

'''simple docstring'''
from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

_UpperCAmelCase  :		Dict  =		'''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''




def 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    Tuple     ,					lowercase_						:    int=8	)		->	Union[str, Any]:




  '''simple docstring'''



  lowercase									=h // scale_factor**2
  if h % scale_factor**2 != 0:
    new_h += 1
  lowercase									=w // scale_factor**2
  if w % scale_factor**2 != 0:
    new_w += 1
  return new_h * scale_factor, new_w * scale_factor




class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







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

   self.register_modules(
       text_encoder=snake_case_  , tokenizer=snake_case_  , unet=snake_case_  , scheduler=snake_case_  , movq=snake_case_  , )
   lowercase									=2 ** (len(self.movq.config.block_out_channels   ) - 1)







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_   ):
   if latents is None:
     lowercase									=randn_tensor(snake_case_  , generator=snake_case_  , device=snake_case_  , dtype=snake_case_   )
   else:
     if latents.shape != shape:
       raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'   )
     lowercase									=latents.to(snake_case_   )

   lowercase									=latents * scheduler.init_noise_sigma
   return latents







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_=None  , ):
   lowercase									=len(snake_case_   ) if isinstance(snake_case_  , snake_case_   ) else 1
   # get prompt text embeddings
   lowercase									=self.tokenizer(
       snake_case_  , padding='''max_length'''  , truncation=snake_case_  , max_length=77  , return_attention_mask=snake_case_  , add_special_tokens=snake_case_  , return_tensors='''pt'''  , )

   lowercase									=text_inputs.input_ids
   lowercase									=self.tokenizer(snake_case_  , padding='''longest'''  , return_tensors='''pt'''   ).input_ids

   if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case_  , snake_case_   ):
     lowercase									=self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]   )
     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.to(snake_case_   )
   lowercase									=text_inputs.attention_mask.to(snake_case_   )

   lowercase							, lowercase									=self.text_encoder(
       input_ids=snake_case_  , attention_mask=snake_case_   )

   lowercase									=prompt_embeds.repeat_interleave(snake_case_  , dim=0   )
   lowercase									=text_encoder_hidden_states.repeat_interleave(snake_case_  , dim=0   )
   lowercase									=text_mask.repeat_interleave(snake_case_  , dim=0   )

   if do_classifier_free_guidance:
     lowercase									=42
     if negative_prompt is None:
       lowercase									=[''''''] * batch_size
     elif type(snake_case_   ) is not type(snake_case_   ):
       raise TypeError(
           f'`negative_prompt` should be the same type to `prompt`, but got {type(snake_case_   )} !='
           f' {type(snake_case_   )}.'   )
     elif isinstance(snake_case_  , snake_case_   ):
       lowercase									=[negative_prompt]
     elif batch_size != len(snake_case_   ):
       raise ValueError(
           f'`negative_prompt`: {negative_prompt} has batch size {len(snake_case_   )}, but `prompt`:'
           f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
           ''' the batch size of `prompt`.'''   )
     else:
       lowercase									=negative_prompt

     lowercase									=self.tokenizer(
         snake_case_  , padding='''max_length'''  , max_length=77  , truncation=snake_case_  , return_attention_mask=snake_case_  , add_special_tokens=snake_case_  , return_tensors='''pt'''  , )
     lowercase									=uncond_input.input_ids.to(snake_case_   )
     lowercase									=uncond_input.attention_mask.to(snake_case_   )

     lowercase							, lowercase									=self.text_encoder(
         input_ids=snake_case_  , attention_mask=snake_case_   )

     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

     lowercase									=negative_prompt_embeds.shape[1]
     lowercase									=negative_prompt_embeds.repeat(1  , snake_case_   )
     lowercase									=negative_prompt_embeds.view(batch_size * num_images_per_prompt  , snake_case_   )

     lowercase									=uncond_text_encoder_hidden_states.shape[1]
     lowercase									=uncond_text_encoder_hidden_states.repeat(1  , snake_case_  , 1   )
     lowercase									=uncond_text_encoder_hidden_states.view(
         batch_size * num_images_per_prompt  , snake_case_  , -1   )
     lowercase									=uncond_text_mask.repeat_interleave(snake_case_  , dim=0   )

