



"""simple docstring"""






import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def     _lowercase       (						__snake_case			)		->     int:
			__lowerCAmelCase   :						List[Any]				= []
			embed.append(
			    (
			        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
			        F"""stage{idx}.patch_embed.proj.weight""",
			    )			)
			embed.append(
			    (
			        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
			        F"""stage{idx}.patch_embed.proj.bias""",
			    )			)
			embed.append(
			    (
			        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
			        F"""stage{idx}.patch_embed.norm.weight""",
			    )			)
			embed.append(
			    (
			        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
			        F"""stage{idx}.patch_embed.norm.bias""",
			    )			)
			return embed


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						Union[str, Any]				= []
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
			        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
			    )			)
			attention_weights.append(
			    (
			        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
			        F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
			    )			)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")			)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")			)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")			)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")			)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""")			)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""")			)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""")			)
			attention_weights.append(
			    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""")			)
			return attention_weights


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase   :						Tuple				= []
			token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")			)
			return token


def     _lowercase       (						)		->     Union[str, Any]:
			__lowerCAmelCase   :						Any				= []
			head.append(("layernorm.weight", "norm.weight")			)
			head.append(("layernorm.bias", "norm.bias")			)
			head.append(("classifier.weight", "head.weight")			)
			head.append(("classifier.bias", "head.bias")			)
			return head


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case      ,__snake_case			)		->     Union[str, Any]:
			__lowerCAmelCase   :						Union[str, Any]				= "imagenet-1k-id2label.json"
			__lowerCAmelCase   :						List[str]				= 1_000

			__lowerCAmelCase   :						Tuple				= "huggingface/label-files"
			__lowerCAmelCase   :						Optional[int]				= num_labels
			__lowerCAmelCase   :						int				= json.load(open(cached_download(hf_hub_url(__snake_case      ,__snake_case      ,repo_type="dataset"			)			)      ,"r"			)			)
			__lowerCAmelCase   :						Tuple				= {int(__snake_case			): v for k, v in idalabel.items()}

			__lowerCAmelCase   :						Optional[Any]				= idalabel
			__lowerCAmelCase   :						Optional[int]				= {v: k for k, v in idalabel.items()}

			__lowerCAmelCase   :						List[str]				= CvtConfig(num_labels=__snake_case      ,idalabel=__snake_case      ,labelaid=__snake_case			)

			# For depth size 13 (13 = 1+2+10)
			if cvt_model.rsplit("/"      ,1			)[-1][4:6] == "13":
						__lowerCAmelCase   :						Any				= [1, 2, 10]

			# For depth size 21 (21 = 1+4+16)
			elif cvt_model.rsplit("/"      ,1			)[-1][4:6] == "21":
						__lowerCAmelCase   :						Optional[Any]				= [1, 4, 16]

			# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
			else:
						__lowerCAmelCase   :						Optional[Any]				= [2, 2, 20]
						__lowerCAmelCase   :						Any				= [3, 12, 16]
						__lowerCAmelCase   :						Any				= [192, 768, 1_024]

			__lowerCAmelCase   :						List[str]				= CvtForImageClassification(__snake_case			)
			__lowerCAmelCase   :						Dict				= AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			)
			__lowerCAmelCase   :						str				= image_size
			__lowerCAmelCase   :						Tuple				= torch.load(__snake_case      ,map_location=torch.device("cpu"			)			)

			__lowerCAmelCase   :						List[Any]				= OrderedDict()
			__lowerCAmelCase   :						Optional[Any]				= []

			for idx in range(len(config.depth			)			):
						if config.cls_token[idx]:
									__lowerCAmelCase   :						int				= list_of_state_dict + cls_token(__snake_case			)
						__lowerCAmelCase   :						List[Any]				= list_of_state_dict + embeddings(__snake_case			)
						for cnt in range(config.depth[idx]			):
									__lowerCAmelCase   :						int				= list_of_state_dict + attention(__snake_case      ,__snake_case			)

