


'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase							=logging.get_logger(__name__)

lowercase							={
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="lilt"






       def __init__(      self  ,							snake_case=3_0_5_2_2  ,							snake_case=7_6_8  ,							snake_case=1_2  ,							snake_case=1_2  ,							snake_case=3_0_7_2  ,							snake_case="gelu"  ,							snake_case=0.1  ,							snake_case=0.1  ,							snake_case=5_1_2  ,							snake_case=2  ,							snake_case=0.02  ,							snake_case=1E-1_2  ,							snake_case=0  ,							snake_case="absolute"  ,							snake_case=None  ,							snake_case=4  ,							snake_case=1_0_2_4  ,							**snake_case  ,							)						->  Union[str, Any]:




          '''simple docstring'''
          super().__init__(pad_token_id=snake_case  ,							**snake_case)

          _UpperCAmelCase       :       Optional[int]							   =vocab_size
          _UpperCAmelCase       :       Dict							   =hidden_size
          _UpperCAmelCase       :       List[str]							   =num_hidden_layers
          _UpperCAmelCase       :       Optional[int]							   =num_attention_heads
          _UpperCAmelCase       :       Union[str, Any]							   =hidden_act
          _UpperCAmelCase       :       Optional[int]							   =intermediate_size
          _UpperCAmelCase       :       Any							   =hidden_dropout_prob
          _UpperCAmelCase       :       int							   =attention_probs_dropout_prob
          _UpperCAmelCase       :       Optional[int]							   =max_position_embeddings
          _UpperCAmelCase       :       List[Any]							   =type_vocab_size
          _UpperCAmelCase       :       Union[str, Any]							   =initializer_range
          _UpperCAmelCase       :       Optional[Any]							   =layer_norm_eps
          _UpperCAmelCase       :       str							   =position_embedding_type
          _UpperCAmelCase       :       Optional[Any]							   =classifier_dropout
          _UpperCAmelCase       :       Dict							   =channel_shrink_ratio
          _UpperCAmelCase       :       int							   =max_ad_position_embeddings





'''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


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






class  __magic_name__							(		lowerCAmelCase ,lowerCAmelCase    ):






       @register_to_config
       def __init__(      self  ,							snake_case  ,							snake_case = None  ,							snake_case = None)						->  Union[str, Any]:




          '''simple docstring'''
          super().__init__()

          _UpperCAmelCase       :       List[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       :       str							   =torch.zeros(snake_case  ,							snake_case)
          else:
             _UpperCAmelCase       :       Tuple							   =None

          _UpperCAmelCase       :       int							   =torch.nn.Parameter(snake_case)






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42






       def __init__(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							)						->  Tuple:




          '''simple docstring'''
          super().__init__()

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






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =len(snake_case) if isinstance(snake_case  ,							snake_case) else 1

          # get prompt text embeddings
          _UpperCAmelCase       :       Optional[Any]							   =self.tokenizer(
              snake_case  ,							padding='max_length'  ,							max_length=self.tokenizer.model_max_length  ,							return_tensors='pt'  ,							)
          _UpperCAmelCase       :       Union[str, Any]							   =text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             _UpperCAmelCase       :       str							   =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       :       Union[str, Any]							   =text_input_ids[:, : self.tokenizer.model_max_length]
          _UpperCAmelCase       :       Optional[int]							   =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       :       List[str]							   =prompt_embeds / prompt_embeds.norm(dim=-1  ,							keepdim=snake_case)

          # duplicate text embeddings for each generation per prompt
          _UpperCAmelCase       :       Optional[Any]							   =prompt_embeds.repeat_interleave(snake_case  ,							dim=0)

          if do_classifier_free_guidance:
             if self.learned_classifier_free_sampling_embeddings.learnable:
                _UpperCAmelCase       :       Dict							   =self.learned_classifier_free_sampling_embeddings.embeddings
                _UpperCAmelCase       :       Any							   =negative_prompt_embeds.unsqueeze(0).repeat(snake_case  ,							1  ,							1)
             else:
                _UpperCAmelCase       :       str							   =[''] * batch_size

