





"""simple docstring"""


from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCamelCase__				  =   logging.get_logger(__name__)  # pylint: disable=invalid-name



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  _UpperCamelCase   ):







  '''simple docstring'''


  @register_to_config
  def __init__(		self   :       Optional[Any]					,   __a   :       bool					,   __a   :       Optional[int] = None					,   __a   :       Optional[int] = None	)		->				Any:
        super().__init__()

        _UpperCamelCase      :	Any						=       learnable

        if self.learnable:
              assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
              assert length is not None, "learnable=True requires `length` to be set"

              _UpperCamelCase      :	int						=       torch.zeros(__a					,   __a	)
        else:
              _UpperCamelCase      :	List[str]						=       None

        _UpperCamelCase      :	str						=       torch.nn.Parameter(__a	)





class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:VQModel
  SCREAMING_SNAKE_CASE__							:CLIPTextModel
  SCREAMING_SNAKE_CASE__							:CLIPTokenizer
  SCREAMING_SNAKE_CASE__							:TransformeraDModel
  SCREAMING_SNAKE_CASE__							:LearnedClassifierFreeSamplingEmbeddings
  SCREAMING_SNAKE_CASE__							:VQDiffusionScheduler

  def __init__(		self   :       int					,   __a   :       VQModel					,   __a   :       CLIPTextModel					,   __a   :       CLIPTokenizer					,   __a   :       TransformeraDModel					,   __a   :       VQDiffusionScheduler					,   __a   :       LearnedClassifierFreeSamplingEmbeddings					,   )		->				List[Any]:
        super().__init__()

        self.register_modules(
            vqvae=__a					,   transformer=__a					,   text_encoder=__a					,   tokenizer=__a					,   scheduler=__a					,   learned_classifier_free_sampling_embeddings=__a					,   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       Any					,   __a   :       Optional[Any]					,   __a   :       str	)		->				List[str]:
        _UpperCamelCase      :	int						=       len(__a	) if isinstance(__a					,   __a	) else 1

        # get prompt text embeddings
        _UpperCamelCase      :	Dict						=       self.tokenizer(
            __a					,   padding="max_length"					,   max_length=self.tokenizer.model_max_length					,   return_tensors="pt"					,   )
        _UpperCamelCase      :	Optional[int]						=       text_inputs.input_ids

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
              _UpperCamelCase      :	Optional[Any]						=       self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
              logger.warning(
                  "The following part of your input was truncated because CLIP can only handle sequences up to"
                  F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''	)
              _UpperCamelCase      :	List[str]						=       text_input_ids[:, : self.tokenizer.model_max_length]
        _UpperCamelCase      :	List[Any]						=       self.text_encoder(text_input_ids.to(self.device	)	)[0]

        # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
        # While CLIP does normalize the pooled output of the text transformer when combining
        # the image and text embeddings, CLIP does not directly normalize the last hidden state.
        #
        # CLIP normalizing the pooled output.
        # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
        _UpperCamelCase      :	int						=       prompt_embeds / prompt_embeds.norm(dim=-1					,   keepdim=__a	)

        # duplicate text embeddings for each generation per prompt
        _UpperCamelCase      :	List[str]						=       prompt_embeds.repeat_interleave(__a					,   dim=0	)

        if do_classifier_free_guidance:
              if self.learned_classifier_free_sampling_embeddings.learnable:
                    _UpperCamelCase      :	Any						=       self.learned_classifier_free_sampling_embeddings.embeddings
                    _UpperCamelCase      :	str						=       negative_prompt_embeds.unsqueeze(0	).repeat(__a					,   1					,   1	)
              else:
                    _UpperCamelCase      :	Tuple						=       [""] * batch_size

                    _UpperCamelCase      :	Any						=       text_input_ids.shape[-1]
                    _UpperCamelCase      :	Optional[int]						=       self.tokenizer(
                        __a					,   padding="max_length"					,   max_length=__a					,   truncation=__a					,   return_tensors="pt"					,   )
                    _UpperCamelCase      :	Optional[int]						=       self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]
                    # See comment for normalizing text embeddings
                    _UpperCamelCase      :	Optional[int]						=       negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1					,   keepdim=__a	)

              # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
              _UpperCamelCase      :	Tuple						=       negative_prompt_embeds.shape[1]
              _UpperCamelCase      :	Dict						=       negative_prompt_embeds.repeat(1					,   __a					,   1	)
              _UpperCamelCase      :	List[Any]						=       negative_prompt_embeds.view(batch_size * num_images_per_prompt					,   __a					,   -1	)

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              _UpperCamelCase      :	str						=       torch.cat([negative_prompt_embeds, prompt_embeds]	)

        return prompt_embeds

  @torch.no_grad()
  def __call__(		self   :       List[Any]					,   __a   :       Union[str, List[str]]					,   __a   :       int = 100					,   __a   :       float = 5.0					,   __a   :       float = 1.0					,   __a   :       int = 1					,   __a   :       Optional[Union[torch.Generator, List[torch.Generator]]] = None					,   __a   :       Optional[torch.FloatTensor] = None					,   __a   :       Optional[str] = "pil"					,   __a   :       bool = True					,   __a   :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,   __a   :       int = 1					,   )		->				Union[ImagePipelineOutput, Tuple]:
        if isinstance(__a					,   __a	):
              _UpperCamelCase      :	Tuple						=       1
        elif isinstance(__a					,   __a	):
              _UpperCamelCase      :	List[Any]						=       len(__a	)
        else:
              raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__a	)}'''	)

