





"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__				  =   logging.get_logger(__name__)




def 					lowercase__    (					lowercase_			)					->		List[Any]:




						"""simple docstring"""

						_UpperCamelCase      :	List[str]						=       SwinConfig(
						    embed_dim=192		,depths=(2, 2, 18, 2)		,num_heads=(6, 12, 24, 48)		,window_size=12		,out_features=["stage2", "stage3", "stage4"]		,)

						_UpperCamelCase      :	List[Any]						=       DetaConfig(
						    backbone_config=lowercase_		,num_queries=900		,encoder_ffn_dim=2_048		,decoder_ffn_dim=2_048		,num_feature_levels=5		,assign_first_stage=lowercase_		,with_box_refine=lowercase_		,two_stage=lowercase_		,)

						# set labels
						_UpperCamelCase      :	int						=       "huggingface/label-files"
						if "o365" in model_name:
												_UpperCamelCase      :	Optional[Any]						=       366
												_UpperCamelCase      :	List[str]						=       "object365-id2label.json"
						else:
												_UpperCamelCase      :	List[str]						=       91
												_UpperCamelCase      :	List[str]						=       "coco-detection-id2label.json"

						_UpperCamelCase      :	Union[str, Any]						=       num_labels
						_UpperCamelCase      :	Optional[Any]						=       json.load(open(cached_download(hf_hub_url(lowercase_		,lowercase_		,repo_type="dataset"			)			)		,"r"			)			)
						_UpperCamelCase      :	Any						=       {int(lowercase_			): v for k, v in idalabel.items()}
						_UpperCamelCase      :	Tuple						=       idalabel
						_UpperCamelCase      :	List[Any]						=       {v: k for k, v in idalabel.items()}

						return config




def 					lowercase__    (					lowercase_			)					->		str:




						"""simple docstring"""

						_UpperCamelCase      :	List[str]						=       []

						# stem
						# fmt: off
						rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")			)
						rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")			)
						rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")			)
						rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")			)
						# stages
						for i in range(len(config.backbone_config.depths			)			):
												for j in range(config.backbone_config.depths[i]			):
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')			)

												if i < 3:
																		rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.reduction.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')			)
																		rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.bias''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')			)

						rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")			)
						rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")			)
						rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")			)
						rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")			)
						rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")			)
						rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")			)

						# transformer encoder
						for i in range(config.encoder_layers			):
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight''')			)
												rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias''')			)

						# transformer decoder
						for i in range(config.decoder_layers			):
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight''')			)
												rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias''')			)

						# fmt: on

						return rename_keys




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




						"""simple docstring"""

						_UpperCamelCase      :	List[Any]						=       dct.pop(lowercase_			)
						_UpperCamelCase      :	Optional[Any]						=       val




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Tuple:




						"""simple docstring"""

						_UpperCamelCase      :	Dict						=       [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
						for i in range(len(backbone_config.depths			)			):
												_UpperCamelCase      :	List[str]						=       num_features[i]
												for j in range(backbone_config.depths[i]			):
																		# fmt: off
																		# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																		_UpperCamelCase      :	Optional[Any]						=       state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
																		_UpperCamelCase      :	Optional[int]						=       state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''			)
																		# next, add query, keys and values (in that order) to the state dict
																		_UpperCamelCase      :	int						=       in_proj_weight[:dim, :]
																		_UpperCamelCase      :	Any						=       in_proj_bias[: dim]
																		_UpperCamelCase      :	List[str]						=       in_proj_weight[
																		    dim : dim * 2, :
																		]
																		_UpperCamelCase      :	Union[str, Any]						=       in_proj_bias[
																		    dim : dim * 2
																		]
																		_UpperCamelCase      :	List[Any]						=       in_proj_weight[
																		    -dim :, :
																		]
																		_UpperCamelCase      :	Optional[int]						=       in_proj_bias[-dim :]
																		# fmt: on




def 					lowercase__    (					lowercase_		,lowercase_			)					->		int:




						"""simple docstring"""

						_UpperCamelCase      :	Optional[Any]						=       config.d_model
						for i in range(config.decoder_layers			):
												# read in weights + bias of input projection layer of self-attention
												_UpperCamelCase      :	str						=       state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''			)
												_UpperCamelCase      :	Optional[int]						=       state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''			)
												# next, add query, keys and values (in that order) to the state dict
												_UpperCamelCase      :	List[str]						=       in_proj_weight[:hidden_size, :]
												_UpperCamelCase      :	Dict						=       in_proj_bias[:hidden_size]
												_UpperCamelCase      :	Dict						=       in_proj_weight[
												    hidden_size : hidden_size * 2, :
												]
												_UpperCamelCase      :	Any						=       in_proj_bias[hidden_size : hidden_size * 2]
												_UpperCamelCase      :	int						=       in_proj_weight[-hidden_size:, :]
												_UpperCamelCase      :	int						=       in_proj_bias[-hidden_size:]




def 					lowercase__    (					)					->		Optional[int]:




