

"""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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


__A     :    Union[str, Any]    =		logging.get_logger(__name__)





class 			__UpperCamelCase (   _A						):
		SCREAMING_SNAKE_CASE								=    ["pixel_values"]


		def __init__(self :		int     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int] = None     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int] = None     ,     __SCREAMING_SNAKE_CASE :		PILImageResampling = PILImageResampling.BICUBIC     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		Union[int, float] = 1 / 2_5_5     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[float, List[float]]] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[float, List[float]]] = None     ,     **__SCREAMING_SNAKE_CASE :		Dict     ,     ):
							super().__init__(**__SCREAMING_SNAKE_CASE)
							A			=					crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE     ,     param_name="crop_size")
							A			=					size if size is not None else {"height": 2_2_4, "width": 2_2_4}
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE)

							A			=					do_center_crop
							A			=					crop_size
							A			=					do_resize
							A			=					size
							A			=					resample
							A			=					do_rescale
							A			=					rescale_factor
							A			=					do_normalize
							A			=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							A			=					image_std if image_std is not None else IMAGENET_DEFAULT_STD


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		np.ndarray     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int]     ,     __SCREAMING_SNAKE_CASE :		Optional[int] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, ChannelDimension]] = None     ,     **__SCREAMING_SNAKE_CASE :		Dict     ,     ):
							A			=					self.size if size is None else size
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE)
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE     ,     param_name="crop_size")

							A			,    A			=					get_image_size(__SCREAMING_SNAKE_CASE)
							A			=					min(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					(size["height"] / crop_size["height"]) * min_dim
							A			=					(size["width"] / crop_size["width"]) * min_dim
							return center_crop(__SCREAMING_SNAKE_CASE     ,     size=(cropped_height, cropped_width)     ,     data_format=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict     ,     __SCREAMING_SNAKE_CASE :		np.ndarray     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int]     ,     __SCREAMING_SNAKE_CASE :		PILImageResampling = PIL.Image.BICUBIC     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, ChannelDimension]] = None     ,     **__SCREAMING_SNAKE_CASE :		Tuple     ,     ):
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE)
							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 resize(
							    __SCREAMING_SNAKE_CASE     ,     size=(size["height"], size["width"])     ,     resample=__SCREAMING_SNAKE_CASE     ,     data_format=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		np.ndarray     ,     __SCREAMING_SNAKE_CASE :		Union[int, float]     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, ChannelDimension]] = None     ,     **__SCREAMING_SNAKE_CASE :		Optional[Any]     ,     ):
							return rescale(__SCREAMING_SNAKE_CASE     ,     scale=__SCREAMING_SNAKE_CASE     ,     data_format=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		np.ndarray     ,     __SCREAMING_SNAKE_CASE :		Union[float, List[float]]     ,     __SCREAMING_SNAKE_CASE :		Union[float, List[float]]     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, ChannelDimension]] = None     ,     **__SCREAMING_SNAKE_CASE :		Any     ,     ):
							return normalize(__SCREAMING_SNAKE_CASE     ,     mean=__SCREAMING_SNAKE_CASE     ,     std=__SCREAMING_SNAKE_CASE     ,     data_format=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple     ,     __SCREAMING_SNAKE_CASE :		ImageInput     ,     __SCREAMING_SNAKE_CASE :		Optional[bool] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Dict[str, int]] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[bool] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Dict[str, int]] = None     ,     __SCREAMING_SNAKE_CASE :		PILImageResampling = None     ,     __SCREAMING_SNAKE_CASE :		Optional[bool] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[float] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[bool] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[float, List[float]]] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[float, List[float]]] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, TensorType]] = None     ,     __SCREAMING_SNAKE_CASE :		ChannelDimension = ChannelDimension.FIRST     ,     **__SCREAMING_SNAKE_CASE :		Any     ,     ):
							A			=					do_center_crop if do_center_crop is not None else self.do_center_crop
							A			=					crop_size if crop_size is not None else self.crop_size
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE     ,     param_name="crop_size")
							A			=					do_resize if do_resize is not None else self.do_resize
							A			=					size if size is not None else self.size
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE)
							A			=					resample if resample is not None else self.resample
							A			=					do_rescale if do_rescale is not None else self.do_rescale
							A			=					rescale_factor if rescale_factor is not None else self.rescale_factor
							A			=					do_normalize if do_normalize is not None else self.do_normalize
							A			=					image_mean if image_mean is not None else self.image_mean
							A			=					image_std if image_std is not None else self.image_std

							A			=					make_list_of_images(__SCREAMING_SNAKE_CASE)

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

							if do_center_crop and crop_size is None:
												raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided.")

							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_normalize and (image_mean is None or image_std is None):
												raise ValueError("Image mean and image standard deviation must be specified if do_normalize is True.")

