

import math
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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =42
							__UpperCAmelCase       =None


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=0.999		,					UpperCAmelCase__="cosine"		,					):

					"""simple docstring"""






					if alpha_transform_type == "cosine":

										def alpha_bar_fn(UpperCAmelCase__      ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(UpperCAmelCase__      ):
															return math.exp(t * -12.0      )

					else:
										raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""      )

					__lowerCAmelCase					=							[]
					for i in range(UpperCAmelCase__      ):
										__lowerCAmelCase					=							i / num_diffusion_timesteps
										__lowerCAmelCase					=							(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(UpperCAmelCase__      ) / alpha_bar_fn(UpperCAmelCase__      )		,					UpperCAmelCase__      )      )
					return torch.tensor(UpperCAmelCase__		,					dtype=torch.floataa      )





class     snake_case_			(				_a			, _a							):


							"""simple docstring"""





							@register_to_config
							def __init__( self					,						_A = 1_0_0_0					,						_A = "fixed_small_log"					,						_A = True					,						_A = 1.0					,						_A = "epsilon"					,						_A = "squaredcos_cap_v2"					,						):
												if beta_schedule != "squaredcos_cap_v2":
																	raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''							)

												__lowerCAmelCase					=							betas_for_alpha_bar(_A							)

												__lowerCAmelCase					=							1.0 - self.betas
												__lowerCAmelCase					=							torch.cumprod(self.alphas					,						dim=0							)
												__lowerCAmelCase					=							torch.tensor(1.0							)

												# standard deviation of the initial noise distribution
												__lowerCAmelCase					=							1.0

												# setable values
												__lowerCAmelCase					=							None
												__lowerCAmelCase					=							torch.from_numpy(np.arange(0					,						_A							)[::-1].copy()							)

												__lowerCAmelCase					=							variance_type




							def        A__  ( self					,						_A					,						_A = None							):
												return sample




							def        A__  ( self					,						_A					,						_A = None							):
												__lowerCAmelCase					=							num_inference_steps
												__lowerCAmelCase					=							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
												__lowerCAmelCase					=							(np.arange(0					,						_A							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
												__lowerCAmelCase					=							torch.from_numpy(_A							).to(_A							)




							def        A__  ( self					,						_A					,						_A=None					,						_A=None					,						_A=None							):
												if prev_timestep is None:
																	__lowerCAmelCase					=							t - 1

												__lowerCAmelCase					=							self.alphas_cumprod[t]
												__lowerCAmelCase					=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
												__lowerCAmelCase					=							1 - alpha_prod_t
												__lowerCAmelCase					=							1 - alpha_prod_t_prev

												if prev_timestep == t - 1:
																	__lowerCAmelCase					=							self.betas[t]
												else:
																	__lowerCAmelCase					=							1 - alpha_prod_t / alpha_prod_t_prev

												# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
												# and sample from it to get previous sample
												# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
												__lowerCAmelCase					=							beta_prod_t_prev / beta_prod_t * beta

												if variance_type is None:
																	__lowerCAmelCase					=							self.config.variance_type

												# hacks - were probably added for training stability
												if variance_type == "fixed_small_log":
																	__lowerCAmelCase					=							torch.log(torch.clamp(_A					,						min=1e-2_0							)							)
																	__lowerCAmelCase					=							torch.exp(0.5 * variance							)
												elif variance_type == "learned_range":
																	# NOTE difference with DDPM scheduler
																	__lowerCAmelCase					=							variance.log()
																	__lowerCAmelCase					=							beta.log()

																	__lowerCAmelCase					=							(predicted_variance + 1) / 2
																	__lowerCAmelCase					=							frac * max_log + (1 - frac) * min_log

												return variance




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A = None					,						_A=None					,						_A = True					,						):
												__lowerCAmelCase					=							timestep

												if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																	__lowerCAmelCase,	__lowerCAmelCase					=							torch.split(_A					,						sample.shape[1]					,						dim=1							)
												else:
																	__lowerCAmelCase					=							None

