
import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
					import jax
					import jax.numpy as jnp





@slow
@require_flax
class 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Dict							,A					: List[Any]							,A					: Dict ):
					return f'''gaussian_noise_s={seed}_shape={'_'.join([str(A ) for s in shape] )}.npy'''






	def 				UpperCamelCase_  (       self					: Dict ):
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: List[Any]=0							,A					: Any=(4, 4, 64, 64)							,A					: Tuple=False ):
					__A   			= jnp.bfloataa if fpaa else jnp.floataa
					__A   			= jnp.array(load_hf_numpy(self.get_file_format(A							,A ) )							,dtype=A )
					return image






	def 				UpperCamelCase_  (       self					: Any							,A					: int=False							,A					: Union[str, Any]="CompVis/stable-diffusion-v1-4" ):
					__A   			= jnp.bfloataa if fpaa else jnp.floataa
					__A   			= "bf16" if fpaa else None

					__A			,							__A   			= FlaxUNetaDConditionModel.from_pretrained(
					    A							,subfolder="unet"							,dtype=A							,revision=A )
					return model, params






	def 				UpperCamelCase_  (       self					: str							,A					: Union[str, Any]=0							,A					: int=(4, 77, 7_68)							,A					: Dict=False ):
					__A   			= jnp.bfloataa if fpaa else jnp.floataa
					__A   			= jnp.array(load_hf_numpy(self.get_file_format(A							,A ) )							,dtype=A )
					return hidden_states






	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [-0.23_23, -0.13_04, 0.08_13, -0.30_93, -0.09_19, -0.15_71, -0.11_25, -0.58_06]],
	        [17, 0.55, [-0.08_31, -0.24_43, 0.09_01, -0.09_19, 0.33_96, 0.01_03, -0.37_43, 0.07_01]],
	        [8, 0.89, [-0.48_63, 0.08_59, 0.08_75, -0.16_58, 0.91_99, -0.01_14, 0.48_39, 0.46_39]],
	        [3, 10_00, [-0.56_49, 0.24_02, -0.55_18, 0.12_48, 1.13_28, -0.24_43, -0.03_25, -1.00_78]],
	        # fmt: on
	    ] )
	def 				UpperCamelCase_  (       self					: int							,A					: Dict							,A					: Optional[Any]							,A					: Optional[int] ):
					__A			,							__A   			= self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"							,fpaa=A )
					__A   			= self.get_latents(A							,fpaa=A )
					__A   			= self.get_encoder_hidden_states(A							,fpaa=A )

					__A   			= model.apply(
					    {"params": params}							,A							,jnp.array(A							,dtype=jnp.intaa )							,encoder_hidden_states=A							,).sample

					assert sample.shape == latents.shape

					__A   			= jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )							,dtype=jnp.floataa )
					__A   			= jnp.array(A							,dtype=jnp.floataa )

					# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
					assert jnp.allclose(A							,A							,atol=1E-2 )







	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [0.15_14, 0.08_07, 0.16_24, 0.10_16, -0.18_96, 0.02_63, 0.06_77, 0.23_10]],
	        [17, 0.55, [0.11_64, -0.02_16, 0.01_70, 0.15_89, -0.31_20, 0.10_05, -0.05_81, -0.14_58]],
	        [8, 0.89, [-0.17_58, -0.01_69, 0.10_04, -0.14_11, 0.13_12, 0.11_03, -0.19_96, 0.21_39]],
	        [3, 10_00, [0.12_14, 0.03_52, -0.07_31, -0.15_62, -0.09_94, -0.09_06, -0.23_40, -0.05_39]],
	        # fmt: on
	    ] )
	def 				UpperCamelCase_  (       self					: Any							,A					: Union[str, Any]							,A					: Optional[int]							,A					: Any ):
					__A			,							__A   			= self.get_unet_model(model_id="stabilityai/stable-diffusion-2"							,fpaa=A )
					__A   			= self.get_latents(A							,shape=(4, 4, 96, 96)							,fpaa=A )
					__A   			= self.get_encoder_hidden_states(A							,shape=(4, 77, 10_24)							,fpaa=A )

					__A   			= model.apply(
					    {"params": params}							,A							,jnp.array(A							,dtype=jnp.intaa )							,encoder_hidden_states=A							,).sample

					assert sample.shape == latents.shape

					__A   			= jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )							,dtype=jnp.floataa )
					__A   			= jnp.array(A							,dtype=jnp.floataa )

					# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
					assert jnp.allclose(A							,A							,atol=1E-2 )


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests


def 						UpperCAmelCase			(	a_						)		->	str:







				"""simple docstring"""



				__A   			= {}

				__A   			= job["started_at"]
				__A   			= job["completed_at"]

				__A   			= date_parser.parse(a_						)
				__A   			= date_parser.parse(a_						)

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

				__A   			= start
				__A   			= end
				__A   			= duration_in_min

				return job_info


def 						UpperCAmelCase			(	a_		,   a_=None						)		->	str:







				"""simple docstring"""



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

				__A   			= F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
				__A   			= requests.get(a_		,   headers=a_						).json()
				__A   			= {}

				try:
								job_time.update({job["name"]: extract_time_from_single_job(a_						) for job in result["jobs"]}						)
								__A   			= math.ceil((result["total_count"] - 1_0_0) / 1_0_0						)

								for i in range(a_						):
												__A   			= 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__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				get_job_time(args.workflow_run_id)
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				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"]}''')