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

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             _UpperCAmelCase       :       int							   =negative_prompt_embeds.shape[1]
             _UpperCAmelCase       :       List[str]							   =negative_prompt_embeds.repeat(1  ,							snake_case  ,							1)
             _UpperCAmelCase       :       Optional[int]							   =negative_prompt_embeds.view(batch_size * num_images_per_prompt  ,							snake_case  ,							-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  ,							snake_case  ,							snake_case = 1_0_0  ,							snake_case = 5.0  ,							snake_case = 1.0  ,							snake_case = 1  ,							snake_case = None  ,							snake_case = None  ,							snake_case = "pil"  ,							snake_case = True  ,							snake_case = None  ,							snake_case = 1  ,							)						->  Union[ImagePipelineOutput, Tuple]:




          '''simple docstring'''
          if isinstance(snake_case  ,							snake_case):
             _UpperCAmelCase       :       Tuple							   =1
          elif isinstance(snake_case  ,							snake_case):
             _UpperCAmelCase       :       int							   =len(snake_case)
          else:
             raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(snake_case)}")

          _UpperCAmelCase       :       Optional[Any]							   =batch_size * num_images_per_prompt

          _UpperCAmelCase       :       Union[str, Any]							   =guidance_scale > 1.0

          _UpperCAmelCase       :       Any							   =self._encode_prompt(snake_case  ,							snake_case  ,							snake_case)

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

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

          _UpperCAmelCase       :       List[Any]							   =(batch_size, self.transformer.num_latent_pixels)
          if latents is None:
             _UpperCAmelCase       :       Optional[Any]							   =self.transformer.num_vector_embeds - 1
             _UpperCAmelCase       :       Optional[int]							   =torch.full(snake_case  ,							snake_case).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       :       Optional[Any]							   =latents.to(self.device)

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

          _UpperCAmelCase       :       int							   =self.scheduler.timesteps.to(self.device)

          _UpperCAmelCase       :       Dict							   =latents

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

             # predict the un-noised image
             # model_output == `log_p_x_0`
             _UpperCAmelCase       :       Optional[Any]							   =self.transformer(snake_case  ,							encoder_hidden_states=snake_case  ,							timestep=snake_case).sample

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

             _UpperCAmelCase       :       Any							   =self.truncate(snake_case  ,							snake_case)

             # remove `log(0)`'s (`-inf`s)
             _UpperCAmelCase       :       int							   =model_output.clamp(-7_0)

             # compute the previous noisy sample x_t -> x_t-1
             _UpperCAmelCase       :       List[Any]							   =self.scheduler.step(snake_case  ,							timestep=snake_case  ,							sample=snake_case  ,							generator=snake_case).prev_sample

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

          _UpperCAmelCase       :       List[str]							   =self.vqvae.config.vq_embed_dim
          _UpperCAmelCase       :       Optional[int]							   =(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
          _UpperCAmelCase       :       int							   =self.vqvae.quantize.get_codebook_entry(snake_case  ,							shape=snake_case)
          _UpperCAmelCase       :       str							   =self.vqvae.decode(snake_case  ,							force_not_quantize=snake_case).sample

          _UpperCAmelCase       :       str							   =(image / 2 + 0.5).clamp(0  ,							1)
          _UpperCAmelCase       :       Tuple							   =image.cpu().permute(0  ,							2  ,							3  ,							1).numpy()

          if output_type == "pil":
             _UpperCAmelCase       :       Optional[int]							   =self.numpy_to_pil(snake_case)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  torch.FloatTensor:




          '''simple docstring'''
          _UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =torch.sort(snake_case  ,							1  ,							descending=snake_case)
          _UpperCAmelCase       :       Dict							   =torch.exp(snake_case)
          _UpperCAmelCase       :       str							   =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, :]  ,							snake_case)
          _UpperCAmelCase       :       Any							   =torch.cat((all_true, keep_mask)  ,							dim=1)
          _UpperCAmelCase       :       Dict							   =keep_mask[:, :-1, :]

          _UpperCAmelCase       :       Any							   =keep_mask.gather(1  ,							indices.argsort(1))

          _UpperCAmelCase       :       str							   =log_p_x_0.clone()

          _UpperCAmelCase       :       Any							   =-torch.inf  # -inf = log(0)

          return rv

