

from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
       from ..tf_utils import stable_softmax

UpperCamelCase__  :      Any			=    logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase__       )
class       __snake_case  (    lowerCAmelCase__       ):

  def __init__(					self						,		**_A):
      super().__init__(**_A)

      requires_backends(self						,		'vision')
      self.check_model_type(
          TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == 'tf'
          else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING)

  def __call__(					self						,		_A						,		**_A):
      return super().__call__(_A						,		**_A)

  def 						lowerCAmelCase__     (					self						,		**_A):
      SCREAMING_SNAKE_CASE_									=						{}
      if "candidate_labels" in kwargs:
          SCREAMING_SNAKE_CASE_									=						kwargs['candidate_labels']
      if "hypothesis_template" in kwargs:
          SCREAMING_SNAKE_CASE_									=						kwargs['hypothesis_template']

      return preprocess_params, {}, {}

  def 						lowerCAmelCase__     (					self						,		_A						,		_A=None						,		_A="This is a photo of {}."):
      SCREAMING_SNAKE_CASE_									=						load_image(_A)
      SCREAMING_SNAKE_CASE_									=						self.image_processor(images=[image]						,		return_tensors=self.framework)
      SCREAMING_SNAKE_CASE_									=						candidate_labels
      SCREAMING_SNAKE_CASE_									=						[hypothesis_template.format(_A) for x in candidate_labels]
      SCREAMING_SNAKE_CASE_									=						self.tokenizer(_A						,		return_tensors=self.framework						,		padding=_A)
      SCREAMING_SNAKE_CASE_									=						[text_inputs]
      return inputs

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						model_inputs.pop('candidate_labels')
      SCREAMING_SNAKE_CASE_									=						model_inputs.pop('text_inputs')
      if isinstance(text_inputs[0]						,		_A):
          SCREAMING_SNAKE_CASE_									=						text_inputs[0]
      else:
          # Batching case.
          SCREAMING_SNAKE_CASE_									=						text_inputs[0][0]

      SCREAMING_SNAKE_CASE_									=						self.model(**_A						,		**_A)

      SCREAMING_SNAKE_CASE_									=						{
          'candidate_labels': candidate_labels,
          'logits': outputs.logits_per_image,
      }
      return model_outputs


  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						model_outputs.pop('candidate_labels')
      SCREAMING_SNAKE_CASE_									=						model_outputs['logits'][0]
      if self.framework == "pt":
          SCREAMING_SNAKE_CASE_									=						logits.softmax(dim=-1).squeeze(-1)
          SCREAMING_SNAKE_CASE_									=						probs.tolist()
          if not isinstance(_A						,		_A):
              SCREAMING_SNAKE_CASE_									=						[scores]
      elif self.framework == "tf":
          SCREAMING_SNAKE_CASE_									=						stable_softmax(_A						,		axis=-1)
          SCREAMING_SNAKE_CASE_									=						probs.numpy().tolist()
      else:
          raise ValueError(f"""Unsupported framework: {self.framework}""")

      SCREAMING_SNAKE_CASE_									=						[
          {'score': score, 'label': candidate_label}
          for score, candidate_label in sorted(zip(_A						,		_A)						,		key=lambda _A: -x[0])
      ]
      return result





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)


