

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     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      SwinConfig(
						    embed_dim=192    ,						depths=(2, 2, 18, 2)    ,						num_heads=(6, 12, 24, 48)    ,						window_size=12    ,						out_features=['''stage2''', '''stage3''', '''stage4''']    ,						)

						snake_case_  =      DetaConfig(
						    backbone_config=SCREAMING_SNAKE_CASE__    ,						num_queries=900    ,						encoder_ffn_dim=2048    ,						decoder_ffn_dim=2048    ,						num_feature_levels=5    ,						assign_first_stage=SCREAMING_SNAKE_CASE__    ,						with_box_refine=SCREAMING_SNAKE_CASE__    ,						two_stage=SCREAMING_SNAKE_CASE__    ,						)

						# set labels
						snake_case_  =      '''huggingface/label-files'''
						if "o365" in model_name:
												snake_case_  =      366
												snake_case_  =      '''object365-id2label.json'''
						else:
												snake_case_  =      91
												snake_case_  =      '''coco-detection-id2label.json'''

						snake_case_  =      num_labels
						snake_case_  =      json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						repo_type='''dataset'''			)			)    ,						'''r'''			)			)
						snake_case_  =      {int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
						snake_case_  =      idalabel
						snake_case_  =      {v: k for k, v in idalabel.items()}

						return config


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      []

						# 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     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      dct.pop(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      val


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
						for i in range(len(backbone_config.depths			)			):
												snake_case_  =      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)
																		snake_case_  =      state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
																		snake_case_  =      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
																		snake_case_  =      in_proj_weight[:dim, :]
																		snake_case_  =      in_proj_bias[: dim]
																		snake_case_  =      in_proj_weight[
																		    dim : dim * 2, :
																		]
																		snake_case_  =      in_proj_bias[
																		    dim : dim * 2
																		]
																		snake_case_  =      in_proj_weight[
																		    -dim :, :
																		]
																		snake_case_  =      in_proj_bias[-dim :]
																		# fmt: on


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						# transformer decoder self-attention layers
						snake_case_  =      config.d_model
						for i in range(config.decoder_layers			):
												# read in weights + bias of input projection layer of self-attention
												snake_case_  =      state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''			)
												snake_case_  =      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
												snake_case_  =      in_proj_weight[:hidden_size, :]
												snake_case_  =      in_proj_bias[:hidden_size]
												snake_case_  =      in_proj_weight[
												    hidden_size : hidden_size * 2, :
												]
												snake_case_  =      in_proj_bias[hidden_size : hidden_size * 2]
												snake_case_  =      in_proj_weight[-hidden_size:, :]
												snake_case_  =      in_proj_bias[-hidden_size:]


def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case_  =      Image.open(requests.get(SCREAMING_SNAKE_CASE__    ,						stream=SCREAMING_SNAKE_CASE__			).raw			)

						return im


@torch.no_grad()
def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      get_deta_config(SCREAMING_SNAKE_CASE__			)

						# load original state dict
						if model_name == "deta-swin-large":
												snake_case_  =      hf_hub_download(repo_id='''nielsr/deta-checkpoints'''    ,						filename='''adet_swin_ft.pth'''			)
						elif model_name == "deta-swin-large-o365":
												snake_case_  =      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'''			)

						snake_case_  =      torch.load(SCREAMING_SNAKE_CASE__    ,						map_location='''cpu'''			)['''model''']

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

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

						# fix some prefixes
						for key in state_dict.copy().keys():
												if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																		snake_case_  =      state_dict.pop(SCREAMING_SNAKE_CASE__			)
																		snake_case_  =      val
												if "input_proj" in key:
																		snake_case_  =      state_dict.pop(SCREAMING_SNAKE_CASE__			)
																		snake_case_  =      val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		snake_case_  =      state_dict.pop(SCREAMING_SNAKE_CASE__			)
																		snake_case_  =      val

    # finally, create HuggingFace model and load state dict
						snake_case_  =      DetaForObjectDetection(SCREAMING_SNAKE_CASE__			)
						model.load_state_dict(SCREAMING_SNAKE_CASE__			)
						model.eval()

						snake_case_  =      '''cuda''' if torch.cuda.is_available() else '''cpu'''
						model.to(SCREAMING_SNAKE_CASE__			)

						# load image processor
						snake_case_  =      DetaImageProcessor(format='''coco_detection'''			)

						# verify our conversion on image
						snake_case_  =      prepare_img()
						snake_case_  =      processor(images=SCREAMING_SNAKE_CASE__    ,						return_tensors='''pt'''			)
						snake_case_  =      encoding['''pixel_values''']
						snake_case_  =      model(pixel_values.to(SCREAMING_SNAKE_CASE__			)			)

						# verify logits
						print('''Logits:'''    ,						outputs.logits[0, :3, :3]			)
						print('''Boxes:'''    ,						outputs.pred_boxes[0, :3, :3]			)
						if model_name == "deta-swin-large":
												snake_case_  =      torch.tensor(
												    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]			)
												snake_case_  =      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":
												snake_case_  =      torch.tensor(
												    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]			)
												snake_case_  =      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(SCREAMING_SNAKE_CASE__			)    ,						atol=1E-4			)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,						expected_boxes.to(SCREAMING_SNAKE_CASE__			)    ,						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(SCREAMING_SNAKE_CASE__			).mkdir(exist_ok=SCREAMING_SNAKE_CASE__			)
												model.save_pretrained(SCREAMING_SNAKE_CASE__			)
												processor.save_pretrained(SCREAMING_SNAKE_CASE__			)

