



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[Any]											=						(EulerDiscreteScheduler,)
       UpperCamelCase__  :    List[str]											=						10



       def 		_lowerCamelCase			(		self							:     Tuple     ,		**__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           __a						       =				{
               '''num_train_timesteps''': 1_100,
               '''beta_start''': 0.00_01,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
           }

           config.update(**__SCREAMING_SNAKE_CASE)
           return config



       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           for timesteps in [10, 50, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]     ,		[0.00_02, 0.0_02, 0.02]):
               self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE     ,		beta_end=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps)

           __a						       =				torch.manual_seed(0)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter * scheduler.init_noise_sigma
           __a						       =				sample.to(__SCREAMING_SNAKE_CASE)

           for i, t in enumerate(scheduler.timesteps):
               __a						       =				scheduler.scale_model_input(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE)
               __a						       =				output.prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 10.08_07) < 1E-2
           assert abs(result_mean.item() - 0.01_31) < 1E-3



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config(prediction_type='''v_prediction''')
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps)

           __a						       =				torch.manual_seed(0)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter * scheduler.init_noise_sigma
           __a						       =				sample.to(__SCREAMING_SNAKE_CASE)

           for i, t in enumerate(scheduler.timesteps):
               __a						       =				scheduler.scale_model_input(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE)
               __a						       =				output.prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 0.00_02) < 1E-2
           assert abs(result_mean.item() - 2.2676E-06) < 1E-3



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps     ,		device=__SCREAMING_SNAKE_CASE)

           __a						       =				torch.manual_seed(0)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           __a						       =				sample.to(__SCREAMING_SNAKE_CASE)

           for t in scheduler.timesteps:
               __a						       =				scheduler.scale_model_input(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE)
               __a						       =				output.prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 10.08_07) < 1E-2
           assert abs(result_mean.item() - 0.01_31) < 1E-3





       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE     ,		use_karras_sigmas=__SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps     ,		device=__SCREAMING_SNAKE_CASE)

           __a						       =				torch.manual_seed(0)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           __a						       =				sample.to(__SCREAMING_SNAKE_CASE)

           for t in scheduler.timesteps:
               __a						       =				scheduler.scale_model_input(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE)
               __a						       =				output.prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19) < 1E-2
           assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63) < 1E-3










import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    # Initialise PyTorch model
    __a						       =				LxmertConfig.from_json_file(_UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)
    __a						       =				LxmertForPreTraining(_UpperCAmelCase				)

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    torch.save(model.state_dict()   , _UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:List[Any]      		=     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(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 __snake_case						:Optional[Any]      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