     # done duplicates

     # 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]   )
     lowercase									=torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

     lowercase									=torch.cat([uncond_text_mask, text_mask]   )

   return prompt_embeds, text_encoder_hidden_states, text_mask







 def 						_A(       self  , snake_case_=0   ):
   if is_accelerate_available():
     from accelerate import cpu_offload
   else:
     raise ImportError('''Please install accelerate via `pip install accelerate`'''   )

   lowercase									=torch.device(f'cuda:{gpu_id}'   )

   lowercase									=[
       self.unet,
       self.text_encoder,
       self.movq,
   ]
   for cpu_offloaded_model in models:
     if cpu_offloaded_model is not None:
       cpu_offload(snake_case_  , snake_case_   )







 def 						_A(       self  , snake_case_=0   ):
   if is_accelerate_available() and is_accelerate_version('''>='''  , '''0.17.0.dev0'''   ):
     from accelerate import cpu_offload_with_hook
   else:
     raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''   )

   lowercase									=torch.device(f'cuda:{gpu_id}'   )

   if self.device.type != "cpu":
     self.to('''cpu'''  , silence_dtype_warnings=snake_case_   )
     torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

   lowercase									=None
   for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
     lowercase							, lowercase									=cpu_offload_with_hook(snake_case_  , snake_case_  , prev_module_hook=snake_case_   )

   if self.safety_checker is not None:
     lowercase							, lowercase									=cpu_offload_with_hook(self.safety_checker  , snake_case_  , prev_module_hook=snake_case_   )

   # We'll offload the last model manually.
   lowercase									=hook







 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 						_A(       self   ):
   if not hasattr(self.unet  , '''_hf_hook'''   ):
     return self.device
   for module in self.unet.modules():
     if (
         hasattr(snake_case_  , '''_hf_hook'''   )
         and hasattr(module._hf_hook  , '''execution_device'''   )
         and module._hf_hook.execution_device is not None
     ):
       return torch.device(module._hf_hook.execution_device   )
   return self.device







 @torch.no_grad()
 @replace_example_docstring(snake_case_   )
 def __call__(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_ = None  , snake_case_ = 5_12  , snake_case_ = 5_12  , snake_case_ = 1_00  , snake_case_ = 4.0  , snake_case_ = 1  , snake_case_ = None  , snake_case_ = None  , snake_case_ = "pil"  , snake_case_ = True  , ):

   if isinstance(snake_case_  , snake_case_   ):
     lowercase									=1
   elif isinstance(snake_case_  , snake_case_   ):
     lowercase									=len(snake_case_   )
   else:
     raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(snake_case_   )}'   )

   lowercase									=self._execution_device

   lowercase									=batch_size * num_images_per_prompt
   lowercase									=guidance_scale > 1.0

   lowercase							, lowercase							, lowercase									=self._encode_prompt(
       snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_   )

   if isinstance(snake_case_  , snake_case_   ):
     lowercase									=torch.cat(snake_case_  , dim=0   )
   if isinstance(snake_case_  , snake_case_   ):
     lowercase									=torch.cat(snake_case_  , dim=0   )

   if do_classifier_free_guidance:
     lowercase									=image_embeds.repeat_interleave(snake_case_  , dim=0   )
     lowercase									=negative_image_embeds.repeat_interleave(snake_case_  , dim=0   )

   lowercase									=torch.cat([negative_image_embeds, image_embeds]  , dim=0   ).to(
       dtype=prompt_embeds.dtype  , device=snake_case_   )

   self.scheduler.set_timesteps(snake_case_  , device=snake_case_   )
   lowercase									=self.scheduler.timesteps

   lowercase									=self.unet.config.in_channels

   lowercase							, lowercase									=get_new_h_w(snake_case_  , snake_case_  , self.movq_scale_factor   )

   # create initial latent
   lowercase									=self.prepare_latents(
       (batch_size, num_channels_latents, height, width)  , text_encoder_hidden_states.dtype  , snake_case_  , snake_case_  , snake_case_  , self.scheduler  , )

