



"""simple docstring"""
from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


SCREAMING_SNAKE_CASE_				= transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Any:
		if isinstance(SCREAMING_SNAKE_CASE__, torch.Tensor	):
				return image
		elif isinstance(SCREAMING_SNAKE_CASE__, PIL.Image.Image	):
				a_     :      Optional[Any] 						=     [image]

		a_     :      Union[str, Any] 						=     [trans(img.convert("RGB"	)	) for img in image]
		a_     :      int 						=     torch.stack(SCREAMING_SNAKE_CASE__	)
		return image




class 			snake_case_						(						a_				):






							def __init__(   self			,     a_			,     a_      ):
									super().__init__()

									# make sure scheduler can always be converted to DDIM
									a_     :      Optional[Any] 						=     DDIMScheduler.from_config(scheduler.config      )

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






							def snake_case_  (   self			,     a_      ):
									if strength < 0 or strength > 1:
											raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""      )






							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
									# get the original timestep using init_timestep
									a_     :      Tuple 						=     min(int(num_inference_steps * strength      )			,     a_      )

									a_     :      Dict 						=     max(num_inference_steps - init_timestep			,     0      )
									a_     :      Union[str, Any] 						=     self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_=None      ):
									if not isinstance(a_			,     (torch.Tensor, PIL.Image.Image, list)      ):
											raise ValueError(
											    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a_      )}"""      )

									a_     :      Optional[Any] 						=     image.to(device=a_			,     dtype=a_      )

									if isinstance(a_			,     a_      ) and len(a_      ) != batch_size:
											raise ValueError(
											    F"""You have passed a list of generators of length {len(a_      )}, but requested an effective batch"""
											    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""      )

									a_     :      List[Any] 						=     init_latents.shape
									a_     :      str 						=     randn_tensor(a_			,     generator=a_			,     device=a_			,     dtype=a_      )

									# get latents
									print("add noise to latents at timestep"			,     a_      )
									a_     :      Tuple 						=     self.scheduler.add_noise(a_			,     a_			,     a_      )
									a_     :      List[str] 						=     init_latents

									return latents






							@torch.no_grad()
							def __call__(   self			,     a_ = None			,     a_ = 0.8			,     a_ = 1			,     a_ = None			,     a_ = 0.0			,     a_ = 5_0			,     a_ = None			,     a_ = "pil"			,     a_ = True			,     ):
									self.check_inputs(a_      )

									# 2. Preprocess image
									a_     :      Dict 						=     preprocess(a_      )

									# 3. set timesteps
									self.scheduler.set_timesteps(a_			,     device=self.device      )
									a_						,				a_     :      Optional[int] 						=     self.get_timesteps(a_			,     a_			,     self.device      )
									a_     :      str 						=     timesteps[:1].repeat(a_      )

									# 4. Prepare latent variables
									a_     :      List[Any] 						=     self.prepare_latents(a_			,     a_			,     a_			,     self.unet.dtype			,     self.device			,     a_      )
									a_     :      Dict 						=     latents

									# 5. Denoising loop
									for t in self.progress_bar(a_      ):
											# 1. predict noise model_output
											a_     :      Union[str, Any] 						=     self.unet(a_			,     a_      ).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											a_     :      str 						=     self.scheduler.step(
											    a_			,     a_			,     a_			,     eta=a_			,     use_clipped_model_output=a_			,     generator=a_			,     ).prev_sample

									a_     :      str 						=     (image / 2 + 0.5).clamp(0			,     1      )
									a_     :      Tuple 						=     image.cpu().permute(0			,     2			,     3			,     1      ).numpy()
									if output_type == "pil":
											a_     :      int 						=     self.numpy_to_pil(a_      )

									if not return_dict:
											return (image, latent_timestep.item())

									return ImagePipelineOutput(images=a_      )




"""simple docstring"""
import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


SCREAMING_SNAKE_CASE_				= {
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None	)			->      int:
		# Initialise PyTorch model
		a_     :      Optional[int] 						=     XLNetConfig.from_json_file(SCREAMING_SNAKE_CASE__	)

		a_     :      List[Any] 						=     finetuning_task.lower() if finetuning_task is not None else ""
		if finetuning_task in GLUE_TASKS_NUM_LABELS:
				print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""	)
				a_     :      Tuple 						=     finetuning_task
				a_     :      Dict 						=     GLUE_TASKS_NUM_LABELS[finetuning_task]
				a_     :      Tuple 						=     XLNetForSequenceClassification(SCREAMING_SNAKE_CASE__	)
		elif "squad" in finetuning_task:
				a_     :      Tuple 						=     finetuning_task
				a_     :      List[Any] 						=     XLNetForQuestionAnswering(SCREAMING_SNAKE_CASE__	)
		else:
				a_     :      Any 						=     XLNetLMHeadModel(SCREAMING_SNAKE_CASE__	)

		# Load weights from tf checkpoint
		load_tf_weights_in_xlnet(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		# Save pytorch-model
		a_     :      Tuple 						=     os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		a_     :      str 						=     os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		print(F"""Save PyTorch model to {os.path.abspath(SCREAMING_SNAKE_CASE__	)}"""	)
		torch.save(model.state_dict(), SCREAMING_SNAKE_CASE__	)
		print(F"""Save configuration file to {os.path.abspath(SCREAMING_SNAKE_CASE__	)}"""	)
		with open(SCREAMING_SNAKE_CASE__, "w", encoding="utf-8"	) as f:
				f.write(config.to_json_string()	)


if __name__ == "__main__":
					SCREAMING_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(
					    """--xlnet_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained XLNet model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					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(
					    """--finetuning_task""",
					    default=None,
					    type=str,
					    help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
					)
					SCREAMING_SNAKE_CASE_				= parser.parse_args()
					print(args)

					convert_xlnet_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
					)