



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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_snake_case		=  logging.get_logger(__name__)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		['pixel_values']







							def __init__(   self,			__a = True,			__a = None,			__a = PILImageResampling.BICUBIC,			__a = True,			__a = True,			__a = 1 / 255,			__a = None,			__a = True,			__a = None,			__a = None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)
														_lowerCAmelCase     :							Optional[int]    =    size if size is not None else {"height": 224, "width": 224}
														_lowerCAmelCase     :							Optional[Any]    =    get_size_dict(__a)
														_lowerCAmelCase     :							str    =    crop_size if crop_size is not None else {"height": 224, "width": 224}
														_lowerCAmelCase     :							Tuple    =    get_size_dict(__a,			default_to_square=__a,			param_name="crop_size")

														_lowerCAmelCase     :							Optional[int]    =    do_resize
														_lowerCAmelCase     :							Optional[int]    =    do_rescale
														_lowerCAmelCase     :							List[str]    =    do_normalize
														_lowerCAmelCase     :							int    =    do_center_crop
														_lowerCAmelCase     :							int    =    crop_size
														_lowerCAmelCase     :							List[Any]    =    size
														_lowerCAmelCase     :							Union[str, Any]    =    resample
														_lowerCAmelCase     :							Union[str, Any]    =    rescale_factor
														_lowerCAmelCase     :							List[str]    =    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
														_lowerCAmelCase     :							int    =    image_std if image_std is not None else IMAGENET_DEFAULT_STD







							def    snake_case__      (   self,			__a,			__a,			__a = PILImageResampling.BILINEAR,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    get_size_dict(__a)

														if "shortest_edge" in size:
																					_lowerCAmelCase     :							Dict    =    get_resize_output_image_size(__a,			size=size["shortest_edge"],			default_to_square=__a)
																					# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
														elif "height" in size and "width" in size:
																					_lowerCAmelCase     :							int    =    (size["height"], size["width"])
														else:
																					raise ValueError(f"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}")
														return resize(__a,			size=__a,			resample=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    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, width). Got {size.keys()}")
														return center_crop(__a,			size=(size["height"], size["width"]),			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a = None,			**__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

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







							def    snake_case__      (   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,			):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    do_resize if do_resize is not None else self.do_resize
														_lowerCAmelCase     :							str    =    do_rescale if do_rescale is not None else self.do_rescale
														_lowerCAmelCase     :							Any    =    do_normalize if do_normalize is not None else self.do_normalize
														_lowerCAmelCase     :							Optional[int]    =    do_center_crop if do_center_crop is not None else self.do_center_crop
														_lowerCAmelCase     :							Union[str, Any]    =    crop_size if crop_size is not None else self.crop_size
														_lowerCAmelCase     :							Union[str, Any]    =    get_size_dict(__a,			param_name="crop_size",			default_to_square=__a)
														_lowerCAmelCase     :							int    =    resample if resample is not None else self.resample
														_lowerCAmelCase     :							Optional[Any]    =    rescale_factor if rescale_factor is not None else self.rescale_factor
														_lowerCAmelCase     :							str    =    image_mean if image_mean is not None else self.image_mean
														_lowerCAmelCase     :							str    =    image_std if image_std is not None else self.image_std

														_lowerCAmelCase     :							str    =    size if size is not None else self.size
														_lowerCAmelCase     :							Union[str, Any]    =    get_size_dict(__a)

														if not is_batched(__a):
																					_lowerCAmelCase     :							int    =    [images]

														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.")

														# All transformations expect numpy arrays.
														_lowerCAmelCase     :							str    =    [to_numpy_array(__a) for image in images]

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

														if do_center_crop:
																					_lowerCAmelCase     :							Optional[int]    =    [self.center_crop(image=__a,			size=__a) for image in images]

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

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

														_lowerCAmelCase     :							str    =    [to_channel_dimension_format(__a,			__a) for image in images]

														_lowerCAmelCase     :							str    =    {"pixel_values": images}
														return BatchFeature(data=__a,			tensor_type=__a)





import argparse
from collections import defaultdict

import yaml


_snake_case		=  "docs/source/en/_toctree.yml"






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    defaultdict(_lowerCamelCase  )
							_lowerCAmelCase     :							Any    =    []
							_lowerCAmelCase     :							List[str]    =    []
							for doc in doc_list:
														if "local" in doc:
																					counts[doc["local"]] += 1

														if doc["title"].lower() == "overview":
																					overview_doc.append({"local": doc["local"], "title": doc["title"]}  )
														else:
																					new_doc_list.append(_lowerCamelCase  )

