




"""simple docstring"""





import argparse
import os
import re


A   =       """src/transformers"""

# Pattern that looks at the indentation in a line.
A   =       re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
A   =       re.compile(r"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
A   =       re.compile(r"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
A   =       re.compile(r"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
A   =       re.compile(r"""\[([^\]]+)\]""")

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Optional[int]:


			"""simple docstring"""
			__UpperCAmelCase	: str		=					_re_indent.search(UpperCamelCase							)
			return "" if search is None else search.groups()[0]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase=""				,       UpperCamelCase=None				,       UpperCamelCase=None							)			->			str:


			"""simple docstring"""
			__UpperCAmelCase	: List[str]		=					0
			__UpperCAmelCase	: str		=					code.split("\n"							)
			if start_prompt is not None:
						while not lines[index].startswith(UpperCamelCase							):
									index += 1
						__UpperCAmelCase	: List[Any]		=					["\n".join(lines[:index]							)]
			else:
						__UpperCAmelCase	: str		=					[]

			# We split into blocks until we get to the `end_prompt` (or the end of the block).
			__UpperCAmelCase	: str		=					[lines[index]]
			index += 1
			while index < len(UpperCamelCase							) and (end_prompt is None or not lines[index].startswith(UpperCamelCase							)):
						if len(lines[index]							) > 0 and get_indent(lines[index]							) == indent_level:
									if len(UpperCamelCase							) > 0 and get_indent(current_block[-1]							).startswith(indent_level + " "							):
												current_block.append(lines[index]							)
												blocks.append("\n".join(UpperCamelCase							)							)
												if index < len(UpperCamelCase							) - 1:
															__UpperCAmelCase	: Optional[Any]		=					[lines[index + 1]]
															index += 1
												else:
															__UpperCAmelCase	: Any		=					[]
									else:
												blocks.append("\n".join(UpperCamelCase							)							)
												__UpperCAmelCase	: int		=					[lines[index]]
						else:
									current_block.append(lines[index]							)
						index += 1

			# Adds current block if it's nonempty.
			if len(UpperCamelCase							) > 0:
						blocks.append("\n".join(UpperCamelCase							)							)

			# Add final block after end_prompt if provided.
			if end_prompt is not None and index < len(UpperCamelCase							):
						blocks.append("\n".join(lines[index:]							)							)

			return blocks

def 					_UpperCamelCase    (			UpperCamelCase							)			->			List[str]:


			"""simple docstring"""

			def _inner(UpperCamelCase							):
						return key(UpperCamelCase							).lower().replace("_"				,       ""							)

			return _inner

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase=None							)			->			Any:


			"""simple docstring"""

			# If no key is provided, we use a noop.
			def noop(UpperCamelCase							):
						return x

			if key is None:
						__UpperCAmelCase	: Tuple		=					noop
			# Constants are all uppercase, they go first.
			__UpperCAmelCase	: Any		=					[obj for obj in objects if key(UpperCamelCase							).isupper()]
			# Classes are not all uppercase but start with a capital, they go second.
			__UpperCAmelCase	: Tuple		=					[obj for obj in objects if key(UpperCamelCase							)[0].isupper() and not key(UpperCamelCase							).isupper()]
			# Functions begin with a lowercase, they go last.
			__UpperCAmelCase	: str		=					[obj for obj in objects if not key(UpperCamelCase							)[0].isupper()]

			__UpperCAmelCase	: Tuple		=					ignore_underscore(UpperCamelCase							)
			return sorted(UpperCamelCase				,       key=UpperCamelCase							) + sorted(UpperCamelCase				,       key=UpperCamelCase							) + sorted(UpperCamelCase				,       key=UpperCamelCase							)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			List[str]:


			"""simple docstring"""

