'''simple docstring'''




import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests






def 				snake_case__							(			_A:					List[str] )    ->	Any:

		'''simple docstring'''


		lowerCAmelCase   					=  {}

		lowerCAmelCase   					=  job["""started_at"""]
		lowerCAmelCase   					=  job["""completed_at"""]

		lowerCAmelCase   					=  date_parser.parse(_A )
		lowerCAmelCase   					=  date_parser.parse(_A )

		lowerCAmelCase   					=  round((end_datetime - start_datetime).total_seconds() / 60.0 )

		lowerCAmelCase   					=  start
		lowerCAmelCase   					=  end
		lowerCAmelCase   					=  duration_in_min

		return job_info






def 				snake_case__							(			_A:					List[str]      ,   _A:					List[str]=None )    ->	Union[str, Any]:

		'''simple docstring'''


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

		lowerCAmelCase   					=  f"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
		lowerCAmelCase   					=  requests.get(_A      ,   headers=_A ).json()
		lowerCAmelCase   					=  {}

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

				for i in range(_A ):
						lowerCAmelCase   					=  requests.get(url + f"&page={i + 2}"      ,   headers=_A ).json()
						job_time.update({job["""name"""]: extract_time_from_single_job(_A ) 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__":
						__lowercase          =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
						__lowercase          =  parser.parse_args()

						__lowercase          =  get_job_time(args.workflow_run_id)
						__lowercase          =  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"]}')





'''simple docstring'''




import math

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






def 				snake_case__							(			_A:					jnp.ndarray      ,   _A:					int      ,   _A:					float = 1      ,   _A:					float = 1      ,   _A:					float = 1.0e4      ,   _A:					bool = False      ,   _A:					float = 1.0      ,   )    ->	jnp.ndarray:

		'''simple docstring'''


		assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
		assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
		lowerCAmelCase   					=  float(embedding_dim // 2 )
		lowerCAmelCase   					=  math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
		lowerCAmelCase   					=  min_timescale * jnp.exp(jnp.arange(_A      ,   dtype=jnp.floataa ) * -log_timescale_increment )
		lowerCAmelCase   					=  jnp.expand_dims(_A      ,   1 ) * jnp.expand_dims(_A      ,   0 )

		# scale embeddings
		lowerCAmelCase   					=  scale * emb

		if flip_sin_to_cos:
				lowerCAmelCase   					=  jnp.concatenate([jnp.cos(_A ), jnp.sin(_A )]      ,   axis=1 )
		else:
				lowerCAmelCase   					=  jnp.concatenate([jnp.sin(_A ), jnp.cos(_A )]      ,   axis=1 )
		lowerCAmelCase   					=  jnp.reshape(_A      ,   [jnp.shape(_A )[0], embedding_dim] )
		return signal






class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     jnp.dtype      =       jnp.floataa



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_1""")(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.silu(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_2""")(__lowerCAmelCase)
					return temb







class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     bool      =       False
			UpperCAmelCase_			:     float      =       1



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return get_sinusoidal_embeddings(
					    __lowerCAmelCase       ,    embedding_dim=self.dim       ,    flip_sin_to_cos=self.flip_sin_to_cos       ,    freq_shift=self.freq_shift)




