




'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (						_a       ):
							lowercase_							     =							(DDPMParallelScheduler,)







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     **lowerCAmelCase_			:    Optional[int]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							{
														    '''num_train_timesteps''': 10_00,
														    '''beta_start''': 0.0_0_0_1,
														    '''beta_end''': 0.0_2,
														    '''beta_schedule''': '''linear''',
														    '''variance_type''': '''fixed_small''',
														    '''clip_sample''': True,
														}

														config.update(**lowerCAmelCase_   )
														return config







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														for timesteps in [1, 5, 1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]				,     [0.0_0_2, 0.0_2, 0.2, 2]   ):
																					self.check_over_configs(beta_start=lowerCAmelCase_				,     beta_end=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				List[Any]:


														"""simple docstring"""


														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														for variance in ["fixed_small", "fixed_large", "other"]:
																					self.check_over_configs(variance_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														self.check_over_configs(thresholding=lowerCAmelCase_   )
														for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "sample", "v_prediction"]:
																												self.check_over_configs(
																												    thresholding=lowerCAmelCase_				,     prediction_type=lowerCAmelCase_				,     sample_max_value=lowerCAmelCase_				,     )







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																					self.check_over_configs(prediction_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				List[str]:


														"""simple docstring"""


														for t in [0, 5_00, 9_99]:
																					self.check_over_forward(time_step=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.0_0_9_7_9   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.0_2   )   ) < 1e-5







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				str:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							self.dummy_sample_deter + 0.1
														_a      	=							self.dummy_sample_deter - 0.1

														_a      	=							samplea.shape[0]
														_a      	=							torch.stack([samplea, samplea, samplea]				,     dim=0   )
														_a      	=							torch.arange(lowerCAmelCase_   )[0:3, None].repeat(1				,     lowerCAmelCase_   )

														_a      	=							model(samples.flatten(0				,     1   )				,     timesteps.flatten(0				,     1   )   )
														_a      	=							scheduler.batch_step_no_noise(lowerCAmelCase_				,     timesteps.flatten(0				,     1   )				,     samples.flatten(0				,     1   )   )

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 1_1_5_3.1_8_3_3   ) < 1e-2
														assert abs(result_mean.item() - 0.5_0_0_5   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							torch.manual_seed(0   )

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

																					# 2. predict previous mean of sample x_t-1
																					_a      	=							scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     generator=lowerCAmelCase_   ).prev_sample

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
														assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config(prediction_type='''v_prediction'''   )
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							torch.manual_seed(0   )

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

																					# 2. predict previous mean of sample x_t-1
																					_a      	=							scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     generator=lowerCAmelCase_   ).prev_sample

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
														assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    int   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 1, 0]

														scheduler.set_timesteps(timesteps=lowerCAmelCase_   )

														_a      	=							scheduler.timesteps

														for i, timestep in enumerate(lowerCAmelCase_   ):
																					if i == len(lowerCAmelCase_   ) - 1:
																												_a      	=							-1
																					else:
																												_a      	=							timesteps[i + 1]

																					_a      	=							scheduler.previous_timestep(lowerCAmelCase_   )
																					_a      	=							prev_t.item()

																					self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 51, 0]

														with self.assertRaises(lowerCAmelCase_				,     msg='''`custom_timesteps` must be in descending order.'''   ):
																					scheduler.set_timesteps(timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 1, 0]
														_a      	=							len(lowerCAmelCase_   )

														with self.assertRaises(lowerCAmelCase_				,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''   ):
																					scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_				,     timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[scheduler.config.num_train_timesteps]

														with self.assertRaises(
														    lowerCAmelCase_				,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,     ):
																					scheduler.set_timesteps(timesteps=lowerCAmelCase_   )




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Tuple						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      BigBirdConfig.from_json_file(_UpperCamelCase						)
 print(f"""Building PyTorch model from configuration: {config}"""						)

 if is_trivia_qa:
  SCREAMING_SNAKE_CASE		    =      BigBirdForQuestionAnswering(_UpperCamelCase						)
 else:
  SCREAMING_SNAKE_CASE		    =      BigBirdForPreTraining(_UpperCamelCase						)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_UpperCamelCase		,				_UpperCamelCase		,				is_trivia_qa=_UpperCamelCase						)

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
 model.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    int     		=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--big_bird_config_file",
          default=None,
          type=str,
          required=True,
          help=(
              "The config json file corresponding to the pre-trained BERT model. \n"
              "This specifies the model architecture."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
      )
      a_     :    Any     		=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
      )

