import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests




def       _snake_case	(   lowerCAmelCase							:   Any   , lowerCAmelCase							:   str=None	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    None
		if token is not None:
				SCREAMING_SNAKE_CASE_  :		Any				      =    {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

		SCREAMING_SNAKE_CASE_  :		Tuple				      =    f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    requests.get(__lowerCamelCase   , headers=__lowerCamelCase	).json()
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    {}

		try:
				job_links.update({job["name"]: job["html_url"] for job in result["jobs"]}	)
				SCREAMING_SNAKE_CASE_  :		Any				      =    math.ceil((result["total_count"] - 1_0_0) / 1_0_0	)

				for i in range(__lowerCamelCase	):
						SCREAMING_SNAKE_CASE_  :		int				      =    requests.get(url + f'&page={i + 2}'   , headers=__lowerCamelCase	).json()
						job_links.update({job["name"]: job["html_url"] for job in result["jobs"]}	)

				return job_links
		except Exception:
				print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}'	)

		return {}




def       _snake_case	(   lowerCAmelCase							:   Optional[int]   , lowerCAmelCase							:   List[Any]=None	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    None
		if token is not None:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
		SCREAMING_SNAKE_CASE_  :		str				      =    requests.get(__lowerCamelCase   , headers=__lowerCamelCase	).json()
		SCREAMING_SNAKE_CASE_  :		Dict				      =    {}

		try:
				artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}	)
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    math.ceil((result["total_count"] - 1_0_0) / 1_0_0	)

				for i in range(__lowerCamelCase	):
						SCREAMING_SNAKE_CASE_  :		Dict				      =    requests.get(url + f'&page={i + 2}'   , headers=__lowerCamelCase	).json()
						artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}	)

				return artifacts
		except Exception:
				print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}'	)

		return {}




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   List[Any]   , lowerCAmelCase							:   List[Any]   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    None
		if token is not None:
				SCREAMING_SNAKE_CASE_  :		str				      =    {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

		SCREAMING_SNAKE_CASE_  :		str				      =    requests.get(__lowerCamelCase   , headers=__lowerCamelCase   , allow_redirects=__lowerCamelCase	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    result.headers["""Location"""]
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    requests.get(__lowerCamelCase   , allow_redirects=__lowerCamelCase	)
		SCREAMING_SNAKE_CASE_  :		Any				      =    os.path.join(__lowerCamelCase   , f'{artifact_name}.zip'	)
		with open(__lowerCamelCase   , "wb"	) as fp:
				fp.write(response.content	)




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   Union[str, Any]=None	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    []
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    []
		SCREAMING_SNAKE_CASE_  :		Dict				      =    None

		with zipfile.ZipFile(__lowerCamelCase	) as z:
				for filename in z.namelist():
						if not os.path.isdir(__lowerCamelCase	):
								# read the file
								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
										with z.open(__lowerCamelCase	) as f:
												for line in f:
														SCREAMING_SNAKE_CASE_  :		Dict				      =    line.decode("UTF-8"	).strip()
														if filename == "failures_line.txt":
																try:
																		# `error_line` is the place where `error` occurs
																		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    line[: line.index(": "	)]
																		SCREAMING_SNAKE_CASE_  :		int				      =    line[line.index(": "	) + len(": "	) :]
																		errors.append([error_line, error]	)
																except Exception:
																		# skip un-related lines
																		pass
														elif filename == "summary_short.txt" and line.startswith("FAILED "	):
																# `test` is the test method that failed
																SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    line[len("FAILED "	) :]
																failed_tests.append(__lowerCamelCase	)
														elif filename == "job_name.txt":
																SCREAMING_SNAKE_CASE_  :		List[str]				      =    line

		if len(__lowerCamelCase	) != len(__lowerCamelCase	):
				raise ValueError(
				    f'`errors` and `failed_tests` should have the same number of elements. Got {len(__lowerCamelCase	)} for `errors` '
				    f'and {len(__lowerCamelCase	)} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
				    " problem."	)

		SCREAMING_SNAKE_CASE_  :		Dict				      =    None
		if job_name and job_links:
				SCREAMING_SNAKE_CASE_  :		str				      =    job_links.get(__lowerCamelCase   , __lowerCamelCase	)

