

'''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 			lowercase_	(_snake_case				,       _snake_case      ):




  """simple docstring"""




  @register_to_config
  def __init__(      self   :     List[str]					,lowercase__   :     Optional[int]					,lowercase__   :     Tuple = None					,lowercase__   :     Optional[Any] = None			):
   super().__init__()

   __lowercase							  = 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"

    __lowercase							  = torch.zeros(_UpperCamelCase					,_UpperCamelCase			)
   else:
    __lowercase							  = None

   __lowercase							  = torch.nn.Parameter(_UpperCamelCase			)






class 			lowercase_	(_snake_case      ):




  """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   :     Union[str, Any]					,lowercase__   :     List[Any]					,lowercase__   :     Tuple					,lowercase__   :     List[str]					,lowercase__   :     Any					,lowercase__   :     str					,lowercase__   :     int					,):
   super().__init__()

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     List[Any]					,lowercase__   :     Any					,lowercase__   :     Optional[Any]			):
   __lowercase							  = len(_UpperCamelCase			) if isinstance(_UpperCamelCase					,_UpperCamelCase			) else 1

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

   if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
    __lowercase							  = 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}"			)
    __lowercase							  = text_input_ids[:, : self.tokenizer.model_max_length]
   __lowercase							  = 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
   __lowercase							  = prompt_embeds / prompt_embeds.norm(dim=-1					,keepdim=_UpperCamelCase			)

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

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

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

    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
    __lowercase							  = negative_prompt_embeds.shape[1]
    __lowercase							  = negative_prompt_embeds.repeat(1					,_UpperCamelCase					,1			)
    __lowercase							  = negative_prompt_embeds.view(batch_size * num_images_per_prompt					,_UpperCamelCase					,-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
    __lowercase							  = torch.cat([negative_prompt_embeds, prompt_embeds]			)

   return prompt_embeds






  @torch.no_grad()
  def __call__(      self   :     Union[str, Any]					,lowercase__   :     int					,lowercase__   :     Union[str, Any] = 1_0_0					,lowercase__   :     Dict = 5.0					,lowercase__   :     Any = 1.0					,lowercase__   :     List[Any] = 1					,lowercase__   :     List[str] = None					,lowercase__   :     str = None					,lowercase__   :     int = "pil"					,lowercase__   :     List[Any] = True					,lowercase__   :     Optional[Any] = None					,lowercase__   :     Optional[int] = 1					,):
   if isinstance(_UpperCamelCase					,_UpperCamelCase			):
    __lowercase							  = 1
   elif isinstance(_UpperCamelCase					,_UpperCamelCase			):
    __lowercase							  = len(_UpperCamelCase			)
   else:
    raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase			)}"			)

   __lowercase							  = batch_size * num_images_per_prompt

   __lowercase							  = guidance_scale > 1.0

   __lowercase							  = self._encode_prompt(_UpperCamelCase					,_UpperCamelCase					,_UpperCamelCase			)

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

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

   __lowercase							  = (batch_size, self.transformer.num_latent_pixels)
   if latents is None:
    __lowercase							  = self.transformer.num_vector_embeds - 1
    __lowercase							  = torch.full(_UpperCamelCase					,_UpperCamelCase			).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)."			)
    __lowercase							  = latents.to(self.device			)

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

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

   __lowercase							  = latents

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

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

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

    __lowercase							  = self.truncate(_UpperCamelCase					,_UpperCamelCase			)

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

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

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

   __lowercase							  = self.vqvae.config.vq_embed_dim
   __lowercase							  = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
   __lowercase							  = self.vqvae.quantize.get_codebook_entry(_UpperCamelCase					,shape=_UpperCamelCase			)
   __lowercase							  = self.vqvae.decode(_UpperCamelCase					,force_not_quantize=_UpperCamelCase			).sample

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

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

   if not return_dict:
    return (image,)

   return ImagePipelineOutput(images=_UpperCamelCase			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     Dict					,lowercase__   :     Tuple			):
   __lowercase							  = torch.sort(_UpperCamelCase					,1					,descending=_UpperCamelCase			)
   __lowercase							  = torch.exp(_UpperCamelCase			)
   __lowercase							  = sorted_p_x_0.cumsum(dim=1			) < truncation_rate

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

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

   __lowercase							  = log_p_x_0.clone()

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

   return rv









from typing import List

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


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Optional[int]       						=  '''efficientformer'''



		def __init__(							self				,		_UpperCamelCase = [3, 2, 6, 4]				,		_UpperCamelCase = [4_8, 9_6, 2_2_4, 4_4_8]				,		_UpperCamelCase = [True, True, True, True]				,		_UpperCamelCase = 4_4_8				,		_UpperCamelCase = 3_2				,		_UpperCamelCase = 4				,		_UpperCamelCase = 7				,		_UpperCamelCase = 5				,		_UpperCamelCase = 8				,		_UpperCamelCase = 4				,		_UpperCamelCase = 0.0				,		_UpperCamelCase = 1_6				,		_UpperCamelCase = 3				,		_UpperCamelCase = 3				,		_UpperCamelCase = 3				,		_UpperCamelCase = 2				,		_UpperCamelCase = 1				,		_UpperCamelCase = 0.0				,		_UpperCamelCase = 1				,		_UpperCamelCase = True				,		_UpperCamelCase = True				,		_UpperCamelCase = 1E-5				,		_UpperCamelCase = "gelu"				,		_UpperCamelCase = 0.02				,		_UpperCamelCase = 1E-12				,		_UpperCamelCase = 2_2_4				,		_UpperCamelCase = 1E-05				,		**_UpperCamelCase				,		)	->							None:
							super().__init__(**_UpperCamelCase   )

							UpperCAmelCase_    :      int            =						hidden_act
							UpperCAmelCase_    :      Union[str, Any]            =						hidden_dropout_prob
							UpperCAmelCase_    :      Tuple            =						hidden_sizes
							UpperCAmelCase_    :      Union[str, Any]            =						num_hidden_layers
							UpperCAmelCase_    :      List[str]            =						num_attention_heads
							UpperCAmelCase_    :      List[Any]            =						initializer_range
							UpperCAmelCase_    :      int            =						layer_norm_eps
							UpperCAmelCase_    :      List[str]            =						patch_size
							UpperCAmelCase_    :      Union[str, Any]            =						num_channels
							UpperCAmelCase_    :      Optional[Any]            =						depths
							UpperCAmelCase_    :      List[Any]            =						mlp_expansion_ratio
							UpperCAmelCase_    :      List[str]            =						downsamples
							UpperCAmelCase_    :      List[Any]            =						dim
							UpperCAmelCase_    :      Tuple            =						key_dim
							UpperCAmelCase_    :      Optional[int]            =						attention_ratio
							UpperCAmelCase_    :      str            =						resolution
							UpperCAmelCase_    :      Dict            =						pool_size
							UpperCAmelCase_    :      Union[str, Any]            =						downsample_patch_size
							UpperCAmelCase_    :      List[str]            =						downsample_stride
							UpperCAmelCase_    :      List[str]            =						downsample_pad
							UpperCAmelCase_    :      Any            =						drop_path_rate
							UpperCAmelCase_    :      Dict            =						num_metaad_blocks
							UpperCAmelCase_    :      Dict            =						distillation
							UpperCAmelCase_    :      int            =						use_layer_scale
							UpperCAmelCase_    :      Any            =						layer_scale_init_value
							UpperCAmelCase_    :      Any            =						image_size
							UpperCAmelCase_    :      Dict            =						batch_norm_eps






