



import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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
lowerCamelCase		       =      """src/transformers"""
lowerCamelCase		       =      """docs/source/en"""
lowerCamelCase		       =      """."""






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Tuple:
				with open(__UpperCamelCase	,				"r"	,				encoding="utf-8"	,				newline="\n" ) as f:
								a__	:							Optional[int]      						= f.readlines()
				# Find the start prompt.
				a__	:							Optional[Any]      						= 0
				while not lines[start_index].startswith(__UpperCamelCase ):
								start_index += 1
				start_index += 1

				a__	:							int      						= start_index
				while not lines[end_index].startswith(__UpperCamelCase ):
								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 |
lowerCamelCase		       =      """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
lowerCamelCase		       =      re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowerCamelCase		       =      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.
lowerCamelCase		       =      re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase		       =      direct_transformers_import(TRANSFORMERS_PATH)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Union[str, Any]:
				a__	:							Optional[int]      						= re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"	,				__UpperCamelCase )
				return [m.group(0 ) for m in matches]






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						List[str]:
				a__	:							Tuple      						= 2 if text == "✅" or text == "❌" else len(__UpperCamelCase )
				a__	:							Optional[int]      						= (width - text_length) // 2
				a__	:							Tuple      						= width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent






def     SCREAMING_SNAKE_CASE(      )	->						Dict:
				a__	:							Optional[int]      						= transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				a__	:							List[str]      						= {
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				a__	:							Optional[Any]      						= {name: config.replace("Config"	,				"" ) 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__	:							Optional[Any]      						= collections.defaultdict(__UpperCamelCase )
				a__	:							List[str]      						= collections.defaultdict(__UpperCamelCase )
				a__	:							Optional[Any]      						= collections.defaultdict(__UpperCamelCase )
				a__	:							Union[str, Any]      						= collections.defaultdict(__UpperCamelCase )
				a__	:							List[Any]      						= collections.defaultdict(__UpperCamelCase )

				# Let's lookup through all transformers object (once).
				for attr_name in dir(__UpperCamelCase ):
								a__	:							Tuple      						= None
								if attr_name.endswith("Tokenizer" ):
												a__	:							int      						= slow_tokenizers
												a__	:							Tuple      						= attr_name[:-9]
								elif attr_name.endswith("TokenizerFast" ):
												a__	:							Tuple      						= fast_tokenizers
												a__	:							Optional[Any]      						= attr_name[:-13]
								elif _re_tf_models.match(__UpperCamelCase ) is not None:
												a__	:							List[str]      						= tf_models
												a__	:							List[str]      						= _re_tf_models.match(__UpperCamelCase ).groups()[0]
								elif _re_flax_models.match(__UpperCamelCase ) is not None:
												a__	:							List[Any]      						= flax_models
												a__	:							str      						= _re_flax_models.match(__UpperCamelCase ).groups()[0]
								elif _re_pt_models.match(__UpperCamelCase ) is not None:
												a__	:							Union[str, Any]      						= pt_models
												a__	:							int      						= _re_pt_models.match(__UpperCamelCase ).groups()[0]

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

    # Let's build that table!
				a__	:							List[Any]      						= list(model_name_to_config.keys() )
				model_names.sort(key=str.lower )
				a__	:							str      						= ["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__	:							Tuple      						= [len(__UpperCamelCase ) + 2 for c in columns]
				a__	:							str      						= max([len(__UpperCamelCase ) for name in model_names] ) + 2

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

				a__	:							Any      						= {True: "✅", False: "❌"}
				for name in model_names:
								a__	:							Optional[Any]      						= model_name_to_prefix[name]
								a__	:							List[Any]      						= [
								    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(__UpperCamelCase	,				__UpperCamelCase ) for l, w in zip(__UpperCamelCase	,				__UpperCamelCase )] ) + "|\n"
				return table






