






"""simple docstring"""



import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken




@torch.no_grad()
def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Any      )		->List[str]:
						# Load configuration defined in the metadata file
						with open(UpperCAmelCase__      ) as metadata_file:
												A__						:  Union[str, Any]		  =  json.load(UpperCAmelCase__      )
						A__						:  Tuple		  =  LukeConfig(use_entity_aware_attention=UpperCAmelCase__,						**metadata["""model_config"""]      )

						# Load in the weights from the checkpoint_path
						A__						:  Dict		  =  torch.load(UpperCAmelCase__,						map_location="""cpu"""      )["""module"""]

						# Load the entity vocab file
						A__						:  Dict		  =  load_original_entity_vocab(UpperCAmelCase__      )
						# add an entry for [MASK2]
						A__						:  Optional[Any]		  =  max(entity_vocab.values()      ) + 1
						config.entity_vocab_size += 1

						A__						:  Optional[Any]		  =  XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]      )

						# Add special tokens to the token vocabulary for downstream tasks
						A__						:  Optional[Any]		  =  AddedToken("""<ent>""",						lstrip=UpperCAmelCase__,						rstrip=UpperCAmelCase__      )
						A__						:  Tuple		  =  AddedToken("""<ent2>""",						lstrip=UpperCAmelCase__,						rstrip=UpperCAmelCase__      )
						tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}      )
						config.vocab_size += 2

						print(f'Saving tokenizer to {pytorch_dump_folder_path}'      )
						tokenizer.save_pretrained(UpperCAmelCase__      )
						with open(os.path.join(UpperCAmelCase__,						"""tokenizer_config.json"""      ),						"""r"""      ) as f:
												A__						:  Tuple		  =  json.load(UpperCAmelCase__      )
						A__						:  int		  =  """MLukeTokenizer"""
						with open(os.path.join(UpperCAmelCase__,						"""tokenizer_config.json"""      ),						"""w"""      ) as f:
												json.dump(UpperCAmelCase__,						UpperCAmelCase__      )

						with open(os.path.join(UpperCAmelCase__,						MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]      ),						"""w"""      ) as f:
												json.dump(UpperCAmelCase__,						UpperCAmelCase__      )

						A__						:  Dict		  =  MLukeTokenizer.from_pretrained(UpperCAmelCase__      )

						# Initialize the embeddings of the special tokens
						A__						:  Optional[int]		  =  tokenizer.convert_tokens_to_ids(["""@"""]      )[0]
						A__						:  List[Any]		  =  tokenizer.convert_tokens_to_ids(["""#"""]      )[0]

						A__						:  Dict		  =  state_dict["""embeddings.word_embeddings.weight"""]
						A__						:  Tuple		  =  word_emb[ent_init_index].unsqueeze(0      )
						A__						:  Tuple		  =  word_emb[enta_init_index].unsqueeze(0      )
						A__						:  Any		  =  torch.cat([word_emb, ent_emb, enta_emb]      )
						# add special tokens for 'entity_predictions.bias'
						for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
												A__						:  List[Any]		  =  state_dict[bias_name]
												A__						:  str		  =  decoder_bias[ent_init_index].unsqueeze(0      )
												A__						:  List[str]		  =  decoder_bias[enta_init_index].unsqueeze(0      )
												A__						:  Any		  =  torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]      )

						# Initialize the query layers of the entity-aware self-attention mechanism
						for layer_index in range(config.num_hidden_layers      ):
												for matrix_name in ["query.weight", "query.bias"]:
																		A__						:  Dict		  =  f'encoder.layer.{layer_index}.attention.self.'
																		A__						:  Tuple		  =  state_dict[prefix + matrix_name]
																		A__						:  int		  =  state_dict[prefix + matrix_name]
																		A__						:  List[str]		  =  state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
						A__						:  Any		  =  state_dict["""entity_embeddings.entity_embeddings.weight"""]
						A__						:  List[str]		  =  entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0      )
						A__						:  Dict		  =  torch.cat([entity_emb, entity_mask_emb]      )
						# add [MASK2] for 'entity_predictions.bias'
						A__						:  Dict		  =  state_dict["""entity_predictions.bias"""]
						A__						:  Optional[Any]		  =  entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0      )
						A__						:  List[Any]		  =  torch.cat([entity_prediction_bias, entity_mask_bias]      )

