





import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a      : List[str] 				=				logging.get_logger(__name__)

__a      : List[Any] 				=				torch.device("""cpu""")
def 		__magic_name__				( )		->					Union[str, Any]:


						'''simple docstring'''


						UpperCamelCase  							= 'http://images.cocodataset.org/val2017/000000039769.jpg'
						UpperCamelCase  							= Image.open(requests.get(_lowerCAmelCase						,		stream=_lowerCAmelCase				).raw				)
						return im
def 		__magic_name__				( lowercase_				)		->					List[str]:


						'''simple docstring'''


						if swiftformer_name == "swiftformer_xs":
												return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]				)

						elif swiftformer_name == "swiftformer_s":
												return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]				)

						elif swiftformer_name == "swiftformer_l1":
												return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]				)

						elif swiftformer_name == "swiftformer_l3":
												return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]				)
def 		__magic_name__				( lowercase_						,		lowercase_						,		lowercase_				)		->					Dict:


						'''simple docstring'''


						UpperCamelCase  							= dct.pop(_lowerCAmelCase				)
						UpperCamelCase  							= val
def 		__magic_name__				( lowercase_				)		->					int:


						'''simple docstring'''


						UpperCamelCase  							= []
						for k in state_dict.keys():
												UpperCamelCase  							= k
												if ".pwconv" in k:
																		UpperCamelCase  							= k_new.replace(".pwconv"						,		".point_wise_conv"				)
												if ".dwconv" in k:
																		UpperCamelCase  							= k_new.replace(".dwconv"						,		".depth_wise_conv"				)
												if ".Proj." in k:
																		UpperCamelCase  							= k_new.replace(".Proj."						,		".proj."				)
												if "patch_embed" in k_new:
																		UpperCamelCase  							= k_new.replace("patch_embed"						,		"swiftformer.patch_embed.patch_embedding"				)
												if "network" in k_new:
																		UpperCamelCase  							= k_new.split("."				)
																		if ls[2].isdigit():
																								UpperCamelCase  							= 'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]				)
																		else:
																								UpperCamelCase  							= k_new.replace("network"						,		"swiftformer.encoder.network"				)
												rename_keys.append((k, k_new)				)
						return rename_keys
@torch.no_grad()
def 		__magic_name__				( lowercase_						,		lowercase_						,		lowercase_				)		->					Optional[int]:


						'''simple docstring'''


						UpperCamelCase  							= SwiftFormerConfig()

						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						UpperCamelCase  							= 1000
						UpperCamelCase  							= 'huggingface/label-files'
						UpperCamelCase  							= 'imagenet-1k-id2label.json'
						UpperCamelCase  							= json.load(open(hf_hub_download(_lowerCAmelCase						,		_lowerCAmelCase						,		repo_type="dataset"				)						,		"r"				)				)
						UpperCamelCase  							= {int(_lowerCAmelCase				): v for k, v in idalabel.items()}
						UpperCamelCase  							= idalabel
						UpperCamelCase  							= {v: k for k, v in idalabel.items()}

						# size of the architecture
						if swiftformer_name == "swiftformer_xs":
												UpperCamelCase  							= [3, 3, 6, 4]
												UpperCamelCase  							= [48, 56, 112, 220]

						elif swiftformer_name == "swiftformer_s":
												UpperCamelCase  							= [3, 3, 9, 6]
												UpperCamelCase  							= [48, 64, 168, 224]

						elif swiftformer_name == "swiftformer_l1":
												UpperCamelCase  							= [4, 3, 10, 5]
												UpperCamelCase  							= [48, 96, 192, 384]

						elif swiftformer_name == "swiftformer_l3":
												UpperCamelCase  							= [4, 4, 12, 6]
												UpperCamelCase  							= [64, 128, 320, 512]

						# load state_dict of original model, remove and rename some keys
						if original_ckpt:
												if original_ckpt.startswith("https"				):
																		UpperCamelCase  							= torch.hub.load_state_dict_from_url(_lowerCAmelCase						,		map_location="cpu"						,		check_hash=_lowerCAmelCase				)
												else:
																		UpperCamelCase  							= torch.load(_lowerCAmelCase						,		map_location="cpu"				)
						UpperCamelCase  							= checkpoint

						UpperCamelCase  							= create_rename_keys(_lowerCAmelCase				)
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(_lowerCAmelCase						,		_lowerCAmelCase						,		_lowerCAmelCase				)

						# load HuggingFace model
						UpperCamelCase  							= SwiftFormerForImageClassification(_lowerCAmelCase				).eval()
						hf_model.load_state_dict(_lowerCAmelCase				)

						# prepare test inputs
						UpperCamelCase  							= prepare_img()
						UpperCamelCase  							= ViTImageProcessor.from_pretrained("preprocessor_config"				)
						UpperCamelCase  							= processor(images=_lowerCAmelCase						,		return_tensors="pt"				)

						# compare outputs from both models
						UpperCamelCase  							= get_expected_output(_lowerCAmelCase				)
						UpperCamelCase  							= hf_model(inputs["pixel_values"]				).logits

						assert hf_logits.shape == torch.Size([1, 1000]				)
						assert torch.allclose(hf_logits[0, 0:5]						,		_lowerCAmelCase						,		atol=1E-3				)