							_lowerCAmelCase     :							Optional[Any]    =    new_doc_list
							_lowerCAmelCase     :							List[Any]    =    [key for key, value in counts.items() if value > 1]

							_lowerCAmelCase     :							str    =    []
							for duplicate_key in duplicates:
														_lowerCAmelCase     :							List[str]    =    list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key}  )
														if len(_lowerCamelCase  ) > 1:
																					raise ValueError(
																					    F"{duplicate_key} is present several times in the documentation table of content at "
																					    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
																					    "others."  )
														# Only add this once
														new_doc.append({"local": duplicate_key, "title": titles[0]}  )

							# Add none duplicate-keys
							new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1]  )
							_lowerCAmelCase     :							Optional[Any]    =    sorted(_lowerCamelCase				, key=lambda _lowerCamelCase  : s["title"].lower()  )

							# "overview" gets special treatment and is always first
							if len(_lowerCamelCase  ) > 1:
														raise ValueError("{doc_list} has two 'overview' docs which is not allowed."  )

							overview_doc.extend(_lowerCamelCase  )

							# Sort
							return overview_doc






def 				A				(					_lowerCamelCase=False  ):





							'''simple docstring'''




							with open(_lowerCamelCase				, encoding="utf-8"  ) as f:
														_lowerCAmelCase     :							int    =    yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase     :							Optional[Any]    =    0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase     :							List[str]    =    content[api_idx]["sections"]

							# Then to the model doc
							_lowerCAmelCase     :							Union[str, Any]    =    0
							while api_doc[scheduler_idx]["title"] != "Schedulers":
														scheduler_idx += 1

							_lowerCAmelCase     :							Optional[Any]    =    api_doc[scheduler_idx]["sections"]
							_lowerCAmelCase     :							Optional[Any]    =    clean_doc_toc(_lowerCamelCase  )

							_lowerCAmelCase     :							int    =    False
							if new_scheduler_doc != scheduler_doc:
														_lowerCAmelCase     :							List[Any]    =    True
														if overwrite:
																					_lowerCAmelCase     :							Dict    =    new_scheduler_doc

							if diff:
														if overwrite:
																					_lowerCAmelCase     :							Tuple    =    api_doc
																					with open(_lowerCamelCase				, "w"				, encoding="utf-8"  ) as f:
																												f.write(yaml.dump(_lowerCamelCase				, allow_unicode=_lowerCamelCase  )  )
														else:
																					raise ValueError(
																					    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."  )






def 				A				(					_lowerCamelCase=False  ):





							'''simple docstring'''




							with open(_lowerCamelCase				, encoding="utf-8"  ) as f:
														_lowerCAmelCase     :							Tuple    =    yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase     :							Optional[int]    =    0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase     :							int    =    content[api_idx]["sections"]

							# Then to the model doc
							_lowerCAmelCase     :							List[str]    =    0
							while api_doc[pipeline_idx]["title"] != "Pipelines":
														pipeline_idx += 1

							_lowerCAmelCase     :							Dict    =    False
							_lowerCAmelCase     :							Optional[int]    =    api_doc[pipeline_idx]["sections"]
							_lowerCAmelCase     :							Tuple    =    []

							# sort sub pipeline docs
							for pipeline_doc in pipeline_docs:
														if "section" in pipeline_doc:
																					_lowerCAmelCase     :							List[Any]    =    pipeline_doc["section"]
																					_lowerCAmelCase     :							Union[str, Any]    =    clean_doc_toc(_lowerCamelCase  )
																					if overwrite:
																												_lowerCAmelCase     :							Optional[Any]    =    new_sub_pipeline_doc
														new_pipeline_docs.append(_lowerCamelCase  )

							# sort overall pipeline doc
							_lowerCAmelCase     :							Union[str, Any]    =    clean_doc_toc(_lowerCamelCase  )

							if new_pipeline_docs != pipeline_docs:
														_lowerCAmelCase     :							Dict    =    True
														if overwrite:
																					_lowerCAmelCase     :							Optional[int]    =    new_pipeline_docs

							if diff:
														if overwrite:
																					_lowerCAmelCase     :							Optional[int]    =    api_doc
																					with open(_lowerCamelCase				, "w"				, encoding="utf-8"  ) as f:
																												f.write(yaml.dump(_lowerCamelCase				, allow_unicode=_lowerCamelCase  )  )
														else:
																					raise ValueError(
																					    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."  )


if __name__ == "__main__":
						_snake_case		=  argparse.ArgumentParser()
						parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
						_snake_case		=  parser.parse_args()

						check_scheduler_doc(args.fix_and_overwrite)
						check_pipeline_doc(args.fix_and_overwrite)