						A__						:  str		  =  LukeForMaskedLM(config=UpperCAmelCase__      ).eval()

						state_dict.pop("""entity_predictions.decoder.weight"""      )
						state_dict.pop("""lm_head.decoder.weight"""      )
						state_dict.pop("""lm_head.decoder.bias"""      )
						A__						:  Any		  =  OrderedDict()
						for key, value in state_dict.items():
												if not (key.startswith("""lm_head"""      ) or key.startswith("""entity_predictions"""      )):
																		A__						:  Optional[int]		  =  state_dict[key]
												else:
																		A__						:  Optional[int]		  =  state_dict[key]

						A__  ,  A__						:  List[str]		  =  model.load_state_dict(UpperCAmelCase__,						strict=UpperCAmelCase__      )

						if set(UpperCAmelCase__      ) != {"luke.embeddings.position_ids"}:
												raise ValueError(f'Unexpected unexpected_keys: {unexpected_keys}'      )
						if set(UpperCAmelCase__      ) != {
						    "lm_head.decoder.weight",
						    "lm_head.decoder.bias",
						    "entity_predictions.decoder.weight",
						}:
												raise ValueError(f'Unexpected missing_keys: {missing_keys}'      )

						model.tie_weights()
						assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
						assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

						# Check outputs
						A__						:  List[str]		  =  MLukeTokenizer.from_pretrained(UpperCAmelCase__,						task="""entity_classification"""      )

						A__						:  str		  =  """ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
						A__						:  List[str]		  =  (0, 9)
						A__						:  List[Any]		  =  tokenizer(UpperCAmelCase__,						entity_spans=[span],						return_tensors="""pt"""      )

						A__						:  Optional[Any]		  =  model(**UpperCAmelCase__      )

						# Verify word hidden states
						if model_size == "large":
												raise NotImplementedError
						else:  # base
												A__						:  int		  =  torch.Size((1, 3_3, 7_6_8)      )
												A__						:  List[str]		  =  torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]      )

						if not (outputs.last_hidden_state.shape == expected_shape):
												raise ValueError(
												    f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'      )
						if not torch.allclose(outputs.last_hidden_state[0, :3, :3],						UpperCAmelCase__,						atol=1e-4      ):
												raise ValueError

						# Verify entity hidden states
						if model_size == "large":
												raise NotImplementedError
						else:  # base
												A__						:  Tuple		  =  torch.Size((1, 1, 7_6_8)      )
												A__						:  int		  =  torch.tensor([[-0.1482, 0.0609, 0.0322]]      )

						if not (outputs.entity_last_hidden_state.shape == expected_shape):
												raise ValueError(
												    f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
												    f' {expected_shape}'      )
						if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3],						UpperCAmelCase__,						atol=1e-4      ):
												raise ValueError

						# Verify masked word/entity prediction
						A__						:  Union[str, Any]		  =  MLukeTokenizer.from_pretrained(UpperCAmelCase__      )
						A__						:  Union[str, Any]		  =  """Tokyo is the capital of <mask>."""
						A__						:  Tuple		  =  (2_4, 3_0)
						A__						:  Optional[Any]		  =  tokenizer(UpperCAmelCase__,						entity_spans=[span],						return_tensors="""pt"""      )

						A__						:  Any		  =  model(**UpperCAmelCase__      )

						A__						:  List[Any]		  =  encoding["""input_ids"""][0].tolist()
						A__						:  Tuple		  =  input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""      )      )
						A__						:  Dict		  =  outputs.logits[0][mask_position_id].argmax(dim=-1      )
						assert "Japan" == tokenizer.decode(UpperCAmelCase__      )

