





'''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


_A:					Union[str, Any]							=				logging.get_logger(__name__)  # pylint: disable=invalid-name






class UpperCAmelCase							(		UpperCAmelCase_					,			UpperCAmelCase_							):



    @register_to_config
    def __init__(  self     ,			__A     ,			__A = None     ,			__A = None					):
     super().__init__()

     __UpperCAmelCase					=   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					=   torch.zeros(__A     ,			__A					)
     else:
      __UpperCAmelCase					=   None

     __UpperCAmelCase					=   torch.nn.Parameter(__A					)






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			VQModel
    _A :			CLIPTextModel
    _A :			CLIPTokenizer
    _A :			TransformeraDModel
    _A :			LearnedClassifierFreeSamplingEmbeddings
    _A :			VQDiffusionScheduler



    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			):
     super().__init__()

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



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   len(__A					) if isinstance(__A     ,			__A					) else 1

     # get prompt text embeddings
     __UpperCAmelCase					=   self.tokenizer(
         __A     ,			padding='max_length'     ,			max_length=self.tokenizer.model_max_length     ,			return_tensors='pt'     ,			)
     __UpperCAmelCase					=   text_inputs.input_ids

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
      __UpperCAmelCase					=   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					=   text_input_ids[:, : self.tokenizer.model_max_length]
     __UpperCAmelCase					=   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					=   prompt_embeds / prompt_embeds.norm(dim=-1     ,			keepdim=__A					)

     # duplicate text embeddings for each generation per prompt
     __UpperCAmelCase					=   prompt_embeds.repeat_interleave(__A     ,			dim=0					)

     if do_classifier_free_guidance:
      if self.learned_classifier_free_sampling_embeddings.learnable:
       __UpperCAmelCase					=   self.learned_classifier_free_sampling_embeddings.embeddings
       __UpperCAmelCase					=   negative_prompt_embeds.unsqueeze(0					).repeat(__A     ,			1     ,			1					)
      else:
       __UpperCAmelCase					=   [''] * batch_size

       __UpperCAmelCase					=   text_input_ids.shape[-1]
       __UpperCAmelCase					=   self.tokenizer(
           __A     ,			padding='max_length'     ,			max_length=__A     ,			truncation=__A     ,			return_tensors='pt'     ,			)
       __UpperCAmelCase					=   self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]
       # See comment for normalizing text embeddings
       __UpperCAmelCase					=   negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1     ,			keepdim=__A					)

      # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
      __UpperCAmelCase					=   negative_prompt_embeds.shape[1]
      __UpperCAmelCase					=   negative_prompt_embeds.repeat(1     ,			__A     ,			1					)
      __UpperCAmelCase					=   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					=   torch.cat([negative_prompt_embeds, prompt_embeds]					)

     return prompt_embeds



    @torch.no_grad()
    def __call__(  self     ,			__A     ,			__A = 100     ,			__A = 5.0     ,			__A = 1.0     ,			__A = 1     ,			__A = None     ,			__A = None     ,			__A = "pil"     ,			__A = True     ,			__A = None     ,			__A = 1     ,			):
     if isinstance(__A     ,			__A					):
      __UpperCAmelCase					=   1
     elif isinstance(__A     ,			__A					):
      __UpperCAmelCase					=   len(__A					)
     else:
      raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(__A					)}'					)

     __UpperCAmelCase					=   batch_size * num_images_per_prompt

     __UpperCAmelCase					=   guidance_scale > 1.0

     __UpperCAmelCase					=   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					=   (batch_size, self.transformer.num_latent_pixels)
     if latents is None:
      __UpperCAmelCase					=   self.transformer.num_vector_embeds - 1
      __UpperCAmelCase					=   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					=   latents.to(self.device					)

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

     __UpperCAmelCase					=   self.scheduler.timesteps.to(self.device					)

     __UpperCAmelCase					=   latents

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

      # predict the un-noised image
      # model_output == `log_p_x_0`
      __UpperCAmelCase					=   self.transformer(__A     ,			encoder_hidden_states=__A     ,			timestep=__A					).sample

      if do_classifier_free_guidance:
       __UpperCAmelCase					,    __UpperCAmelCase					=   model_output.chunk(2					)
       __UpperCAmelCase					=   model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
       model_output -= torch.logsumexp(__A     ,			dim=1     ,			keepdim=__A					)

      __UpperCAmelCase					=   self.truncate(__A     ,			__A					)

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

      # compute the previous noisy sample x_t -> x_t-1
      __UpperCAmelCase					=   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					=   self.vqvae.config.vq_embed_dim
     __UpperCAmelCase					=   (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
     __UpperCAmelCase					=   self.vqvae.quantize.get_codebook_entry(__A     ,			shape=__A					)
     __UpperCAmelCase					=   self.vqvae.decode(__A     ,			force_not_quantize=__A					).sample

     __UpperCAmelCase					=   (image / 2 + 0.5).clamp(0     ,			1					)
     __UpperCAmelCase					=   image.cpu().permute(0     ,			2     ,			3     ,			1					).numpy()

     if output_type == "pil":
      __UpperCAmelCase					=   self.numpy_to_pil(__A					)

     if not return_dict:
      return (image,)

     return ImagePipelineOutput(images=__A					)




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   torch.sort(__A     ,			1     ,			descending=__A					)
     __UpperCAmelCase					=   torch.exp(__A					)
     __UpperCAmelCase					=   sorted_p_x_0.cumsum(dim=1					) < truncation_rate

     # Ensure that at least the largest probability is not zeroed out
     __UpperCAmelCase					=   torch.full_like(keep_mask[:, 0:1, :]     ,			__A					)
     __UpperCAmelCase					=   torch.cat((all_true, keep_mask)     ,			dim=1					)
     __UpperCAmelCase					=   keep_mask[:, :-1, :]

     __UpperCAmelCase					=   keep_mask.gather(1     ,			indices.argsort(1					)					)

     __UpperCAmelCase					=   log_p_x_0.clone()

     __UpperCAmelCase					=   -torch.inf  # -inf = log(0)

     return rv












'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A:					List[str]							=				{
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A:					Union[str, Any]							=				[
        """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """LukeForEntityClassification""",
        """LukeForEntityPairClassification""",
        """LukeForEntitySpanClassification""",
        """LukeForMultipleChoice""",
        """LukeForQuestionAnswering""",
        """LukeForSequenceClassification""",
        """LukeForTokenClassification""",
        """LukeForMaskedLM""",
        """LukeModel""",
        """LukePreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

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





