"""simple docstring"""


import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


__UpperCamelCase									=			logging.get_logger(__name__)




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "linear"
       SCREAMING_SNAKE_CASE_			=      "cosine"
       SCREAMING_SNAKE_CASE_			=      "cosine_with_restarts"
       SCREAMING_SNAKE_CASE_			=      "polynomial"
       SCREAMING_SNAKE_CASE_			=      "constant"
       SCREAMING_SNAKE_CASE_			=      "constant_with_warmup"
       SCREAMING_SNAKE_CASE_			=      "piecewise_constant"







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase = -1					)				->      Optional[Any]:
     return LambdaLR(UpperCAmelCase						,					lambda UpperCAmelCase					: 1						,					last_epoch=UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase = -1					)				->      Optional[Any]:

     def lr_lambda(UpperCAmelCase					):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase					) / float(max(1.0						,					UpperCAmelCase					)					)
          return 1.0

     return LambdaLR(UpperCAmelCase						,					UpperCAmelCase						,					last_epoch=UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase = -1					)				->      List[Any]:
     snake_case_					       =					{}
     snake_case_					       =					step_rules.split(','					)
     for rule_str in rule_list[:-1]:
          snake_case_       ,	snake_case_					       =					rule_str.split(':'					)
          snake_case_					       =					int(UpperCAmelCase					)
          snake_case_					       =					float(UpperCAmelCase					)
          snake_case_					       =					value
     snake_case_					       =					float(rule_list[-1]					)

     def create_rules_function(UpperCAmelCase						,					UpperCAmelCase					):
          def rule_func(UpperCAmelCase					) -> float:
               snake_case_					       =					sorted(rules_dict.keys()					)
               for i, sorted_step in enumerate(UpperCAmelCase					):
                    if steps < sorted_step:
                         return rules_dict[sorted_steps[i]]
               return last_lr_multiple

          return rule_func

     snake_case_					       =					create_rules_function(UpperCAmelCase						,					UpperCAmelCase					)

     return LambdaLR(UpperCAmelCase						,					UpperCAmelCase						,					last_epoch=UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=-1					)				->      Optional[int]:

     def lr_lambda(UpperCAmelCase					):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase					) / float(max(1						,					UpperCAmelCase					)					)
          return max(
              0.0						,					float(num_training_steps - current_step					) / float(max(1						,					num_training_steps - num_warmup_steps					)					)					)

     return LambdaLR(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase = 0.5						,					UpperCAmelCase = -1					)				->      Optional[Any]:

     def lr_lambda(UpperCAmelCase					):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase					) / float(max(1						,					UpperCAmelCase					)					)
          snake_case_					       =					float(current_step - num_warmup_steps					) / float(max(1						,					num_training_steps - num_warmup_steps					)					)
          return max(0.0						,					0.5 * (1.0 + math.cos(math.pi * float(UpperCAmelCase					) * 2.0 * progress					))					)

     return LambdaLR(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase = 1						,					UpperCAmelCase = -1					)				->      Optional[int]:

     def lr_lambda(UpperCAmelCase					):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase					) / float(max(1						,					UpperCAmelCase					)					)
          snake_case_					       =					float(current_step - num_warmup_steps					) / float(max(1						,					num_training_steps - num_warmup_steps					)					)
          if progress >= 1.0:
               return 0.0
          return max(0.0						,					0.5 * (1.0 + math.cos(math.pi * ((float(UpperCAmelCase					) * progress) % 1.0)					))					)

     return LambdaLR(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=1e-7						,					UpperCAmelCase=1.0						,					UpperCAmelCase=-1					)				->      List[str]:
     snake_case_					       =					optimizer.defaults['lr']
     if not (lr_init > lr_end):
          raise ValueError(f'lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'					)

     def lr_lambda(UpperCAmelCase					):
          if current_step < num_warmup_steps:
               return float(UpperCAmelCase					) / float(max(1						,					UpperCAmelCase					)					)
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               snake_case_					       =					lr_init - lr_end
               snake_case_					       =					num_training_steps - num_warmup_steps
               snake_case_					       =					1 - (current_step - num_warmup_steps) / decay_steps
               snake_case_					       =					lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)


