




import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  {}

						lowerCAmelCase_      			=  job["started_at"]
						lowerCAmelCase_      			=  job["completed_at"]

						lowerCAmelCase_      			=  date_parser.parse(_snake_case  )
						lowerCAmelCase_      			=  date_parser.parse(_snake_case  )

						lowerCAmelCase_      			=  round((end_datetime - start_datetime).total_seconds() / 6_0.0  )

						lowerCAmelCase_      			=  start
						lowerCAmelCase_      			=  end
						lowerCAmelCase_      			=  duration_in_min

						return job_info





def __UpperCamelCase			(       _A ,  _A=None  ):
						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(_snake_case ,  headers=_snake_case  ).json()
						lowerCAmelCase_      			=  {}

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

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

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






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

snake_case :		List[str]							      =  "facebook/wmt19-en-de"
snake_case :		Dict							      =  FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
snake_case :		List[str]							      =  FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

snake_case :		int							      =  FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
snake_case :		Optional[Any]							      =  tokenizer(["Making tiny model"], return_tensors="pt")
snake_case :		List[str]							      =  tiny_model(**batch)

print("test output:", len(outputs.logits[0]))

# Save
snake_case :		Dict							      =  "tiny-wmt19-en-de"
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de
