
'''simple docstring'''
import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM







@require_torch
class  __A				:
 def __init__(   self			:    Tuple				,   lowerCamelCase			:    int				,   lowerCamelCase			:    int=99				,   lowerCamelCase			:    Dict=13				,   lowerCamelCase			:    int=16				,   lowerCamelCase			:    Optional[Any]=7				,   lowerCamelCase			:    str=True				,   lowerCamelCase			:    Optional[int]=True				,   lowerCamelCase			:    int=True				,   lowerCamelCase			:    Optional[Any]=False				,   lowerCamelCase			:    Any=True				,   lowerCamelCase			:    Union[str, Any]=2				,   lowerCamelCase			:    str=32				,   lowerCamelCase			:    Union[str, Any]=4				,   lowerCamelCase			:    Union[str, Any]=4				,   lowerCamelCase			:    List[Any]=30				,   lowerCamelCase			:    int=0				,   lowerCamelCase			:    List[str]=1				,   lowerCamelCase			:    List[str]=2				,   lowerCamelCase			:    Tuple=None				,   ):



   """simple docstring"""





   __A		:     Tuple												=		parent
   __A		:     Optional[int]												=		batch_size
   __A		:     int												=		decoder_seq_length
   # For common tests
   __A		:     Tuple												=		self.decoder_seq_length
   __A		:     Dict												=		is_training
   __A		:     int												=		use_attention_mask
   __A		:     Optional[Any]												=		use_labels

   __A		:     Dict												=		vocab_size
   __A		:     str												=		d_model
   __A		:     Optional[int]												=		d_model
   __A		:     Optional[int]												=		decoder_layers
   __A		:     Tuple												=		decoder_layers
   __A		:     int												=		decoder_ffn_dim
   __A		:     str												=		decoder_attention_heads
   __A		:     Optional[int]												=		decoder_attention_heads
   __A		:     str												=		eos_token_id
   __A		:     List[str]												=		bos_token_id
   __A		:     str												=		pad_token_id
   __A		:     Union[str, Any]												=		decoder_start_token_id
   __A		:     List[str]												=		use_cache
   __A		:     List[Any]												=		max_position_embeddings

   __A		:     List[str]												=		None
   __A		:     str												=		decoder_seq_length
   __A		:     List[str]												=		2
   __A		:     int												=		1
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     str												=		ids_tensor([self.batch_size, self.decoder_seq_length]				,   self.vocab_size       )

   __A		:     Dict												=		None
   if self.use_attention_mask:
     __A		:     Any												=		ids_tensor([self.batch_size, self.decoder_seq_length]				,   vocab_size=2       )

   __A		:     Any												=		None
   if self.use_labels:
     __A		:     Optional[Any]												=		ids_tensor([self.batch_size, self.decoder_seq_length]				,   self.vocab_size       )

   __A		:     List[Any]												=		TrOCRConfig(
       vocab_size=self.vocab_size				,   d_model=self.d_model				,   decoder_layers=self.decoder_layers				,   decoder_ffn_dim=self.decoder_ffn_dim				,   decoder_attention_heads=self.decoder_attention_heads				,   eos_token_id=self.eos_token_id				,   bos_token_id=self.bos_token_id				,   use_cache=self.use_cache				,   pad_token_id=self.pad_token_id				,   decoder_start_token_id=self.decoder_start_token_id				,   max_position_embeddings=self.max_position_embeddings				,   )

   return (config, input_ids, attention_mask, lm_labels)
 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    Tuple				,   lowerCamelCase			:    Union[str, Any]				,   lowerCamelCase			:    Any				,   lowerCamelCase			:    Union[str, Any]				,   ):



   """simple docstring"""





