"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
					if is_torch_available():
										import torch
					if is_tf_available():
										import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =					{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__   =					{
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE__   =					{
    "Salesforce/codegen-350M-mono": 2_048,
}



class 		lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[Any]     					=  VOCAB_FILES_NAMES
				_lowerCAmelCase       :      List[str]     					=  PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase       :      str     					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase       :      Dict     					=  ["""input_ids""", """attention_mask"""]
				_lowerCAmelCase       :      Union[str, Any]     					=  CodeGenTokenizer

				def __init__(			self						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase="<|endoftext|>"						,      lowerCAmelCase="<|endoftext|>"						,      lowerCAmelCase="<|endoftext|>"						,      lowerCAmelCase=False						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(
										    lowerCAmelCase						,      lowerCAmelCase						,      tokenizer_file=lowerCAmelCase						,      unk_token=lowerCAmelCase						,      bos_token=lowerCAmelCase						,      eos_token=lowerCAmelCase						,      add_prefix_space=lowerCAmelCase						,      **lowerCAmelCase						,      )

										if kwargs.pop('add_bos_token'						,      lowerCAmelCase      ):
																snake_case        =     kwargs.pop('name_or_path'						,      ''      )
																raise ValueError(
																    'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
																    'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
																    F"""`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n"""
																    F"""`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n"""
																    'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
																    ' so that the fast tokenizer works correctly.'      )

										snake_case        =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('add_prefix_space'						,      lowerCAmelCase      ) != add_prefix_space:
																snake_case        =     getattr(lowerCAmelCase						,      pre_tok_state.pop('type'      )      )
																snake_case        =     add_prefix_space
																snake_case        =     pre_tok_class(**lowerCAmelCase      )

										snake_case        =     add_prefix_space

				def 						snake_case	(			self						,      *lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     kwargs.get('is_split_into_words'						,      lowerCAmelCase      )
										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*lowerCAmelCase						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      *lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     kwargs.get('is_split_into_words'						,      lowerCAmelCase      )

										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*lowerCAmelCase						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None      ):






										"""simple docstring"""

										snake_case        =     self._tokenizer.model.save(lowerCAmelCase						,      name=lowerCAmelCase      )
										return tuple(lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = False						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     super().decode(
										    token_ids=lowerCAmelCase						,      skip_special_tokens=lowerCAmelCase						,      clean_up_tokenization_spaces=lowerCAmelCase						,      **lowerCAmelCase						,      )

										if truncate_before_pattern is not None and len(lowerCAmelCase      ) > 0:
																snake_case        =     self.truncate(lowerCAmelCase						,      lowerCAmelCase      )

										return decoded_text

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										def find_re(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):
																snake_case        =     pattern.search(lowerCAmelCase						,      lowerCAmelCase      )
																return m.start() if m else -1

										snake_case        =     [re.compile(lowerCAmelCase						,      re.MULTILINE      ) for pattern in truncate_before_pattern]

										snake_case        =     list(re.finditer('^print'						,      lowerCAmelCase						,      re.MULTILINE      )      )

										if len(lowerCAmelCase      ) > 1:
																snake_case        =     completion[: prints[1].start()]

										snake_case        =     list(re.finditer('^def'						,      lowerCAmelCase						,      re.MULTILINE      )      )

										if len(lowerCAmelCase      ) > 1:
																snake_case        =     completion[: defs[1].start()]

										snake_case        =     0

										snake_case        =     [
										    pos for pos in [find_re(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ) for terminal in terminals] if pos != -1
										]

										if len(lowerCAmelCase      ) > 0:
																return completion[: min(lowerCAmelCase      )]
										else:
																return completion



"""simple docstring"""
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Any   )   ->			int:





						"""simple docstring"""






						snake_case        =     {}

						snake_case        =     job['started_at']
						snake_case        =     job['completed_at']

						snake_case        =     date_parser.parse(_UpperCamelCase   )
						snake_case        =     date_parser.parse(_UpperCamelCase   )

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

						snake_case        =     start
						snake_case        =     end
						snake_case        =     duration_in_min

						return job_info





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Any=None   )   ->			Union[str, Any]:





						"""simple docstring"""






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

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

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

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

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


