

'''simple docstring'''




import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple    ):
  UpperCAmelCase    				=						SwinConfig()
  UpperCAmelCase    				=						swin_name.split('_'    )

  UpperCAmelCase    				=						name_split[1]
  UpperCAmelCase    				=						int(name_split[4]    )
  UpperCAmelCase    				=						int(name_split[3][-1]    )

  if model_size == "tiny":
    UpperCAmelCase    				=						96
    UpperCAmelCase    				=						(2, 2, 6, 2)
    UpperCAmelCase    				=						(3, 6, 12, 24)
  elif model_size == "small":
    UpperCAmelCase    				=						96
    UpperCAmelCase    				=						(2, 2, 18, 2)
    UpperCAmelCase    				=						(3, 6, 12, 24)
  elif model_size == "base":
    UpperCAmelCase    				=						128
    UpperCAmelCase    				=						(2, 2, 18, 2)
    UpperCAmelCase    				=						(4, 8, 16, 32)
  else:
    UpperCAmelCase    				=						192
    UpperCAmelCase    				=						(2, 2, 18, 2)
    UpperCAmelCase    				=						(6, 12, 24, 48)

  if "in22k" in swin_name:
    UpperCAmelCase    				=						2_1841
  else:
    UpperCAmelCase    				=						1000
    UpperCAmelCase    				=						'huggingface/label-files'
    UpperCAmelCase    				=						'imagenet-1k-id2label.json'
    UpperCAmelCase    				=						json.load(open(hf_hub_download(__UpperCAmelCase							, __UpperCAmelCase							, repo_type='dataset'    )							, 'r'    )    )
    UpperCAmelCase    				=						{int(__UpperCAmelCase    ): v for k, v in idalabel.items()}
    UpperCAmelCase    				=						idalabel
    UpperCAmelCase    				=						{v: k for k, v in idalabel.items()}

  UpperCAmelCase    				=						img_size
  UpperCAmelCase    				=						num_classes
  UpperCAmelCase    				=						embed_dim
  UpperCAmelCase    				=						depths
  UpperCAmelCase    				=						num_heads
  UpperCAmelCase    				=						window_size

  return config


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[int]    ):

  if "patch_embed.proj" in name:
    UpperCAmelCase    				=						name.replace('patch_embed.proj'							, 'embeddings.patch_embeddings.projection'    )
  if "patch_embed.norm" in name:
    UpperCAmelCase    				=						name.replace('patch_embed.norm'							, 'embeddings.norm'    )
  if "layers" in name:
    UpperCAmelCase    				=						'encoder.' + name
  if "attn.proj" in name:
    UpperCAmelCase    				=						name.replace('attn.proj'							, 'attention.output.dense'    )
  if "attn" in name:
    UpperCAmelCase    				=						name.replace('attn'							, 'attention.self'    )
  if "norm1" in name:
    UpperCAmelCase    				=						name.replace('norm1'							, 'layernorm_before'    )
  if "norm2" in name:
    UpperCAmelCase    				=						name.replace('norm2'							, 'layernorm_after'    )
  if "mlp.fc1" in name:
    UpperCAmelCase    				=						name.replace('mlp.fc1'							, 'intermediate.dense'    )
  if "mlp.fc2" in name:
    UpperCAmelCase    				=						name.replace('mlp.fc2'							, 'output.dense'    )

  if name == "norm.weight":
    UpperCAmelCase    				=						'layernorm.weight'
  if name == "norm.bias":
    UpperCAmelCase    				=						'layernorm.bias'

  if "head" in name:
    UpperCAmelCase    				=						name.replace('head'							, 'classifier'    )
  else:
    UpperCAmelCase    				=						'swin.' + name

  return name


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : List[str]    ):

  for key in orig_state_dict.copy().keys():
    UpperCAmelCase    				=						orig_state_dict.pop(__UpperCAmelCase    )

    if "mask" in key:
      continue
    elif "qkv" in key:
      UpperCAmelCase    				=						key.split('.'    )
      UpperCAmelCase    				=						int(key_split[1]    )
      UpperCAmelCase    				=						int(key_split[3]    )
      UpperCAmelCase    				=						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        UpperCAmelCase    				=						val[:dim, :]
        UpperCAmelCase    				=						val[
            dim : dim * 2, :
        ]
        UpperCAmelCase    				=						val[-dim:, :]
      else:
        UpperCAmelCase    				=						val[
            :dim
        ]
        UpperCAmelCase    				=						val[
            dim : dim * 2
        ]
        UpperCAmelCase    				=						val[
            -dim:
        ]
    else:
      UpperCAmelCase    				=						val

  return orig_state_dict


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : Any    ):
  UpperCAmelCase    				=						timm.create_model(__UpperCAmelCase							, pretrained=__UpperCAmelCase    )
  timm_model.eval()