						"""simple docstring"""

						_UpperCamelCase      :	List[str]						=       "http://images.cocodataset.org/val2017/000000039769.jpg"
						_UpperCamelCase      :	int						=       Image.open(requests.get(lowercase_		,stream=lowercase_			).raw			)

						return im




@torch.no_grad()
def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		Union[str, Any]:




						"""simple docstring"""

						_UpperCamelCase      :	List[Any]						=       get_deta_config(lowercase_			)

						# load original state dict
						if model_name == "deta-swin-large":
												_UpperCamelCase      :	Optional[int]						=       hf_hub_download(repo_id="nielsr/deta-checkpoints"		,filename="adet_swin_ft.pth"			)
						elif model_name == "deta-swin-large-o365":
												_UpperCamelCase      :	Any						=       hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"		,filename="deta_swin_pt_o365.pth"			)
						else:
												raise ValueError(F'''Model name {model_name} not supported'''			)

						_UpperCamelCase      :	Dict						=       torch.load(lowercase_		,map_location="cpu"			)["model"]

						# original state dict
						for name, param in state_dict.items():
												print(lowercase_		,param.shape			)

						# rename keys
						_UpperCamelCase      :	Dict						=       create_rename_keys(lowercase_			)
						for src, dest in rename_keys:
												rename_key(lowercase_		,lowercase_		,lowercase_			)
						read_in_swin_q_k_v(lowercase_		,config.backbone_config			)
						read_in_decoder_q_k_v(lowercase_		,lowercase_			)

						# fix some prefixes
						for key in state_dict.copy().keys():
												if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																		_UpperCamelCase      :	Optional[int]						=       state_dict.pop(lowercase_			)
																		_UpperCamelCase      :	Tuple						=       val
												if "input_proj" in key:
																		_UpperCamelCase      :	Union[str, Any]						=       state_dict.pop(lowercase_			)
																		_UpperCamelCase      :	Union[str, Any]						=       val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		_UpperCamelCase      :	List[Any]						=       state_dict.pop(lowercase_			)
																		_UpperCamelCase      :	Optional[Any]						=       val

    # finally, create HuggingFace model and load state dict
						_UpperCamelCase      :	Union[str, Any]						=       DetaForObjectDetection(lowercase_			)
						model.load_state_dict(lowercase_			)
						model.eval()

						_UpperCamelCase      :	List[str]						=       "cuda" if torch.cuda.is_available() else "cpu"
						model.to(lowercase_			)

						# load image processor
						_UpperCamelCase      :	int						=       DetaImageProcessor(format="coco_detection"			)

						# verify our conversion on image
						_UpperCamelCase      :	Optional[int]						=       prepare_img()
						_UpperCamelCase      :	Dict						=       processor(images=lowercase_		,return_tensors="pt"			)
						_UpperCamelCase      :	Optional[Any]						=       encoding["pixel_values"]
						_UpperCamelCase      :	Any						=       model(pixel_values.to(lowercase_			)			)

						# verify logits
						print("Logits:"		,outputs.logits[0, :3, :3]			)
						print("Boxes:"		,outputs.pred_boxes[0, :3, :3]			)
						if model_name == "deta-swin-large":
												_UpperCamelCase      :	Tuple						=       torch.tensor(
												    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]			)
												_UpperCamelCase      :	List[Any]						=       torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]			)
						elif model_name == "deta-swin-large-o365":
												_UpperCamelCase      :	Dict						=       torch.tensor(
												    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]			)
												_UpperCamelCase      :	int						=       torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]			)
						assert torch.allclose(outputs.logits[0, :3, :3]		,expected_logits.to(lowercase_			)		,atol=1e-4			)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,expected_boxes.to(lowercase_			)		,atol=1e-4			)
						print("Everything ok!"			)

						if pytorch_dump_folder_path:
												# Save model and processor
												logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''			)
												Path(lowercase_			).mkdir(exist_ok=lowercase_			)
												model.save_pretrained(lowercase_			)
												processor.save_pretrained(lowercase_			)

						# Push to hub
						if push_to_hub:
												print("Pushing model and processor to hub..."			)
												model.push_to_hub(F'''jozhang97/{model_name}'''			)
												processor.push_to_hub(F'''jozhang97/{model_name}'''			)


if __name__ == "__main__":
		lowerCamelCase__				  =   argparse.ArgumentParser()

		parser.add_argument(
		    "--model_name",
		    type=str,
		    default="deta-swin-large",
		    choices=["deta-swin-large", "deta-swin-large-o365"],
		    help="Name of the model you'd like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path",
		    default=None,
		    type=str,
		    help="Path to the folder to output PyTorch model.",
		)
		parser.add_argument(
		    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
		)
		lowerCamelCase__				  =   parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""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,  _UpperCamelCase,  _UpperCamelCase,  _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)
