

import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links

def        lowerCamelCase__       (    _a							,	_a=7):
	SCREAMING_SNAKE_CASE      :					Dict    =				None
	if token is not None:
		SCREAMING_SNAKE_CASE      :					Any    =				{'Accept': 'application/vnd.github+json', 'Authorization': f"Bearer {token}"}

	# The id of a workflow (not of a workflow run)
	SCREAMING_SNAKE_CASE      :					int    =				'636036'

	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				f"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
	# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
	url += f"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"

	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				requests.get(UpperCAmelCase_							,	headers=UpperCAmelCase_).json()

	return result["workflow_runs"]

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					Tuple    =				get_daily_ci_runs(UpperCAmelCase_)
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				None
	for workflow_run in workflow_runs:
		if workflow_run["status"] == "completed":
			SCREAMING_SNAKE_CASE      :					List[Any]    =				workflow_run['id']
			break

	return workflow_run_id

def        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					List[str]    =				get_last_daily_ci_runs(UpperCAmelCase_)
	if workflow_run_id is not None:
		SCREAMING_SNAKE_CASE      :					Tuple    =				get_artifacts_links(worflow_run_id=UpperCAmelCase_							,	token=UpperCAmelCase_)
		for artifact_name in artifact_names:
			if artifact_name in artifacts_links:
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				artifacts_links[artifact_name]
				download_artifact(
				    artifact_name=UpperCAmelCase_							,	artifact_url=UpperCAmelCase_							,	output_dir=UpperCAmelCase_							,	token=UpperCAmelCase_)


def        lowerCamelCase__       (    _a							,	_a							,	_a):
	get_last_daily_ci_artifacts(UpperCAmelCase_							,	UpperCAmelCase_							,	UpperCAmelCase_)

	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				{}
	for artifact_name in artifact_names:
		SCREAMING_SNAKE_CASE      :					List[str]    =				os.path.join(UpperCAmelCase_							,	f"{artifact_name}.zip")
		if os.path.isfile(UpperCAmelCase_):
			SCREAMING_SNAKE_CASE      :					Optional[int]    =				{}
			with zipfile.ZipFile(UpperCAmelCase_) as z:
				for filename in z.namelist():
					if not os.path.isdir(UpperCAmelCase_):
						# read the file
						with z.open(UpperCAmelCase_) as f:
							SCREAMING_SNAKE_CASE      :					Optional[Any]    =				f.read().decode("UTF-8")

	return results



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__		:					List[Any]												=  {
    """configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					int												=  [
          """MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MegatronBertForCausalLM""",
          """MegatronBertForMaskedLM""",
          """MegatronBertForMultipleChoice""",
          """MegatronBertForNextSentencePrediction""",
          """MegatronBertForPreTraining""",
          """MegatronBertForQuestionAnswering""",
          """MegatronBertForSequenceClassification""",
          """MegatronBertForTokenClassification""",
          """MegatronBertModel""",
          """MegatronBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_megatron_bert import (
                MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MegatronBertForCausalLM,
                MegatronBertForMaskedLM,
                MegatronBertForMultipleChoice,
                MegatronBertForNextSentencePrediction,
                MegatronBertForPreTraining,
                MegatronBertForQuestionAnswering,
                MegatronBertForSequenceClassification,
                MegatronBertForTokenClassification,
                MegatronBertModel,
                MegatronBertPreTrainedModel,
            )

else:
      import sys

      A__		:					List[Any]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