		# A list with elements of the form (line of error, error, failed test)
		SCREAMING_SNAKE_CASE_  :		int				      =    [x + [y] + [job_link] for x, y in zip(__lowerCamelCase   , __lowerCamelCase	)]

		return result




def       _snake_case	(   lowerCAmelCase							:   Any   , lowerCAmelCase							:   Tuple=None	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    []

		SCREAMING_SNAKE_CASE_  :		Dict				      =    [os.path.join(__lowerCamelCase   , __lowerCamelCase	) for p in os.listdir(__lowerCamelCase	) if p.endswith(".zip"	)]
		for p in paths:
				errors.extend(get_errors_from_single_artifact(__lowerCamelCase   , job_links=__lowerCamelCase	)	)

		return errors




def       _snake_case	(   lowerCAmelCase							:   List[Any]   , lowerCAmelCase							:   Optional[int]=None	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    Counter()
		counter.update([x[1] for x in logs]	)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    counter.most_common()
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    {}
		for error, count in counts:
				if error_filter is None or error not in error_filter:
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

		SCREAMING_SNAKE_CASE_  :		int				      =    dict(sorted(r.items()   , key=lambda lowerCAmelCase	: item[1]["count"]   , reverse=__lowerCamelCase	)	)
		return r




def       _snake_case	(   lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    test.split("::"	)[0]
		if test.startswith("tests/models/"	):
				SCREAMING_SNAKE_CASE_  :		Any				      =    test.split("/"	)[2]
		else:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    None

		return test




def       _snake_case	(   lowerCAmelCase							:   Any   , lowerCAmelCase							:   Dict=None	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [(x[0], x[1], get_model(x[2]	)) for x in logs]
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    [x for x in logs if x[2] is not None]
		SCREAMING_SNAKE_CASE_  :		int				      =    {x[2] for x in logs}

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {}
		for test in tests:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    Counter()
				# count by errors in `test`
				counter.update([x[1] for x in logs if x[2] == test]	)
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    counter.most_common()
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    sum(error_counts.values()	)
				if n_errors > 0:
						SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {"""count""": n_errors, """errors""": error_counts}

		SCREAMING_SNAKE_CASE_  :		Dict				      =    dict(sorted(r.items()   , key=lambda lowerCAmelCase	: item[1]["count"]   , reverse=__lowerCamelCase	)	)
		return r




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict				      =    """| no. | error | status |"""
		SCREAMING_SNAKE_CASE_  :		Dict				      =    """|-:|:-|:-|"""
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    [header, sep]
		for error in reduced_by_error:
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    reduced_by_error[error]["""count"""]
				SCREAMING_SNAKE_CASE_  :		Dict				      =    f'| {count} | {error[:1_0_0]} |  |'
				lines.append(__lowerCamelCase	)

		return "\n".join(__lowerCamelCase	)




def       _snake_case	(   lowerCAmelCase							:   List[Any]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    """| model | no. of errors | major error | count |"""
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    """|-:|-:|-:|-:|"""
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [header, sep]
		for model in reduced_by_model:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    reduced_by_model[model]["""count"""]
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    list(reduced_by_model[model]["errors"].items()	)[0]
				SCREAMING_SNAKE_CASE_  :		Any				      =    f'| {model} | {count} | {error[:6_0]} | {_count} |'
				lines.append(__lowerCamelCase	)

		return "\n".join(__lowerCamelCase	)


if __name__ == "__main__":
		__lowerCamelCase					:	List[str]       =  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.''')
		__lowerCamelCase					:	List[str]       =  parser.parse_args()

		os.makedirs(args.output_dir, exist_ok=True)

		__lowerCamelCase					:	Tuple       =  get_job_links(args.workflow_run_id, token=args.token)
		__lowerCamelCase					:	str       =  {}
		# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
		# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
		if _job_links:
				for k, v in _job_links.items():
						# This is how GitHub actions combine job names.
						if " / " in k:
								__lowerCamelCase					:	Union[str, Any]       =  k.find(''' / ''')
								__lowerCamelCase					:	str       =  k[index + len(''' / ''') :]
						__lowerCamelCase					:	List[str]       =  v
		with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
				json.dump(job_links, fp, ensure_ascii=False, indent=4)

