





"""simple docstring"""


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 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		List[str]:




						"""simple docstring"""

						with open(lowercase_		,"r"		,encoding="utf-8"		,newline="\n"			) as f:
												_UpperCamelCase      :	Union[str, Any]						=       f.readlines()
						# Find the start prompt.
						_UpperCamelCase      :	Dict						=       0
						while not lines[start_index].startswith(lowercase_			):
												start_index += 1
						start_index += 1

						_UpperCamelCase      :	Optional[int]						=       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 |
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 					lowercase__    (					lowercase_			)					->		Any:




						"""simple docstring"""

						_UpperCamelCase      :	Tuple						=       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,lowercase_			)
						return [m.group(0			) for m in matches]




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Union[str, Any]:




						"""simple docstring"""

						_UpperCamelCase      :	str						=       2 if text == "✅" or text == "❌" else len(lowercase_			)
						_UpperCamelCase      :	Union[str, Any]						=       (width - text_length) // 2
						_UpperCamelCase      :	Dict						=       width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent




def 					lowercase__    (					)					->		str:




						"""simple docstring"""

						_UpperCamelCase      :	Optional[Any]						=       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						_UpperCamelCase      :	str						=       {
						    name: config_maping_names[code]
						    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_maping_names
						}
						_UpperCamelCase      :	Dict						=       {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.
						_UpperCamelCase      :	int						=       collections.defaultdict(lowercase_			)
						_UpperCamelCase      :	Dict						=       collections.defaultdict(lowercase_			)
						_UpperCamelCase      :	Dict						=       collections.defaultdict(lowercase_			)
						_UpperCamelCase      :	int						=       collections.defaultdict(lowercase_			)
						_UpperCamelCase      :	str						=       collections.defaultdict(lowercase_			)

						# Let's lookup through all transformers object (once).
						for attr_name in dir(lowercase_			):
												_UpperCamelCase      :	List[str]						=       None
												if attr_name.endswith("Tokenizer"			):
																		_UpperCamelCase      :	Tuple						=       slow_tokenizers
																		_UpperCamelCase      :	Any						=       attr_name[:-9]
												elif attr_name.endswith("TokenizerFast"			):
																		_UpperCamelCase      :	Optional[Any]						=       fast_tokenizers
																		_UpperCamelCase      :	List[str]						=       attr_name[:-13]
												elif _re_tf_models.match(lowercase_			) is not None:
																		_UpperCamelCase      :	List[Any]						=       tf_models
																		_UpperCamelCase      :	Dict						=       _re_tf_models.match(lowercase_			).groups()[0]
												elif _re_flax_models.match(lowercase_			) is not None:
																		_UpperCamelCase      :	Dict						=       flax_models
																		_UpperCamelCase      :	Union[str, Any]						=       _re_flax_models.match(lowercase_			).groups()[0]
												elif _re_pt_models.match(lowercase_			) is not None:
																		_UpperCamelCase      :	Optional[int]						=       pt_models
																		_UpperCamelCase      :	Any						=       _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():
																														_UpperCamelCase      :	Dict						=       True
																														break
																								# Try again after removing the last word in the name
																								_UpperCamelCase      :	List[str]						=       "".join(camel_case_split(lowercase_			)[:-1]			)

    # Let's build that table!
						_UpperCamelCase      :	Any						=       list(model_name_to_config.keys()			)
						model_names.sort(key=str.lower			)
						_UpperCamelCase      :	List[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).
						_UpperCamelCase      :	Union[str, Any]						=       [len(lowercase_			) + 2 for c in columns]
						_UpperCamelCase      :	Any						=       max([len(lowercase_			) for name in model_names]			) + 2

						# Build the table per se
						_UpperCamelCase      :	Tuple						=       "|" + "|".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"

						_UpperCamelCase      :	Union[str, Any]						=       {True: "✅", False: "❌"}
						for name in model_names:
												_UpperCamelCase      :	Optional[int]						=       model_name_to_prefix[name]
												_UpperCamelCase      :	Tuple						=       [
												    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 					lowercase__    (					lowercase_=False			)					->		List[Any]:




						"""simple docstring"""

						_UpperCamelCase      :	str						=       _find_text_in_file(
						    filename=os.path.join(lowercase_		,"index.md"			)		,start_prompt="<!--This table is updated automatically from the auto modules"		,end_prompt="<!-- End table-->"		,)
						_UpperCamelCase      :	Any						=       get_model_table_from_auto_modules()

						if current_table != new_table:
												if overwrite:
																		with open(os.path.join(lowercase_		,"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)







"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple	     =					"yolos"

  def __init__(		self   :       Dict					,   __a   :       Optional[Any]=768					,   __a   :       List[Any]=12					,   __a   :       Any=12					,   __a   :       List[Any]=3072					,   __a   :       Optional[int]="gelu"					,   __a   :       Dict=0.0					,   __a   :       Optional[Any]=0.0					,   __a   :       Any=0.02					,   __a   :       Optional[int]=1e-1_2					,   __a   :       List[Any]=[512, 864]					,   __a   :       List[str]=16					,   __a   :       str=3					,   __a   :       Optional[Any]=True					,   __a   :       Optional[Any]=100					,   __a   :       List[str]=True					,   __a   :       Any=False					,   __a   :       List[str]=1					,   __a   :       str=5					,   __a   :       Optional[Any]=2					,   __a   :       Tuple=5					,   __a   :       Any=2					,   __a   :       Union[str, Any]=0.1					,   **__a   :       List[str]					,   )		->				List[str]:
        super().__init__(**__a	)

        _UpperCamelCase      :	Dict						=       hidden_size
        _UpperCamelCase      :	Any						=       num_hidden_layers
        _UpperCamelCase      :	str						=       num_attention_heads
        _UpperCamelCase      :	Dict						=       intermediate_size
        _UpperCamelCase      :	List[str]						=       hidden_act
        _UpperCamelCase      :	List[str]						=       hidden_dropout_prob
        _UpperCamelCase      :	str						=       attention_probs_dropout_prob
        _UpperCamelCase      :	Tuple						=       initializer_range
        _UpperCamelCase      :	List[str]						=       layer_norm_eps
        _UpperCamelCase      :	Tuple						=       image_size
        _UpperCamelCase      :	Tuple						=       patch_size
        _UpperCamelCase      :	Dict						=       num_channels
        _UpperCamelCase      :	Any						=       qkv_bias
        _UpperCamelCase      :	str						=       num_detection_tokens
        _UpperCamelCase      :	str						=       use_mid_position_embeddings
        _UpperCamelCase      :	List[str]						=       auxiliary_loss
        # Hungarian matcher
        _UpperCamelCase      :	List[Any]						=       class_cost
        _UpperCamelCase      :	int						=       bbox_cost
        _UpperCamelCase      :	Optional[int]						=       giou_cost
        # Loss coefficients
        _UpperCamelCase      :	List[Any]						=       bbox_loss_coefficient
        _UpperCamelCase      :	str						=       giou_loss_coefficient
        _UpperCamelCase      :	Dict						=       eos_coefficient





class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]	     =					version.parse("1.11"   )

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				float:
        return 1e-4


  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				int:
        return 12
