





'''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 						__A						(   lowerCamelCase_     ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE      :							Any						=       SwinConfig()
		SCREAMING_SNAKE_CASE      :							str						=       swin_name.split("""_"""     )

		SCREAMING_SNAKE_CASE      :							Optional[Any]						=       name_split[1]
		SCREAMING_SNAKE_CASE      :							int						=       int(name_split[4]     )
		SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       int(name_split[3][-1]     )

		if model_size == "tiny":
				SCREAMING_SNAKE_CASE      :							Any						=       96
				SCREAMING_SNAKE_CASE      :							List[Any]						=       (2, 2, 6, 2)
				SCREAMING_SNAKE_CASE      :							Dict						=       (3, 6, 12, 24)
		elif model_size == "small":
				SCREAMING_SNAKE_CASE      :							int						=       96
				SCREAMING_SNAKE_CASE      :							Optional[Any]						=       (2, 2, 18, 2)
				SCREAMING_SNAKE_CASE      :							List[str]						=       (3, 6, 12, 24)
		elif model_size == "base":
				SCREAMING_SNAKE_CASE      :							List[Any]						=       1_28
				SCREAMING_SNAKE_CASE      :							Any						=       (2, 2, 18, 2)
				SCREAMING_SNAKE_CASE      :							List[Any]						=       (4, 8, 16, 32)
		else:
				SCREAMING_SNAKE_CASE      :							Any						=       1_92
				SCREAMING_SNAKE_CASE      :							Any						=       (2, 2, 18, 2)
				SCREAMING_SNAKE_CASE      :							Any						=       (6, 12, 24, 48)

		if "in22k" in swin_name:
				SCREAMING_SNAKE_CASE      :							int						=       2_18_41
		else:
				SCREAMING_SNAKE_CASE      :							str						=       10_00
				SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       "huggingface/label-files"
				SCREAMING_SNAKE_CASE      :							List[str]						=       "imagenet-1k-id2label.json"
				SCREAMING_SNAKE_CASE      :							Tuple						=       json.load(open(hf_hub_download(__snake_case       ,       __snake_case       ,       repo_type="""dataset"""     )       ,       """r"""     )     )
				SCREAMING_SNAKE_CASE      :							List[str]						=       {int(__snake_case     ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE      :							int						=       idalabel
				SCREAMING_SNAKE_CASE      :							Optional[Any]						=       {v: k for k, v in idalabel.items()}

		SCREAMING_SNAKE_CASE      :							Optional[Any]						=       img_size
		SCREAMING_SNAKE_CASE      :							int						=       num_classes
		SCREAMING_SNAKE_CASE      :							str						=       embed_dim
		SCREAMING_SNAKE_CASE      :							Any						=       depths
		SCREAMING_SNAKE_CASE      :							List[str]						=       num_heads
		SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       window_size

		return config



def 						__A						(   lowerCamelCase_     ):


		"""simple docstring"""



		if "patch_embed.proj" in name:
				SCREAMING_SNAKE_CASE      :							List[Any]						=       name.replace("""patch_embed.proj"""       ,       """embeddings.patch_embeddings.projection"""     )
		if "patch_embed.norm" in name:
				SCREAMING_SNAKE_CASE      :							Optional[Any]						=       name.replace("""patch_embed.norm"""       ,       """embeddings.norm"""     )
		if "layers" in name:
				SCREAMING_SNAKE_CASE      :							List[Any]						=       "encoder." + name
		if "attn.proj" in name:
				SCREAMING_SNAKE_CASE      :							str						=       name.replace("""attn.proj"""       ,       """attention.output.dense"""     )
		if "attn" in name:
				SCREAMING_SNAKE_CASE      :							Any						=       name.replace("""attn"""       ,       """attention.self"""     )
		if "norm1" in name:
				SCREAMING_SNAKE_CASE      :							List[Any]						=       name.replace("""norm1"""       ,       """layernorm_before"""     )
		if "norm2" in name:
				SCREAMING_SNAKE_CASE      :							Optional[Any]						=       name.replace("""norm2"""       ,       """layernorm_after"""     )
		if "mlp.fc1" in name:
				SCREAMING_SNAKE_CASE      :							int						=       name.replace("""mlp.fc1"""       ,       """intermediate.dense"""     )
		if "mlp.fc2" in name:
				SCREAMING_SNAKE_CASE      :							Optional[int]						=       name.replace("""mlp.fc2"""       ,       """output.dense"""     )

		if name == "norm.weight":
				SCREAMING_SNAKE_CASE      :							Dict						=       "layernorm.weight"
		if name == "norm.bias":
				SCREAMING_SNAKE_CASE      :							Dict						=       "layernorm.bias"

		if "head" in name:
				SCREAMING_SNAKE_CASE      :							Optional[Any]						=       name.replace("""head"""       ,       """classifier"""     )
		else:
				SCREAMING_SNAKE_CASE      :							List[str]						=       "swin." + name

		return name



def 						__A						(   lowerCamelCase_       ,       lowerCamelCase_     ):


		"""simple docstring"""