   __A		:     Optional[Any]												=		True
   __A		:     Optional[int]												=		TrOCRDecoder(config=lowerCAmelCase_       ).to(lowerCAmelCase_       ).eval()
   __A		:     Optional[int]												=		input_ids[:2]

   input_ids[input_ids == 0] += 1
   # first forward pass
   __A		:     str												=		model(lowerCAmelCase_				,   use_cache=lowerCAmelCase_       )
   __A		:     int												=		model(lowerCAmelCase_       )
   __A		:     int												=		model(lowerCAmelCase_				,   use_cache=lowerCAmelCase_       )

   self.parent.assertTrue(len(lowerCAmelCase_       ) == len(lowerCAmelCase_       )       )
   self.parent.assertTrue(len(lowerCAmelCase_       ) == len(lowerCAmelCase_       ) + 1       )

   __A		:     str												=		outputs["""past_key_values"""]

   # create hypothetical next token and extent to next_input_ids
   __A		:     Any												=		ids_tensor((2, 1)				,   config.vocab_size - 1       ) + 1

   # append to next input_ids and
   __A		:     Optional[int]												=		torch.cat([input_ids, next_tokens]				,   dim=-1       )

   __A		:     str												=		model(lowerCAmelCase_       )["""last_hidden_state"""]
   __A		:     Dict												=		model(lowerCAmelCase_				,   past_key_values=lowerCAmelCase_       )["""last_hidden_state"""]

   # select random slice
   __A		:     Optional[int]												=		ids_tensor((1,)				,   output_from_past.shape[-1]       ).item()
   __A		:     Any												=		output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
   __A		:     Optional[Any]												=		output_from_past[:, 0, random_slice_idx].detach()

   # test that outputs are equal for slice
   assert torch.allclose(lowerCAmelCase_				,   lowerCAmelCase_				,   atol=1e-3       )






 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   __A		:     Optional[int]												=		self.prepare_config_and_inputs()
   __A							,					__A							,					__A							,					__A		:     Dict												=		config_and_inputs

   __A		:     List[str]												=		{"""input_ids""": input_ids, """attention_mask""": attention_mask}
   return config, inputs_dict







@require_torch
class  __A				( __UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						unittest.TestCase			):
 lowerCamelCase				       =(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
 lowerCamelCase				       =(TrOCRForCausalLM,) if is_torch_available() else ()
 lowerCamelCase				       ={'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
 lowerCamelCase				       =True
 lowerCamelCase				       =False
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     List[Any]												=		TrOCRStandaloneDecoderModelTester(self				,   is_training=lowerCAmelCase_       )
   __A		:     List[str]												=		ConfigTester(self				,   config_class=lowerCAmelCase_       )
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   self.config_tester.run_common_tests()
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   __A		:     List[Any]												=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_decoder_model_past(*lowerCAmelCase_       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   return






 @unittest.skip("""The model doesn't support left padding"""       )  # and it's not used enough to be worth fixing :)
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   pass





'''simple docstring'''
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Tuple:





  """simple docstring"""

  __A		:     Dict												=		{}

  __A		:     Optional[Any]												=		job["""started_at"""]
  __A		:     Tuple												=		job["""completed_at"""]

  __A		:     Optional[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )

  __A		:     Union[str, Any]												=		round((end_datetime - start_datetime).total_seconds() / 60.0 )

  __A		:     List[Any]												=		start
  __A		:     Tuple												=		end
  __A		:     int												=		duration_in_min

  return job_info







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  int=None )				->       Dict:





  """simple docstring"""

  __A		:     List[Any]												=		None
  if token is not None:
    __A		:     Dict												=		{"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

  __A		:     Tuple												=		F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
  __A		:     List[str]												=		requests.get(__SCREAMING_SNAKE_CASE			,     headers=__SCREAMING_SNAKE_CASE ).json()
  __A		:     List[str]												=		{}

  try:
    job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )
    __A		:     Dict												=		math.ceil((result["""total_count"""] - 100) / 100 )

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

     A__   :		Any 		=get_job_time(args.workflow_run_id)
     A__   :		Dict 		=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']}")



