

import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests






def 					__lowercase      (     _A			)     ->						Tuple:
      SCREAMING_SNAKE_CASE :      Dict       =   {}

      SCREAMING_SNAKE_CASE :      Optional[int]       =   job["""started_at"""]
      SCREAMING_SNAKE_CASE :      Tuple       =   job["""completed_at"""]

      SCREAMING_SNAKE_CASE :      Any       =   date_parser.parse(lowercase_			)
      SCREAMING_SNAKE_CASE :      Tuple       =   date_parser.parse(lowercase_			)

      SCREAMING_SNAKE_CASE :      Dict       =   round((end_datetime - start_datetime).total_seconds() / 60.0			)

      SCREAMING_SNAKE_CASE :      int       =   start
      SCREAMING_SNAKE_CASE :      Tuple       =   end
      SCREAMING_SNAKE_CASE :      int       =   duration_in_min

      return job_info






def 					__lowercase      (     _A						,    _A=None			)     ->						Tuple:
      SCREAMING_SNAKE_CASE :      List[Any]       =   None
      if token is not None:
            SCREAMING_SNAKE_CASE :      str       =   {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

      SCREAMING_SNAKE_CASE :      Any       =   F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
      SCREAMING_SNAKE_CASE :      List[str]       =   requests.get(lowercase_						,    headers=lowercase_			).json()
      SCREAMING_SNAKE_CASE :      str       =   {}

      try:
            job_time.update({job["""name"""]: extract_time_from_single_job(lowercase_			) for job in result["""jobs"""]}			)
            SCREAMING_SNAKE_CASE :      Tuple       =   math.ceil((result["""total_count"""] - 100) / 100			)

            for i in range(lowercase_			):
                  SCREAMING_SNAKE_CASE :      Optional[int]       =   requests.get(url + F"&page={i + 2}"						,    headers=lowercase_			).json()
                  job_time.update({job["""name"""]: extract_time_from_single_job(lowercase_			) for job in result["""jobs"""]}			)

            return job_time
      except Exception:
            print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}"			)

      return {}


if __name__ == "__main__":
     UpperCAmelCase__ :     Tuple				    =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
     UpperCAmelCase__ :     List[str]				    =    parser.parse_args()

     UpperCAmelCase__ :     str				    =    get_job_time(args.workflow_run_id)
     UpperCAmelCase__ :     List[Any]				    =    dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

     for k, v in job_time.items():
          print(F"""{k}: {v['duration']}""")







import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD







class        a__       (   nn.Module       ):







  """simple docstring"""




  UpperCAmelCase__      : int
  UpperCAmelCase__      : int
  UpperCAmelCase__      : float			       =0.0
  UpperCAmelCase__      : int			       =1
  UpperCAmelCase__      : int			       =1
  UpperCAmelCase__      : bool			       =True
  UpperCAmelCase__      : bool			       =False
  UpperCAmelCase__      : bool			       =False
  UpperCAmelCase__      : bool			       =False
  UpperCAmelCase__      : jnp.dtype			       =jnp.floataa


  def  _lowercase      (			self    : Tuple		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   []
        SCREAMING_SNAKE_CASE :      Any       =   []

        for i in range(self.num_layers		):
              SCREAMING_SNAKE_CASE :      List[Any]       =   self.in_channels if i == 0 else self.out_channels

              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   FlaxResnetBlockaD(
                  in_channels=UpperCAmelCase__				,				out_channels=self.out_channels				,				dropout_prob=self.dropout				,				dtype=self.dtype				,				)
              resnets.append(UpperCAmelCase__		)

              SCREAMING_SNAKE_CASE :      List[Any]       =   FlaxTransformeraDModel(
                  in_channels=self.out_channels				,				n_heads=self.num_attention_heads				,				d_head=self.out_channels // self.num_attention_heads				,				depth=1				,				use_linear_projection=self.use_linear_projection				,				only_cross_attention=self.only_cross_attention				,				use_memory_efficient_attention=self.use_memory_efficient_attention				,				dtype=self.dtype				,				)
              attentions.append(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   resnets
        SCREAMING_SNAKE_CASE :      Optional[int]       =   attentions

        if self.add_downsample:
              SCREAMING_SNAKE_CASE :      Tuple       =   FlaxDownsampleaD(self.out_channels				,				dtype=self.dtype		)




  def __call__(			self    : int				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : List[str]=True		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   ()

        for resnet, attn in zip(self.resnets				,				self.attentions		):
              SCREAMING_SNAKE_CASE :      List[str]       =   resnet(UpperCAmelCase__				,				UpperCAmelCase__				,				deterministic=UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      List[str]       =   attn(UpperCAmelCase__				,				UpperCAmelCase__				,				deterministic=UpperCAmelCase__		)
              output_states += (hidden_states,)

        if self.add_downsample:
              SCREAMING_SNAKE_CASE :      int       =   self.downsamplers_a(UpperCAmelCase__		)
              output_states += (hidden_states,)

        return hidden_states, output_states







class        a__       (   nn.Module       ):