			# This inner function sort imports between [ ].
			def _replace(UpperCamelCase							):
						__UpperCAmelCase	: List[str]		=					match.groups()[0]
						if "," not in imports:
									return f"[{imports}]"
						__UpperCAmelCase	: str		=					[part.strip().replace("\""				,       ""							) for part in imports.split(","							)]
						# We will have a final empty element if the line finished with a comma.
						if len(keys[-1]							) == 0:
									__UpperCAmelCase	: Any		=					keys[:-1]
						return "[" + ", ".join([f"\"{k}\"" for k in sort_objects(UpperCamelCase							)]							) + "]"

			__UpperCAmelCase	: Union[str, Any]		=					import_statement.split("\n"							)
			if len(UpperCamelCase							) > 3:
						# Here we have to sort internal imports that are on several lines (one per name):
						# key: [
						#     "object1",
						#     "object2",
						#     ...
						# ]

						# We may have to ignore one or two lines on each side.
						__UpperCAmelCase	: List[str]		=					2 if lines[1].strip() == "[" else 1
						__UpperCAmelCase	: Union[str, Any]		=					[(i, _re_strip_line.search(UpperCamelCase							).groups()[0]) for i, line in enumerate(lines[idx:-idx]							)]
						__UpperCAmelCase	: Optional[Any]		=					sort_objects(UpperCamelCase				,       key=lambda UpperCamelCase							: x[1]							)
						__UpperCAmelCase	: Optional[int]		=					[lines[x[0] + idx] for x in sorted_indices]
						return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]							)
			elif len(UpperCamelCase							) == 3:
						# Here we have to sort internal imports that are on one separate line:
						# key: [
						#     "object1", "object2", ...
						# ]
						if _re_bracket_content.search(lines[1]							) is not None:
									__UpperCAmelCase	: Any		=					_re_bracket_content.sub(_replace				,       lines[1]							)
						else:
									__UpperCAmelCase	: Optional[Any]		=					[part.strip().replace("\""				,       ""							) for part in lines[1].split(","							)]
									# We will have a final empty element if the line finished with a comma.
									if len(keys[-1]							) == 0:
												__UpperCAmelCase	: Any		=					keys[:-1]
									__UpperCAmelCase	: Optional[Any]		=					get_indent(lines[1]							) + ", ".join([f"\"{k}\"" for k in sort_objects(UpperCamelCase							)]							)
						return "\n".join(UpperCamelCase							)
			else:
						# Finally we have to deal with imports fitting on one line
						__UpperCAmelCase	: List[str]		=					_re_bracket_content.sub(_replace				,       UpperCamelCase							)
						return import_statement

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase=True							)			->			Optional[int]:


			"""simple docstring"""
			with open(UpperCamelCase				,       encoding="utf-8"							) as f:
						__UpperCAmelCase	: Any		=					f.read()

			if "_import_structure" not in code:
						return

			# Blocks of indent level 0
			__UpperCAmelCase	: Tuple		=					split_code_in_indented_blocks(
			    UpperCamelCase				,       start_prompt="_import_structure = {"				,       end_prompt="if TYPE_CHECKING:"							)

			# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
			for block_idx in range(1				,       len(UpperCamelCase							) - 1							):
						# Check if the block contains some `_import_structure`s thingy to sort.
						__UpperCAmelCase	: Optional[int]		=					main_blocks[block_idx]
						__UpperCAmelCase	: int		=					block.split("\n"							)

						# Get to the start of the imports.
						__UpperCAmelCase	: List[str]		=					0
						while line_idx < len(UpperCamelCase							) and "_import_structure" not in block_lines[line_idx]:
									# Skip dummy import blocks
									if "import dummy" in block_lines[line_idx]:
												__UpperCAmelCase	: Dict		=					len(UpperCamelCase							)
									else:
												line_idx += 1
						if line_idx >= len(UpperCamelCase							):
									continue

