





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Dict										=    (DDIMParallelScheduler,)
      UpperCamelCase_      :		List[Any]										=    (("eta", 0.0), ("num_inference_steps", 5_0))



      def 						UpperCamelCase_		(      self				:    Dict      , **A__				:    int		)   ->					Tuple:
        _snake_case						=							{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**A__		)
        return config



      def 						UpperCamelCase_		(      self				:    Dict      , **A__				:    str		)   ->					List[str]:
        _snake_case						=							self.scheduler_classes[0]
        _snake_case						=							self.get_scheduler_config(**A__		)
        _snake_case						=							scheduler_class(**A__		)

        _snake_case,					_snake_case						=							10, 0.0

        _snake_case						=							self.dummy_model()
        _snake_case						=							self.dummy_sample_deter

        scheduler.set_timesteps(A__		)

        for t in scheduler.timesteps:
          _snake_case						=							model(A__      , A__		)
          _snake_case						=							scheduler.step(A__      , A__      , A__      , A__		).prev_sample

        return sample



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					str:
        for timesteps in [100, 500, 1000]:
          self.check_over_configs(num_train_timesteps=A__		)



      def 						UpperCamelCase_		(      self				:    Any		)   ->					Dict:
        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=A__		)

        _snake_case						=							self.scheduler_classes[0]
        _snake_case						=							self.get_scheduler_config(steps_offset=1		)
        _snake_case						=							scheduler_class(**A__		)
        scheduler.set_timesteps(5		)
        assert torch.equal(scheduler.timesteps      , torch.LongTensor([801, 601, 401, 201, 1]		)		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[Any]:
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      , [0.002, 0.02, 0.2, 2]		):
          self.check_over_configs(beta_start=A__      , beta_end=A__		)



      def 						UpperCamelCase_		(      self				:    str		)   ->					Any:
        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=A__		)



      def 						UpperCamelCase_		(      self				:    Any		)   ->					List[str]:
        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=A__		)



      def 						UpperCamelCase_		(      self				:    str		)   ->					str:
        for timestep_spacing in ["trailing", "leading"]:
          self.check_over_configs(timestep_spacing=A__		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Tuple:
        for rescale_betas_zero_snr in [True, False]:
          self.check_over_configs(rescale_betas_zero_snr=A__		)



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Optional[Any]:
        self.check_over_configs(thresholding=A__		)
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(
                thresholding=A__      , prediction_type=A__      , sample_max_value=A__      , )



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					str:
        for t in [1, 10, 49]:
          self.check_over_forward(time_step=A__		)



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Union[str, Any]:
        for t, num_inference_steps in zip([1, 10, 50]      , [10, 50, 500]		):
          self.check_over_forward(time_step=A__      , num_inference_steps=A__		)



      def 						UpperCamelCase_		(      self				:    str		)   ->					Any:
        for t, eta in zip([1, 10, 49]      , [0.0, 0.5, 1.0]		):
          self.check_over_forward(time_step=A__      , eta=A__		)



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Union[str, Any]:
        _snake_case						=							self.scheduler_classes[0]
        _snake_case						=							self.get_scheduler_config()
        _snake_case						=							scheduler_class(**A__		)

        assert torch.sum(torch.abs(scheduler._get_variance(0      , 0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420      , 400		) - 0.1_4771		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980      , 960		) - 0.3_2460		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0      , 0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487      , 486		) - 0.0_0979		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999      , 998		) - 0.02		)		) < 1e-5



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[int]:
        _snake_case						=							self.scheduler_classes[0]
        _snake_case						=							self.get_scheduler_config()
        _snake_case						=							scheduler_class(**A__		)

        _snake_case,					_snake_case						=							10, 0.0
        scheduler.set_timesteps(A__		)

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

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

        _snake_case						=							model(samples.flatten(0      , 1		)      , timesteps.flatten(0      , 1		)		)
        _snake_case						=							scheduler.batch_step_no_noise(A__      , timesteps.flatten(0      , 1		)      , samples.flatten(0      , 1		)      , A__		)

        _snake_case						=							torch.sum(torch.abs(A__		)		)
        _snake_case						=							torch.mean(torch.abs(A__		)		)

        assert abs(result_sum.item() - 1147.7904		) < 1e-2
        assert abs(result_mean.item() - 0.4982		) < 1e-3



      def 						UpperCamelCase_		(      self				:    int		)   ->					List[Any]:
        _snake_case						=							self.full_loop()

        _snake_case						=							torch.sum(torch.abs(A__		)		)
        _snake_case						=							torch.mean(torch.abs(A__		)		)

        assert abs(result_sum.item() - 172.0067		) < 1e-2
        assert abs(result_mean.item() - 0.22_3967		) < 1e-3



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[Any]:
        _snake_case						=							self.full_loop(prediction_type='''v_prediction'''		)

        _snake_case						=							torch.sum(torch.abs(A__		)		)
        _snake_case						=							torch.mean(torch.abs(A__		)		)

        assert abs(result_sum.item() - 52.5302		) < 1e-2
        assert abs(result_mean.item() - 0.0684		) < 1e-3



      def 						UpperCamelCase_		(      self				:    Any		)   ->					Union[str, Any]:
        # We specify different beta, so that the first alpha is 0.99
        _snake_case						=							self.full_loop(set_alpha_to_one=A__      , beta_start=0.01		)
        _snake_case						=							torch.sum(torch.abs(A__		)		)
        _snake_case						=							torch.mean(torch.abs(A__		)		)

        assert abs(result_sum.item() - 149.8295		) < 1e-2
        assert abs(result_mean.item() - 0.1951		) < 1e-3







      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Dict:
        # We specify different beta, so that the first alpha is 0.99
        _snake_case						=							self.full_loop(set_alpha_to_one=A__      , beta_start=0.01		)
        _snake_case						=							torch.sum(torch.abs(A__		)		)
        _snake_case						=							torch.mean(torch.abs(A__		)		)

        assert abs(result_sum.item() - 149.0784		) < 1e-2
        assert abs(result_mean.item() - 0.1941		) < 1e-3




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






  _snake_case						=							AlbertConfig.from_json_file(_UpperCamelCase					)
  print(F"""Building PyTorch model from configuration: {config}"""					)
  _snake_case						=							AlbertForPreTraining(_UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_albert(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)

  # Save pytorch-model
  print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
  torch.save(model.state_dict()			,	_UpperCamelCase					)


if __name__ == "__main__":
 __A      		=     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(
     '''--albert_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained ALBERT 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.'''
 )
 __A      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)