						Path(_lowerCAmelCase				).mkdir(exist_ok=_lowerCAmelCase				)
						print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''				)
						hf_model.save_pretrained(_lowerCAmelCase				)


if __name__ == "__main__":
		__a      : Tuple 				=				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--swiftformer_name""",
		    default="""swiftformer_xs""",
		    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
		    type=str,
		    help="""Name of the SwiftFormer model you\'d like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default="""./converted_outputs/""",
		    type=str,
		    help="""Path to the output PyTorch model directory.""",
		)
		parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

		__a      : Tuple 				=				parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









'''simple docstring'''





import argparse
from collections import defaultdict

import yaml


_UpperCamelCase											=		'docs/source/en/_toctree.yml'

def 			a_	(				_lowerCAmelCase			) -> Any:
  __lowerCamelCase			:			Optional[int]       			=					defaultdict(_lowerCAmelCase			)
  __lowerCamelCase			:			Optional[Any]       			=					[]
  __lowerCamelCase			:			Union[str, Any]       			=					[]
  for doc in doc_list:
    if "local" in doc:
      counts[doc["local"]] += 1

    if doc["title"].lower() == "overview":
      overview_doc.append({'local': doc['local'], 'title': doc['title']}			)
    else:
      new_doc_list.append(_lowerCAmelCase			)

  __lowerCamelCase			:			Dict       			=					new_doc_list
  __lowerCamelCase			:			Optional[Any]       			=					[key for key, value in counts.items() if value > 1]

  __lowerCamelCase			:			int       			=					[]
  for duplicate_key in duplicates:
    __lowerCamelCase			:			int       			=					list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}			)
    if len(_lowerCAmelCase			) > 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 doc_list if 'local' not in counts or counts[doc['local']] == 1]			)
  __lowerCamelCase			:			Dict       			=					sorted(_lowerCAmelCase			,key=lambda _lowerCAmelCase			: s["title"].lower()			)

  # "overview" gets special treatment and is always first
  if len(_lowerCAmelCase			) > 1:
    raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'			)

  overview_doc.extend(_lowerCAmelCase			)

  # Sort
  return overview_doc

def 			a_	(				_lowerCAmelCase=False			) -> Optional[Any]:
  with open(_lowerCAmelCase			,encoding='utf-8'			) as f:
    __lowerCamelCase			:			str       			=					yaml.safe_load(f.read()			)

  # Get to the API doc
  __lowerCamelCase			:			Union[str, Any]       			=					0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  __lowerCamelCase			:			List[str]       			=					content[api_idx]['sections']

  # Then to the model doc
  __lowerCamelCase			:			List[Any]       			=					0
  while api_doc[scheduler_idx]["title"] != "Schedulers":
    scheduler_idx += 1

  __lowerCamelCase			:			List[Any]       			=					api_doc[scheduler_idx]['sections']
  __lowerCamelCase			:			Optional[Any]       			=					clean_doc_toc(_lowerCAmelCase			)

  __lowerCamelCase			:			str       			=					False
  if new_scheduler_doc != scheduler_doc:
    __lowerCamelCase			:			int       			=					True
    if overwrite:
      __lowerCamelCase			:			Any       			=					new_scheduler_doc

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

def 			a_	(				_lowerCAmelCase=False			) -> List[Any]:
  with open(_lowerCAmelCase			,encoding='utf-8'			) as f:
    __lowerCamelCase			:			List[str]       			=					yaml.safe_load(f.read()			)

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

  # Then to the model doc
  __lowerCamelCase			:			List[Any]       			=					0
  while api_doc[pipeline_idx]["title"] != "Pipelines":
    pipeline_idx += 1

  __lowerCamelCase			:			int       			=					False
  __lowerCamelCase			:			str       			=					api_doc[pipeline_idx]['sections']
  __lowerCamelCase			:			Optional[Any]       			=					[]

  # sort sub pipeline docs
  for pipeline_doc in pipeline_docs:
    if "section" in pipeline_doc:
      __lowerCamelCase			:			str       			=					pipeline_doc['section']
      __lowerCamelCase			:			Optional[Any]       			=					clean_doc_toc(_lowerCAmelCase			)
      if overwrite:
        __lowerCamelCase			:			Union[str, Any]       			=					new_sub_pipeline_doc
    new_pipeline_docs.append(_lowerCAmelCase			)

  # sort overall pipeline doc
  __lowerCamelCase			:			int       			=					clean_doc_toc(_lowerCAmelCase			)

  if new_pipeline_docs != pipeline_docs:
    __lowerCamelCase			:			Tuple       			=					True
    if overwrite:
      __lowerCamelCase			:			int       			=					new_pipeline_docs

  if diff:
    if overwrite:
      __lowerCamelCase			:			Tuple       			=					api_doc
      with open(_lowerCAmelCase			,'w'			,encoding='utf-8'			) as f:
        f.write(yaml.dump(_lowerCAmelCase			,allow_unicode=_lowerCAmelCase			)			)
    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__":
   _UpperCamelCase											=		argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   _UpperCamelCase											=		parser.parse_args()

   check_scheduler_doc(args.fix_and_overwrite)
   check_pipeline_doc(args.fix_and_overwrite)






