
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Tuple      =					(UnCLIPScheduler,)

						def 							SCREAMING_SNAKE_CASE__(       self ,       **__lowercase  ) ->				str:
									"""simple docstring"""




									a__       :						int							=  {
									    """num_train_timesteps""": 1_0_0_0,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**__lowercase  )
									return config

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									for clip_sample_range in [1, 5, 1_0, 2_0]:
												self.check_over_configs(clip_sample_range=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									for time_step in [0, 5_0_0, 9_9_9]:
												for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
															if prev_timestep is not None and prev_timestep >= time_step:
																		continue

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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						Tuple							=  self.scheduler_classes[0]
									a__       :						Union[str, Any]							=  self.get_scheduler_config(variance_type="""fixed_small_log"""  )
									a__       :						str							=  scheduler_class(**__lowercase  )

									assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 1.0000E-10  )  ) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_8_7  ) - 0.0_5_4_9_6_2_5  )  ) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ) - 0.9_9_9_4_9_8_7  )  ) < 1E-5

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						Optional[int]							=  self.scheduler_classes[0]
									a__       :						Optional[int]							=  self.get_scheduler_config(variance_type="""learned_range"""  )
									a__       :						List[str]							=  scheduler_class(**__lowercase  )

									a__       :						int							=  0.5

									assert scheduler._get_variance(1 ,       predicted_variance=__lowercase  ) - -1_0.1_7_1_2_7_9_0 < 1E-5
									assert scheduler._get_variance(4_8_7 ,       predicted_variance=__lowercase  ) - -5.7_9_9_8_0_5_2 < 1E-5
									assert scheduler._get_variance(9_9_9 ,       predicted_variance=__lowercase  ) - -0.0_0_1_0_0_1_1 < 1E-5

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									a__       :						Dict							=  self.scheduler_classes[0]
									a__       :						Optional[Any]							=  self.get_scheduler_config()
									a__       :						Tuple							=  scheduler_class(**__lowercase  )

									a__       :						str							=  scheduler.timesteps

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

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

												# 2. predict previous mean of sample x_t-1
												a__       :						List[str]							=  scheduler.step(__lowercase ,       __lowercase ,       __lowercase ,       generator=__lowercase  ).prev_sample

												a__       :						List[str]							=  pred_prev_sample

									a__       :						Union[str, Any]							=  torch.sum(torch.abs(__lowercase  )  )
									a__       :						str							=  torch.mean(torch.abs(__lowercase  )  )

									assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5  ) < 1E-2
									assert abs(result_mean.item() - 0.3_2_8_4_7_4_3  ) < 1E-3

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						Any							=  self.scheduler_classes[0]
									a__       :						Optional[int]							=  self.get_scheduler_config()
									a__       :						Dict							=  scheduler_class(**__lowercase  )

									scheduler.set_timesteps(2_5  )

									a__       :						Tuple							=  scheduler.timesteps

									a__       :						Any							=  self.dummy_model()
									a__       :						Any							=  self.dummy_sample_deter
									a__       :						Any							=  torch.manual_seed(0  )

									for i, t in enumerate(__lowercase  ):
												# 1. predict noise residual
												a__       :						str							=  model(__lowercase ,       __lowercase  )

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

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

												a__       :						Dict							=  pred_prev_sample

									a__       :						Tuple							=  torch.sum(torch.abs(__lowercase  )  )
									a__       :						int							=  torch.mean(torch.abs(__lowercase  )  )

									assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3  ) < 1E-2
									assert abs(result_mean.item() - 0.3_3_6_2_0_3_8  ) < 1E-3

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""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
_lowercase		:				Union[str, Any]      		=data_utils.TransfoXLTokenizer
_lowercase		:				Tuple      		=data_utils.TransfoXLCorpus
_lowercase		:				Optional[int]      		=data_utils
_lowercase		:				int      		=data_utils

def      lowerCAmelCase_					(  _lowercase    :       Optional[Any]						,       _lowercase    :       List[Any]						,       _lowercase    :       Optional[Any]						,       _lowercase    :       Optional[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:
									a__       :						Optional[Any]							=  pickle.load(_lowercase						,       encoding="""latin1""")
						# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
						a__       :						Dict							=  pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
						print(F'''Save vocabulary to {pytorch_vocab_dump_path}''')
						a__       :						List[str]							=  corpus.vocab.__dict__
						torch.save(_lowercase						,       _lowercase)

						a__       :						Optional[int]							=  corpus.__dict__
						corpus_dict_no_vocab.pop("""vocab"""						,       _lowercase)
						a__       :						Tuple							=  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
						a__       :						Optional[Any]							=  os.path.abspath(_lowercase)
						a__       :						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 == "":
									a__       :						List[str]							=  TransfoXLConfig()
						else:
									a__       :						Any							=  TransfoXLConfig.from_json_file(_lowercase)
						print(F'''Building PyTorch model from configuration: {config}''')
						a__       :						Any							=  TransfoXLLMHeadModel(_lowercase)

						a__       :						Optional[Any]							=  load_tf_weights_in_transfo_xl(_lowercase						,       _lowercase						,       _lowercase)
						# Save pytorch-model
						a__       :						Tuple							=  os.path.join(_lowercase						,       _lowercase)
						a__       :						Optional[int]							=  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__":
		_lowercase		:				Tuple      		=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.",
		)
		_lowercase		:				Dict      		=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,
		)




