
"""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 		a__						(      a_						):
   def __init__(			self	,    _a	,    _a	,    _a	,    _a = None	,    ):
          super().__init__()
          self.register_modules(transformer=_a	,    vae=_a	,    scheduler=_a				)

          # create a imagenet -> id dictionary for easier use
          lowercase    :				Union[str, Any]       		=							{}
          if idalabel is not None:
                 for key, value in idalabel.items():
                        for label in value.split(","				):
                               lowercase    :				Dict       		=							int(_a				)
                 lowercase    :				Optional[int]       		=							dict(sorted(self.labels.items()				)				)
   def 			__magic_name__			(			self	,    _a				):

          if not isinstance(_a	,    _a				):
                 lowercase    :				int       		=							list(_a				)

          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	,    _a	,    _a = 4.0	,    _a = None	,    _a = 50	,    _a = "pil"	,    _a = True	,    ):
          lowercase    :				Tuple       		=							len(_a				)
          lowercase    :				str       		=							self.transformer.config.sample_size
          lowercase    :				Optional[Any]       		=							self.transformer.config.in_channels

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

          lowercase    :				str       		=							torch.tensor(_a	,    device=self.device				).reshape(-1				)
          lowercase    :				List[str]       		=							torch.tensor([1_000] * batch_size	,    device=self.device				)
          lowercase    :				str       		=							torch.cat([class_labels, class_null]	,    0				) if guidance_scale > 1 else class_labels

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

          for t in self.progress_bar(self.scheduler.timesteps				):
                 if guidance_scale > 1:
                        lowercase    :				Optional[Any]       		=							latent_model_input[: len(_a				) // 2]
                        lowercase    :				str       		=							torch.cat([half, half]	,    dim=0				)
                 lowercase    :				List[Any]       		=							self.scheduler.scale_model_input(_a	,    _a				)

                 lowercase    :				Dict       		=							t
                 if not torch.is_tensor(_a				):
                        # 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+)
                        lowercase    :				int       		=							latent_model_input.device.type == "mps"
                        if isinstance(_a	,    _a				):
                               lowercase    :				Optional[int]       		=							torch.floataa if is_mps else torch.floataa
                        else:
                               lowercase    :				List[str]       		=							torch.intaa if is_mps else torch.intaa
                        lowercase    :				Union[str, Any]       		=							torch.tensor([timesteps]	,    dtype=_a	,    device=latent_model_input.device				)
                 elif len(timesteps.shape				) == 0:
                        lowercase    :				Union[str, Any]       		=							timesteps[None].to(latent_model_input.device				)
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                 lowercase    :				Optional[int]       		=							timesteps.expand(latent_model_input.shape[0]				)
                 # predict noise model_output
                 lowercase    :				List[Any]       		=							self.transformer(
                     _a	,    timestep=_a	,    class_labels=_a				).sample

                 # perform guidance
                 if guidance_scale > 1:
                        lowercase		,    lowercase    :				List[Any]       		=							noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                        lowercase		,    lowercase    :				List[Any]       		=							torch.split(_a	,    len(_a				) // 2	,    dim=0				)

                        lowercase    :				List[str]       		=							uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                        lowercase    :				List[str]       		=							torch.cat([half_eps, half_eps]	,    dim=0				)

                        lowercase    :				Any       		=							torch.cat([eps, rest]	,    dim=1				)

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

                 # compute previous image: x_t -> x_t-1
                 lowercase    :				Optional[Any]       		=							self.scheduler.step(_a	,    _a	,    _a				).prev_sample

          if guidance_scale > 1:
                 lowercase		,    lowercase    :				Union[str, Any]       		=							latent_model_input.chunk(2	,    dim=0				)
          else:
                 lowercase    :				Tuple       		=							latent_model_input

          lowercase    :				Optional[Any]       		=							1 / self.vae.config.scaling_factor * latents
          lowercase    :				List[Any]       		=							self.vae.decode(_a				).sample

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

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

          if output_type == "pil":
                 lowercase    :				Any       		=							self.numpy_to_pil(_a				)

          if not return_dict:
                 return (samples,)

          return ImagePipelineOutput(images=_a				)




"""simple docstring"""




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Dict     , __snake_case				:       Any  ) ->				Any:
       # Initialise PyTorch model
       lowercase    :				Union[str, Any]       		=							RemBertConfig.from_json_file(__snake_case  )
       print("Building PyTorch model from configuration: {}".format(str(__snake_case  )  )  )
       lowercase    :				str       		=							RemBertModel(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(__snake_case     , __snake_case     , __snake_case  )

       # Save pytorch-model
       print("Save PyTorch model to {}".format(__snake_case  )  )
       torch.save(model.state_dict()     , __snake_case  )


if __name__ == "__main__":
   _A			:       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(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT 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."""
   )
   _A			:       List[str]   		=       parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)


