





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    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       def __init__(  self			:   str							,    A			:   TransformeraDModel							,    A			:   AutoencoderKL							,    A			:   KarrasDiffusionSchedulers							,    A			:   Optional[Dict[int, str]] = None							,    ):
         super().__init__()
         self.register_modules(transformer=A							,    vae=A							,    scheduler=A		)

         # create a imagenet -> id dictionary for easier use
         __snake_case:						int			  =						{}
         if idalabel is not None:
           for key, value in idalabel.items():
             for label in value.split(""","""		):
               __snake_case:						List[Any]			  =						int(A		)
           __snake_case:						Tuple			  =						dict(sorted(self.labels.items()		)		)




       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   Union[str, List[str]]		):

         if not isinstance(A							,    A		):
           __snake_case:						str			  =						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			:   int							,    A			:   List[int]							,    A			:   float = 4.0							,    A			:   Optional[Union[torch.Generator, List[torch.Generator]]] = None							,    A			:   int = 50							,    A			:   Optional[str] = "pil"							,    A			:   bool = True							,    ):
         __snake_case:						Tuple			  =						len(A		)
         __snake_case:						Optional[int]			  =						self.transformer.config.sample_size
         __snake_case:						Any			  =						self.transformer.config.in_channels

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

         __snake_case:						int			  =						torch.tensor(A							,    device=self.device		).reshape(-1		)
         __snake_case:						List[str]			  =						torch.tensor([1_000] * batch_size							,    device=self.device		)
         __snake_case:						Union[str, Any]			  =						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:
             __snake_case:						Optional[int]			  =						latent_model_input[: len(A		) // 2]
             __snake_case:						Any			  =						torch.cat([half, half]							,    dim=0		)
           __snake_case:						int			  =						self.scheduler.scale_model_input(A							,    A		)

           __snake_case:						Union[str, Any]			  =						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+)
             __snake_case:						str			  =						latent_model_input.device.type == """mps"""
             if isinstance(A							,    A		):
               __snake_case:						Optional[int]			  =						torch.floataa if is_mps else torch.floataa
             else:
               __snake_case:						Optional[Any]			  =						torch.intaa if is_mps else torch.intaa
             __snake_case:						List[str]			  =						torch.tensor([timesteps]							,    dtype=A							,    device=latent_model_input.device		)
           elif len(timesteps.shape		) == 0:
             __snake_case:						int			  =						timesteps[None].to(latent_model_input.device		)
           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           __snake_case:						Union[str, Any]			  =						timesteps.expand(latent_model_input.shape[0]		)
           # predict noise model_output
           __snake_case:						str			  =						self.transformer(
               A							,    timestep=A							,    class_labels=A		).sample

           # perform guidance
           if guidance_scale > 1:
             __snake_case  ,       __snake_case:						List[str]			  =						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             __snake_case  ,       __snake_case:						int			  =						torch.split(A							,    len(A		) // 2							,    dim=0		)

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

             __snake_case:						List[str]			  =						torch.cat([eps, rest]							,    dim=1		)

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

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

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

         __snake_case:						str			  =						1 / self.vae.config.scaling_factor * latents
         __snake_case:						Union[str, Any]			  =						self.vae.decode(A		).sample

         __snake_case:						int			  =						(samples / 2 + 0.5).clamp(0							,    1		)

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

         if output_type == "pil":
           __snake_case:						Union[str, Any]			  =						self.numpy_to_pil(A		)

         if not return_dict:
           return (samples,)

         return ImagePipelineOutput(images=A		)













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase	:       List[str]    	=							{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[Any]    	=							["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Tuple    	=							[
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[int]    	=							[
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       List[Any]    	=							[
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	:       str    	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