						A__						:  str		  =  outputs.entity_logits[0][0].argmax().item()
						A__						:  Tuple		  =  [
						    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
						]
						assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""      )][0] == "en:Japan"

						# Finally, save our PyTorch model and tokenizer
						print("""Saving PyTorch model to {}""".format(UpperCAmelCase__      )      )
						model.save_pretrained(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple      )		->List[Any]:
						A__						:  int		  =  ["""[MASK]""", """[PAD]""", """[UNK]"""]

						A__						:  List[str]		  =  [json.loads(UpperCAmelCase__      ) for line in open(UpperCAmelCase__      )]

						A__						:  List[Any]		  =  {}
						for entry in data:
												A__						:  Optional[Any]		  =  entry["""id"""]
												for entity_name, language in entry["entities"]:
																		if entity_name in SPECIAL_TOKENS:
																								A__						:  Union[str, Any]		  =  entity_id
																								break
																		A__						:  Optional[Any]		  =  f'{language}:{entity_name}'
																		A__						:  str		  =  entity_id
						return new_mapping


if __name__ == "__main__":
						A_    =							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
						parser.add_argument(
						    '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
						)
						parser.add_argument(
						    '''--entity_vocab_path''',
						    default=None,
						    type=str,
						    help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
						)
						A_    =							parser.parse_args()
						convert_luke_checkpoint(
						    args.checkpoint_path,
						    args.metadata_path,
						    args.entity_vocab_path,
						    args.pytorch_dump_folder_path,
						    args.model_size,
						)














"""simple docstring"""



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

import requests




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Union[str, Any]=None      )		->Tuple:
						A__						:  Dict		  =  None
						if token is not None:
												A__						:  str		  =  {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

						A__						:  Dict		  =  f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
						A__						:  Any		  =  requests.get(UpperCAmelCase__,						headers=UpperCAmelCase__      ).json()
						A__						:  Tuple		  =  {}

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

												for i in range(UpperCAmelCase__      ):
																		A__						:  str		  =  requests.get(url + f'&page={i + 2}',						headers=UpperCAmelCase__      ).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      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					str=None      )		->List[str]:
						A__						:  Optional[Any]		  =  None
						if token is not None:
												A__						:  Any		  =  {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

						A__						:  str		  =  f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
						A__						:  Dict		  =  requests.get(UpperCAmelCase__,						headers=UpperCAmelCase__      ).json()
						A__						:  Any		  =  {}

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

												for i in range(UpperCAmelCase__      ):
																		A__						:  Union[str, Any]		  =  requests.get(url + f'&page={i + 2}',						headers=UpperCAmelCase__      ).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      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					Any      )		->Tuple:
						A__						:  Tuple		  =  None
						if token is not None:
												A__						:  List[Any]		  =  {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

						A__						:  Tuple		  =  requests.get(UpperCAmelCase__,						headers=UpperCAmelCase__,						allow_redirects=UpperCAmelCase__      )
						A__						:  Dict		  =  result.headers["""Location"""]
						A__						:  Union[str, Any]		  =  requests.get(UpperCAmelCase__,						allow_redirects=UpperCAmelCase__      )
						A__						:  int		  =  os.path.join(UpperCAmelCase__,						f'{artifact_name}.zip'      )
						with open(UpperCAmelCase__,						"""wb"""      ) as fp:
												fp.write(response.content      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Tuple=None      )		->Tuple:
						A__						:  int		  =  []
						A__						:  Union[str, Any]		  =  []
						A__						:  Optional[Any]		  =  None

						with zipfile.ZipFile(UpperCAmelCase__      ) as z:
												for filename in z.namelist():
																		if not os.path.isdir(UpperCAmelCase__      ):
																								# read the file
																								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
																														with z.open(UpperCAmelCase__      ) as f:
																																				for line in f:
																																										A__						:  int		  =  line.decode("""UTF-8"""      ).strip()
																																										if filename == "failures_line.txt":
																																																try:
																																																						# `error_line` is the place where `error` occurs
																																																						A__						:  List[str]		  =  line[: line.index(""": """      )]
																																																						A__						:  str		  =  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
																																																A__						:  Any		  =  line[len("""FAILED """      ) :]
																																																failed_tests.append(UpperCAmelCase__      )
																																										elif filename == "job_name.txt":
																																																A__						:  Any		  =  line

