




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

import requests


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=None )->	Tuple:
		SCREAMING_SNAKE_CASE_					      =							None
		if token is not None:
				SCREAMING_SNAKE_CASE_					      =							{'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}

		SCREAMING_SNAKE_CASE_					      =							F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
		SCREAMING_SNAKE_CASE_					      =							requests.get(lowerCAmelCase    ,						headers=lowerCAmelCase ).json()
		SCREAMING_SNAKE_CASE_					      =							{}

		try:
				job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
				SCREAMING_SNAKE_CASE_					      =							math.ceil((result['total_count'] - 100) / 100 )

				for i in range(lowerCAmelCase ):
						SCREAMING_SNAKE_CASE_					      =							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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=None )->	Dict:
		SCREAMING_SNAKE_CASE_					      =							None
		if token is not None:
				SCREAMING_SNAKE_CASE_					      =							{'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}

		SCREAMING_SNAKE_CASE_					      =							F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''
		SCREAMING_SNAKE_CASE_					      =							requests.get(lowerCAmelCase    ,						headers=lowerCAmelCase ).json()
		SCREAMING_SNAKE_CASE_					      =							{}

		try:
				artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
				SCREAMING_SNAKE_CASE_					      =							math.ceil((result['total_count'] - 100) / 100 )

				for i in range(lowerCAmelCase ):
						SCREAMING_SNAKE_CASE_					      =							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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	Union[str, Any]:
		SCREAMING_SNAKE_CASE_					      =							None
		if token is not None:
				SCREAMING_SNAKE_CASE_					      =							{'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}

		SCREAMING_SNAKE_CASE_					      =							requests.get(lowerCAmelCase    ,						headers=lowerCAmelCase    ,						allow_redirects=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							result.headers['Location']
		SCREAMING_SNAKE_CASE_					      =							requests.get(lowerCAmelCase    ,						allow_redirects=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							os.path.join(lowerCAmelCase    ,						F'''{artifact_name}.zip''' )
		with open(lowerCAmelCase    ,						'wb' ) as fp:
				fp.write(response.content )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=None )->	List[Any]:
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							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_					      =							line.decode('UTF-8' ).strip()
														if filename == "failures_line.txt":
																try:
																		# `error_line` is the place where `error` occurs
																		SCREAMING_SNAKE_CASE_					      =							line[: line.index(': ' )]
																		SCREAMING_SNAKE_CASE_					      =							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_					      =							line[len('FAILED ' ) :]
																failed_tests.append(lowerCAmelCase )
														elif filename == "job_name.txt":
																SCREAMING_SNAKE_CASE_					      =							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_					      =							None
		if job_name and job_links:
				SCREAMING_SNAKE_CASE_					      =							job_links.get(lowerCAmelCase    ,						lowerCAmelCase )

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

		return result


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=None )->	List[Any]:
		SCREAMING_SNAKE_CASE_					      =							[]

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

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


def 					_a							(					lowerCAmelCase )->	int:
		SCREAMING_SNAKE_CASE_					      =							test.split('::' )[0]
		if test.startswith('tests/models/' ):
				SCREAMING_SNAKE_CASE_					      =							test.split('/' )[2]
		else:
				SCREAMING_SNAKE_CASE_					      =							None

		return test


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=None )->	List[Any]:
		SCREAMING_SNAKE_CASE_					      =							[(x[0], x[1], get_model(x[2] )) for x in logs]
		SCREAMING_SNAKE_CASE_					      =							[x for x in logs if x[2] is not None]
		SCREAMING_SNAKE_CASE_					      =							{x[2] for x in logs}

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

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


def 					_a							(					lowerCAmelCase )->	Union[str, Any]:
		SCREAMING_SNAKE_CASE_					      =							'| no. | error | status |'
		SCREAMING_SNAKE_CASE_					      =							'|-:|:-|:-|'
		SCREAMING_SNAKE_CASE_					      =							[header, sep]
		for error in reduced_by_error:
				SCREAMING_SNAKE_CASE_					      =							reduced_by_error[error]['count']
				SCREAMING_SNAKE_CASE_					      =							F'''| {count} | {error[:100]} |  |'''
				lines.append(lowerCAmelCase )

		return "\n".join(lowerCAmelCase )


def 					_a							(					lowerCAmelCase )->	List[Any]:
		SCREAMING_SNAKE_CASE_					      =							'| model | no. of errors | major error | count |'
		SCREAMING_SNAKE_CASE_					      =							'|-:|-:|-:|-:|'
		SCREAMING_SNAKE_CASE_					      =							[header, sep]
		for model in reduced_by_model:
				SCREAMING_SNAKE_CASE_					      =							reduced_by_model[model]['count']
				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							list(reduced_by_model[model]['errors'].items() )[0]
				SCREAMING_SNAKE_CASE_					      =							F'''| {model} | {count} | {error[:60]} | {_count} |'''
				lines.append(lowerCAmelCase )

		return "\n".join(lowerCAmelCase )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= 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.''')
						SCREAMING_SNAKE_CASE:     Optional[int]									= parser.parse_args()

