import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Any	      = (UnCLIPScheduler,)



   def 	lowerCAmelCase    (						self       :	int		,	**_lowercase       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			{
        '''num_train_timesteps''': 1_000,
        '''variance_type''': '''fixed_small_log''',
        '''clip_sample''': True,
        '''clip_sample_range''': 1.0,
        '''prediction_type''': '''epsilon''',
    }

    config.update(**_lowercase				)
    return config



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    for timesteps in [1, 5, 100, 1_000]:
     self.check_over_configs(num_train_timesteps=_lowercase				)



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    for variance in ["fixed_small_log", "learned_range"]:
     self.check_over_configs(variance_type=_lowercase				)



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    for clip_sample_range in [1, 5, 10, 20]:
     self.check_over_configs(clip_sample_range=_lowercase				)



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    for time_step in [0, 500, 999]:
     for prev_timestep in [None, 5, 100, 250, 500, 750]:
      if prev_timestep is not None and prev_timestep >= time_step:
       continue

      self.check_over_forward(time_step=_lowercase		,	prev_timestep=_lowercase				)



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			self.scheduler_classes[0]
    _UpperCamelCase:  Union[str, Any]    						=			self.get_scheduler_config(variance_type='''fixed_small_log'''				)
    _UpperCamelCase:  Dict    						=			scheduler_class(**_lowercase				)

    assert torch.sum(torch.abs(scheduler._get_variance(0				) - 1.00_00E-10				)				) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0549625				)				) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.9994987				)				) < 1E-5



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			self.scheduler_classes[0]
    _UpperCamelCase:  List[Any]    						=			self.get_scheduler_config(variance_type='''learned_range'''				)
    _UpperCamelCase:  Optional[Any]    						=			scheduler_class(**_lowercase				)

    _UpperCamelCase:  Dict    						=			0.5

    assert scheduler._get_variance(1		,	predicted_variance=_lowercase				) - -10.1712790 < 1E-5
    assert scheduler._get_variance(487		,	predicted_variance=_lowercase				) - -5.7998052 < 1E-5
    assert scheduler._get_variance(999		,	predicted_variance=_lowercase				) - -0.0010011 < 1E-5



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			self.scheduler_classes[0]
    _UpperCamelCase:  Tuple    						=			self.get_scheduler_config()
    _UpperCamelCase:  List[Any]    						=			scheduler_class(**_lowercase				)

    _UpperCamelCase:  Union[str, Any]    						=			scheduler.timesteps

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

    for i, t in enumerate(_lowercase				):
     # 1. predict noise residual
     _UpperCamelCase:  int    						=			model(_lowercase		,	_lowercase				)

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

     _UpperCamelCase:  Optional[Any]    						=			pred_prev_sample

    _UpperCamelCase:  List[Any]    						=			torch.sum(torch.abs(_lowercase				)				)
    _UpperCamelCase:  Optional[Any]    						=			torch.mean(torch.abs(_lowercase				)				)

    assert abs(result_sum.item() - 252.2682495				) < 1E-2
    assert abs(result_mean.item() - 0.3284743				) < 1E-3



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			self.scheduler_classes[0]
    _UpperCamelCase:  List[str]    						=			self.get_scheduler_config()
    _UpperCamelCase:  List[str]    						=			scheduler_class(**_lowercase				)

    scheduler.set_timesteps(25				)

    _UpperCamelCase:  Union[str, Any]    						=			scheduler.timesteps

    _UpperCamelCase:  Union[str, Any]    						=			self.dummy_model()
    _UpperCamelCase:  int    						=			self.dummy_sample_deter
    _UpperCamelCase:  str    						=			torch.manual_seed(0				)

    for i, t in enumerate(_lowercase				):
     # 1. predict noise residual
     _UpperCamelCase:  List[Any]    						=			model(_lowercase		,	_lowercase				)

     if i + 1 == timesteps.shape[0]:
      _UpperCamelCase:  Union[str, Any]    						=			None
     else:
      _UpperCamelCase:  Optional[Any]    						=			timesteps[i + 1]

     # 2. predict previous mean of sample x_t-1
     _UpperCamelCase:  List[Any]    						=			scheduler.step(
         _lowercase		,	_lowercase		,	_lowercase		,	prev_timestep=_lowercase		,	generator=_lowercase				).prev_sample

     _UpperCamelCase:  Optional[Any]    						=			pred_prev_sample

    _UpperCamelCase:  Optional[Any]    						=			torch.sum(torch.abs(_lowercase				)				)
    _UpperCamelCase:  List[str]    						=			torch.mean(torch.abs(_lowercase				)				)

    assert abs(result_sum.item() - 258.2044983				) < 1E-2
    assert abs(result_mean.item() - 0.3362038				) < 1E-3



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    pass
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    lowerCAmelCase_  (		lowercase:				Tuple	,       lowercase:				Union[str, Any]	,       lowercase:				Any	,       lowercase:				List[Any]		)     ->					List[Any]:







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

  _UpperCamelCase:  str    						=			corpus.__dict__
  corpus_dict_no_vocab.pop('''vocab'''	,       lowercase		)
  _UpperCamelCase:  str    						=			pytorch_dump_folder_path + '''/''' + CORPUS_NAME
  print(F"""Save dataset to {pytorch_dataset_dump_path}"""		)
  torch.save(lowercase	,       lowercase		)

 if tf_checkpoint_path:
  # Convert a pre-trained TensorFlow model
  _UpperCamelCase:  Optional[Any]    						=			os.path.abspath(lowercase		)
  _UpperCamelCase:  List[str]    						=			os.path.abspath(lowercase		)

  print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""		)
  # Initialise PyTorch model
  if transfo_xl_config_file == "":
   _UpperCamelCase:  Tuple    						=			TransfoXLConfig()
  else:
   _UpperCamelCase:  Union[str, Any]    						=			TransfoXLConfig.from_json_file(lowercase		)
  print(F"""Building PyTorch model from configuration: {config}"""		)
  _UpperCamelCase:  Optional[int]    						=			TransfoXLLMHeadModel(lowercase		)

  _UpperCamelCase:  int    						=			load_tf_weights_in_transfo_xl(lowercase	,       lowercase	,       lowercase		)
  # Save pytorch-model
  _UpperCamelCase:  List[str]    						=			os.path.join(lowercase	,       lowercase		)
  _UpperCamelCase:  Union[str, Any]    						=			os.path.join(lowercase	,       lowercase		)
  print(F"""Save PyTorch model to {os.path.abspath(lowercase		)}"""		)
  torch.save(model.state_dict()	,       lowercase		)
  print(F"""Save configuration file to {os.path.abspath(lowercase		)}"""		)
  with open(lowercase	,       '''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,
  )