



'''simple docstring'''





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=(EulerDiscreteScheduler,)
  a_												=10




  def 						_lowercase     ( self      :	List[str]			,	**_a      :	Optional[int]   )					->					str:
    __lowerCamelCase       :       Dict           = {
        'num_train_timesteps': 1100,
        'beta_start': 0.0001,
        'beta_end': 0.02,
        'beta_schedule': 'linear',
    }

    config.update(**_a   )
    return config




  def 						_lowercase     ( self      :	List[str]   )					->					Optional[Any]:
    for timesteps in [10, 50, 100, 1000]:
      self.check_over_configs(num_train_timesteps=_a   )




  def 						_lowercase     ( self      :	Dict   )					->					Dict:
    for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]			,	[0.0002, 0.002, 0.02]   ):
      self.check_over_configs(beta_start=_a			,	beta_end=_a   )




  def 						_lowercase     ( self      :	str   )					->					Optional[Any]:
    for schedule in ["linear", "scaled_linear"]:
      self.check_over_configs(beta_schedule=_a   )




  def 						_lowercase     ( self      :	Tuple   )					->					int:
    for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=_a   )




  def 						_lowercase     ( self      :	Any   )					->					Union[str, Any]:
    __lowerCamelCase       :       int           = self.scheduler_classes[0]
    __lowerCamelCase       :       List[Any]           = self.get_scheduler_config()
    __lowerCamelCase       :       Dict           = scheduler_class(**_a   )

    scheduler.set_timesteps(self.num_inference_steps   )

    __lowerCamelCase       :       List[str]           = torch.manual_seed(0   )

    __lowerCamelCase       :       Dict           = self.dummy_model()
    __lowerCamelCase       :       List[Any]           = self.dummy_sample_deter * scheduler.init_noise_sigma
    __lowerCamelCase       :       List[Any]           = sample.to(_a   )

    for i, t in enumerate(scheduler.timesteps   ):
      __lowerCamelCase       :       List[Any]           = scheduler.scale_model_input(_a			,	_a   )

      __lowerCamelCase       :       List[Any]           = model(_a			,	_a   )

      __lowerCamelCase       :       Any           = scheduler.step(_a			,	_a			,	_a			,	generator=_a   )
      __lowerCamelCase       :       List[str]           = output.prev_sample

    __lowerCamelCase       :       int           = torch.sum(torch.abs(_a   )   )
    __lowerCamelCase       :       Optional[int]           = torch.mean(torch.abs(_a   )   )

    assert abs(result_sum.item() - 10.0807   ) < 1e-2
    assert abs(result_mean.item() - 0.0131   ) < 1e-3




  def 						_lowercase     ( self      :	Dict   )					->					List[str]:
    __lowerCamelCase       :       Union[str, Any]           = self.scheduler_classes[0]
    __lowerCamelCase       :       Union[str, Any]           = self.get_scheduler_config(prediction_type='v_prediction'   )
    __lowerCamelCase       :       str           = scheduler_class(**_a   )

    scheduler.set_timesteps(self.num_inference_steps   )

    __lowerCamelCase       :       Optional[int]           = torch.manual_seed(0   )

    __lowerCamelCase       :       str           = self.dummy_model()
    __lowerCamelCase       :       Any           = self.dummy_sample_deter * scheduler.init_noise_sigma
    __lowerCamelCase       :       Dict           = sample.to(_a   )

    for i, t in enumerate(scheduler.timesteps   ):
      __lowerCamelCase       :       str           = scheduler.scale_model_input(_a			,	_a   )

      __lowerCamelCase       :       int           = model(_a			,	_a   )

      __lowerCamelCase       :       Dict           = scheduler.step(_a			,	_a			,	_a			,	generator=_a   )
      __lowerCamelCase       :       Union[str, Any]           = output.prev_sample

    __lowerCamelCase       :       Any           = torch.sum(torch.abs(_a   )   )
    __lowerCamelCase       :       Any           = torch.mean(torch.abs(_a   )   )

    assert abs(result_sum.item() - 0.0002   ) < 1e-2
    assert abs(result_mean.item() - 2.2_676e-06   ) < 1e-3




  def 						_lowercase     ( self      :	Dict   )					->					Optional[Any]:
    __lowerCamelCase       :       Dict           = self.scheduler_classes[0]
    __lowerCamelCase       :       Dict           = self.get_scheduler_config()
    __lowerCamelCase       :       Tuple           = scheduler_class(**_a   )

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

    __lowerCamelCase       :       List[str]           = torch.manual_seed(0   )

    __lowerCamelCase       :       int           = self.dummy_model()
    __lowerCamelCase       :       List[Any]           = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    __lowerCamelCase       :       List[str]           = sample.to(_a   )

    for t in scheduler.timesteps:
      __lowerCamelCase       :       Union[str, Any]           = scheduler.scale_model_input(_a			,	_a   )

      __lowerCamelCase       :       List[Any]           = model(_a			,	_a   )

      __lowerCamelCase       :       Optional[int]           = scheduler.step(_a			,	_a			,	_a			,	generator=_a   )
      __lowerCamelCase       :       Any           = output.prev_sample

