

from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Tuple						 =  ['image_processor', 'tokenizer']
  __lowerCAmelCase     :	Dict						 =  'Pix2StructImageProcessor'
  __lowerCAmelCase     :	List[Any]						 =  ('T5Tokenizer', 'T5TokenizerFast')

  def __init__(					self						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						False
      super().__init__(_A						,		_A)

  def __call__(					self						,		_A=None						,		_A = None						,		_A = True						,		_A = False						,		_A = None						,		_A = None						,		_A = 2048						,		_A = 0						,		_A = None						,		_A = None						,		_A = False						,		_A = False						,		_A = False						,		_A = False						,		_A = False						,		_A = True						,		_A = None						,		**_A						,		):
      if images is None and text is None:
          raise ValueError('You have to specify either images or text.')

      # Get only text
      if images is None and not self.image_processor.is_vqa:
          SCREAMING_SNAKE_CASE_									=						self.tokenizer
          SCREAMING_SNAKE_CASE_									=						self.tokenizer(
              text=_A						,		add_special_tokens=_A						,		padding=_A						,		truncation=_A						,		max_length=_A						,		stride=_A						,		pad_to_multiple_of=_A						,		return_attention_mask=_A						,		return_overflowing_tokens=_A						,		return_special_tokens_mask=_A						,		return_offsets_mapping=_A						,		return_token_type_ids=_A						,		return_length=_A						,		verbose=_A						,		return_tensors=_A						,		**_A						,		)
          return text_encoding

      if not self.image_processor.is_vqa:
          # add pixel_values
          SCREAMING_SNAKE_CASE_									=						self.image_processor(
              _A						,		return_tensors=_A						,		max_patches=_A						,		**_A)
      else:
          # add pixel_values and bbox
          SCREAMING_SNAKE_CASE_									=						self.image_processor(
              _A						,		return_tensors=_A						,		max_patches=_A						,		header_text=_A						,		**_A)

      if text is not None and not self.image_processor.is_vqa:
          SCREAMING_SNAKE_CASE_									=						self.tokenizer(
              text=_A						,		add_special_tokens=_A						,		padding=_A						,		truncation=_A						,		max_length=_A						,		stride=_A						,		pad_to_multiple_of=_A						,		return_attention_mask=_A						,		return_overflowing_tokens=_A						,		return_special_tokens_mask=_A						,		return_offsets_mapping=_A						,		return_token_type_ids=_A						,		return_length=_A						,		verbose=_A						,		return_tensors=_A						,		**_A						,		)

          if "attention_mask" in text_encoding:
              SCREAMING_SNAKE_CASE_									=						text_encoding.pop('attention_mask')
          if "input_ids" in text_encoding:
              SCREAMING_SNAKE_CASE_									=						text_encoding.pop('input_ids')
      else:
          SCREAMING_SNAKE_CASE_									=						None

      if text_encoding is not None:
          encoding_image_processor.update(_A)

      return encoding_image_processor

  def 						lowerCAmelCase__     (					self						,		*_A						,		**_A):
      return self.tokenizer.batch_decode(*_A						,		**_A)

  def 						lowerCAmelCase__     (					self						,		*_A						,		**_A):
      return self.tokenizer.decode(*_A						,		**_A)


  @property
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.tokenizer.model_input_names
      SCREAMING_SNAKE_CASE_									=						self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





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						,		_A						,		_A						,		_A						,		_A = None						,		):
      super().__init__()
      self.register_modules(transformer=_A						,		vae=_A						,		scheduler=_A)

      # create a imagenet -> id dictionary for easier use
      SCREAMING_SNAKE_CASE_									=						{}
      if idalabel is not None:
          for key, value in idalabel.items():
              for label in value.split(','):
                  SCREAMING_SNAKE_CASE_									=						int(_A)
          SCREAMING_SNAKE_CASE_									=						dict(sorted(self.labels.items()))

  def 						lowerCAmelCase__     (					self						,		_A):

      if not isinstance(_A						,		_A):
          SCREAMING_SNAKE_CASE_									=						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						,		):
      SCREAMING_SNAKE_CASE_									=						len(_A)
      SCREAMING_SNAKE_CASE_									=						self.transformer.config.sample_size
      SCREAMING_SNAKE_CASE_									=						self.transformer.config.in_channels

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

      SCREAMING_SNAKE_CASE_									=						torch.tensor(_A						,		device=self.device).reshape(-1)
      SCREAMING_SNAKE_CASE_									=						torch.tensor([1000] * batch_size						,		device=self.device)
      SCREAMING_SNAKE_CASE_									=						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:
              SCREAMING_SNAKE_CASE_									=						latent_model_input[: len(_A) // 2]
              SCREAMING_SNAKE_CASE_									=						torch.cat([half, half]						,		dim=0)
          SCREAMING_SNAKE_CASE_									=						self.scheduler.scale_model_input(_A						,		_A)

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

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

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

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

          # learned sigma
          if self.transformer.config.out_channels // 2 == latent_channels:
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						torch.split(_A						,		_A						,		dim=1)
          else:
              SCREAMING_SNAKE_CASE_									=						noise_pred

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

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

      SCREAMING_SNAKE_CASE_									=						1 / self.vae.config.scaling_factor * latents
      SCREAMING_SNAKE_CASE_									=						self.vae.decode(_A).sample

      SCREAMING_SNAKE_CASE_									=						(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_									=						samples.cpu().permute(0						,		2						,		3						,		1).float().numpy()

      if output_type == "pil":
          SCREAMING_SNAKE_CASE_									=						self.numpy_to_pil(_A)

      if not return_dict:
          return (samples,)

      return ImagePipelineOutput(images=_A)


