


import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=7       ):
  A_      :				Optional[int]		     =					None
  if token is not None:
    A_      :				Tuple		     =					{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}

  # The id of a workflow (not of a workflow run)
  A_      :				List[str]		     =					'''636036'''

  A_      :				int		     =					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}'''

  A_      :				Tuple		     =					requests.get(SCREAMING_SNAKE_CASE ,			headers=SCREAMING_SNAKE_CASE       ).json()

  return result["workflow_runs"]


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				int		     =					get_daily_ci_runs(SCREAMING_SNAKE_CASE       )
  A_      :				Optional[int]		     =					None
  for workflow_run in workflow_runs:
    if workflow_run["status"] == "completed":
      A_      :				Optional[int]		     =					workflow_run['''id''']
      break

  return workflow_run_id


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					get_last_daily_ci_runs(SCREAMING_SNAKE_CASE       )
  if workflow_run_id is not None:
    A_      :				List[str]		     =					get_artifacts_links(worflow_run_id=SCREAMING_SNAKE_CASE ,			token=SCREAMING_SNAKE_CASE       )
    for artifact_name in artifact_names:
      if artifact_name in artifacts_links:
        A_      :				Optional[int]		     =					artifacts_links[artifact_name]
        download_artifact(
            artifact_name=SCREAMING_SNAKE_CASE ,			artifact_url=SCREAMING_SNAKE_CASE ,			output_dir=SCREAMING_SNAKE_CASE ,			token=SCREAMING_SNAKE_CASE       )







def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  get_last_daily_ci_artifacts(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

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

  return results








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "t5"
       snake_case      =    ["past_key_values"]
       snake_case      =    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3_2128				,       _SCREAMING_SNAKE_CASE=512				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=2048				,       _SCREAMING_SNAKE_CASE=6				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=8				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=128				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=1.0				,       _SCREAMING_SNAKE_CASE="relu"				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=1				,       **_SCREAMING_SNAKE_CASE				,       )->List[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					vocab_size
         A_      :				int		     =					d_model
         A_      :				Optional[Any]		     =					d_kv
         A_      :				str		     =					d_ff
         A_      :				int		     =					num_layers
         A_      :				Any		     =					(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         A_      :				Optional[Any]		     =					num_heads
         A_      :				Union[str, Any]		     =					relative_attention_num_buckets
         A_      :				Dict		     =					relative_attention_max_distance
         A_      :				List[str]		     =					dropout_rate
         A_      :				Dict		     =					layer_norm_epsilon
         A_      :				str		     =					initializer_factor
         A_      :				Dict		     =					feed_forward_proj
         A_      :				int		     =					use_cache

         A_      :				Optional[int]		     =					self.feed_forward_proj.split('''-'''		)
         A_      :				Optional[Any]		     =					act_info[-1]
         A_      :				Optional[Any]		     =					act_info[0] == '''gated'''

         if len(_SCREAMING_SNAKE_CASE		) > 1 and act_info[0] != "gated" or len(_SCREAMING_SNAKE_CASE		) > 2:
           raise ValueError(
               F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
               '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
               '''\'gated-gelu\' or \'relu\''''		)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
           A_      :				Tuple		     =					'''gelu_new'''

         super().__init__(
             pad_token_id=_SCREAMING_SNAKE_CASE				,       eos_token_id=_SCREAMING_SNAKE_CASE				,       is_encoder_decoder=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       @property
       def        _snake_case       (      self		)->Mapping[str, Mapping[int, str]]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					{
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
           A_      :				List[str]		     =					'''past_encoder_sequence + sequence'''
           A_      :				Optional[int]		     =					{0: '''batch'''}
           A_      :				str		     =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
           A_      :				Optional[int]		     =					{0: '''batch''', 1: '''decoder_sequence'''}
           A_      :				List[Any]		     =					{0: '''batch''', 1: '''decoder_sequence'''}

         if self.use_past:
           self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE				,       direction='''inputs'''		)

         return common_inputs







       @property
       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         return 13