  """simple docstring"""




  UpperCAmelCase__      : int
  UpperCAmelCase__      : int
  UpperCAmelCase__      : float			       =0.0
  UpperCAmelCase__      : int			       =1
  UpperCAmelCase__      : bool			       =True
  UpperCAmelCase__      : jnp.dtype			       =jnp.floataa


  def  _lowercase      (			self    : Dict		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   []

        for i in range(self.num_layers		):
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.in_channels if i == 0 else self.out_channels

              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   FlaxResnetBlockaD(
                  in_channels=UpperCAmelCase__				,				out_channels=self.out_channels				,				dropout_prob=self.dropout				,				dtype=self.dtype				,				)
              resnets.append(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   resnets

        if self.add_downsample:
              SCREAMING_SNAKE_CASE :      List[str]       =   FlaxDownsampleaD(self.out_channels				,				dtype=self.dtype		)




  def __call__(			self    : Tuple				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Any=True		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   ()

        for resnet in self.resnets:
              SCREAMING_SNAKE_CASE :      List[str]       =   resnet(UpperCAmelCase__				,				UpperCAmelCase__				,				deterministic=UpperCAmelCase__		)
              output_states += (hidden_states,)

        if self.add_downsample:
              SCREAMING_SNAKE_CASE :      Dict       =   self.downsamplers_a(UpperCAmelCase__		)
              output_states += (hidden_states,)

        return hidden_states, output_states







class        a__       (   nn.Module       ):







  """simple docstring"""




  UpperCAmelCase__      : int
  UpperCAmelCase__      : int
  UpperCAmelCase__      : int
  UpperCAmelCase__      : float			       =0.0
  UpperCAmelCase__      : int			       =1
  UpperCAmelCase__      : int			       =1
  UpperCAmelCase__      : bool			       =True
  UpperCAmelCase__      : bool			       =False
  UpperCAmelCase__      : bool			       =False
  UpperCAmelCase__      : bool			       =False
  UpperCAmelCase__      : jnp.dtype			       =jnp.floataa


  def  _lowercase      (			self    : int		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   []
        SCREAMING_SNAKE_CASE :      List[Any]       =   []

        for i in range(self.num_layers		):
              SCREAMING_SNAKE_CASE :      Any       =   self.in_channels if (i == self.num_layers - 1) else self.out_channels
              SCREAMING_SNAKE_CASE :      Any       =   self.prev_output_channel if i == 0 else self.out_channels

              SCREAMING_SNAKE_CASE :      Tuple       =   FlaxResnetBlockaD(
                  in_channels=resnet_in_channels + res_skip_channels				,				out_channels=self.out_channels				,				dropout_prob=self.dropout				,				dtype=self.dtype				,				)
              resnets.append(UpperCAmelCase__		)

              SCREAMING_SNAKE_CASE :      Tuple       =   FlaxTransformeraDModel(
                  in_channels=self.out_channels				,				n_heads=self.num_attention_heads				,				d_head=self.out_channels // self.num_attention_heads				,				depth=1				,				use_linear_projection=self.use_linear_projection				,				only_cross_attention=self.only_cross_attention				,				use_memory_efficient_attention=self.use_memory_efficient_attention				,				dtype=self.dtype				,				)
              attentions.append(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   resnets
        SCREAMING_SNAKE_CASE :      List[Any]       =   attentions

        if self.add_upsample:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   FlaxUpsampleaD(self.out_channels				,				dtype=self.dtype		)




  def __call__(			self    : Union[str, Any]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Dict=True		)					->Tuple:
        """simple docstring"""







        for resnet, attn in zip(self.resnets				,				self.attentions		):
              # pop res hidden states
              SCREAMING_SNAKE_CASE :      str       =   res_hidden_states_tuple[-1]
              SCREAMING_SNAKE_CASE :      Dict       =   res_hidden_states_tuple[:-1]
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   jnp.concatenate((hidden_states, res_hidden_states)				,				axis=-1		)