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase=False )	->						Union[str, Any]:
				a__						,   a__						,   a__						,   a__	:							Tuple      						= _find_text_in_file(
				    filename=os.path.join(__UpperCamelCase	,				"index.md" )	,				start_prompt="<!--This table is updated automatically from the auto modules"	,				end_prompt="<!-- End table-->"	,				)
				a__	:							Optional[Any]      						= get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(__UpperCamelCase	,				"index.md" )	,				"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.md` has not been updated. Run `make fix-copies` to fix this." )


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

						check_model_table(args.fix_and_overwrite)







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
						import PIL

lowerCamelCase		       =      logging.get_logger(__name__)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List[List[ImageInput]]:
				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						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:List[str] =       ["pixel_values"]



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







											"""simple docstring"""



											super().__init__(**__UpperCAmelCase				)
											a__	:							int      						= size if size is not None else {"shortest_edge": 256}
											a__	:							str      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= crop_size if crop_size is not None else {"height": 224, "width": 224}
											a__	:							int      						= get_size_dict(__UpperCAmelCase ,					param_name="crop_size"				)

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



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = PILImageResampling.BILINEAR ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											if "shortest_edge" in size:
															a__	:							Optional[Any]      						= get_resize_output_image_size(__UpperCAmelCase ,					size["shortest_edge"] ,					default_to_square=__UpperCAmelCase				)
											elif "height" in size and "width" in size:
															a__	:							Union[str, Any]      						= (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 ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= 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 ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= image.astype(np.floataa				)
											if offset:
															a__	:							Dict      						= image - (scale / 2)
											return rescale(__UpperCAmelCase ,					scale=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



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



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = 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."				)

											if offset and not do_rescale:
															raise ValueError("For offset, do_rescale must also be set to True."				)

											# All transformations expect numpy arrays.
											a__	:							int      						= to_numpy_array(__UpperCAmelCase				)

											if do_resize:
															a__	:							List[Any]      						= self.resize(image=__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase				)

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

											if do_rescale:
															a__	:							Optional[Any]      						= self.rescale(image=__UpperCAmelCase ,					scale=__UpperCAmelCase ,					offset=__UpperCAmelCase				)

											if do_normalize:
															a__	:							Union[str, Any]      						= self.normalize(image=__UpperCAmelCase ,					mean=__UpperCAmelCase ,					std=__UpperCAmelCase				)

											a__	:							Optional[int]      						= to_channel_dimension_format(__UpperCAmelCase ,					__UpperCAmelCase				)
											return image




							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = ChannelDimension.FIRST ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							List[Any]      						= do_resize if do_resize is not None else self.do_resize
											a__	:							Dict      						= resample if resample is not None else self.resample
											a__	:							Tuple      						= do_center_crop if do_center_crop is not None else self.do_center_crop
											a__	:							Optional[Any]      						= do_rescale if do_rescale is not None else self.do_rescale
											a__	:							Optional[int]      						= rescale_factor if rescale_factor is not None else self.rescale_factor
											a__	:							List[Any]      						= offset if offset is not None else self.offset
											a__	:							Optional[Any]      						= do_normalize if do_normalize is not None else self.do_normalize
											a__	:							int      						= image_mean if image_mean is not None else self.image_mean
											a__	:							List[Any]      						= image_std if image_std is not None else self.image_std

											a__	:							int      						= size if size is not None else self.size
											a__	:							Tuple      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											a__	:							List[Any]      						= crop_size if crop_size is not None else self.crop_size
											a__	:							Union[str, Any]      						= 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."				)

											a__	:							Union[str, Any]      						= make_batched(__UpperCAmelCase				)

											a__	:							Optional[Any]      						= [
											    [
											        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 ,					offset=__UpperCAmelCase ,					do_normalize=__UpperCAmelCase ,					image_mean=__UpperCAmelCase ,					image_std=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					)
											        for img in video
											    ]
											    for video in videos
											]

											a__	:							Any      						= {"pixel_values": videos}
											return BatchFeature(data=__UpperCAmelCase ,					tensor_type=__UpperCAmelCase				)


