import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def 						__lowerCamelCase				(UpperCAmelCase__  :				Optional[int]	):
					SCREAMING_SNAKE_CASE           =							image.size
					SCREAMING_SNAKE_CASE           =							(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
					SCREAMING_SNAKE_CASE           =							image.resize((w, h)   ,			resample=PIL_INTERPOLATION["lanczos"]	)
					SCREAMING_SNAKE_CASE           =							np.array(UpperCAmelCase__	).astype(np.floataa	) / 255.0
					SCREAMING_SNAKE_CASE           =							image[None].transpose(0   ,			3   ,			1   ,			2	)
					SCREAMING_SNAKE_CASE           =							torch.from_numpy(UpperCAmelCase__	)
					return 2.0 * image - 1.0


class     lowercase     (  snake_case_  ):




							def __init__(    self :       Tuple						,				_UpperCamelCase :       List[Any]						,				_UpperCamelCase :       Union[str, Any]						,				_UpperCamelCase :       Optional[int]						,				)       ->					Optional[Any]:



												'''simple docstring'''

												super().__init__()
												self.register_modules(vqvae=UpperCamelCase__						,				unet=UpperCamelCase__						,				scheduler=UpperCamelCase__   )





							@torch.no_grad()
							def __call__(    self :       Tuple						,				_UpperCamelCase :       List[str] = None						,				_UpperCamelCase :       Optional[int] = 1						,				_UpperCamelCase :       Tuple = 100						,				_UpperCamelCase :       Dict = 0.0						,				_UpperCamelCase :       int = None						,				_UpperCamelCase :       Tuple = "pil"						,				_UpperCamelCase :       str = True						,				)       ->					Union[Tuple, ImagePipelineOutput]:



												'''simple docstring'''

												if isinstance(UpperCamelCase__						,				PIL.Image.Image   ):
																	SCREAMING_SNAKE_CASE           =							1
												elif isinstance(UpperCamelCase__						,				torch.Tensor   ):
																	SCREAMING_SNAKE_CASE           =							image.shape[0]
												else:
																	raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__   )}"   )

												if isinstance(UpperCamelCase__						,				PIL.Image.Image   ):
																	SCREAMING_SNAKE_CASE           =							preprocess(UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							image.shape[-2:]

												# in_channels should be 6: 3 for latents, 3 for low resolution image
												SCREAMING_SNAKE_CASE           =							(batch_size, self.unet.config.in_channels // 2, height, width)
												SCREAMING_SNAKE_CASE           =							next(self.unet.parameters()   ).dtype

												SCREAMING_SNAKE_CASE           =							randn_tensor(UpperCamelCase__						,				generator=UpperCamelCase__						,				device=self.device						,				dtype=UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							image.to(device=self.device						,				dtype=UpperCamelCase__   )

												# set timesteps and move to the correct device
												self.scheduler.set_timesteps(UpperCamelCase__						,				device=self.device   )
												SCREAMING_SNAKE_CASE           =							self.scheduler.timesteps

												# scale the initial noise by the standard deviation required by the scheduler
												SCREAMING_SNAKE_CASE           =							latents * self.scheduler.init_noise_sigma

												# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
												# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
												# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
												# and should be between [0, 1]
												SCREAMING_SNAKE_CASE           =							"eta" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
												SCREAMING_SNAKE_CASE           =							{}
												if accepts_eta:
																	SCREAMING_SNAKE_CASE           =							eta

												for t in self.progress_bar(UpperCamelCase__   ):
																	# concat latents and low resolution image in the channel dimension.
																	SCREAMING_SNAKE_CASE           =							torch.cat([latents, image]						,				dim=1   )
																	SCREAMING_SNAKE_CASE           =							self.scheduler.scale_model_input(UpperCamelCase__						,				UpperCamelCase__   )
																	# predict the noise residual
																	SCREAMING_SNAKE_CASE           =							self.unet(UpperCamelCase__						,				UpperCamelCase__   ).sample
																	# compute the previous noisy sample x_t -> x_t-1
																	SCREAMING_SNAKE_CASE           =							self.scheduler.step(UpperCamelCase__						,				UpperCamelCase__						,				UpperCamelCase__						,				**UpperCamelCase__   ).prev_sample

												# decode the image latents with the VQVAE
												SCREAMING_SNAKE_CASE           =							self.vqvae.decode(UpperCamelCase__   ).sample
												SCREAMING_SNAKE_CASE           =							torch.clamp(UpperCamelCase__						,				-1.0						,				1.0   )
												SCREAMING_SNAKE_CASE           =							image / 2 + 0.5
												SCREAMING_SNAKE_CASE           =							image.cpu().permute(0						,				2						,				3						,				1   ).numpy()

												if output_type == "pil":
																	SCREAMING_SNAKE_CASE           =							self.numpy_to_pil(UpperCamelCase__   )

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=UpperCamelCase__   )








"""simple docstring"""



import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    Dict		,				lowercase    :    str					)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : Tuple    =       AlbertConfig.from_json_file(lowercase					)
    print(F'Building PyTorch model from configuration: {config}'					)
    snake_case       : int    =       AlbertForPreTraining(lowercase					)

    # Load weights from tf checkpoint
    load_tf_weights_in_albert(lowercase		,				lowercase		,				lowercase					)

    # Save pytorch-model
    print(F'Save PyTorch model to {pytorch_dump_path}'					)
    torch.save(model.state_dict()		,				lowercase					)


if __name__ == "__main__":
  __snake_case       =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--albert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained ALBERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __snake_case       =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