__UpperCamelCase									=			{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase = None						,					UpperCAmelCase = None						,					UpperCAmelCase = None						,					UpperCAmelCase = 1						,					UpperCAmelCase = 1.0						,					UpperCAmelCase = -1						,					)				->      Union[str, Any]:
     snake_case_					       =					SchedulerType(UpperCAmelCase					)
     snake_case_					       =					TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(UpperCAmelCase						,					last_epoch=UpperCAmelCase					)

     if name == SchedulerType.PIECEWISE_CONSTANT:
          return schedule_func(UpperCAmelCase						,					step_rules=UpperCAmelCase						,					last_epoch=UpperCAmelCase					)

     # All other schedulers require `num_warmup_steps`
     if num_warmup_steps is None:
          raise ValueError(f'{name} requires `num_warmup_steps`, please provide that argument.'					)

     if name == SchedulerType.CONSTANT_WITH_WARMUP:
          return schedule_func(UpperCAmelCase						,					num_warmup_steps=UpperCAmelCase						,					last_epoch=UpperCAmelCase					)

     # All other schedulers require `num_training_steps`
     if num_training_steps is None:
          raise ValueError(f'{name} requires `num_training_steps`, please provide that argument.'					)

     if name == SchedulerType.COSINE_WITH_RESTARTS:
          return schedule_func(
              UpperCAmelCase						,					num_warmup_steps=UpperCAmelCase						,					num_training_steps=UpperCAmelCase						,					num_cycles=UpperCAmelCase						,					last_epoch=UpperCAmelCase						,					)

     if name == SchedulerType.POLYNOMIAL:
          return schedule_func(
              UpperCAmelCase						,					num_warmup_steps=UpperCAmelCase						,					num_training_steps=UpperCAmelCase						,					power=UpperCAmelCase						,					last_epoch=UpperCAmelCase						,					)

     return schedule_func(
         UpperCAmelCase						,					num_warmup_steps=UpperCAmelCase						,					num_training_steps=UpperCAmelCase						,					last_epoch=UpperCAmelCase					)







"""simple docstring"""


import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					Any=7    ):
  """simple docstring"""

  _snake_case							:		Any								=  None
  if token is not None:
    _snake_case							:		Any								=  {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

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

  _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}"

  _snake_case							:		str								=  requests.get(snake_case__		,   headers=snake_case__    ).json()

  return result["workflow_runs"]


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  _snake_case							:		str								=  get_daily_ci_runs(snake_case__    )
  _snake_case							:		str								=  None
  for workflow_run in workflow_runs:
    if workflow_run["status"] == "completed":
      _snake_case							:		List[str]								=  workflow_run["""id"""]
      break

  return workflow_run_id


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  get_last_daily_ci_runs(snake_case__    )
  if workflow_run_id is not None:
    _snake_case							:		Optional[Any]								=  get_artifacts_links(worflow_run_id=snake_case__		,   token=snake_case__    )
    for artifact_name in artifact_names:
      if artifact_name in artifacts_links:
        _snake_case							:		Optional[int]								=  artifacts_links[artifact_name]
        download_artifact(
            artifact_name=snake_case__		,   artifact_url=snake_case__		,   output_dir=snake_case__		,   token=snake_case__    )






def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]		,   snake_case__							:					List[str]		,   snake_case__							:					int    ):
  """simple docstring"""

  get_last_daily_ci_artifacts(snake_case__		,   snake_case__		,   snake_case__    )

  _snake_case							:		int								=  {}
  for artifact_name in artifact_names:
    _snake_case							:		int								=  os.path.join(snake_case__		,   F"{artifact_name}.zip"    )
    if os.path.isfile(snake_case__    ):
      _snake_case							:		Tuple								=  {}
      with zipfile.ZipFile(snake_case__    ) as z:
        for filename in z.namelist():
          if not os.path.isdir(snake_case__    ):
            # read the file
            with z.open(snake_case__    ) as f:
              _snake_case							:		Any								=  f.read().decode("""UTF-8"""    )

  return results


