

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class  _A		(  UpperCAmelCase_):
  SCREAMING_SNAKE_CASE    :      int       =  (DDPMScheduler,)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):
        SCREAMING_SNAKE_CASE_				:  Dict           =     {
            'num_train_timesteps': 1000,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'variance_type': 'fixed_small',
            'clip_sample': True,
        }

        config.update(**_snake_case					)
        return config

  def  UpperCAmelCase    (				self					):


        for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_snake_case					)

  def  UpperCAmelCase    (				self					):


        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=_snake_case ,					beta_end=_snake_case					)

  def  UpperCAmelCase    (				self					):


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

  def  UpperCAmelCase    (				self					):


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

  def  UpperCAmelCase    (				self					):


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

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

  def  UpperCAmelCase    (				self					):


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

  def  UpperCAmelCase    (				self					):


        for t in [0, 500, 999]:
              self.check_over_forward(time_step=_snake_case					)

  def  UpperCAmelCase    (				self					):
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_				:  int           =     scheduler_class(**_snake_case					)

        assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.00979					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.02					)					) < 1e-5

  def  UpperCAmelCase    (				self					):
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler_class(**_snake_case					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     len(_snake_case					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.dummy_model()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.dummy_sample_deter
        SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.manual_seed(0					)

        for t in reversed(range(_snake_case					)					):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE_				:  List[str]           =     model(_snake_case ,					_snake_case					)

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE_				:  Dict           =     scheduler.step(_snake_case ,					_snake_case ,					_snake_case ,					generator=_snake_case					).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              SCREAMING_SNAKE_CASE_				:  str           =     pred_prev_sample

        SCREAMING_SNAKE_CASE_				:  int           =     torch.sum(torch.abs(_snake_case					)					)
        SCREAMING_SNAKE_CASE_				:  int           =     torch.mean(torch.abs(_snake_case					)					)

        assert abs(result_sum.item() - 258.9606					) < 1e-2
        assert abs(result_mean.item() - 0.3372					) < 1e-3

  def  UpperCAmelCase    (				self					):
        SCREAMING_SNAKE_CASE_				:  Any           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.get_scheduler_config(prediction_type='v_prediction'					)
        SCREAMING_SNAKE_CASE_				:  Any           =     scheduler_class(**_snake_case					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     len(_snake_case					)

        SCREAMING_SNAKE_CASE_				:  str           =     self.dummy_model()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.dummy_sample_deter
        SCREAMING_SNAKE_CASE_				:  Any           =     torch.manual_seed(0					)

        for t in reversed(range(_snake_case					)					):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE_				:  Tuple           =     model(_snake_case ,					_snake_case					)

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE_				:  int           =     scheduler.step(_snake_case ,					_snake_case ,					_snake_case ,					generator=_snake_case					).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              SCREAMING_SNAKE_CASE_				:  List[str]           =     pred_prev_sample

        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.sum(torch.abs(_snake_case					)					)
        SCREAMING_SNAKE_CASE_				:  int           =     torch.mean(torch.abs(_snake_case					)					)

        assert abs(result_sum.item() - 202.0296					) < 1e-2
        assert abs(result_mean.item() - 0.2631					) < 1e-3

  def  UpperCAmelCase    (				self					):
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class(**_snake_case					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     [100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=_snake_case					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.timesteps

        for i, timestep in enumerate(_snake_case					):
              if i == len(_snake_case					) - 1:
                    SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     -1
              else:
                    SCREAMING_SNAKE_CASE_				:  int           =     timesteps[i + 1]

              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     scheduler.previous_timestep(_snake_case					)
              SCREAMING_SNAKE_CASE_				:  List[str]           =     prev_t.item()

              self.assertEqual(_snake_case ,					_snake_case					)

  def  UpperCAmelCase    (				self					):
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_snake_case					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     [100, 87, 50, 51, 0]

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

  def  UpperCAmelCase    (				self					):
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  int           =     self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_				:  int           =     scheduler_class(**_snake_case					)

        SCREAMING_SNAKE_CASE_				:  Any           =     [100, 87, 50, 1, 0]
        SCREAMING_SNAKE_CASE_				:  Any           =     len(_snake_case					)

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



  def  UpperCAmelCase    (				self					):
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_				:  Dict           =     scheduler_class(**_snake_case					)

        SCREAMING_SNAKE_CASE_				:  str           =     [scheduler.config.num_train_timesteps]

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









import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     TaConfig.from_json_file(a  )
      print(f"Building PyTorch model from configuration: {config}"  )
      SCREAMING_SNAKE_CASE_				:  Tuple           =     TaForConditionalGeneration(a  )

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(a					,   a					,   a  )

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


if __name__ == "__main__":
    lowerCAmelCase     :   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(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained T5 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.'
    )
    lowerCAmelCase     :   List[str]						     =  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