              SCREAMING_SNAKE_CASE :      Tuple       =   resnet(UpperCAmelCase__				,				UpperCAmelCase__				,				deterministic=UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      Tuple       =   attn(UpperCAmelCase__				,				UpperCAmelCase__				,				deterministic=UpperCAmelCase__		)

        if self.add_upsample:
              SCREAMING_SNAKE_CASE :      Tuple       =   self.upsamplers_a(UpperCAmelCase__		)

        return hidden_states







class        a__       (   nn.Module       ):







  """simple docstring"""




  UpperCAmelCase__      : int
  UpperCAmelCase__      : int
  UpperCAmelCase__      : int
  UpperCAmelCase__      : float			       =0.0
  UpperCAmelCase__      : int			       =1
  UpperCAmelCase__      : bool			       =True
  UpperCAmelCase__      : jnp.dtype			       =jnp.floataa


  def  _lowercase      (			self    : Union[str, Any]		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   []

        for i in range(self.num_layers		):
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.in_channels if (i == self.num_layers - 1) else self.out_channels
              SCREAMING_SNAKE_CASE :      str       =   self.prev_output_channel if i == 0 else self.out_channels

              SCREAMING_SNAKE_CASE :      Optional[int]       =   FlaxResnetBlockaD(
                  in_channels=resnet_in_channels + res_skip_channels				,				out_channels=self.out_channels				,				dropout_prob=self.dropout				,				dtype=self.dtype				,				)
              resnets.append(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Dict       =   resnets

        if self.add_upsample:
              SCREAMING_SNAKE_CASE :      int       =   FlaxUpsampleaD(self.out_channels				,				dtype=self.dtype		)




  def __call__(			self    : Optional[Any]				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : Tuple				,				UpperCAmelCase__    : Optional[int]=True		)					->Union[str, Any]:
        """simple docstring"""







        for resnet in self.resnets:
              # pop res hidden states
              SCREAMING_SNAKE_CASE :      Dict       =   res_hidden_states_tuple[-1]
              SCREAMING_SNAKE_CASE :      Any       =   res_hidden_states_tuple[:-1]
              SCREAMING_SNAKE_CASE :      Tuple       =   jnp.concatenate((hidden_states, res_hidden_states)				,				axis=-1		)

              SCREAMING_SNAKE_CASE :      Optional[int]       =   resnet(UpperCAmelCase__				,				UpperCAmelCase__				,				deterministic=UpperCAmelCase__		)

        if self.add_upsample:
              SCREAMING_SNAKE_CASE :      Optional[int]       =   self.upsamplers_a(UpperCAmelCase__		)

        return hidden_states







class        a__       (   nn.Module       ):







  """simple docstring"""




  UpperCAmelCase__      : int
  UpperCAmelCase__      : float			       =0.0
  UpperCAmelCase__      : int			       =1
  UpperCAmelCase__      : int			       =1
  UpperCAmelCase__      : bool			       =False
  UpperCAmelCase__      : bool			       =False
  UpperCAmelCase__      : jnp.dtype			       =jnp.floataa


  def  _lowercase      (			self    : Optional[Any]		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   [
            FlaxResnetBlockaD(
                in_channels=self.in_channels				,				out_channels=self.in_channels				,				dropout_prob=self.dropout				,				dtype=self.dtype				,				)
        ]

        SCREAMING_SNAKE_CASE :      List[str]       =   []

        for _ in range(self.num_layers		):
              SCREAMING_SNAKE_CASE :      Tuple       =   FlaxTransformeraDModel(
                  in_channels=self.in_channels				,				n_heads=self.num_attention_heads				,				d_head=self.in_channels // self.num_attention_heads				,				depth=1				,				use_linear_projection=self.use_linear_projection				,				use_memory_efficient_attention=self.use_memory_efficient_attention				,				dtype=self.dtype				,				)
              attentions.append(UpperCAmelCase__		)

              SCREAMING_SNAKE_CASE :      List[str]       =   FlaxResnetBlockaD(
                  in_channels=self.in_channels				,				out_channels=self.in_channels				,				dropout_prob=self.dropout				,				dtype=self.dtype				,				)
              resnets.append(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      int       =   resnets
        SCREAMING_SNAKE_CASE :      Tuple       =   attentions




  def __call__(			self    : Union[str, Any]				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : str=True		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   self.resnets[0](UpperCAmelCase__				,				UpperCAmelCase__		)
        for attn, resnet in zip(self.attentions				,				self.resnets[1:]		):
              SCREAMING_SNAKE_CASE :      Optional[int]       =   attn(UpperCAmelCase__				,				UpperCAmelCase__				,				deterministic=UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      List[str]       =   resnet(UpperCAmelCase__				,				UpperCAmelCase__				,				deterministic=UpperCAmelCase__		)

        return hidden_states