    __lowerCamelCase       :       Optional[int]           = torch.sum(torch.abs(_a   )   )
    __lowerCamelCase       :       Any           = torch.mean(torch.abs(_a   )   )

    assert abs(result_sum.item() - 10.0807   ) < 1e-2
    assert abs(result_mean.item() - 0.0131   ) < 1e-3




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Dict:
    __lowerCamelCase       :       Union[str, Any]           = self.scheduler_classes[0]
    __lowerCamelCase       :       List[Any]           = self.get_scheduler_config()
    __lowerCamelCase       :       int           = scheduler_class(**_a			,	use_karras_sigmas=_a   )

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

    __lowerCamelCase       :       Optional[Any]           = torch.manual_seed(0   )

    __lowerCamelCase       :       Union[str, Any]           = self.dummy_model()
    __lowerCamelCase       :       List[Any]           = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    __lowerCamelCase       :       Union[str, Any]           = sample.to(_a   )

    for t in scheduler.timesteps:
      __lowerCamelCase       :       str           = scheduler.scale_model_input(_a			,	_a   )

      __lowerCamelCase       :       Optional[Any]           = model(_a			,	_a   )

      __lowerCamelCase       :       Union[str, Any]           = scheduler.step(_a			,	_a			,	_a			,	generator=_a   )
      __lowerCamelCase       :       Optional[int]           = output.prev_sample

    __lowerCamelCase       :       Union[str, Any]           = torch.sum(torch.abs(_a   )   )
    __lowerCamelCase       :       Optional[Any]           = torch.mean(torch.abs(_a   )   )

    assert abs(result_sum.item() - 124.52299499511719   ) < 1e-2
    assert abs(result_mean.item() - 0.16213932633399963   ) < 1e-3











'''simple docstring'''





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_UpperCamelCase											=						data_utils.TransfoXLTokenizer
_UpperCamelCase											=						data_utils.TransfoXLCorpus
_UpperCamelCase											=						data_utils
_UpperCamelCase											=						data_utils

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[Any]:
  if transfo_xl_dataset_file:
    # Convert a pre-processed corpus (see original TensorFlow repo)
    with open(_lowerCAmelCase       ,'rb'       ) as fp:
      __lowerCamelCase       :       Optional[Any]           = pickle.load(_lowerCAmelCase       ,encoding='latin1'       )
    # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
    __lowerCamelCase       :       Tuple           = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
    print(F'Save vocabulary to {pytorch_vocab_dump_path}'       )
    __lowerCamelCase       :       str           = corpus.vocab.__dict__
    torch.save(_lowerCAmelCase       ,_lowerCAmelCase       )

    __lowerCamelCase       :       Optional[Any]           = corpus.__dict__
    corpus_dict_no_vocab.pop('vocab'       ,_lowerCAmelCase       )
    __lowerCamelCase       :       Optional[Any]           = pytorch_dump_folder_path + '/' + CORPUS_NAME
    print(F'Save dataset to {pytorch_dataset_dump_path}'       )
    torch.save(_lowerCAmelCase       ,_lowerCAmelCase       )

  if tf_checkpoint_path:
    # Convert a pre-trained TensorFlow model
    __lowerCamelCase       :       int           = os.path.abspath(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = os.path.abspath(_lowerCAmelCase       )

    print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'       )
    # Initialise PyTorch model
    if transfo_xl_config_file == "":
      __lowerCamelCase       :       Optional[int]           = TransfoXLConfig()
    else:
      __lowerCamelCase       :       Optional[int]           = TransfoXLConfig.from_json_file(_lowerCAmelCase       )
    print(F'Building PyTorch model from configuration: {config}'       )
    __lowerCamelCase       :       List[str]           = TransfoXLLMHeadModel(_lowerCAmelCase       )

    __lowerCamelCase       :       Dict           = load_tf_weights_in_transfo_xl(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )
    # Save pytorch-model
    __lowerCamelCase       :       List[str]           = os.path.join(_lowerCAmelCase       ,_lowerCAmelCase       )
    __lowerCamelCase       :       int           = os.path.join(_lowerCAmelCase       ,_lowerCAmelCase       )
    print(F'Save PyTorch model to {os.path.abspath(_lowerCAmelCase       )}'       )
    torch.save(model.state_dict()       ,_lowerCAmelCase       )
    print(F'Save configuration file to {os.path.abspath(_lowerCAmelCase       )}'       )
    with open(_lowerCAmelCase       ,'w'       ,encoding='utf-8'       ) as f:
      f.write(config.to_json_string()       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=str,
       required=True,
       help='Path to the folder to store the PyTorch model or dataset/vocab.',
   )
   parser.add_argument(
       '--tf_checkpoint_path',
       default='',
       type=str,
       help='An optional path to a TensorFlow checkpoint path to be converted.',
   )
   parser.add_argument(
       '--transfo_xl_config_file',
       default='',
       type=str,
       help=(
           'An optional config json file corresponding to the pre-trained BERT model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--transfo_xl_dataset_file',
       default='',
       type=str,
       help='An optional dataset file to be converted in a vocabulary.',
   )
   _UpperCamelCase											=						parser.parse_args()
   convert_transfo_xl_checkpoint_to_pytorch(
       args.tf_checkpoint_path,
       args.transfo_xl_config_file,
       args.pytorch_dump_folder_path,
       args.transfo_xl_dataset_file,
   )