		__lowerCamelCase					:	Any       =  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)

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

		__lowerCamelCase					:	str       =  get_all_errors(args.output_dir, job_links=job_links)

		# `e[1]` is the error
		__lowerCamelCase					:	List[Any]       =  Counter()
		counter.update([e[1] for e in errors])

		# print the top 30 most common test errors
		__lowerCamelCase					:	int       =  counter.most_common(30)
		for item in most_common:
				print(item)

		with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
				json.dump(errors, fp, ensure_ascii=False, indent=4)

		__lowerCamelCase					:	Dict       =  reduce_by_error(errors)
		__lowerCamelCase					:	Optional[int]       =  reduce_by_model(errors)

		__lowerCamelCase					:	Tuple       =  make_github_table(reduced_by_error)
		__lowerCamelCase					:	Tuple       =  make_github_table_per_model(reduced_by_model)

		with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
				fp.write(sa)
		with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
				fp.write(sa)








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()
a		:       Dict			     = logging.get_logger(__name__)






def 				lowerCamelCase__      (    __lowerCamelCase			:     str		):
  __UpperCAmelCase							:	Tuple           =  SwinConfig(
      embed_dim=192  ,		depths=(2, 2, 18, 2)  ,		num_heads=(6, 12, 24, 48)  ,		window_size=12  ,		out_features=["""stage2""", """stage3""", """stage4"""]  ,		)

  __UpperCAmelCase							:	Union[str, Any]           =  DetaConfig(
      backbone_config=__lowerCamelCase  ,		num_queries=900  ,		encoder_ffn_dim=2048  ,		decoder_ffn_dim=2048  ,		num_feature_levels=5  ,		assign_first_stage=__lowerCamelCase  ,		with_box_refine=__lowerCamelCase  ,		two_stage=__lowerCamelCase  ,		)

  # set labels
  __UpperCAmelCase							:	List[str]           =  """huggingface/label-files"""
  if "o365" in model_name:
    __UpperCAmelCase							:	Any           =  366
    __UpperCAmelCase							:	Union[str, Any]           =  """object365-id2label.json"""
  else:
    __UpperCAmelCase							:	Tuple           =  91
    __UpperCAmelCase							:	str           =  """coco-detection-id2label.json"""

  __UpperCAmelCase							:	Any           =  num_labels
  __UpperCAmelCase							:	Union[str, Any]           =  json.load(open(cached_download(hf_hub_url(__lowerCamelCase  ,		__lowerCamelCase  ,		repo_type="""dataset"""		)		)  ,		"""r"""		)		)
  __UpperCAmelCase							:	Optional[Any]           =  {int(__lowerCamelCase		): v for k, v in idalabel.items()}
  __UpperCAmelCase							:	List[str]           =  idalabel
  __UpperCAmelCase							:	List[str]           =  {v: k for k, v in idalabel.items()}

  return config






def 				lowerCamelCase__      (    __lowerCamelCase			:     Union[str, Any]		):
  __UpperCAmelCase							:	Dict           =  []

  # 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 				lowerCamelCase__      (    __lowerCamelCase			:     List[str]  ,		__lowerCamelCase			:     Optional[int]  ,		__lowerCamelCase			:     Union[str, Any]		):
  __UpperCAmelCase							:	Union[str, Any]           =  dct.pop(__lowerCamelCase		)
  __UpperCAmelCase							:	List[Any]           =  val






def 				lowerCamelCase__      (    __lowerCamelCase			:     Tuple  ,		__lowerCamelCase			:     Optional[Any]		):
  __UpperCAmelCase							:	List[Any]           =  [int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
  for i in range(len(backbone_config.depths		)		):
    __UpperCAmelCase							:	Any           =  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)
      __UpperCAmelCase							:	int           =  state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""		)
      __UpperCAmelCase							:	str           =  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
      __UpperCAmelCase							:	str           =  in_proj_weight[:dim, :]
      __UpperCAmelCase							:	Union[str, Any]           =  in_proj_bias[: dim]
      __UpperCAmelCase							:	List[str]           =  in_proj_weight[
          dim : dim * 2, :
      ]
      __UpperCAmelCase							:	Dict           =  in_proj_bias[
          dim : dim * 2
      ]
      __UpperCAmelCase							:	List[str]           =  in_proj_weight[
          -dim :, :
      ]
      __UpperCAmelCase							:	Optional[Any]           =  in_proj_bias[-dim :]
      # fmt: on