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

						A__						:  List[str]		  =  None
						if job_name and job_links:
												A__						:  Any		  =  job_links.get(UpperCAmelCase__,						UpperCAmelCase__      )

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

						return result




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					int=None      )		->str:
						A__						:  List[Any]		  =  []

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

						return errors




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Dict=None      )		->List[Any]:
						A__						:  Dict		  =  Counter()
						counter.update([x[1] for x in logs]      )
						A__						:  str		  =  counter.most_common()
						A__						:  Dict		  =  {}
						for error, count in counts:
												if error_filter is None or error not in error_filter:
																		A__						:  Optional[int]		  =  {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

						A__						:  List[str]		  =  dict(sorted(r.items(),						key=lambda UpperCAmelCase__      : item[1]["count"],						reverse=UpperCAmelCase__      )      )
						return r




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any]      )		->str:
						A__						:  List[str]		  =  test.split("""::"""      )[0]
						if test.startswith("""tests/models/"""      ):
												A__						:  Union[str, Any]		  =  test.split("""/"""      )[2]
						else:
												A__						:  int		  =  None

						return test




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					str=None      )		->Optional[Any]:
						A__						:  Any		  =  [(x[0], x[1], get_model(x[2]      )) for x in logs]
						A__						:  List[Any]		  =  [x for x in logs if x[2] is not None]
						A__						:  Union[str, Any]		  =  {x[2] for x in logs}

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

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict      )		->List[Any]:
						A__						:  List[Any]		  =  """| no. | error | status |"""
						A__						:  Union[str, Any]		  =  """|-:|:-|:-|"""
						A__						:  Dict		  =  [header, sep]
						for error in reduced_by_error:
												A__						:  List[Any]		  =  reduced_by_error[error]["""count"""]
												A__						:  List[Any]		  =  f'| {count} | {error[:1_0_0]} |  |'
												lines.append(UpperCAmelCase__      )

						return "\n".join(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->int:
						A__						:  str		  =  """| model | no. of errors | major error | count |"""
						A__						:  Optional[int]		  =  """|-:|-:|-:|-:|"""
						A__						:  Tuple		  =  [header, sep]
						for model in reduced_by_model:
												A__						:  Optional[Any]		  =  reduced_by_model[model]["""count"""]
												A__  ,  A__						:  Optional[Any]		  =  list(reduced_by_model[model]["""errors"""].items()      )[0]
												A__						:  Optional[int]		  =  f'| {model} | {count} | {error[:6_0]} | {_count} |'
												lines.append(UpperCAmelCase__      )

						return "\n".join(UpperCAmelCase__      )


if __name__ == "__main__":
						A_    =							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.''')
						A_    =							parser.parse_args()

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

						A_    =							get_job_links(args.workflow_run_id, token=args.token)
						A_    =							{}
						# 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:
																								A_    =							k.find(''' / ''')
																								A_    =							k[index + len(''' / ''') :]
																		A_    =							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)

						A_    =							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)

						A_    =							get_all_errors(args.output_dir, job_links=job_links)

						# `e[1]` is the error
						A_    =							Counter()
						counter.update([e[1] for e in errors])

						# print the top 30 most common test errors
						A_    =							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)

						A_    =							reduce_by_error(errors)
						A_    =							reduce_by_model(errors)

						A_    =							make_github_table(reduced_by_error)
						A_    =							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)