						# 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 os
import re

import packaging.version


lowerCamelCase__						= "examples/"
lowerCamelCase__						= {
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
lowerCamelCase__						= {
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
lowerCamelCase__						= "README.md"




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		Dict:




      """simple docstring"""

      with open(lowercase_		,"r"		,encoding="utf-8"		,newline="\n"      ) as f:
            _UpperCamelCase   :						Any    =							f.read()
      _UpperCamelCase,	_UpperCamelCase   :						str    =							REPLACE_PATTERNS[pattern]
      _UpperCamelCase   :						List[str]    =							replace.replace("VERSION"		,lowercase_      )
      _UpperCamelCase   :						Any    =							re_pattern.sub(lowercase_		,lowercase_      )
      with open(lowercase_		,"w"		,encoding="utf-8"		,newline="\n"      ) as f:
            f.write(lowercase_      )




def        lowercase__     (       lowercase_      )       ->		str:




      """simple docstring"""

      for folder, directories, fnames in os.walk(lowercase_      ):
            # Removing some of the folders with non-actively maintained examples from the walk
            if "research_projects" in directories:
                  directories.remove("research_projects"      )
            if "legacy" in directories:
                  directories.remove("legacy"      )
            for fname in fnames:
                  if fname.endswith(".py"      ):
                        update_version_in_file(os.path.join(lowercase_		,lowercase_      )		,lowercase_		,pattern="examples"      )




def        lowercase__     (       lowercase_		,lowercase_=False      )       ->		Any:




      """simple docstring"""

      for pattern, fname in REPLACE_FILES.items():
            update_version_in_file(lowercase_		,lowercase_		,lowercase_      )
      if not patch:
            update_version_in_examples(lowercase_      )




def        lowercase__     (       )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							"🤗 Transformers currently provides the following architectures"
      _UpperCamelCase   :						int    =							"1. Want to contribute a new model?"
      with open(lowercase_		,"r"		,encoding="utf-8"		,newline="\n"      ) as f:
            _UpperCamelCase   :						int    =							f.readlines()

      # Find the start of the list.
      _UpperCamelCase   :						Optional[Any]    =							0
      while not lines[start_index].startswith(_start_prompt      ):
            start_index += 1
      start_index += 1

      _UpperCamelCase   :						Any    =							start_index
      # Update the lines in the model list.
      while not lines[index].startswith(_end_prompt      ):
            if lines[index].startswith("1."      ):
                  _UpperCamelCase   :						Dict    =							lines[index].replace(
                      "https://huggingface.co/docs/transformers/main/model_doc"		,"https://huggingface.co/docs/transformers/model_doc"		,)
            index += 1

      with open(lowercase_		,"w"		,encoding="utf-8"		,newline="\n"      ) as f:
            f.writelines(lowercase_      )




def        lowercase__     (       )       ->		List[str]:




      """simple docstring"""

      with open(REPLACE_FILES["init"]		,"r"      ) as f:
            _UpperCamelCase   :						Dict    =							f.read()
      _UpperCamelCase   :						List[str]    =							REPLACE_PATTERNS["init"][0].search(lowercase_      ).groups()[0]
      return packaging.version.parse(lowercase_      )




def        lowercase__     (       lowercase_=False      )       ->		Any:




      """simple docstring"""

      _UpperCamelCase   :						Tuple    =							get_version()
      if patch and default_version.is_devrelease:
            raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"      )
      if default_version.is_devrelease:
            _UpperCamelCase   :						Union[str, Any]    =							default_version.base_version
      elif patch:
            _UpperCamelCase   :						Union[str, Any]    =							F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
      else:
            _UpperCamelCase   :						Any    =							F'''{default_version.major}.{default_version.minor + 1}.0'''

      # Now let's ask nicely if that's the right one.
      _UpperCamelCase   :						List[str]    =							input(F'''Which version are you releasing? [{default_version}]'''      )
      if len(lowercase_      ) == 0:
            _UpperCamelCase   :						Any    =							default_version

      print(F'''Updating version to {version}.'''      )
      global_version_update(lowercase_		,patch=lowercase_      )
      if not patch:
            print("Cleaning main README, don't forget to run `make fix-copies`."      )
            clean_main_ref_in_model_list()




def        lowercase__     (       )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							get_version()
      _UpperCamelCase   :						List[Any]    =							F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
      _UpperCamelCase   :						Tuple    =							current_version.base_version

      # Check with the user we got that right.
      _UpperCamelCase   :						Union[str, Any]    =							input(F'''Which version are we developing now? [{dev_version}]'''      )
      if len(lowercase_      ) == 0:
            _UpperCamelCase   :						Optional[int]    =							dev_version

      print(F'''Updating version to {version}.'''      )
      global_version_update(lowercase_      )
      print("Cleaning main README, don't forget to run `make fix-copies`."      )
      clean_main_ref_in_model_list()


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()
  parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
  parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
  lowerCamelCase__						= parser.parse_args()
  if not args.post_release:
    pre_release_work(patch=args.patch)
  elif args.patch:
    print("Nothing to do after a patch :-)")
  else:
    post_release_work()