        _UpperCamelCase      :	Optional[Any]						=       batch_size * num_images_per_prompt

        _UpperCamelCase      :	Optional[int]						=       guidance_scale > 1.0

        _UpperCamelCase      :	int						=       self._encode_prompt(__a					,   __a					,   __a	)

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__a					,   __a	) or callback_steps <= 0)
        ):
              raise ValueError(
                  F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                  F''' {type(__a	)}.'''	)

        # get the initial completely masked latents unless the user supplied it

        _UpperCamelCase      :	Union[str, Any]						=       (batch_size, self.transformer.num_latent_pixels)
        if latents is None:
              _UpperCamelCase      :	Any						=       self.transformer.num_vector_embeds - 1
              _UpperCamelCase      :	List[str]						=       torch.full(__a					,   __a	).to(self.device	)
        else:
              if latents.shape != latents_shape:
                    raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''	)
              if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                    raise ValueError(
                        "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
                        F''' {self.transformer.num_vector_embeds - 1} (inclusive).'''	)
              _UpperCamelCase      :	List[Any]						=       latents.to(self.device	)

        # set timesteps
        self.scheduler.set_timesteps(__a					,   device=self.device	)

        _UpperCamelCase      :	Dict						=       self.scheduler.timesteps.to(self.device	)

        _UpperCamelCase      :	Dict						=       latents

        for i, t in enumerate(self.progress_bar(__a	)	):
              # expand the sample if we are doing classifier free guidance
              _UpperCamelCase      :	int						=       torch.cat([sample] * 2	) if do_classifier_free_guidance else sample

              # predict the un-noised image
              # model_output == `log_p_x_0`
              _UpperCamelCase      :	str						=       self.transformer(__a					,   encoder_hidden_states=__a					,   timestep=__a	).sample

              if do_classifier_free_guidance:
                    _UpperCamelCase,  _UpperCamelCase      :	Dict						=       model_output.chunk(2	)
                    _UpperCamelCase      :	str						=       model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                    model_output -= torch.logsumexp(__a					,   dim=1					,   keepdim=__a	)

              _UpperCamelCase      :	Any						=       self.truncate(__a					,   __a	)

              # remove `log(0)`'s (`-inf`s)
              _UpperCamelCase      :	int						=       model_output.clamp(-70	)

              # compute the previous noisy sample x_t -> x_t-1
              _UpperCamelCase      :	Dict						=       self.scheduler.step(__a					,   timestep=__a					,   sample=__a					,   generator=__a	).prev_sample

              # call the callback, if provided
              if callback is not None and i % callback_steps == 0:
                    callback(__a					,   __a					,   __a	)

        _UpperCamelCase      :	Union[str, Any]						=       self.vqvae.config.vq_embed_dim
        _UpperCamelCase      :	Dict						=       (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
        _UpperCamelCase      :	Tuple						=       self.vqvae.quantize.get_codebook_entry(__a					,   shape=__a	)
        _UpperCamelCase      :	int						=       self.vqvae.decode(__a					,   force_not_quantize=__a	).sample

        _UpperCamelCase      :	Tuple						=       (image / 2 + 0.5).clamp(0					,   1	)
        _UpperCamelCase      :	Optional[Any]						=       image.cpu().permute(0					,   2					,   3					,   1	).numpy()

        if output_type == "pil":
              _UpperCamelCase      :	Dict						=       self.numpy_to_pil(__a	)

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=__a	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       torch.FloatTensor					,   __a   :       float	)		->				torch.FloatTensor:
        _UpperCamelCase,  _UpperCamelCase      :	str						=       torch.sort(__a					,   1					,   descending=__a	)
        _UpperCamelCase      :	Union[str, Any]						=       torch.exp(__a	)
        _UpperCamelCase      :	Any						=       sorted_p_x_0.cumsum(dim=1	) < truncation_rate

        # Ensure that at least the largest probability is not zeroed out
        _UpperCamelCase      :	Optional[int]						=       torch.full_like(keep_mask[:, 0:1, :]					,   __a	)
        _UpperCamelCase      :	List[Any]						=       torch.cat((all_true, keep_mask)					,   dim=1	)
        _UpperCamelCase      :	List[str]						=       keep_mask[:, :-1, :]

        _UpperCamelCase      :	Union[str, Any]						=       keep_mask.gather(1					,   indices.argsort(1	)	)

        _UpperCamelCase      :	List[Any]						=       log_p_x_0.clone()

        _UpperCamelCase      :	Tuple						=       -torch.inf  # -inf = log(0)

        return rv







"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCamelCase__				  =   {
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTOnnxConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   ["OwlViTFeatureExtractor"]
  lowerCamelCase__				  =   ["OwlViTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   [
      "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OwlViTModel",
      "OwlViTPreTrainedModel",
      "OwlViTTextModel",
      "OwlViTVisionModel",
      "OwlViTForObjectDetection",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

  lowerCamelCase__				  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
