




'''simple docstring'''

from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


a  :				int									=  8


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase=BITS	)		->					Any:

					'''simple docstring'''

					snake_case_									=				x.device

					snake_case_									=				(x * 255).int().clamp(0,      255	)

					snake_case_									=				2 ** torch.arange(bits - 1,      -1,      -1,      device=_lowerCAmelCase	)
					snake_case_									=				rearrange(_lowerCAmelCase,      '''d -> d 1 1'''	)
					snake_case_									=				rearrange(_lowerCAmelCase,      '''b c h w -> b c 1 h w'''	)

					snake_case_									=				((x & mask) != 0).float()
					snake_case_									=				rearrange(_lowerCAmelCase,      '''b c d h w -> b (c d) h w'''	)
					snake_case_									=				bits * 2 - 1
					return bits


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase=BITS	)		->					Optional[Any]:

					'''simple docstring'''

					snake_case_									=				x.device

					snake_case_									=				(x > 0).int()
					snake_case_									=				2 ** torch.arange(bits - 1,      -1,      -1,      device=_lowerCAmelCase,      dtype=torch.intaa	)

					snake_case_									=				rearrange(_lowerCAmelCase,      '''d -> d 1 1'''	)
					snake_case_									=				rearrange(_lowerCAmelCase,      '''b (c d) h w -> b c d h w''',      d=8	)
					snake_case_									=				reduce(x * mask,      '''b c d h w -> b c h w''',      '''sum'''	)
					return (dec / 255).clamp(0.0,      1.0	)


def 		__magic_name__    (	self,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase = 0.0,      __UpperCAmelCase = True,      __UpperCAmelCase=None,      __UpperCAmelCase = True,      )		->					Union[DDIMSchedulerOutput, Tuple]:

					'''simple docstring'''

					if self.num_inference_steps is None:
										raise ValueError(
										    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''	)

					# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
					# Ideally, read DDIM paper in-detail understanding

					# Notation (<variable name> -> <name in paper>
					# - pred_noise_t -> e_theta(x_t, t)
					# - pred_original_sample -> f_theta(x_t, t) or x_0
					# - std_dev_t -> sigma_t
					# - eta -> η
					# - pred_sample_direction -> "direction pointing to x_t"
					# - pred_prev_sample -> "x_t-1"

					# 1. get previous step value (=t-1)
					snake_case_									=				timestep - self.config.num_train_timesteps // self.num_inference_steps

					# 2. compute alphas, betas
					snake_case_									=				self.alphas_cumprod[timestep]
					snake_case_									=				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

					snake_case_									=				1 - alpha_prod_t

					# 3. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					snake_case_									=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

					# 4. Clip "predicted x_0"
					snake_case_									=				self.bit_scale
					if self.config.clip_sample:
										snake_case_									=				torch.clamp(_lowerCAmelCase,      -scale,      _lowerCAmelCase	)

					# 5. compute variance: "sigma_t(η)" -> see formula (16)
					# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
					snake_case_									=				self._get_variance(_lowerCAmelCase,      _lowerCAmelCase	)
					snake_case_									=				eta * variance ** 0.5

					if use_clipped_model_output:
										# the model_output is always re-derived from the clipped x_0 in Glide
										snake_case_									=				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

					# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					snake_case_									=				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

					# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					snake_case_									=				alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

					if eta > 0:
										# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
										snake_case_									=				model_output.device if torch.is_tensor(_lowerCAmelCase	) else 'cpu'
										snake_case_									=				torch.randn(model_output.shape,      dtype=model_output.dtype,      generator=_lowerCAmelCase	).to(_lowerCAmelCase	)
										snake_case_									=				self._get_variance(_lowerCAmelCase,      _lowerCAmelCase	) ** 0.5 * eta * noise

										snake_case_									=				prev_sample + variance

					if not return_dict:
										return (prev_sample,)

					return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase,      pred_original_sample=_lowerCAmelCase	)


def 		__magic_name__    (	self,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase="epsilon",      __UpperCAmelCase=None,      __UpperCAmelCase = True,      )		->					Union[DDPMSchedulerOutput, Tuple]:

