





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42
							lowerCamelCase_           =					None




class   UpperCamelCase_       (     UpperCamelCase__				,  UpperCamelCase__    ):
							lowerCamelCase_           =					2


							@register_to_config
							def __init__(							self    :Any							,				__A    :float = 0.0_2							,				__A    :float = 100							,				__A    :float = 1.0_0_7							,				__A    :float = 80							,				__A    :float = 0.0_5							,				__A    :float = 50							,				)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      sigma_max

									# setable values
									SCREAMING_SNAKE_CASE__			=      None
									SCREAMING_SNAKE_CASE__			=      None
									SCREAMING_SNAKE_CASE__			=      None  # sigma(t_i)


							def      _snake_case  (							self    :Optional[int]							,				__A    :torch.FloatTensor							,				__A    :Optional[int] = None		)   ->   torch.FloatTensor:



									"""simple docstring"""



									return sample


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :int							,				__A    :Union[str, torch.device] = None		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      num_inference_steps
									SCREAMING_SNAKE_CASE__			=      np.arange(0							,				self.num_inference_steps		)[::-1].copy()
									SCREAMING_SNAKE_CASE__			=      torch.from_numpy(__A		).to(__A		)
									SCREAMING_SNAKE_CASE__			=      [
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in self.timesteps
									]
									SCREAMING_SNAKE_CASE__			=      torch.tensor(__A							,				dtype=torch.floataa							,				device=__A		)


							def      _snake_case  (							self    :List[Any]							,				__A    :torch.FloatTensor							,				__A    :float							,				__A    :Optional[torch.Generator] = None		)   ->   Tuple[torch.FloatTensor, float]:



									"""simple docstring"""



									if self.config.s_min <= sigma <= self.config.s_max:
											SCREAMING_SNAKE_CASE__			=      min(self.config.s_churn / self.num_inference_steps							,				2**0.5 - 1		)
									else:
											SCREAMING_SNAKE_CASE__			=      0

									# sample eps ~ N(0, S_noise^2 * I)
									SCREAMING_SNAKE_CASE__			=      self.config.s_noise * randn_tensor(sample.shape							,				generator=__A		).to(sample.device		)
									SCREAMING_SNAKE_CASE__			=      sigma + gamma * sigma
									SCREAMING_SNAKE_CASE__			=      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat


							def      _snake_case  (							self    :Optional[int]							,				__A    :torch.FloatTensor							,				__A    :float							,				__A    :float							,				__A    :torch.FloatTensor							,				__A    :bool = True							,				)   ->   Union[KarrasVeOutput, Tuple]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      sample_hat + sigma_hat * model_output
									SCREAMING_SNAKE_CASE__			=      (sample_hat - pred_original_sample) / sigma_hat
									SCREAMING_SNAKE_CASE__			=      sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
											return (sample_prev, derivative)

									return KarrasVeOutput(
									    prev_sample=__A							,				derivative=__A							,				pred_original_sample=__A		)


							def      _snake_case  (							self    :List[Any]							,				__A    :torch.FloatTensor							,				__A    :float							,				__A    :float							,				__A    :torch.FloatTensor							,				__A    :torch.FloatTensor							,				__A    :torch.FloatTensor							,				__A    :bool = True							,				)   ->   Union[KarrasVeOutput, Tuple]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      sample_prev + sigma_prev * model_output
									SCREAMING_SNAKE_CASE__			=      (sample_prev - pred_original_sample) / sigma_prev
									SCREAMING_SNAKE_CASE__			=      sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
											return (sample_prev, derivative)

									return KarrasVeOutput(
									    prev_sample=__A							,				derivative=__A							,				pred_original_sample=__A		)


							def      _snake_case  (							self    :str							,				__A    :str							,				__A    :Dict							,				__A    :Tuple		)   ->   Any:



									"""simple docstring"""



									raise NotImplementedError()






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

				SCREAMING_SNAKE_CASE__			=      corpus.__dict__
				corpus_dict_no_vocab.pop("""vocab"""					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      pytorch_dump_folder_path + """/""" + CORPUS_NAME
				print(f'''Save dataset to {pytorch_dataset_dump_path}'''    )
				torch.save(UpperCamelCase__					,   UpperCamelCase__    )

		if tf_checkpoint_path:
				# Convert a pre-trained TensorFlow model
				SCREAMING_SNAKE_CASE__			=      os.path.abspath(UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      os.path.abspath(UpperCamelCase__    )

				print(f'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''    )
				# Initialise PyTorch model
				if transfo_xl_config_file == "":
						SCREAMING_SNAKE_CASE__			=      TransfoXLConfig()
				else:
						SCREAMING_SNAKE_CASE__			=      TransfoXLConfig.from_json_file(UpperCamelCase__    )
				print(f'''Building PyTorch model from configuration: {config}'''    )
				SCREAMING_SNAKE_CASE__			=      TransfoXLLMHeadModel(UpperCamelCase__    )

				SCREAMING_SNAKE_CASE__			=      load_tf_weights_in_transfo_xl(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
				# Save pytorch-model
				SCREAMING_SNAKE_CASE__			=      os.path.join(UpperCamelCase__					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      os.path.join(UpperCamelCase__					,   UpperCamelCase__    )
				print(f'''Save PyTorch model to {os.path.abspath(UpperCamelCase__    )}'''    )
				torch.save(model.state_dict()					,   UpperCamelCase__    )
				print(f'''Save configuration file to {os.path.abspath(UpperCamelCase__    )}'''    )
				with open(UpperCamelCase__					,   """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,
	)