





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Any     	=	(DDPMParallelScheduler,)




   def    UpperCamelCase__						(					self,	**__magic_name__							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   {
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''variance_type''': '''fixed_small''',
           '''clip_sample''': True,
       }

       config.update(**__magic_name__							)
       return config




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






       """simple docstring"""
       for timesteps in [1, 5, 100, 1000]:
           self.check_over_configs(num_train_timesteps=__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       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=__magic_name__,	beta_end=__magic_name__							)




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






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




   def    UpperCamelCase__						(					self							)				->   int:






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




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   int:






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




   def    UpperCamelCase__						(					self							)				->   int:






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




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






       """simple docstring"""
       for t in [0, 500, 999]:
           self.check_over_forward(time_step=__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   self.scheduler_classes[0]
       UpperCamelCase__							:       int      =   self.get_scheduler_config()
       UpperCamelCase__							:       Tuple      =   scheduler_class(**__magic_name__							)

       assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0979							)							) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.02							)							) < 1E-5




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






       """simple docstring"""
       UpperCamelCase__							:       str      =   self.scheduler_classes[0]
       UpperCamelCase__							:       Any      =   self.get_scheduler_config()
       UpperCamelCase__							:       str      =   scheduler_class(**__magic_name__							)

       UpperCamelCase__							:       Optional[Any]      =   len(__magic_name__							)

       UpperCamelCase__							:       Optional[Any]      =   self.dummy_model()
       UpperCamelCase__							:       str      =   self.dummy_sample_deter
       UpperCamelCase__							:       Optional[int]      =   self.dummy_sample_deter + 0.1
       UpperCamelCase__							:       Optional[Any]      =   self.dummy_sample_deter - 0.1

       UpperCamelCase__							:       Optional[int]      =   samplea.shape[0]
       UpperCamelCase__							:       Tuple      =   torch.stack([samplea, samplea, samplea],	dim=0							)
       UpperCamelCase__							:       Union[str, Any]      =   torch.arange(__magic_name__							)[0:3, None].repeat(1,	__magic_name__							)

       UpperCamelCase__							:       Union[str, Any]      =   model(samples.flatten(0,	1							),	timesteps.flatten(0,	1							)							)
       UpperCamelCase__							:       List[str]      =   scheduler.batch_step_no_noise(__magic_name__,	timesteps.flatten(0,	1							),	samples.flatten(0,	1							)							)

       UpperCamelCase__							:       Any      =   torch.sum(torch.abs(__magic_name__							)							)
       UpperCamelCase__							:       int      =   torch.mean(torch.abs(__magic_name__							)							)

       assert abs(result_sum.item() - 1153.1833							) < 1E-2
       assert abs(result_mean.item() - 0.5005							) < 1E-3




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Any      =   self.scheduler_classes[0]
       UpperCamelCase__							:       Optional[int]      =   self.get_scheduler_config()
       UpperCamelCase__							:       Union[str, Any]      =   scheduler_class(**__magic_name__							)

       UpperCamelCase__							:       int      =   len(__magic_name__							)

       UpperCamelCase__							:       Dict      =   self.dummy_model()
       UpperCamelCase__							:       Optional[int]      =   self.dummy_sample_deter
       UpperCamelCase__							:       List[str]      =   torch.manual_seed(0							)

       for t in reversed(range(__magic_name__							)							):
           # 1. predict noise residual
           UpperCamelCase__							:       str      =   model(__magic_name__,	__magic_name__							)

           # 2. predict previous mean of sample x_t-1
           UpperCamelCase__							:       Any      =   scheduler.step(__magic_name__,	__magic_name__,	__magic_name__,	generator=__magic_name__							).prev_sample

           UpperCamelCase__							:       List[Any]      =   pred_prev_sample

       UpperCamelCase__							:       List[str]      =   torch.sum(torch.abs(__magic_name__							)							)
       UpperCamelCase__							:       Tuple      =   torch.mean(torch.abs(__magic_name__							)							)

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




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






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   self.scheduler_classes[0]
       UpperCamelCase__							:       List[str]      =   self.get_scheduler_config(prediction_type='''v_prediction'''							)
       UpperCamelCase__							:       Union[str, Any]      =   scheduler_class(**__magic_name__							)

       UpperCamelCase__							:       Dict      =   len(__magic_name__							)

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

       for t in reversed(range(__magic_name__							)							):
           # 1. predict noise residual
           UpperCamelCase__							:       Any      =   model(__magic_name__,	__magic_name__							)

           # 2. predict previous mean of sample x_t-1
           UpperCamelCase__							:       Any      =   scheduler.step(__magic_name__,	__magic_name__,	__magic_name__,	generator=__magic_name__							).prev_sample

           UpperCamelCase__							:       List[Any]      =   pred_prev_sample

       UpperCamelCase__							:       Tuple      =   torch.sum(torch.abs(__magic_name__							)							)
       UpperCamelCase__							:       Tuple      =   torch.mean(torch.abs(__magic_name__							)							)

