






"""simple docstring"""


import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase			):




  """simple docstring"""







  __A						      =			{'''small''': (5_1_2, 8, 2_4), '''medium''': (1_0_2_4, 8, 3_6), '''large''': (1_5_3_6, 1_6, 4_8)}
  __A      ,   __A      ,   __A						      =			MODELS[model_size]  # set model hyperparameters
  __A						      =			ImageGPTConfig(n_embd=__UpperCamelCase		,	n_layer=__UpperCamelCase		,	n_head=__UpperCamelCase			)
  __A						      =			ImageGPTForCausalLM(__UpperCamelCase			)

  # Load weights from numpy
  load_tf_weights_in_imagegpt(__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase			)

  # Save pytorch-model
  __A						      =			pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
  __A						      =			pytorch_dump_folder_path + '''/''' + CONFIG_NAME
  print(f'Save PyTorch model to {pytorch_weights_dump_path}'			)
  torch.save(model.state_dict()		,	__UpperCamelCase			)
  print(f'Save configuration file to {pytorch_config_dump_path}'			)
  with open(__UpperCamelCase		,	'''w'''		,	encoding='''utf-8'''			) as f:
    f.write(config.to_json_string()			)


if __name__ == "__main__":
       lowercase_    					=					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--imagegpt_checkpoint_path',
           default=None,
           type=str,
           required=True,
           help='Path to the TensorFlow checkpoint path.',
       )
       parser.add_argument(
           '--model_size',
           default=None,
           type=str,
           required=True,
           help='Size of the model (can be either \'small\', \'medium\' or \'large\').',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       lowercase_    					=					parser.parse_args()
       convert_imagegpt_checkpoint_to_pytorch(
           args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
       )












"""simple docstring"""


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

import numpy as np
import torch

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


lowercase_    					=					logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:     torch.FloatTensor
   A_						:     Optional[torch.FloatTensor]									=			None





def  lowerCAmelCase    (   __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}'			)

  __A						      =			[]
  for i in range(__UpperCamelCase			):
    __A						      =			i / num_diffusion_timesteps
    __A						      =			(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		(							_lowerCAmelCase       ,     _lowerCAmelCase						):


   '''simple docstring'''

   A_						:     Tuple									=			[e.name for e in KarrasDiffusionSchedulers]
   A_						:     Optional[Any]									=			1


   @register_to_config
   def __init__(     self			:					Optional[int],	_lowerCamelCase			:					int = 10_00,	_lowerCamelCase			:					float = 0.00_01,	_lowerCamelCase			:					float = 0.02,	_lowerCamelCase			:					str = "linear",	_lowerCamelCase			:					Optional[Union[np.ndarray, List[float]]] = None,	_lowerCamelCase			:					str = "epsilon",	_lowerCamelCase			:					str = "linear",	_lowerCamelCase			:					Optional[bool] = False,	_lowerCamelCase			:					str = "linspace",	_lowerCamelCase			:					int = 0,	):

     '''simple docstring'''
     if trained_betas is not None:
       __A						      =			torch.tensor(_lowerCamelCase,	dtype=torch.floataa  )
     elif beta_schedule == "linear":
       __A						      =			torch.linspace(_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase,	dtype=torch.floataa  )
     elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       __A						      =			(
           torch.linspace(beta_start**0.5,	beta_end**0.5,	_lowerCamelCase,	dtype=torch.floataa  ) ** 2
       )
     elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       __A						      =			betas_for_alpha_bar(_lowerCamelCase  )
     else:
       raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'  )

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

     __A						      =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
     __A						      =			np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
     __A						      =			torch.from_numpy(_lowerCamelCase  )

     # setable values
     __A						      =			None
     __A						      =			np.linspace(0,	num_train_timesteps - 1,	_lowerCamelCase,	dtype=_lowerCamelCase  )[::-1].copy()
     __A						      =			torch.from_numpy(_lowerCamelCase  )
     __A						      =			False
     __A						      =			use_karras_sigmas


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any]  ):