					'''simple docstring'''

					snake_case_									=				timestep

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

					# 1. compute alphas, betas
					snake_case_									=				self.alphas_cumprod[t]
					snake_case_									=				self.alphas_cumprod[t - 1] if t > 0 else self.one
					snake_case_									=				1 - alpha_prod_t
					snake_case_									=				1 - alpha_prod_t_prev

					# 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 prediction_type == "epsilon":
										snake_case_									=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif prediction_type == "sample":
										snake_case_									=				model_output
					else:
										raise ValueError(F"Unsupported prediction_type {prediction_type}."	)

					# 3. Clip "predicted x_0"
					snake_case_									=				self.bit_scale
					if self.config.clip_sample:
										snake_case_									=				torch.clamp(_lowerCAmelCase,      -scale,      _lowerCAmelCase	)

					# 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
					snake_case_									=				(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
					snake_case_									=				self.alphas[t] ** 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
					snake_case_									=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					snake_case_									=				0
					if t > 0:
										snake_case_									=				torch.randn(
										    model_output.size(),      dtype=model_output.dtype,      layout=model_output.layout,      generator=_lowerCAmelCase	).to(model_output.device	)
										snake_case_									=				(self._get_variance(_lowerCAmelCase,      predicted_variance=_lowerCAmelCase	) ** 0.5) * noise

					snake_case_									=				pred_prev_sample + variance

					if not return_dict:
										return (pred_prev_sample,)

					return DDPMSchedulerOutput(prev_sample=_lowerCAmelCase,      pred_original_sample=_lowerCAmelCase	)






class 						a				(   SCREAMING_SNAKE_CASE_     ):


				def __init__(		self    :					List[str]       ,				lowercase_    :					UNetaDConditionModel       ,				lowercase_    :					Union[DDIMScheduler, DDPMScheduler]       ,				lowercase_    :					Optional[float] = 1.0       ,				):
									super().__init__()
									snake_case_									=				bit_scale
									snake_case_									=				(
									    ddim_bit_scheduler_step if isinstance(_a       ,				_a					) else ddpm_bit_scheduler_step
									)

									self.register_modules(unet=_a       ,				scheduler=_a					)


				@torch.no_grad()
				def __call__(		self    :					Tuple       ,				lowercase_    :					Optional[int] = 256       ,				lowercase_    :					Optional[int] = 256       ,				lowercase_    :					Optional[int] = 50       ,				lowercase_    :					Optional[torch.Generator] = None       ,				lowercase_    :					Optional[int] = 1       ,				lowercase_    :					Optional[str] = "pil"       ,				lowercase_    :					bool = True       ,				**lowercase_    :					Dict       ,				):
									snake_case_									=				torch.randn(
									    (batch_size, self.unet.config.in_channels, height, width)       ,				generator=_a       ,				)
									snake_case_									=				decimal_to_bits(_a					) * self.bit_scale
									snake_case_									=				latents.to(self.device					)

									self.scheduler.set_timesteps(_a					)

									for t in self.progress_bar(self.scheduler.timesteps					):
														# predict the noise residual
														snake_case_									=				self.unet(_a       ,				_a					).sample

														# compute the previous noisy sample x_t -> x_t-1
														snake_case_									=				self.scheduler.step(_a       ,				_a       ,				_a					).prev_sample

									snake_case_									=				bits_to_decimal(_a					)

									if output_type == "pil":
														snake_case_									=				self.numpy_to_pil(_a					)

									if not return_dict:
														return (image,)

									return ImagePipelineOutput(images=_a					)






'''simple docstring'''





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

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

  if tf_checkpoint_path:
    # Convert a pre-trained TensorFlow model
    __lowerCamelCase       :       int           = os.path.abspath(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = os.path.abspath(_lowerCAmelCase       )

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

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






