
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


__A							=transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def      lowerCamelCase_					(							lowerCamelCase__       ):
				if isinstance(lowerCamelCase__      ,					torch.Tensor       ):
								return image
				elif isinstance(lowerCamelCase__      ,					PIL.Image.Image       ):
								lowerCamelCase_         =     [image]

				lowerCamelCase_         =     [trans(img.convert("RGB"       )       ) for img in image]
				lowerCamelCase_         =     torch.stack(lowerCamelCase__       )
				return image





class        _SCREAMING_SNAKE_CASE	( snake_case_		):



	def __init__(       self		,    lowercase		,    lowercase					)							->			List[Any]:
					super().__init__()

					# make sure scheduler can always be converted to DDIM
					lowerCamelCase_         =     DDIMScheduler.from_config(scheduler.config					)

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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			Optional[int]:
					if strength < 0 or strength > 1:
									raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}'					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase					)							->			Optional[int]:
					# get the original timestep using init_timestep
					lowerCamelCase_         =     min(int(num_inference_steps * strength					)		,    lowercase					)

					lowerCamelCase_         =     max(num_inference_steps - init_timestep		,    0					)
					lowerCamelCase_         =     self.scheduler.timesteps[t_start:]

					return timesteps, num_inference_steps - t_start



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase=None					)							->			Union[str, Any]:
					if not isinstance(lowercase		,    (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(lowercase					)}'					)

					lowerCamelCase_         =     image.to(device=lowercase		,    dtype=lowercase					)

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

					lowerCamelCase_         =     init_latents.shape
					lowerCamelCase_         =     randn_tensor(lowercase		,    generator=lowercase		,    device=lowercase		,    dtype=lowercase					)

					# get latents
					print("add noise to latents at timestep"		,    lowercase					)
					lowerCamelCase_         =     self.scheduler.add_noise(lowercase		,    lowercase		,    lowercase					)
					lowerCamelCase_         =     init_latents

					return latents



	@torch.no_grad()
	def __call__(       self		,    lowercase = None		,    lowercase = 0.8		,    lowercase = 1		,    lowercase = None		,    lowercase = 0.0		,    lowercase = 50		,    lowercase = None		,    lowercase = "pil"		,    lowercase = True		,    )							->			Union[ImagePipelineOutput, Tuple]:
					self.check_inputs(lowercase					)

					# 2. Preprocess image
					lowerCamelCase_         =     preprocess(lowercase					)

					# 3. set timesteps
					self.scheduler.set_timesteps(lowercase		,    device=self.device					)
					lowerCamelCase_      ,       lowerCamelCase_         =     self.get_timesteps(lowercase		,    lowercase		,    self.device					)
					lowerCamelCase_         =     timesteps[:1].repeat(lowercase					)

					# 4. Prepare latent variables
					lowerCamelCase_         =     self.prepare_latents(lowercase		,    lowercase		,    lowercase		,    self.unet.dtype		,    self.device		,    lowercase					)
					lowerCamelCase_         =     latents

					# 5. Denoising loop
					for t in self.progress_bar(lowercase					):
									# 1. predict noise model_output
									lowerCamelCase_         =     self.unet(lowercase		,    lowercase					).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
									lowerCamelCase_         =     self.scheduler.step(
									    lowercase		,    lowercase		,    lowercase		,    eta=lowercase		,    use_clipped_model_output=lowercase		,    generator=lowercase		,    ).prev_sample

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

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

					return ImagePipelineOutput(images=lowercase					)




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


__A							={
    '''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_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__=None       ):
				# Initialise PyTorch model
				lowerCamelCase_         =     XLNetConfig.from_json_file(lowerCamelCase__       )

				lowerCamelCase_         =     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}'       )
								lowerCamelCase_         =     finetuning_task
								lowerCamelCase_         =     GLUE_TASKS_NUM_LABELS[finetuning_task]
								lowerCamelCase_         =     XLNetForSequenceClassification(lowerCamelCase__       )
				elif "squad" in finetuning_task:
								lowerCamelCase_         =     finetuning_task
								lowerCamelCase_         =     XLNetForQuestionAnswering(lowerCamelCase__       )
				else:
								lowerCamelCase_         =     XLNetLMHeadModel(lowerCamelCase__       )

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

				# Save pytorch-model
				lowerCamelCase_         =     os.path.join(lowerCamelCase__      ,					lowerCamelCase__       )
				lowerCamelCase_         =     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__":
			__A							=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''',
			)
			__A							=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
			)