     '''simple docstring'''
     # standard deviation of the initial noise distribution
     if self.config.timestep_spacing in ["linspace", "trailing"]:
       return self.sigmas.max()

     return (self.sigmas.max() ** 2 + 1) ** 0.5


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str],	_lowerCamelCase			:					torch.FloatTensor,	_lowerCamelCase			:					Union[float, torch.FloatTensor]  ):

     '''simple docstring'''
     if isinstance(_lowerCamelCase,	torch.Tensor  ):
       __A						      =			timestep.to(self.timesteps.device  )
     __A						      =			(self.timesteps == timestep).nonzero().item()
     __A						      =			self.sigmas[step_index]

     __A						      =			sample / ((sigma**2 + 1) ** 0.5)

     __A						      =			True
     return sample


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	_lowerCamelCase			:					int,	_lowerCamelCase			:					Union[str, torch.device] = None  ):

     '''simple docstring'''
     __A						      =			num_inference_steps

     # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
     if self.config.timestep_spacing == "linspace":
       __A						      =			np.linspace(0,	self.config.num_train_timesteps - 1,	_lowerCamelCase,	dtype=_lowerCamelCase  )[
           ::-1
       ].copy()
     elif self.config.timestep_spacing == "leading":
       __A						      =			self.config.num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       __A						      =			(np.arange(0,	_lowerCamelCase  ) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase  )
       timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
       __A						      =			self.config.num_train_timesteps / self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       __A						      =			(np.arange(self.config.num_train_timesteps,	0,	-step_ratio  )).round().copy().astype(_lowerCamelCase  )
       timesteps -= 1
     else:
       raise ValueError(
           f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'  )

     __A						      =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
     __A						      =			np.log(_lowerCamelCase  )

     if self.config.interpolation_type == "linear":
       __A						      =			np.interp(_lowerCamelCase,	np.arange(0,	len(_lowerCamelCase  )  ),	_lowerCamelCase  )
     elif self.config.interpolation_type == "log_linear":
       __A						      =			torch.linspace(np.log(sigmas[-1]  ),	np.log(sigmas[0]  ),	num_inference_steps + 1  ).exp()
     else:
       raise ValueError(
           f'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
           ''' \'linear\' or \'log_linear\''''  )

     if self.use_karras_sigmas:
       __A						      =			self._convert_to_karras(in_sigmas=_lowerCamelCase,	num_inference_steps=self.num_inference_steps  )
       __A						      =			np.array([self._sigma_to_t(_lowerCamelCase,	_lowerCamelCase  ) for sigma in sigmas]  )

     __A						      =			np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
     __A						      =			torch.from_numpy(_lowerCamelCase  ).to(device=_lowerCamelCase  )
     if str(_lowerCamelCase  ).startswith('''mps'''  ):
       # mps does not support float64
       __A						      =			torch.from_numpy(_lowerCamelCase  ).to(_lowerCamelCase,	dtype=torch.floataa  )
     else:
       __A						      =			torch.from_numpy(_lowerCamelCase  ).to(device=_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple,	_lowerCamelCase			:					Any,	_lowerCamelCase			:					List[Any]  ):

     '''simple docstring'''
     # get log sigma
     __A						      =			np.log(_lowerCamelCase  )

     # get distribution
     __A						      =			log_sigma - log_sigmas[:, np.newaxis]

     # get sigmas range
     __A						      =			np.cumsum((dists >= 0),	axis=0  ).argmax(axis=0  ).clip(max=log_sigmas.shape[0] - 2  )
     __A						      =			low_idx + 1

     __A						      =			log_sigmas[low_idx]
     __A						      =			log_sigmas[high_idx]

     # interpolate sigmas
     __A						      =			(low - log_sigma) / (low - high)
     __A						      =			np.clip(_lowerCamelCase,	0,	1  )

     # transform interpolation to time range
     __A						      =			(1 - w) * low_idx + w * high_idx
     __A						      =			t.reshape(sigma.shape  )
     return t


   def     _SCREAMING_SNAKE_CASE    (     self			:					str,	_lowerCamelCase			:					torch.FloatTensor,	_lowerCamelCase			:					int  ):

