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

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

			if tf_checkpoint_path:
						# Convert a pre-trained TensorFlow model
						lowerCAmelCase__      :      Tuple         =		os.path.abspath(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Optional[Any]         =		os.path.abspath(SCREAMING_SNAKE_CASE_     )

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

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


if __name__ == "__main__":
					lowerCamelCase__          =		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.""",
					)
					lowerCamelCase__          =		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,
					)

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      A__							(     __magic_name__	):
				lowercase			    =  42
				lowercase			    =  42






				def __init__(						self					:      Any    ,     a					:      UNetaDModel    ,     a					:      ScoreSdeVeScheduler   ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a    ,     scheduler=a   )






				@torch.no_grad()
				def __call__(						self					:      List[str]    ,     a					:      int = 1    ,     a					:      int = 2_000    ,     a					:      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     a					:      Optional[str] = "pil"    ,     a					:      bool = True    ,     **a					:      int    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.unet.config.sample_size
							lowerCAmelCase__      :      Union[str, Any]         =		(batch_size, 3, img_size, img_size)

							lowerCAmelCase__      :      Tuple         =		self.unet

							lowerCAmelCase__      :      Optional[Any]         =		randn_tensor(a    ,     generator=a   ) * self.scheduler.init_noise_sigma
							lowerCAmelCase__      :      int         =		sample.to(self.device   )

							self.scheduler.set_timesteps(a   )
							self.scheduler.set_sigmas(a   )

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
										lowerCAmelCase__      :      Dict         =		self.scheduler.sigmas[i] * torch.ones(shape[0]    ,     device=self.device   )

										# correction step
										for _ in range(self.scheduler.config.correct_steps   ):
													lowerCAmelCase__      :      Optional[Any]         =		self.unet(a    ,     a   ).sample
													lowerCAmelCase__      :      Dict         =		self.scheduler.step_correct(a    ,     a    ,     generator=a   ).prev_sample

										# prediction step
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     a   ).sample
										lowerCAmelCase__      :      Optional[Any]         =		self.scheduler.step_pred(a    ,     a    ,     a    ,     generator=a   )

										lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		output.prev_sample, output.prev_sample_mean

							lowerCAmelCase__      :      Any         =		sample_mean.clamp(0    ,     1   )
							lowerCAmelCase__      :      List[str]         =		sample.cpu().permute(0    ,     2    ,     3    ,     1   ).numpy()
							if output_type == "pil":
										lowerCAmelCase__      :      int         =		self.numpy_to_pil(a   )

							if not return_dict:
										return (sample,)

							return ImagePipelineOutput(images=a   )