		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE      :							int						=       orig_state_dict.pop(__snake_case     )

				if "mask" in key:
						continue
				elif "qkv" in key:
						SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       key.split("""."""     )
						SCREAMING_SNAKE_CASE      :							str						=       int(key_split[1]     )
						SCREAMING_SNAKE_CASE      :							int						=       int(key_split[3]     )
						SCREAMING_SNAKE_CASE      :							Dict						=       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								SCREAMING_SNAKE_CASE      :							List[str]						=       val[:dim, :]
								SCREAMING_SNAKE_CASE      :							Dict						=       val[
								    dim : dim * 2, :
								]
								SCREAMING_SNAKE_CASE      :							int						=       val[-dim:, :]
						else:
								SCREAMING_SNAKE_CASE      :							List[Any]						=       val[
								    :dim
								]
								SCREAMING_SNAKE_CASE      :							Optional[int]						=       val[
								    dim : dim * 2
								]
								SCREAMING_SNAKE_CASE      :							str						=       val[
								    -dim:
								]
				else:
						SCREAMING_SNAKE_CASE      :							Dict						=       val

		return orig_state_dict



def 						__A						(   lowerCamelCase_       ,       lowerCamelCase_     ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE      :							str						=       timm.create_model(__snake_case       ,       pretrained=__snake_case     )
		timm_model.eval()

		SCREAMING_SNAKE_CASE      :							Dict						=       get_swin_config(__snake_case     )
		SCREAMING_SNAKE_CASE      :							Tuple						=       SwinForImageClassification(__snake_case     )
		model.eval()

		SCREAMING_SNAKE_CASE      :							Tuple						=       convert_state_dict(timm_model.state_dict()       ,       __snake_case     )
		model.load_state_dict(__snake_case     )

		SCREAMING_SNAKE_CASE      :							List[str]						=       "http://images.cocodataset.org/val2017/000000039769.jpg"

		SCREAMING_SNAKE_CASE      :							Optional[int]						=       AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""       ,       """-"""     )     )     )
		SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       Image.open(requests.get(__snake_case       ,       stream=__snake_case     ).raw     )
		SCREAMING_SNAKE_CASE      :							Optional[int]						=       image_processor(images=__snake_case       ,       return_tensors="""pt"""     )

		SCREAMING_SNAKE_CASE      :							Any						=       timm_model(inputs["""pixel_values"""]     )
		SCREAMING_SNAKE_CASE      :							Optional[Any]						=       model(**__snake_case     ).logits

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

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

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


if __name__ == "__main__":
							__UpperCAmelCase			    =    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."""
							)

							__UpperCAmelCase			    =    parser.parse_args()
							convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def   __magic_name__	(      __snake_case				:       Union[str, Any]     , __snake_case				:       List[str]=7  ) ->				str:
       lowercase    :				int       		=							None
       if token is not None:
              lowercase    :				Any       		=							{"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}

       # The id of a workflow (not of a workflow run)
       lowercase    :				int       		=							"636036"

       lowercase    :				Dict       		=							f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
       # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
       url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

       lowercase    :				int       		=							requests.get(__snake_case     , headers=__snake_case  ).json()

       return result["workflow_runs"]


def   __magic_name__	(      __snake_case				:       Dict  ) ->				Tuple:
       lowercase    :				Tuple       		=							get_daily_ci_runs(__snake_case  )
       lowercase    :				Union[str, Any]       		=							None
       for workflow_run in workflow_runs:
              if workflow_run["status"] == "completed":
                     lowercase    :				List[Any]       		=							workflow_run["id"]
                     break

       return workflow_run_id


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Optional[Any]     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase    :				Dict       		=							get_last_daily_ci_runs(__snake_case  )
       if workflow_run_id is not None:
              lowercase    :				Dict       		=							get_artifacts_links(worflow_run_id=__snake_case     , token=__snake_case  )
              for artifact_name in artifact_names:
                     if artifact_name in artifacts_links:
                            lowercase    :				Optional[int]       		=							artifacts_links[artifact_name]
                            download_artifact(
                                artifact_name=__snake_case     , artifact_url=__snake_case     , output_dir=__snake_case     , token=__snake_case  )


def   __magic_name__	(      __snake_case				:       List[Any]     , __snake_case				:       Optional[int]     , __snake_case				:       Tuple  ) ->				Optional[int]:
       get_last_daily_ci_artifacts(__snake_case     , __snake_case     , __snake_case  )

       lowercase    :				str       		=							{}
       for artifact_name in artifact_names:
              lowercase    :				Optional[Any]       		=							os.path.join(__snake_case     , f"""{artifact_name}.zip"""  )
              if os.path.isfile(__snake_case  ):
                     lowercase    :				List[Any]       		=							{}
                     with zipfile.ZipFile(__snake_case  ) as z:
                            for filename in z.namelist():
                                   if not os.path.isdir(__snake_case  ):
                                          # read the file
                                          with z.open(__snake_case  ) as f:
                                                 lowercase    :				str       		=							f.read().decode("UTF-8"  )

       return results


