is not None else self.size
       SCREAMING_SNAKE_CASE__					:					Dict      =		get_size_dict(a_  ,			default_to_square=a_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__					:					List[str]      =		get_size_dict(a_  ,			param_name='crop_size'					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		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_rescale and rescale_factor is None:
            raise ValueError('Rescale factor must be specified if do_rescale is True.'					)

       if do_center_crop and crop_size is None:
            raise ValueError('Crop size must be specified if do_center_crop is True.'					)

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__					:					List[str]      =		[to_numpy_array(a_					) for image in images]

       if do_resize:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		[self.resize(image=a_  ,			size=a_  ,			resample=a_					) for image in images]

       if do_center_crop:
            SCREAMING_SNAKE_CASE__					:					int      =		[self.center_crop(image=a_  ,			size=a_					) for image in images]

       if do_rescale:
            SCREAMING_SNAKE_CASE__					:					Tuple      =		[self.rescale(image=a_  ,			scale=a_					) for image in images]

       # the pretrained checkpoints assume images are BGR, not RGB
       if do_flip_channel_order:
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		[self.flip_channel_order(image=a_					) for image in images]

       SCREAMING_SNAKE_CASE__					:					int      =		[to_channel_dimension_format(a_  ,			a_					) for image in images]

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		{'pixel_values': images}
       return BatchFeature(data=a_  ,			tensor_type=a_					)




  def 						__lowercase(   self  :						Optional[Any]  ,			a_  :						Any  ,			a_  :						List[Tuple] = None					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		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_					):
                 SCREAMING_SNAKE_CASE__					:					Optional[int]      =		target_sizes.numpy()

            SCREAMING_SNAKE_CASE__					:					Any      =		[]

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

       return semantic_segmentation






import argparse
import os
import re


a_     :    List[str]     		=   "src/transformers/models/auto"


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a_     :    Optional[Any]     		=   re.compile(R"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
a_     :    Optional[int]     		=   re.compile(R"\s*\(\s*\"(\S[^\"]+)\"")


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	bool = False						)		->				Optional[int]:
 '''simple docstring'''


 with open(_UpperCamelCase		,				'r'		,				encoding='utf-8'						) as f:
  SCREAMING_SNAKE_CASE		    =      f.read()

 SCREAMING_SNAKE_CASE		    =      content.split('\n'						)
 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      0
 while line_idx < len(_UpperCamelCase						):
  if _re_intro_mapping.search(lines[line_idx]						) is not None:
   SCREAMING_SNAKE_CASE		    =      len(re.search(R'^(\s*)\S'		,				lines[line_idx]						).groups()[0]						) + 8
   # Start of a new mapping!
   while not lines[line_idx].startswith(' ' * indent + '('						):
    new_lines.append(lines[line_idx]						)
    line_idx += 1

   SCREAMING_SNAKE_CASE		    =      []
   while lines[line_idx].strip() != "]":
    # Blocks either fit in one line or not
    if lines[line_idx].strip() == "(":
     SCREAMING_SNAKE_CASE		    =      line_idx
     while not lines[line_idx].startswith(' ' * indent + ')'						):
      line_idx += 1
     blocks.append('\n'.join(lines[start_idx : line_idx + 1]						)						)
    else:
     blocks.append(lines[line_idx]						)
    line_idx += 1

   # Sort blocks by their identifiers
   SCREAMING_SNAKE_CASE		    =      sorted(_UpperCamelCase		,				key=lambda _UpperCamelCase						: _re_identifier.search(_UpperCamelCase						).groups()[0]						)
   new_lines += blocks
  else:
   new_lines.append(lines[line_idx]						)
   line_idx += 1

 if overwrite:
  with open(_UpperCamelCase		,				'w'		,				encoding='utf-8'						) as f:
   f.write('\n'.join(_UpperCamelCase						)						)
 elif "\n".join(_UpperCamelCase						) != content:
  return True


def 							__lowerCAmelCase     (							_UpperCamelCase		:	bool = False						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      [os.path.join(_UpperCamelCase		,				_UpperCamelCase						) for f in os.listdir(_UpperCamelCase						) if f.endswith('.py'						)]
 SCREAMING_SNAKE_CASE		    =      [sort_auto_mapping(_UpperCamelCase		,				overwrite=_UpperCamelCase						) for fname in fnames]

 if not overwrite and any(_UpperCamelCase						):
  SCREAMING_SNAKE_CASE		    =      [f for f, d in zip(_UpperCamelCase		,				_UpperCamelCase						) if d]
  raise ValueError(
      f"""The following files have auto mappings that need sorting: {', '.join(_UpperCamelCase						)}. Run `make style` to fix"""
      ' this.'						)


if __name__ == "__main__":
      a_     :    Union[str, Any]     		=   argparse.ArgumentParser()
      parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
      a_     :    List[str]     		=   parser.parse_args()

      sort_all_auto_mappings(not args.check_only)