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




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   self.scheduler_classes[0]
       UpperCamelCase__							:       List[Any]      =   self.get_scheduler_config()
       UpperCamelCase__							:       Optional[int]      =   scheduler_class(**__magic_name__							)

       UpperCamelCase__							:       int      =   [100, 87, 50, 1, 0]

       scheduler.set_timesteps(timesteps=__magic_name__							)

       UpperCamelCase__							:       List[str]      =   scheduler.timesteps

       for i, timestep in enumerate(__magic_name__							):
           if i == len(__magic_name__							) - 1:
               UpperCamelCase__							:       Any      =   -1
           else:
               UpperCamelCase__							:       Tuple      =   timesteps[i + 1]

           UpperCamelCase__							:       str      =   scheduler.previous_timestep(__magic_name__							)
           UpperCamelCase__							:       str      =   prev_t.item()

           self.assertEqual(__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   self.scheduler_classes[0]
       UpperCamelCase__							:       int      =   self.get_scheduler_config()
       UpperCamelCase__							:       List[str]      =   scheduler_class(**__magic_name__							)

       UpperCamelCase__							:       List[str]      =   [100, 87, 50, 51, 0]

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




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






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   self.scheduler_classes[0]
       UpperCamelCase__							:       Optional[Any]      =   self.get_scheduler_config()
       UpperCamelCase__							:       List[Any]      =   scheduler_class(**__magic_name__							)

       UpperCamelCase__							:       Any      =   [100, 87, 50, 1, 0]
       UpperCamelCase__							:       List[Any]      =   len(__magic_name__							)

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




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






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   self.scheduler_classes[0]
       UpperCamelCase__							:       str      =   self.get_scheduler_config()
       UpperCamelCase__							:       str      =   scheduler_class(**__magic_name__							)

       UpperCamelCase__							:       List[Any]      =   [scheduler.config.num_train_timesteps]

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








import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    str   )   ->			Optional[int]:
    # Initialise PyTorch model.
    # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
    # TapasConfig to False.

    # initialize configuration from json file
    UpperCamelCase__							:       Any      =   TapasConfig.from_json_file(__UpperCAmelCase   )
    # set absolute/relative position embeddings parameter
    UpperCamelCase__							:       Optional[Any]      =   reset_position_index_per_cell

    # set remaining parameters of TapasConfig as well as the model based on the task
    if task == "SQA":
        UpperCamelCase__							:       List[Any]      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "WTQ":
        # run_task_main.py hparams
        UpperCamelCase__							:       Any      =   4
        UpperCamelCase__							:       List[str]      =   True
        # hparam_utils.py hparams
        UpperCamelCase__							:       int      =   0.664694
        UpperCamelCase__							:       Union[str, Any]      =   0.207951
        UpperCamelCase__							:       Any      =   0.121194
        UpperCamelCase__							:       int      =   True
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       str      =   False
        UpperCamelCase__							:       int      =   0.0352513

        UpperCamelCase__							:       Dict      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        UpperCamelCase__							:       Any      =   4
        UpperCamelCase__							:       List[str]      =   False
        # hparam_utils.py hparams
        UpperCamelCase__							:       int      =   36.4519
        UpperCamelCase__							:       int      =   0.903421
        UpperCamelCase__							:       List[str]      =   222.088
        UpperCamelCase__							:       Dict      =   True
        UpperCamelCase__							:       List[str]      =   True
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       int      =   0.763141

        UpperCamelCase__							:       Union[str, Any]      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "TABFACT":
        UpperCamelCase__							:       List[Any]      =   TapasForSequenceClassification(config=__UpperCAmelCase   )
    elif task == "MLM":
        UpperCamelCase__							:       Optional[Any]      =   TapasForMaskedLM(config=__UpperCAmelCase   )
    elif task == "INTERMEDIATE_PRETRAINING":
        UpperCamelCase__							:       str      =   TapasModel(config=__UpperCAmelCase   )
    else:
        raise ValueError(f"Task {task} not supported."   )

    print(f"Building PyTorch model from configuration: {config}"   )
    # Load weights from tf checkpoint
    load_tf_weights_in_tapas(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

    # Save pytorch-model (weights and configuration)
    print(f"Save PyTorch model to {pytorch_dump_path}"   )
    model.save_pretrained(__UpperCAmelCase   )

    # Save tokenizer files
    print(f"Save tokenizer files to {pytorch_dump_path}"   )
    UpperCamelCase__							:       str      =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + '''vocab.txt'''	,     model_max_length=512   )
    tokenizer.save_pretrained(__UpperCAmelCase   )

    print('''Used relative position embeddings:'''	,     model.config.reset_position_index_per_cell   )


if __name__ == "__main__":
  UpperCAmelCase_  							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
  )
  parser.add_argument(
      '--reset_position_index_per_cell',
      default=False,
      action='store_true',
      help='Whether to use relative position embeddings or not. Defaults to True.',
  )
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--tapas_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained TAPAS 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.'
  )
  UpperCAmelCase_  							=				parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )