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

     lowercase									={'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
     lowercase									=self.unet(
         sample=snake_case_  , timestep=snake_case_  , encoder_hidden_states=snake_case_  , added_cond_kwargs=snake_case_  , return_dict=snake_case_  , )[0]

     if do_classifier_free_guidance:
       lowercase							, lowercase									=noise_pred.split(latents.shape[1]  , dim=1   )
       lowercase							, lowercase									=noise_pred.chunk(2   )
       lowercase							, lowercase									=variance_pred.chunk(2   )
       lowercase									=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
       lowercase									=torch.cat([noise_pred, variance_pred_text]  , dim=1   )

     if not (
         hasattr(self.scheduler.config  , '''variance_type'''   )
         and self.scheduler.config.variance_type in ["learned", "learned_range"]
     ):
       lowercase							, lowercase									=noise_pred.split(latents.shape[1]  , dim=1   )

     # compute the previous noisy sample x_t -> x_t-1
     lowercase									=self.scheduler.step(
         snake_case_  , snake_case_  , snake_case_  , generator=snake_case_  , ).prev_sample
   # post-processing
   lowercase									=self.movq.decode(snake_case_  , force_not_quantize=snake_case_   )['''sample''']

   if output_type not in ["pt", "np", "pil"]:
     raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'   )

   if output_type in ["np", "pil"]:
     lowercase									=image * 0.5 + 0.5
     lowercase									=image.clamp(0  , 1   )
     lowercase									=image.cpu().permute(0  , 2  , 3  , 1   ).float().numpy()

   if output_type == "pil":
     lowercase									=self.numpy_to_pil(snake_case_   )

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=snake_case_   )



"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__UpperCamelCase    : Tuple				 =						logging.get_logger(__name__)

__UpperCamelCase    : Optional[int]				 =						{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}







class 							SCREAMING_SNAKE_CASE   (						a_       ,				a_			):






					"""simple docstring"""



					lowercase__												=			"bit"
					lowercase__												=			["preactivation", "bottleneck"]
					lowercase__												=			["SAME", "VALID"]







					def __init__(			self     :							int  ,lowercase_     :							Optional[int]=3  ,lowercase_     :							int=6_4  ,lowercase_     :							Optional[int]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]  ,lowercase_     :							Union[str, Any]=[3, 4, 6, 3]  ,lowercase_     :							List[Any]="preactivation"  ,lowercase_     :							Dict="relu"  ,lowercase_     :							Dict=None  ,lowercase_     :							Optional[Any]=3_2  ,lowercase_     :							Any=0.0  ,lowercase_     :							Optional[Any]=False  ,lowercase_     :							List[Any]=3_2  ,lowercase_     :							Optional[Any]=1  ,lowercase_     :							Optional[int]=None  ,lowercase_     :							int=None  ,**lowercase_     :							List[str]  ,):
						super().__init__(**lowercase_						)
						if layer_type not in self.layer_types:
							raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
						if global_padding is not None:
							if global_padding.upper() in self.supported_padding:
								lowerCAmelCase__      :		Optional[Any]    							=      global_padding.upper()
							else:
								raise ValueError(F'Padding strategy {global_padding} not supported'						)
						lowerCAmelCase__      :		int    							=      num_channels
						lowerCAmelCase__      :		int    							=      embedding_size
						lowerCAmelCase__      :		Any    							=      hidden_sizes
						lowerCAmelCase__      :		int    							=      depths
						lowerCAmelCase__      :		Union[str, Any]    							=      layer_type
						lowerCAmelCase__      :		Optional[int]    							=      hidden_act
						lowerCAmelCase__      :		int    							=      global_padding
						lowerCAmelCase__      :		List[Any]    							=      num_groups
						lowerCAmelCase__      :		List[Any]    							=      drop_path_rate
						lowerCAmelCase__      :		Union[str, Any]    							=      embedding_dynamic_padding
						lowerCAmelCase__      :		int    							=      output_stride
						lowerCAmelCase__      :		Optional[Any]    							=      width_factor

						lowerCAmelCase__      :		List[str]    							=      ['''stem'''] + [F'stage{idx}' for idx in range(1  ,len(lowercase_						) + 1						)]
						lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      get_aligned_output_features_output_indices(
						    out_features=lowercase_  ,out_indices=lowercase_  ,stage_names=self.stage_names						)




