



"""simple docstring"""

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

import requests







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[int]				,    SCREAMING_SNAKE_CASE__ :  int=None			):



					"""simple docstring"""





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

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

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

										for i in range(SCREAMING_SNAKE_CASE__			):
															snake_case_					:		Dict 					=					requests.get(url + f'&page={i + 2}'				,    headers=SCREAMING_SNAKE_CASE__			).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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  int=None			):



					"""simple docstring"""





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

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

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

										for i in range(SCREAMING_SNAKE_CASE__			):
															snake_case_					:		int 					=					requests.get(url + f'&page={i + 2}'				,    headers=SCREAMING_SNAKE_CASE__			).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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[int]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]			):



					"""simple docstring"""





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

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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[Any]=None			):



					"""simple docstring"""





					snake_case_					:		str 					=					[]
					snake_case_					:		Optional[int] 					=					[]
					snake_case_					:		Union[str, Any] 					=					None

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

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

					snake_case_					:		Optional[int] 					=					None
					if job_name and job_links:
										snake_case_					:		Any 					=					job_links.get(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)

					# A list with elements of the form (line of error, error, failed test)
					snake_case_					:		Optional[Any] 					=					[x + [y] + [job_link] for x, y in zip(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)]

					return result







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  List[Any]=None			):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					[]

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

					return errors







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]=None			):



					"""simple docstring"""





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

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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





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

					return test







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  Dict=None			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					[(x[0], x[1], get_model(x[2]			)) for x in logs]
					snake_case_					:		Any 					=					[x for x in logs if x[2] is not None]
					snake_case_					:		List[str] 					=					{x[2] for x in logs}

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

					snake_case_					:		Union[str, Any] 					=					dict(sorted(r.items()				,    key=lambda SCREAMING_SNAKE_CASE__			: item[1]["count"]				,    reverse=SCREAMING_SNAKE_CASE__			)			)
					return r







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					"""| no. | error | status |"""
					snake_case_					:		int 					=					"""|-:|:-|:-|"""
					snake_case_					:		str 					=					[header, sep]
					for error in reduced_by_error:
										snake_case_					:		List[str] 					=					reduced_by_error[error]["""count"""]
										snake_case_					:		List[str] 					=					f'| {count} | {error[:1_0_0]} |  |'
										lines.append(SCREAMING_SNAKE_CASE__			)

					return "\n".join(SCREAMING_SNAKE_CASE__			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]			):



					"""simple docstring"""





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

					return "\n".join(SCREAMING_SNAKE_CASE__			)


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)









"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


a_			   =					r'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `" / "`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `" // "`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `"train"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `"compressed"`)
            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
            `"compressed"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a "dummy" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
'''







@add_start_docstrings(_UpperCAmelCase)
class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		"""rag"""
						_A		:							Optional[Any]							       =		True







						def __init__(self   ,		lowercase__=None   ,		lowercase__=True   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=" / "   ,		lowercase__=" // "   ,		lowercase__=5   ,		lowercase__=3_00   ,		lowercase__=7_68   ,		lowercase__=8   ,		lowercase__="wiki_dpr"   ,		lowercase__="train"   ,		lowercase__="compressed"   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=False   ,		lowercase__=False   ,		lowercase__=0.0   ,		lowercase__=True   ,		lowercase__=False   ,		lowercase__=False   ,		lowercase__=False   ,		lowercase__=True   ,		lowercase__=None   ,		**lowercase__   ,		):
											super().__init__(
											    bos_token_id=lowercase__   ,		pad_token_id=lowercase__   ,		eos_token_id=lowercase__   ,		decoder_start_token_id=lowercase__   ,		forced_eos_token_id=lowercase__   ,		is_encoder_decoder=lowercase__   ,		prefix=lowercase__   ,		vocab_size=lowercase__   ,		**lowercase__   ,		)
											assert (
											    "question_encoder" in kwargs and "generator" in kwargs
											), "Config has to be initialized with question_encoder and generator config"
											snake_case_					:		List[Any] 					=					kwargs.pop("""question_encoder"""       )
											snake_case_					:		Tuple 					=					question_encoder_config.pop("""model_type"""       )
											snake_case_					:		List[str] 					=					kwargs.pop("""generator"""       )
											snake_case_					:		List[str] 					=					decoder_config.pop("""model_type"""       )

											from ..auto.configuration_auto import AutoConfig

											snake_case_					:		List[str] 					=					AutoConfig.for_model(lowercase__   ,		**lowercase__       )
											snake_case_					:		Tuple 					=					AutoConfig.for_model(lowercase__   ,		**lowercase__       )

											snake_case_					:		int 					=					reduce_loss
											snake_case_					:		Optional[int] 					=					label_smoothing
											snake_case_					:		Dict 					=					exclude_bos_score
											snake_case_					:		Union[str, Any] 					=					do_marginalize

											snake_case_					:		Union[str, Any] 					=					title_sep
											snake_case_					:		int 					=					doc_sep
											snake_case_					:		int 					=					n_docs
											snake_case_					:		List[str] 					=					max_combined_length

											snake_case_					:		Tuple 					=					dataset
											snake_case_					:		int 					=					dataset_split
											snake_case_					:		str 					=					index_name

											snake_case_					:		List[str] 					=					retrieval_vector_size
											snake_case_					:		Dict 					=					retrieval_batch_size
											snake_case_					:		str 					=					passages_path
											snake_case_					:		Union[str, Any] 					=					index_path
											snake_case_					:		Tuple 					=					use_dummy_dataset

											snake_case_					:		Dict 					=					output_retrieved

											snake_case_					:		str 					=					do_deduplication

											snake_case_					:		Any 					=					use_cache

											if self.forced_eos_token_id is None:
																snake_case_					:		Any 					=					getattr(self.generator   ,		"""forced_eos_token_id"""   ,		lowercase__       )







						@classmethod
						def 						__UpperCamelCase							(cls   ,		lowercase__   ,		lowercase__   ,		**lowercase__       ):
											return cls(question_encoder=question_encoder_config.to_dict()   ,		generator=generator_config.to_dict()   ,		**lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					copy.deepcopy(self.__dict__       )
											snake_case_					:		Any 					=					self.question_encoder.to_dict()
											snake_case_					:		Dict 					=					self.generator.to_dict()
											snake_case_					:		Union[str, Any] 					=					self.__class__.model_type
											return output