def 				lowerCamelCase__      (    __lowerCamelCase			:     int  ,		__lowerCamelCase			:     Any		):
  # transformer decoder self-attention layers
  __UpperCAmelCase							:	Union[str, Any]           =  config.d_model
  for i in range(config.decoder_layers		):
    # read in weights + bias of input projection layer of self-attention
    __UpperCAmelCase							:	List[Any]           =  state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""		)
    __UpperCAmelCase							:	Union[str, Any]           =  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
    __UpperCAmelCase							:	int           =  in_proj_weight[:hidden_size, :]
    __UpperCAmelCase							:	Any           =  in_proj_bias[:hidden_size]
    __UpperCAmelCase							:	Dict           =  in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    __UpperCAmelCase							:	Union[str, Any]           =  in_proj_bias[hidden_size : hidden_size * 2]
    __UpperCAmelCase							:	List[Any]           =  in_proj_weight[-hidden_size:, :]
    __UpperCAmelCase							:	int           =  in_proj_bias[-hidden_size:]






def 				lowerCamelCase__      (    ):
  __UpperCAmelCase							:	Tuple           =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  __UpperCAmelCase							:	Tuple           =  Image.open(requests.get(__lowerCamelCase  ,		stream=__lowerCamelCase		).raw		)

  return im






@torch.no_grad()
def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     Union[str, Any]  ,		__lowerCamelCase			:     Optional[int]		):
  __UpperCAmelCase							:	Any           =  get_deta_config(__lowerCamelCase		)

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

  __UpperCAmelCase							:	int           =  torch.load(__lowerCamelCase  ,		map_location="""cpu"""		)["""model"""]

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

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

  # fix some prefixes
  for key in state_dict.copy().keys():
    if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
      __UpperCAmelCase							:	str           =  state_dict.pop(__lowerCamelCase		)
      __UpperCAmelCase							:	Optional[Any]           =  val
    if "input_proj" in key:
      __UpperCAmelCase							:	Any           =  state_dict.pop(__lowerCamelCase		)
      __UpperCAmelCase							:	Union[str, Any]           =  val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      __UpperCAmelCase							:	Dict           =  state_dict.pop(__lowerCamelCase		)
      __UpperCAmelCase							:	Optional[int]           =  val

    # finally, create HuggingFace model and load state dict
  __UpperCAmelCase							:	Optional[Any]           =  DetaForObjectDetection(__lowerCamelCase		)
  model.load_state_dict(__lowerCamelCase		)
  model.eval()

  __UpperCAmelCase							:	Optional[int]           =  """cuda""" if torch.cuda.is_available() else """cpu"""
  model.to(__lowerCamelCase		)

  # load image processor
  __UpperCAmelCase							:	int           =  DetaImageProcessor(format="""coco_detection"""		)

  # verify our conversion on image
  __UpperCAmelCase							:	Dict           =  prepare_img()
  __UpperCAmelCase							:	Tuple           =  processor(images=__lowerCamelCase  ,		return_tensors="""pt"""		)
  __UpperCAmelCase							:	Optional[int]           =  encoding["""pixel_values"""]
  __UpperCAmelCase							:	List[str]           =  model(pixel_values.to(__lowerCamelCase		)		)

  # verify logits
  print("""Logits:"""  ,		outputs.logits[0, :3, :3]		)
  print("""Boxes:"""  ,		outputs.pred_boxes[0, :3, :3]		)
  if model_name == "deta-swin-large":
    __UpperCAmelCase							:	List[Any]           =  torch.tensor(
        [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]		)
    __UpperCAmelCase							:	int           =  torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]		)
  elif model_name == "deta-swin-large-o365":
    __UpperCAmelCase							:	Optional[int]           =  torch.tensor(
        [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]		)
    __UpperCAmelCase							:	List[Any]           =  torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]		)
  assert torch.allclose(outputs.logits[0, :3, :3]  ,		expected_logits.to(__lowerCamelCase		)  ,		atol=1E-4		)
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,		expected_boxes.to(__lowerCamelCase		)  ,		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(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
    model.save_pretrained(__lowerCamelCase		)
    processor.save_pretrained(__lowerCamelCase		)

  # 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__":
       a		:       List[str]			     = 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."
       )
       a		:       List[Any]			     = parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