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

						SCREAMING_SNAKE_CASE:     List[Any]									= get_job_links(args.workflow_run_id, token=args.token)
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= {}
						# 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:
																								SCREAMING_SNAKE_CASE:     Dict									= k.find(''' / ''')
																								SCREAMING_SNAKE_CASE:     Optional[Any]									= k[index + len(''' / ''') :]
																		SCREAMING_SNAKE_CASE:     Optional[int]									= 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)

						SCREAMING_SNAKE_CASE:     int									= 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)

						SCREAMING_SNAKE_CASE:     List[Any]									= get_all_errors(args.output_dir, job_links=job_links)

						# `e[1]` is the error
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= Counter()
						counter.update([e[1] for e in errors])

						# print the top 30 most common test errors
						SCREAMING_SNAKE_CASE:     str									= counter.most_common(3_0)
						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)

						SCREAMING_SNAKE_CASE:     int									= reduce_by_error(errors)
						SCREAMING_SNAKE_CASE:     int									= reduce_by_model(errors)

						SCREAMING_SNAKE_CASE:     str									= make_github_table(reduced_by_error)
						SCREAMING_SNAKE_CASE:     int									= 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 os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


SCREAMING_SNAKE_CASE:     int									= ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
SCREAMING_SNAKE_CASE:     Tuple									= {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
						raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE:     int									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Any									= ''' Hello world! cécé herlolip'''

SCREAMING_SNAKE_CASE:     Dict									= [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]


def 					_a							(					lowerCAmelCase )->	Any:
		SCREAMING_SNAKE_CASE_					      =							[
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    '_float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase    ,						lowerCAmelCase )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	str:
		SCREAMING_SNAKE_CASE_					      =							dct.pop(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							val


def 					_a							(					lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase    ,						map_location='cpu' )
		SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						'bart.large.cnn' ).eval()
		hub_interface.model.load_state_dict(sd['model'] )
		return hub_interface


def 					_a							(					lowerCAmelCase )->	Optional[Any]:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							emb.weight.shape
		SCREAMING_SNAKE_CASE_					      =							nn.Linear(lowerCAmelCase    ,						lowerCAmelCase    ,						bias=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							emb.weight.data
		return lin_layer


@torch.no_grad()
def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None )->	Tuple:
		if not os.path.exists(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						lowerCAmelCase ).eval()
		else:
				SCREAMING_SNAKE_CASE_					      =							load_xsum_checkpoint(lowerCAmelCase )

		bart.model.upgrade_state_dict(bart.model.state_dict() )
		if hf_checkpoint_name is None:
				SCREAMING_SNAKE_CASE_					      =							checkpoint_path.replace('.'    ,						'-' )
		SCREAMING_SNAKE_CASE_					      =							BartConfig.from_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							bart.encode(lowerCAmelCase ).unsqueeze(0 )
		SCREAMING_SNAKE_CASE_					      =							BartTokenizer.from_pretrained(lowerCAmelCase ).encode(lowerCAmelCase    ,						return_tensors='pt' ).unsqueeze(0 )
		if not torch.eq(lowerCAmelCase    ,						lowerCAmelCase ).all():
				raise ValueError(
				    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}''' )

		if checkpoint_path == "bart.large.mnli":
				SCREAMING_SNAKE_CASE_					      =							bart.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['model.decoder.embed_tokens.weight']
				for src, dest in mnli_rename_keys:
						rename_key(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							BartForSequenceClassification(lowerCAmelCase ).eval()
				model.load_state_dict(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							bart.predict('mnli'    ,						lowerCAmelCase    ,						return_logits=lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase )[0]  # logits
		else:  # no classification heads to worry about
				SCREAMING_SNAKE_CASE_					      =							bart.model.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['decoder.embed_tokens.weight']
				SCREAMING_SNAKE_CASE_					      =							bart.extract_features(lowerCAmelCase )
				if hf_checkpoint_name == "facebook/bart-large":
						SCREAMING_SNAKE_CASE_					      =							BartModel(lowerCAmelCase ).eval()
						model.load_state_dict(lowerCAmelCase )
						SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase ).model[0]
				else:
						SCREAMING_SNAKE_CASE_					      =							BartForConditionalGeneration(lowerCAmelCase ).eval()  # an existing summarization ckpt
						model.model.load_state_dict(lowerCAmelCase )
						if hasattr(lowerCAmelCase    ,						'lm_head' ):
								SCREAMING_SNAKE_CASE_					      =							make_linear_from_emb(model.model.shared )
						SCREAMING_SNAKE_CASE_					      =							model.model(lowerCAmelCase )[0]

    # Check results
		if fairseq_output.shape != new_model_outputs.shape:
				raise ValueError(
				    F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}''' )
		if (fairseq_output != new_model_outputs).any().item():
				raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`' )
		Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
		model.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Dict									= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
						)
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument(
						    '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
						)
						SCREAMING_SNAKE_CASE:     List[str]									= parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)