						# Ignore beginning and last line: they don't contain anything.
						__UpperCAmelCase	: List[Any]		=					"\n".join(block_lines[line_idx:-1]							)
						__UpperCAmelCase	: Optional[int]		=					get_indent(block_lines[1]							)
						# Slit the internal block into blocks of indent level 1.
						__UpperCAmelCase	: Optional[int]		=					split_code_in_indented_blocks(UpperCamelCase				,       indent_level=UpperCamelCase							)
						# We have two categories of import key: list or _import_structure[key].append/extend
						__UpperCAmelCase	: Dict		=					_re_direct_key if "_import_structure = {" in block_lines[0] else _re_indirect_key
						# Grab the keys, but there is a trap: some lines are empty or just comments.
						__UpperCAmelCase	: str		=					[(pattern.search(UpperCamelCase							).groups()[0] if pattern.search(UpperCamelCase							) is not None else None) for b in internal_blocks]
						# We only sort the lines with a key.
						__UpperCAmelCase	: List[Any]		=					[(i, key) for i, key in enumerate(UpperCamelCase							) if key is not None]
						__UpperCAmelCase	: Dict		=					[x[0] for x in sorted(UpperCamelCase				,       key=lambda UpperCamelCase							: x[1]							)]

						# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
						__UpperCAmelCase	: List[str]		=					0
						__UpperCAmelCase	: Optional[Any]		=					[]
						for i in range(len(UpperCamelCase							)							):
									if keys[i] is None:
												reorderded_blocks.append(internal_blocks[i]							)
									else:
												__UpperCAmelCase	: Union[str, Any]		=					sort_objects_in_import(internal_blocks[sorted_indices[count]]							)
												reorderded_blocks.append(UpperCamelCase							)
												count += 1

        # And we put our main block back together with its first and last line.
						__UpperCAmelCase	: Union[str, Any]		=					"\n".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]							)

			if code != "\n".join(UpperCamelCase							):
						if check_only:
									return True
						else:
									print(f"Overwriting {file}."							)
									with open(UpperCamelCase				,       "w"				,       encoding="utf-8"							) as f:
												f.write("\n".join(UpperCamelCase							)							)

def 					_UpperCamelCase    (			UpperCamelCase=True							)			->			Optional[int]:


			"""simple docstring"""
			__UpperCAmelCase	: Union[str, Any]		=					[]
			for root, _, files in os.walk(UpperCamelCase							):
						if "__init__.py" in files:
									__UpperCAmelCase	: Dict		=					sort_imports(os.path.join(UpperCamelCase				,       "__init__.py"							)				,       check_only=UpperCamelCase							)
									if result:
												__UpperCAmelCase	: Dict		=					[os.path.join(UpperCamelCase				,       "__init__.py"							)]
			if len(UpperCamelCase							) > 0:
						raise ValueError(f"Would overwrite {len(UpperCamelCase							)} files, run `make style`."							)


if __name__ == "__main__":
					A   =       argparse.ArgumentParser()
					parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
					A   =       parser.parse_args()

					sort_imports_in_all_inits(check_only=args.check_only)








"""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


A   =       logging.get_logger(__name__)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			List[List[ImageInput]]:


			"""simple docstring"""
			if isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(videos[0]				,       (list, tuple)							) and is_valid_image(videos[0][0]							):
						return videos

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

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

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





class      a__				(	__magic_name__   ):
			lowercase_    =	["pixel_values"]


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


						"""simple docstring"""




						super().__init__(**UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					size if size is not None else {"shortest_edge": 224}
						__UpperCAmelCase	: Union[str, Any]		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						__UpperCAmelCase	: Optional[int]		=					crop_size if crop_size is not None else {"height": 224, "width": 224}
						__UpperCAmelCase	: List[str]		=					get_size_dict(UpperCamelCase_      ,  param_name="crop_size")

