"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =					{
    "google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
    "google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
    "google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
}



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[int]     					=  """owlvit_text_model"""

				def __init__(			self						,      lowerCAmelCase=4_94_08						,      lowerCAmelCase=5_12						,      lowerCAmelCase=20_48						,      lowerCAmelCase=12						,      lowerCAmelCase=8						,      lowerCAmelCase=16						,      lowerCAmelCase="quick_gelu"						,      lowerCAmelCase=1E-5						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.02						,      lowerCAmelCase=1.0						,      lowerCAmelCase=0						,      lowerCAmelCase=4_94_06						,      lowerCAmelCase=4_94_07						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(pad_token_id=lowerCAmelCase						,      bos_token_id=lowerCAmelCase						,      eos_token_id=lowerCAmelCase						,      **lowerCAmelCase      )

										snake_case        =     vocab_size
										snake_case        =     hidden_size
										snake_case        =     intermediate_size
										snake_case        =     num_hidden_layers
										snake_case        =     num_attention_heads
										snake_case        =     max_position_embeddings
										snake_case        =     hidden_act
										snake_case        =     layer_norm_eps
										snake_case        =     attention_dropout
										snake_case        =     initializer_range
										snake_case        =     initializer_factor

				@classmethod
				def 						snake_case	(			cls						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										cls._set_token_in_kwargs(lowerCAmelCase      )

										snake_case   ,snake_case        =     cls.get_config_dict(lowerCAmelCase						,      **lowerCAmelCase      )

										# get the text config dict if we are loading from OwlViTConfig
										if config_dict.get('model_type'      ) == "owlvit":
																snake_case        =     config_dict['text_config']

										if "model_type" in config_dict and hasattr(cls						,      'model_type'      ) and config_dict["model_type"] != cls.model_type:
																logger.warning(
																    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""      )

										return cls.from_dict(lowerCAmelCase						,      **lowerCAmelCase      )





class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      int     					=  """owlvit_vision_model"""

				def __init__(			self						,      lowerCAmelCase=7_68						,      lowerCAmelCase=30_72						,      lowerCAmelCase=12						,      lowerCAmelCase=12						,      lowerCAmelCase=3						,      lowerCAmelCase=7_68						,      lowerCAmelCase=32						,      lowerCAmelCase="quick_gelu"						,      lowerCAmelCase=1E-5						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.02						,      lowerCAmelCase=1.0						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )

										snake_case        =     hidden_size
										snake_case        =     intermediate_size
										snake_case        =     num_hidden_layers
										snake_case        =     num_attention_heads
										snake_case        =     num_channels
										snake_case        =     image_size
										snake_case        =     patch_size
										snake_case        =     hidden_act
										snake_case        =     layer_norm_eps
										snake_case        =     attention_dropout
										snake_case        =     initializer_range
										snake_case        =     initializer_factor

				@classmethod
				def 						snake_case	(			cls						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										cls._set_token_in_kwargs(lowerCAmelCase      )

										snake_case   ,snake_case        =     cls.get_config_dict(lowerCAmelCase						,      **lowerCAmelCase      )

										# get the vision config dict if we are loading from OwlViTConfig
										if config_dict.get('model_type'      ) == "owlvit":
																snake_case        =     config_dict['vision_config']

										if "model_type" in config_dict and hasattr(cls						,      'model_type'      ) and config_dict["model_type"] != cls.model_type:
																logger.warning(
																    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""      )

										return cls.from_dict(lowerCAmelCase						,      **lowerCAmelCase      )





class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Dict     					=  """owlvit"""
				_lowerCAmelCase       :      str     					=  True

				def __init__(			self						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase=5_12						,      lowerCAmelCase=2.65_92						,      lowerCAmelCase=True						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )

										if text_config is None:
																snake_case        =     {}
																logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.'      )

										if vision_config is None:
																snake_case        =     {}
																logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.'      )

										snake_case        =     OwlViTTextConfig(**lowerCAmelCase      )
										snake_case        =     OwlViTVisionConfig(**lowerCAmelCase      )

										snake_case        =     projection_dim
										snake_case        =     logit_scale_init_value
										snake_case        =     return_dict
										snake_case        =     1.0

				@classmethod
				def 						snake_case	(			cls						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										cls._set_token_in_kwargs(lowerCAmelCase      )

										snake_case   ,snake_case        =     cls.get_config_dict(lowerCAmelCase						,      **lowerCAmelCase      )

										if "model_type" in config_dict and hasattr(cls						,      'model_type'      ) and config_dict["model_type"] != cls.model_type:
																logger.warning(
																    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""      )

										return cls.from_dict(lowerCAmelCase						,      **lowerCAmelCase      )

				@classmethod
				def 						snake_case	(			cls						,      lowerCAmelCase						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     {}
										snake_case        =     text_config
										snake_case        =     vision_config

										return cls.from_dict(lowerCAmelCase						,      **lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     copy.deepcopy(self.__dict__      )
										snake_case        =     self.text_config.to_dict()
										snake_case        =     self.vision_config.to_dict()
										snake_case        =     self.__class__.model_type
										return output





class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return OrderedDict(
										    [
										        ('input_ids', {0: 'batch', 1: 'sequence'}),
										        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
										        ('attention_mask', {0: 'batch', 1: 'sequence'}),
										    ]      )

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return OrderedDict(
										    [
										        ('logits_per_image', {0: 'batch'}),
										        ('logits_per_text', {0: 'batch'}),
										        ('text_embeds', {0: 'batch'}),
										        ('image_embeds', {0: 'batch'}),
										    ]      )

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return 1E-4

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = -1						,      lowerCAmelCase = -1						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     super().generate_dummy_inputs(
										    processor.tokenizer						,      batch_size=lowerCAmelCase						,      seq_length=lowerCAmelCase						,      framework=lowerCAmelCase      )
										snake_case        =     super().generate_dummy_inputs(
										    processor.image_processor						,      batch_size=lowerCAmelCase						,      framework=lowerCAmelCase      )
										return {**text_input_dict, **image_input_dict}

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return 14



"""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']}""")


