"""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        __snake_case	(					__lowerCAmelCase    ):



      def __init__(						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      )     ->      List[str]:





        '''simple docstring'''





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

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



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      List[int]:





        '''simple docstring'''





        if not isinstance(__a   ,      __a):
          a__:					Tuple      							=     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   ,      lowercase   ,      lowercase = 4.0   ,      lowercase = None   ,      lowercase = 50   ,      lowercase = "pil"   ,      lowercase = True   ,      )     ->      Union[ImagePipelineOutput, Tuple]:





        '''simple docstring'''





        a__:					int      							=     len(__a)
        a__:					List[str]      							=     self.transformer.config.sample_size
        a__:					Dict      							=     self.transformer.config.in_channels

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

        a__:					Tuple      							=     torch.tensor(__a   ,      device=self.device).reshape(-1)
        a__:					List[Any]      							=     torch.tensor([10_00] * batch_size   ,      device=self.device)
        a__:					Dict      							=     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:
            a__:					int      							=     latent_model_input[: len(__a) // 2]
            a__:					Optional[Any]      							=     torch.cat([half, half]   ,      dim=0)
          a__:					Any      							=     self.scheduler.scale_model_input(__a   ,      __a)

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

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

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

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

          # learned sigma
          if self.transformer.config.out_channels // 2 == latent_channels:
            a__							,   a__:					Optional[Any]      							=     torch.split(__a   ,      __a   ,      dim=1)
          else:
            a__:					Optional[int]      							=     noise_pred

          # compute previous image: x_t -> x_t-1
          a__:					Optional[int]      							=     self.scheduler.step(__a   ,      __a   ,      __a).prev_sample

        if guidance_scale > 1:
          a__							,   a__:					Optional[int]      							=     latent_model_input.chunk(2   ,      dim=0)
        else:
          a__:					Tuple      							=     latent_model_input

        a__:					int      							=     1 / self.vae.config.scaling_factor * latents
        a__:					Any      							=     self.vae.decode(__a).sample

        a__:					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
        a__:					Union[str, Any]      							=     samples.cpu().permute(0   ,      2   ,      3   ,      1).float().numpy()

        if output_type == "pil":
          a__:					Any      							=     self.numpy_to_pil(__a)

        if not return_dict:
          return (samples,)

        return ImagePipelineOutput(images=__a)






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

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


lowercase__ 	=     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__ 	=     {
    'vocab_file': {
        'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'unc-nlp/lxmert-base-uncased': (
            'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'
        ),
    },
}

lowercase__ 	=     {
    'unc-nlp/lxmert-base-uncased': 512,
}

lowercase__ 	=     {
    'unc-nlp/lxmert-base-uncased': {'do_lower_case': True},
}




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		PRETRAINED_VOCAB_FILES_MAP
      a__				=		PRETRAINED_INIT_CONFIGURATION
      a__				=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		LxmertTokenizer



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      lowercase=True   ,      lowercase="[UNK]"   ,      lowercase="[SEP]"   ,      lowercase="[PAD]"   ,      lowercase="[CLS]"   ,      lowercase="[MASK]"   ,      lowercase=True   ,      lowercase=None   ,      **lowercase   ,      )     ->      Dict:





        '''simple docstring'''





        super().__init__(
            lowercase   ,      tokenizer_file=lowercase   ,      do_lower_case=lowercase   ,      unk_token=lowercase   ,      sep_token=lowercase   ,      pad_token=lowercase   ,      cls_token=lowercase   ,      mask_token=lowercase   ,      tokenize_chinese_chars=lowercase   ,      strip_accents=lowercase   ,      **lowercase   ,      )

        a__:					Dict      							=     json.loads(self.backend_tokenizer.normalizer.__getstate__())
        if (
            normalizer_state.get('lowercase'   ,      lowercase) != do_lower_case
            or normalizer_state.get('strip_accents'   ,      lowercase) != strip_accents
            or normalizer_state.get('handle_chinese_chars'   ,      lowercase) != tokenize_chinese_chars
        ):
          a__:					int      							=     getattr(lowercase   ,      normalizer_state.pop('type'))
          a__:					Dict      							=     do_lower_case
          a__:					Dict      							=     strip_accents
          a__:					Optional[int]      							=     tokenize_chinese_chars
          a__:					List[Any]      							=     normalizer_class(**lowercase)

        a__:					Optional[int]      							=     do_lower_case



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=None)     ->      Tuple:





        '''simple docstring'''





        a__:					Optional[Any]      							=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

        return output



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[int]:





        '''simple docstring'''





        a__:					List[Any]      							=     [self.sep_token_id]
        a__:					List[Any]      							=     [self.cls_token_id]
        if token_ids_a is None:
          return len(cls + token_ids_a + sep) * [0]
        return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      Tuple[str]:





        '''simple docstring'''





        a__:					List[Any]      							=     self._tokenizer.model.save(lowercase   ,      name=lowercase)
        return tuple(lowercase)


