






"""simple docstring"""






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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 							lowerCAmelCase__       (    lowercase__				):






      '''simple docstring'''

      def __init__(		self  :						Tuple		,			lowercase_  :						TransformeraDModel		,			lowercase_  :						AutoencoderKL		,			lowercase_  :						KarrasDiffusionSchedulers		,			lowercase_  :						Optional[Dict[int, str]] = None		,			):




          '''simple docstring'''


          super().__init__()
          self.register_modules(transformer=__lowerCamelCase		,			vae=__lowerCamelCase		,			scheduler=__lowerCamelCase)

          # create a imagenet -> id dictionary for easier use
          SCREAMING_SNAKE_CASE_   :						Dict							=	{}
          if idalabel is not None:
              for key, value in idalabel.items():
                  for label in value.split(''','''):
                      SCREAMING_SNAKE_CASE_   :						Dict							=	int(__lowerCamelCase)
              SCREAMING_SNAKE_CASE_   :						str							=	dict(sorted(self.labels.items()))
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						Union[str, List[str]]):




          '''simple docstring'''


          if not isinstance(__lowerCamelCase		,			__lowerCamelCase):
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	list(__lowerCamelCase)

          for l in label:
              if l not in self.labels:
                  raise ValueError(
                      F'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.')

          return [self.labels[l] for l in label]



      @torch.no_grad()
      def __call__(		self  :						List[Any]		,			lowercase_  :						List[int]		,			lowercase_  :						float = 4.0		,			lowercase_  :						Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			lowercase_  :						int = 50		,			lowercase_  :						Optional[str] = "pil"		,			lowercase_  :						bool = True		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	len(__lowerCamelCase)
          SCREAMING_SNAKE_CASE_   :						Dict							=	self.transformer.config.sample_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	self.transformer.config.in_channels

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	randn_tensor(
              shape=(batch_size, latent_channels, latent_size, latent_size)		,			generator=__lowerCamelCase		,			device=self.device		,			dtype=self.transformer.dtype		,			)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	torch.cat([latents] * 2) if guidance_scale > 1 else latents

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.tensor(__lowerCamelCase		,			device=self.device).reshape(-1)
          SCREAMING_SNAKE_CASE_   :						str							=	torch.tensor([1000] * batch_size		,			device=self.device)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.cat([class_labels, class_null]		,			0) if guidance_scale > 1 else class_labels

          # set step values
          self.scheduler.set_timesteps(__lowerCamelCase)

          for t in self.progress_bar(self.scheduler.timesteps):
              if guidance_scale > 1:
                  SCREAMING_SNAKE_CASE_   :						int							=	latent_model_input[: len(__lowerCamelCase) // 2]
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.cat([half, half]		,			dim=0)
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.scheduler.scale_model_input(__lowerCamelCase		,			__lowerCamelCase)

              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	t
              if not torch.is_tensor(__lowerCamelCase):
                  # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                  # This would be a good case for the `match` statement (Python 3.10+)
                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	latent_model_input.device.type == "mps"
                  if isinstance(__lowerCamelCase		,			__lowerCamelCase):
                      SCREAMING_SNAKE_CASE_   :						List[str]							=	torch.floataa if is_mps else torch.floataa
                  else:
                      SCREAMING_SNAKE_CASE_   :						Any							=	torch.intaa if is_mps else torch.intaa
                  SCREAMING_SNAKE_CASE_   :						Dict							=	torch.tensor([timesteps]		,			dtype=__lowerCamelCase		,			device=latent_model_input.device)
              elif len(timesteps.shape) == 0:
                  SCREAMING_SNAKE_CASE_   :						Any							=	timesteps[None].to(latent_model_input.device)
              # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
              SCREAMING_SNAKE_CASE_   :						Any							=	timesteps.expand(latent_model_input.shape[0])
              # predict noise model_output
              SCREAMING_SNAKE_CASE_   :						List[str]							=	self.transformer(
                  __lowerCamelCase		,			timestep=__lowerCamelCase		,			class_labels=__lowerCamelCase).sample

              # perform guidance
              if guidance_scale > 1:
                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                  SCREAMING_SNAKE_CASE_   :						Dict							=	torch.split(__lowerCamelCase		,			len(__lowerCamelCase) // 2		,			dim=0)

                  SCREAMING_SNAKE_CASE_   :						Any							=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.cat([half_eps, half_eps]		,			dim=0)

                  SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.cat([eps, rest]		,			dim=1)

              # learned sigma
              if self.transformer.config.out_channels // 2 == latent_channels:
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	torch.split(__lowerCamelCase		,			__lowerCamelCase		,			dim=1)
              else:
                  SCREAMING_SNAKE_CASE_   :						str							=	noise_pred

              # compute previous image: x_t -> x_t-1
              SCREAMING_SNAKE_CASE_   :						Dict							=	self.scheduler.step(__lowerCamelCase		,			__lowerCamelCase		,			__lowerCamelCase).prev_sample

          if guidance_scale > 1:
              SCREAMING_SNAKE_CASE_   :						int							=	latent_model_input.chunk(2		,			dim=0)
          else:
              SCREAMING_SNAKE_CASE_   :						str							=	latent_model_input

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	1 / self.vae.config.scaling_factor * latents
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.vae.decode(__lowerCamelCase).sample

          SCREAMING_SNAKE_CASE_   :						List[str]							=	(samples / 2 + 0.5).clamp(0		,			1)

          # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
          SCREAMING_SNAKE_CASE_   :						Dict							=	samples.cpu().permute(0		,			2		,			3		,			1).float().numpy()

          if output_type == "pil":
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.numpy_to_pil(__lowerCamelCase)

          if not return_dict:
              return (samples,)

          return ImagePipelineOutput(images=__lowerCamelCase)










"""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


UpperCAmelCase_     :				int         =				{
    """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       _A    (__a					,       __a					,       __a					,       __a=None							)				-> int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						str							=	XLNetConfig.from_json_file(__a							)

    SCREAMING_SNAKE_CASE_   :						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}'							)
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	finetuning_task
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	GLUE_TASKS_NUM_LABELS[finetuning_task]
        SCREAMING_SNAKE_CASE_   :						Dict							=	XLNetForSequenceClassification(__a							)
    elif "squad" in finetuning_task:
        SCREAMING_SNAKE_CASE_   :						str							=	finetuning_task
        SCREAMING_SNAKE_CASE_   :						Dict							=	XLNetForQuestionAnswering(__a							)
    else:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	XLNetLMHeadModel(__a							)

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

    # Save pytorch-model
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	os.path.join(__a					,       __a							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	os.path.join(__a					,       __a							)
    print(f'Save PyTorch model to {os.path.abspath(__a							)}'							)
    torch.save(model.state_dict()					,       __a							)
    print(f'Save configuration file to {os.path.abspath(__a							)}'							)
    with open(__a					,       '''w'''					,       encoding='''utf-8'''							) as f:
        f.write(config.to_json_string()							)


if __name__ == "__main__":
  UpperCAmelCase_     :				str         =				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""",
  )
  UpperCAmelCase_     :				int         =				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
  )