  UpperCAmelCase    				=						get_swin_config(__UpperCAmelCase    )
  UpperCAmelCase    				=						SwinForImageClassification(__UpperCAmelCase    )
  model.eval()

  UpperCAmelCase    				=						convert_state_dict(timm_model.state_dict()							, __UpperCAmelCase    )
  model.load_state_dict(__UpperCAmelCase    )

  UpperCAmelCase    				=						'http://images.cocodataset.org/val2017/000000039769.jpg'

  UpperCAmelCase    				=						AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'							, '-'    )    )    )
  UpperCAmelCase    				=						Image.open(requests.get(__UpperCAmelCase							, stream=__UpperCAmelCase    ).raw    )
  UpperCAmelCase    				=						image_processor(images=__UpperCAmelCase							, return_tensors='pt'    )

  UpperCAmelCase    				=						timm_model(inputs['pixel_values']    )
  UpperCAmelCase    				=						model(**__UpperCAmelCase    ).logits

  assert torch.allclose(__UpperCAmelCase							, __UpperCAmelCase							, atol=1E-3    )

  print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''    )
  model.save_pretrained(__UpperCAmelCase    )

  print(f'''Saving image processor to {pytorch_dump_folder_path}'''    )
  image_processor.save_pretrained(__UpperCAmelCase    )


if __name__ == "__main__":
  _a					:			int							=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--swin_name',
      default='swin_tiny_patch4_window7_224',
      type=str,
      help='Name of the Swin timm model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
  )

  _a					:			str							=    parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)








'''simple docstring'''

import argparse
from collections import defaultdict

import yaml


a    :							List[Any]										=    'docs/source/en/_toctree.yml'


def 						__magic_name__       (      __UpperCAmelCase      )						->   str:

					'''simple docstring'''

					snake_case_		     =							defaultdict(__UpperCAmelCase      )
					for doc in model_doc:
										counts[doc["local"]] += 1
					snake_case_		     =							[key for key, value in counts.items() if value > 1]

					snake_case_		     =							[]
					for duplicate_key in duplicates:
										snake_case_		     =							list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}      )
										if len(__UpperCAmelCase      ) > 1:
															raise ValueError(
															    F"{duplicate_key} is present several times in the documentation table of content at "
															    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
															    '''others.'''      )
										# Only add this once
										new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}      )

					# Add none duplicate-keys
					new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]      )

					# Sort
					return sorted(__UpperCAmelCase, key=lambda __UpperCAmelCase      : s["title"].lower()      )


def 						__magic_name__       (      __UpperCAmelCase=False      )						->   List[Any]:

					'''simple docstring'''

					with open(__UpperCAmelCase, encoding='''utf-8'''      ) as f:
										snake_case_		     =							yaml.safe_load(f.read()      )

					# Get to the API doc
					snake_case_		     =							0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					snake_case_		     =							content[api_idx]['''sections''']

					# Then to the model doc
					snake_case_		     =							0
					while api_doc[model_idx]["title"] != "Models":
										model_idx += 1

					snake_case_		     =							api_doc[model_idx]['''sections''']

					snake_case_		     =							[(idx, section) for idx, section in enumerate(__UpperCAmelCase      ) if '''sections''' in section]
					snake_case_		     =							False
					for idx, modality_doc in modalities_docs:
										snake_case_		     =							modality_doc['''sections''']
										snake_case_		     =							clean_model_doc_toc(__UpperCAmelCase      )

										if old_modality_doc != new_modality_doc:
															snake_case_		     =							True
															if overwrite:
																				snake_case_		     =							new_modality_doc

					if diff:
										if overwrite:
															snake_case_		     =							model_doc
															snake_case_		     =							api_doc
															with open(__UpperCAmelCase, '''w''', encoding='''utf-8'''      ) as f:
																				f.write(yaml.dump(__UpperCAmelCase, allow_unicode=__UpperCAmelCase      )      )
										else:
															raise ValueError(
															    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''      )


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

							check_model_doc(args.fix_and_overwrite)