							# All transformations expect numpy arrays.
							A			=					[to_numpy_array(__SCREAMING_SNAKE_CASE) for image in images]

							if do_center_crop:
												A			=					[self.center_crop(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     size=__SCREAMING_SNAKE_CASE) for image in images]

							if do_resize:
												A			=					[self.resize(image=__SCREAMING_SNAKE_CASE     ,     size=__SCREAMING_SNAKE_CASE     ,     resample=__SCREAMING_SNAKE_CASE) for image in images]

							if do_rescale:
												A			=					[self.rescale(image=__SCREAMING_SNAKE_CASE     ,     scale=__SCREAMING_SNAKE_CASE) for image in images]

							if do_normalize:
												A			=					[self.normalize(image=__SCREAMING_SNAKE_CASE     ,     mean=__SCREAMING_SNAKE_CASE     ,     std=__SCREAMING_SNAKE_CASE) for image in images]

							A			=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE) for image in images]

							A			=					{"pixel_values": images}
							return BatchFeature(data=__SCREAMING_SNAKE_CASE     ,     tensor_type=__SCREAMING_SNAKE_CASE)








"""simple docstring"""




import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__A     :    List[str]    =		'src/diffusers'
__A     :    Optional[int]    =		'docs/source/en'
__A     :    Union[str, Any]    =		'.'
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							"r"			,							encoding="utf-8"			,							newline="\n"      ) as f:
										A			=					f.readlines()
					# Find the start prompt.
					A			=					0
					while not lines[start_index].startswith(lowercase__      ):
										start_index += 1
					start_index += 1

					A			=					start_index
					while not lines[end_index].startswith(lowercase__      ):
										end_index += 1
					end_index -= 1

					while len(lines[start_index]      ) <= 1:
										start_index += 1
					while len(lines[end_index]      ) <= 1:
										end_index -= 1
					end_index += 1
					return "".join(lines[start_index:end_index]      ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__A     :    Any    =		'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__A     :    Union[str, Any]    =		re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__A     :    int    =		re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__A     :    Any    =		re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
__A     :    Optional[int]    =		importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__A     :    Tuple    =		spec.loader.load_module()
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"			,							lowercase__      )
					return [m.group(0      ) for m in matches]
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					2 if text == "✅" or text == "❌" else len(lowercase__      )
					A			=					(width - text_length) // 2
					A			=					width - text_length - left_indent
					return " " * left_indent + text + " " * right_indent
def    __SCREAMING_SNAKE_CASE						(						):
					"""simple docstring"""
					A			=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					A			=					{
					    name: config_mapping_names[code]
					    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
					    if code in config_mapping_names
					}
					A			=					{name: config.replace("ConfigMixin"			,							""      ) for name, config in model_name_to_config.items()}

					# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )

					# Let's lookup through all diffusers object (once).
					for attr_name in dir(lowercase__      ):
										A			=					None
										if attr_name.endswith("Tokenizer"      ):
															A			=					slow_tokenizers
															A			=					attr_name[:-9]
										elif attr_name.endswith("TokenizerFast"      ):
															A			=					fast_tokenizers
															A			=					attr_name[:-13]
										elif _re_tf_models.match(lowercase__      ) is not None:
															A			=					tf_models
															A			=					_re_tf_models.match(lowercase__      ).groups()[0]
										elif _re_flax_models.match(lowercase__      ) is not None:
															A			=					flax_models
															A			=					_re_flax_models.match(lowercase__      ).groups()[0]
										elif _re_pt_models.match(lowercase__      ) is not None:
															A			=					pt_models
															A			=					_re_pt_models.match(lowercase__      ).groups()[0]

										if lookup_dict is not None:
															while len(lowercase__      ) > 0:
																				if attr_name in model_name_to_prefix.values():
																									A			=					True
																									break
																				# Try again after removing the last word in the name
																				A			=					"".join(camel_case_split(lowercase__      )[:-1]      )

    # Let's build that table!
					A			=					list(model_name_to_config.keys()      )
					model_names.sort(key=str.lower      )
					A			=					["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
					# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
					A			=					[len(lowercase__      ) + 2 for c in columns]
					A			=					max([len(lowercase__      ) for name in model_names]      ) + 2

					# Build the table per se
					A			=					"|" + "|".join([_center_text(lowercase__			,							lowercase__      ) for c, w in zip(lowercase__			,							lowercase__      )]      ) + "|\n"
					# Use ":-----:" format to center-aligned table cell texts
					table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]      ) + "|\n"

					A			=					{True: "✅", False: "❌"}
					for name in model_names:
										A			=					model_name_to_prefix[name]
										A			=					[
										    name,
										    check[slow_tokenizers[prefix]],
										    check[fast_tokenizers[prefix]],
										    check[pt_models[prefix]],
										    check[tf_models[prefix]],
										    check[flax_models[prefix]],
										]
										table += "|" + "|".join([_center_text(lowercase__			,							lowercase__      ) for l, w in zip(lowercase__			,							lowercase__      )]      ) + "|\n"
					return table
def    __SCREAMING_SNAKE_CASE						(						lowercase__=False      ):
					"""simple docstring"""
					A			,    A			,    A			,    A			=					_find_text_in_file(
					    filename=os.path.join(lowercase__			,							"index.mdx"      )			,							start_prompt="<!--This table is updated automatically from the auto modules"			,							end_prompt="<!-- End table-->"			,							)
					A			=					get_model_table_from_auto_modules()

					if current_table != new_table:
										if overwrite:
															with open(os.path.join(lowercase__			,							"index.mdx"      )			,							"w"			,							encoding="utf-8"			,							newline="\n"      ) as f:
																				f.writelines(lines[:start_index] + [new_table] + lines[end_index:]      )
										else:
															raise ValueError(
															    "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."      )


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

	check_model_table(args.fix_and_overwrite)