     '''simple docstring'''
     __A						      =			in_sigmas[-1].item()
     __A						      =			in_sigmas[0].item()

     __A						      =			7.0  # 7.0 is the value used in the paper
     __A						      =			np.linspace(0,	1,	_lowerCamelCase  )
     __A						      =			sigma_min ** (1 / rho)
     __A						      =			sigma_max ** (1 / rho)
     __A						      =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str],	_lowerCamelCase			:					torch.FloatTensor,	_lowerCamelCase			:					Union[float, torch.FloatTensor],	_lowerCamelCase			:					torch.FloatTensor,	_lowerCamelCase			:					float = 0.0,	_lowerCamelCase			:					float = 0.0,	_lowerCamelCase			:					float = float('''inf'''  ),	_lowerCamelCase			:					float = 1.0,	_lowerCamelCase			:					Optional[torch.Generator] = None,	_lowerCamelCase			:					bool = True,	):

     '''simple docstring'''

     if (
         isinstance(_lowerCamelCase,	_lowerCamelCase  )
         or isinstance(_lowerCamelCase,	torch.IntTensor  )
         or isinstance(_lowerCamelCase,	torch.LongTensor  )
     ):
       raise ValueError(
           (
               '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
               ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
               ''' one of the `scheduler.timesteps` as a timestep.'''
           ),	)

     if not self.is_scale_input_called:
       logger.warning(
           '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
           '''See `StableDiffusionPipeline` for a usage example.'''  )

     if isinstance(_lowerCamelCase,	torch.Tensor  ):
       __A						      =			timestep.to(self.timesteps.device  )

     __A						      =			(self.timesteps == timestep).nonzero().item()
     __A						      =			self.sigmas[step_index]

     __A						      =			min(s_churn / (len(self.sigmas  ) - 1),	2**0.5 - 1  ) if s_tmin <= sigma <= s_tmax else 0.0

     __A						      =			randn_tensor(
         model_output.shape,	dtype=model_output.dtype,	device=model_output.device,	generator=_lowerCamelCase  )

     __A						      =			noise * s_noise
     __A						      =			sigma * (gamma + 1)

     if gamma > 0:
       __A						      =			sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     # NOTE: "original_sample" should not be an expected prediction_type but is left in for
     # backwards compatibility
     if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
       __A						      =			model_output
     elif self.config.prediction_type == "epsilon":
       __A						      =			sample - sigma_hat * model_output
     elif self.config.prediction_type == "v_prediction":
       # * c_out + input * c_skip
       __A						      =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     else:
       raise ValueError(
           f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'  )

     # 2. Convert to an ODE derivative
     __A						      =			(sample - pred_original_sample) / sigma_hat

     __A						      =			self.sigmas[step_index + 1] - sigma_hat

     __A						      =			sample + derivative * dt

     if not return_dict:
       return (prev_sample,)

     return EulerDiscreteSchedulerOutput(prev_sample=_lowerCamelCase,	pred_original_sample=_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any,	_lowerCamelCase			:					torch.FloatTensor,	_lowerCamelCase			:					torch.FloatTensor,	_lowerCamelCase			:					torch.FloatTensor,	):

     '''simple docstring'''
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     __A						      =			self.sigmas.to(device=original_samples.device,	dtype=original_samples.dtype  )
     if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase  ):
       # mps does not support float64
       __A						      =			self.timesteps.to(original_samples.device,	dtype=torch.floataa  )
       __A						      =			timesteps.to(original_samples.device,	dtype=torch.floataa  )
     else:
       __A						      =			self.timesteps.to(original_samples.device  )
       __A						      =			timesteps.to(original_samples.device  )

     __A						      =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

     __A						      =			sigmas[step_indices].flatten()
     while len(sigma.shape  ) < len(original_samples.shape  ):
       __A						      =			sigma.unsqueeze(-1  )

     __A						      =			original_samples + noise * sigma
     return noisy_samples






   def __len__(     self			:					Tuple  ):

     '''simple docstring'''
     return self.config.num_train_timesteps




