"""simple docstring"""
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      List[str]     					=  ["""image_processor""", """tokenizer"""]
				_lowerCAmelCase       :      List[str]     					=  """Pix2StructImageProcessor"""
				_lowerCAmelCase       :      List[Any]     					=  ("""T5Tokenizer""", """T5TokenizerFast""")

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     False
										super().__init__(lowerCAmelCase						,      lowerCAmelCase      )

				def __call__(			self						,      lowerCAmelCase=None						,      lowerCAmelCase = None						,      lowerCAmelCase = True						,      lowerCAmelCase = False						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = 20_48						,      lowerCAmelCase = 0						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										if images is None and text is None:
																raise ValueError('You have to specify either images or text.'      )

										# Get only text
										if images is None and not self.image_processor.is_vqa:
																snake_case        =     self.tokenizer
																snake_case        =     self.tokenizer(
																    text=lowerCAmelCase						,      add_special_tokens=lowerCAmelCase						,      padding=lowerCAmelCase						,      truncation=lowerCAmelCase						,      max_length=lowerCAmelCase						,      stride=lowerCAmelCase						,      pad_to_multiple_of=lowerCAmelCase						,      return_attention_mask=lowerCAmelCase						,      return_overflowing_tokens=lowerCAmelCase						,      return_special_tokens_mask=lowerCAmelCase						,      return_offsets_mapping=lowerCAmelCase						,      return_token_type_ids=lowerCAmelCase						,      return_length=lowerCAmelCase						,      verbose=lowerCAmelCase						,      return_tensors=lowerCAmelCase						,      **lowerCAmelCase						,      )
																return text_encoding

										if not self.image_processor.is_vqa:
																# add pixel_values
																snake_case        =     self.image_processor(
																    lowerCAmelCase						,      return_tensors=lowerCAmelCase						,      max_patches=lowerCAmelCase						,      **lowerCAmelCase      )
										else:
																# add pixel_values and bbox
																snake_case        =     self.image_processor(
																    lowerCAmelCase						,      return_tensors=lowerCAmelCase						,      max_patches=lowerCAmelCase						,      header_text=lowerCAmelCase						,      **lowerCAmelCase      )

										if text is not None and not self.image_processor.is_vqa:
																snake_case        =     self.tokenizer(
																    text=lowerCAmelCase						,      add_special_tokens=lowerCAmelCase						,      padding=lowerCAmelCase						,      truncation=lowerCAmelCase						,      max_length=lowerCAmelCase						,      stride=lowerCAmelCase						,      pad_to_multiple_of=lowerCAmelCase						,      return_attention_mask=lowerCAmelCase						,      return_overflowing_tokens=lowerCAmelCase						,      return_special_tokens_mask=lowerCAmelCase						,      return_offsets_mapping=lowerCAmelCase						,      return_token_type_ids=lowerCAmelCase						,      return_length=lowerCAmelCase						,      verbose=lowerCAmelCase						,      return_tensors=lowerCAmelCase						,      **lowerCAmelCase						,      )

																if "attention_mask" in text_encoding:
																						snake_case        =     text_encoding.pop('attention_mask'      )
																if "input_ids" in text_encoding:
																						snake_case        =     text_encoding.pop('input_ids'      )
										else:
																snake_case        =     None

										if text_encoding is not None:
																encoding_image_processor.update(lowerCAmelCase      )

										return encoding_image_processor

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






										"""simple docstring"""

										return self.tokenizer.batch_decode(*lowerCAmelCase						,      **lowerCAmelCase      )

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






										"""simple docstring"""

										return self.tokenizer.decode(*lowerCAmelCase						,      **lowerCAmelCase      )

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.tokenizer.model_input_names
										snake_case        =     self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )



"""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']}""")