			__lowerCAmelCase   :						Optional[Any]				= list_of_state_dict + final()
			for gg in list_of_state_dict:
						print(__snake_case			)
			for i in range(len(__snake_case			)			):
						__lowerCAmelCase   :						List[str]				= original_weights[list_of_state_dict[i][1]]

			model.load_state_dict(__snake_case			)
			model.save_pretrained(__snake_case			)
			image_processor.save_pretrained(__snake_case			)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
	__snake_case							:     int   =					argparse.ArgumentParser()
	parser.add_argument(
	    '--cvt_model',
	    default='cvt-w24',
	    type=str,
	    help='Name of the cvt model you\'d like to convert.',
	)
	parser.add_argument(
	    '--image_size',
	    default=384,
	    type=int,
	    help='Input Image Size',
	)
	parser.add_argument(
	    '--cvt_file_name',
	    default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
	    type=str,
	    help='Input Image Size',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)

	__snake_case							:     str   =					parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




"""simple docstring"""






import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__snake_case							:     Tuple   =					logging.get_logger(__name__)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Dict:
			__lowerCAmelCase   :						Optional[Any]				= set()
			__lowerCAmelCase   :						List[Any]				= []

			def parse_line(__snake_case			):
						for line in fp:
									if isinstance(__snake_case      ,__snake_case			):
												__lowerCAmelCase   :						Tuple				= line.decode("UTF-8"			)
									if "warnings summary (final)" in line:
												continue
									# This means we are outside the body of a warning
									elif not line.startswith(" "			):
												# process a single warning and move it to `selected_warnings`.
												if len(__snake_case			) > 0:
															__lowerCAmelCase   :						List[Any]				= "\n".join(__snake_case			)
															# Only keep the warnings specified in `targets`
															if any(F""": {x}: """ in warning for x in targets			):
																		selected_warnings.add(__snake_case			)
															buffer.clear()
												continue
									else:
												__lowerCAmelCase   :						List[str]				= line.strip()
												buffer.append(__snake_case			)

			if from_gh:
						for filename in os.listdir(__snake_case			):
									__lowerCAmelCase   :						List[str]				= os.path.join(__snake_case      ,__snake_case			)
									if not os.path.isdir(__snake_case			):
												# read the file
												if filename != "warnings.txt":
															continue
												with open(__snake_case			) as fp:
															parse_line(__snake_case			)
			else:
						try:
									with zipfile.ZipFile(__snake_case			) as z:
												for filename in z.namelist():
															if not os.path.isdir(__snake_case			):
																		# read the file
																		if filename != "warnings.txt":
																					continue
																		with z.open(__snake_case			) as fp:
																					parse_line(__snake_case			)
						except Exception:
									logger.warning(
									    F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""			)

			return selected_warnings


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						Any				= set()

			__lowerCAmelCase   :						str				= [os.path.join(__snake_case      ,__snake_case			) for p in os.listdir(__snake_case			) if (p.endswith(".zip"			) or from_gh)]
			for p in paths:
						selected_warnings.update(extract_warnings_from_single_artifact(__snake_case      ,__snake_case			)			)

			return selected_warnings


if __name__ == "__main__":


	def     _lowercase       (						__snake_case			)		->     Optional[Any]:
				return values.split(","			)

	__snake_case							:     Optional[int]   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
	parser.add_argument(
	    '--output_dir',
	    type=str,
	    required=True,
	    help='Where to store the downloaded artifacts and other result files.',
	)
	parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
	# optional parameters
	parser.add_argument(
	    '--targets',
	    default='DeprecationWarning,UserWarning,FutureWarning',
	    type=list_str,
	    help='Comma-separated list of target warning(s) which we want to extract.',
	)
	parser.add_argument(
	    '--from_gh',
	    action='store_true',
	    help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
	)

	__snake_case							:     Tuple   =					parser.parse_args()

	__snake_case							:     Any   =					args.from_gh
	if from_gh:
		# The artifacts have to be downloaded using `actions/download-artifact@v3`
		pass
	else:
		os.makedirs(args.output_dir, exist_ok=True)

		# get download links
		__snake_case							:     str   =					get_artifacts_links(args.workflow_run_id, token=args.token)
		with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
			json.dump(artifacts, fp, ensure_ascii=False, indent=4)

		# download artifacts
		for idx, (name, url) in enumerate(artifacts.items()):
			print(name)
			print(url)
			print('=' * 80)
			download_artifact(name, url, args.output_dir, args.token)
			# Be gentle to GitHub
			time.sleep(1)

    # extract warnings from artifacts
	__snake_case							:     Tuple   =					extract_warnings(args.output_dir, args.targets)
	__snake_case							:     List[str]   =					sorted(selected_warnings)
	with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
		json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)