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


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


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						if "shortest_edge" in size:
									__UpperCAmelCase	: Dict		=					get_resize_output_image_size(UpperCamelCase_      ,  size["shortest_edge"]      ,  default_to_square=UpperCamelCase_)
						elif "height" in size and "width" in size:
									__UpperCAmelCase	: Dict		=					(size["height"], size["width"])
						else:
									raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
						return resize(UpperCamelCase_      ,  size=UpperCamelCase_      ,  resample=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Dict[str, int]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    Tuple      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					get_size_dict(UpperCamelCase_)
						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(UpperCamelCase_      ,  size=(size["height"], size["width"])      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Union[int, float]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    Optional[int]      ,  ):


						"""simple docstring"""




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


			def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Union[float, List[float]]      ,  UpperCamelCase_      :    Union[float, List[float]]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    Union[str, Any]      ,  ):


						"""simple docstring"""




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


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    ImageInput      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    PILImageResampling = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    float = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[ChannelDimension] = ChannelDimension.FIRST      ,  ):


						"""simple docstring"""




						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.
						__UpperCAmelCase	: List[str]		=					to_numpy_array(UpperCamelCase_)

						if do_resize:
									__UpperCAmelCase	: Tuple		=					self.resize(image=UpperCamelCase_      ,  size=UpperCamelCase_      ,  resample=UpperCamelCase_)

						if do_center_crop:
									__UpperCAmelCase	: str		=					self.center_crop(UpperCamelCase_      ,  size=UpperCamelCase_)

						if do_rescale:
									__UpperCAmelCase	: Union[str, Any]		=					self.rescale(image=UpperCamelCase_      ,  scale=UpperCamelCase_)

						if do_normalize:
									__UpperCAmelCase	: Optional[int]		=					self.normalize(image=UpperCamelCase_      ,  mean=UpperCamelCase_      ,  std=UpperCamelCase_)

						__UpperCAmelCase	: Any		=					to_channel_dimension_format(UpperCamelCase_      ,  UpperCamelCase_)
						return image


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


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					do_resize if do_resize is not None else self.do_resize
						__UpperCAmelCase	: Optional[Any]		=					resample if resample is not None else self.resample
						__UpperCAmelCase	: Any		=					do_center_crop if do_center_crop is not None else self.do_center_crop
						__UpperCAmelCase	: str		=					do_rescale if do_rescale is not None else self.do_rescale
						__UpperCAmelCase	: str		=					rescale_factor if rescale_factor is not None else self.rescale_factor
						__UpperCAmelCase	: Tuple		=					do_normalize if do_normalize is not None else self.do_normalize
						__UpperCAmelCase	: str		=					image_mean if image_mean is not None else self.image_mean
						__UpperCAmelCase	: Optional[Any]		=					image_std if image_std is not None else self.image_std

						__UpperCAmelCase	: Tuple		=					size if size is not None else self.size
						__UpperCAmelCase	: str		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						__UpperCAmelCase	: Any		=					crop_size if crop_size is not None else self.crop_size
						__UpperCAmelCase	: int		=					get_size_dict(UpperCamelCase_      ,  param_name="crop_size")

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

						__UpperCAmelCase	: Dict		=					make_batched(UpperCamelCase_)

						__UpperCAmelCase	: List[str]		=					[
						    [
						        self._preprocess_image(
						            image=UpperCamelCase_      ,  do_resize=UpperCamelCase_      ,  size=UpperCamelCase_      ,  resample=UpperCamelCase_      ,  do_center_crop=UpperCamelCase_      ,  crop_size=UpperCamelCase_      ,  do_rescale=UpperCamelCase_      ,  rescale_factor=UpperCamelCase_      ,  do_normalize=UpperCamelCase_      ,  image_mean=UpperCamelCase_      ,  image_std=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  )
						        for img in video
						    ]
						    for video in videos
						]

						__UpperCAmelCase	: int		=					{"pixel_values": videos}
						return BatchFeature(data=UpperCamelCase_      ,  tensor_type=UpperCamelCase_)