												# 1. compute alphas, betas
												if prev_timestep is None:
																	__lowerCAmelCase					=							t - 1

												__lowerCAmelCase					=							self.alphas_cumprod[t]
												__lowerCAmelCase					=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
												__lowerCAmelCase					=							1 - alpha_prod_t
												__lowerCAmelCase					=							1 - alpha_prod_t_prev

												if prev_timestep == t - 1:
																	__lowerCAmelCase					=							self.betas[t]
																	__lowerCAmelCase					=							self.alphas[t]
												else:
																	__lowerCAmelCase					=							1 - alpha_prod_t / alpha_prod_t_prev
																	__lowerCAmelCase					=							1 - beta

												# 2. compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
												if self.config.prediction_type == "epsilon":
																	__lowerCAmelCase					=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
												elif self.config.prediction_type == "sample":
																	__lowerCAmelCase					=							model_output
												else:
																	raise ValueError(
																	    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																	    ' for the UnCLIPScheduler.'							)

												# 3. Clip "predicted x_0"
												if self.config.clip_sample:
																	__lowerCAmelCase					=							torch.clamp(
																	    _A					,						-self.config.clip_sample_range					,						self.config.clip_sample_range							)

												# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
												# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
												__lowerCAmelCase					=							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
												__lowerCAmelCase					=							alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

												# 5. Compute predicted previous sample µ_t
												# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
												__lowerCAmelCase					=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

												# 6. Add noise
												__lowerCAmelCase					=							0
												if t > 0:
																	__lowerCAmelCase					=							randn_tensor(
																	    model_output.shape					,						dtype=model_output.dtype					,						generator=_A					,						device=model_output.device							)

																	__lowerCAmelCase					=							self._get_variance(
																	    _A					,						predicted_variance=_A					,						prev_timestep=_A					,						)

																	if self.variance_type == "fixed_small_log":
																						__lowerCAmelCase					=							variance
																	elif self.variance_type == "learned_range":
																						__lowerCAmelCase					=							(0.5 * variance).exp()
																	else:
																						raise ValueError(
																						    F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																						    ' for the UnCLIPScheduler.'							)

																	__lowerCAmelCase					=							variance * variance_noise

												__lowerCAmelCase					=							pred_prev_sample + variance

												if not return_dict:
																	return (pred_prev_sample,)

												return UnCLIPSchedulerOutput(prev_sample=_A					,						pred_original_sample=_A							)




							def        A__  ( self					,						_A					,						_A					,						_A					,						):
												# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
												__lowerCAmelCase					=							self.alphas_cumprod.to(device=original_samples.device					,						dtype=original_samples.dtype							)
												__lowerCAmelCase					=							timesteps.to(original_samples.device							)

												__lowerCAmelCase					=							alphas_cumprod[timesteps] ** 0.5
												__lowerCAmelCase					=							sqrt_alpha_prod.flatten()
												while len(sqrt_alpha_prod.shape							) < len(original_samples.shape							):
																	__lowerCAmelCase					=							sqrt_alpha_prod.unsqueeze(-1							)

												__lowerCAmelCase					=							(1 - alphas_cumprod[timesteps]) ** 0.5
												__lowerCAmelCase					=							sqrt_one_minus_alpha_prod.flatten()
												while len(sqrt_one_minus_alpha_prod.shape							) < len(original_samples.shape							):
																	__lowerCAmelCase					=							sqrt_one_minus_alpha_prod.unsqueeze(-1							)

												__lowerCAmelCase					=							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
												return noisy_samples









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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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

										__lowerCAmelCase					=							corpus.__dict__
										corpus_dict_no_vocab.pop('vocab'		,					UpperCAmelCase__      )
										__lowerCAmelCase					=							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
										__lowerCAmelCase					=							os.path.abspath(UpperCAmelCase__      )
										__lowerCAmelCase					=							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 == "":
															__lowerCAmelCase					=							TransfoXLConfig()
										else:
															__lowerCAmelCase					=							TransfoXLConfig.from_json_file(UpperCAmelCase__      )
										print(F"""Building PyTorch model from configuration: {config}"""      )
										__lowerCAmelCase					=							TransfoXLLMHeadModel(UpperCAmelCase__      )

										__lowerCAmelCase					=							load_tf_weights_in_transfo_xl(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      )
										# Save pytorch-model
										__lowerCAmelCase					=							os.path.join(UpperCAmelCase__		,					UpperCAmelCase__      )
										__lowerCAmelCase					=							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,
		)






