





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


class    __SCREAMING_SNAKE_CASE			( lowerCAmelCase_     ,lowerCAmelCase_					):







       @register_to_config
       def __init__(      self     :	Dict		,					A     :	str		,					A     :	Optional[Any] = None		,					A     :	List[str] = None			)   ->str:
          super().__init__()

          lowerCamelCase__   :						str					   =							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"

             lowerCamelCase__   :						Tuple					   =							torch.zeros(A		,					A			)
          else:
             lowerCamelCase__   :						Any					   =							None

          lowerCamelCase__   :						Union[str, Any]					   =							torch.nn.Parameter(A			)


class    __SCREAMING_SNAKE_CASE			( lowerCAmelCase_					):
       _UpperCAmelCase      :  List[str]     =    4_2
       _UpperCAmelCase      :  Optional[Any]     =    4_2
       _UpperCAmelCase      :  List[str]     =    4_2
       _UpperCAmelCase      :  List[Any]     =    4_2
       _UpperCAmelCase      :  str     =    4_2
       _UpperCAmelCase      :  Tuple     =    4_2







       def __init__(      self     :	Tuple		,					A     :	List[Any]		,					A     :	Optional[Any]		,					A     :	Dict		,					A     :	Dict		,					A     :	Union[str, Any]		,					A     :	str		,					)   ->int:
          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     :	Optional[Any]		,					A     :	int		,					A     :	List[str]		,					A     :	List[Any]			)   ->Union[str, Any]:
          lowerCamelCase__   :						int					   =							len(A			) if isinstance(A		,					A			) else 1

          # get prompt text embeddings
          lowerCamelCase__   :						Any					   =							self.tokenizer(
              A		,					padding='''max_length'''		,					max_length=self.tokenizer.model_max_length		,					return_tensors='''pt'''		,					)
          lowerCamelCase__   :						Tuple					   =							text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             lowerCamelCase__   :						Optional[int]					   =							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}"			)
             lowerCamelCase__   :						List[str]					   =							text_input_ids[:, : self.tokenizer.model_max_length]
          lowerCamelCase__   :						Optional[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
          lowerCamelCase__   :						str					   =							prompt_embeds / prompt_embeds.norm(dim=-1		,					keepdim=A			)

          # duplicate text embeddings for each generation per prompt
          lowerCamelCase__   :						Any					   =							prompt_embeds.repeat_interleave(A		,					dim=0			)

          if do_classifier_free_guidance:
             if self.learned_classifier_free_sampling_embeddings.learnable:
                lowerCamelCase__   :						List[Any]					   =							self.learned_classifier_free_sampling_embeddings.embeddings
                lowerCamelCase__   :						Optional[int]					   =							negative_prompt_embeds.unsqueeze(0			).repeat(A		,					1		,					1			)
             else:
                lowerCamelCase__   :						str					   =							[''''''] * batch_size

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

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             lowerCamelCase__   :						Dict					   =							negative_prompt_embeds.shape[1]
             lowerCamelCase__   :						List[Any]					   =							negative_prompt_embeds.repeat(1		,					A		,					1			)
             lowerCamelCase__   :						Optional[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
             lowerCamelCase__   :						Tuple					   =							torch.cat([negative_prompt_embeds, prompt_embeds]			)

          return prompt_embeds







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

          lowerCamelCase__   :						Optional[int]					   =							batch_size * num_images_per_prompt

          lowerCamelCase__   :						str					   =							guidance_scale > 1.0

          lowerCamelCase__   :						Any					   =							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

          lowerCamelCase__   :						Union[str, Any]					   =							(batch_size, self.transformer.num_latent_pixels)
          if latents is None:
             lowerCamelCase__   :						int					   =							self.transformer.num_vector_embeds - 1
             lowerCamelCase__   :						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)."			)
             lowerCamelCase__   :						str					   =							latents.to(self.device			)

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

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

          lowerCamelCase__   :						Optional[int]					   =							latents

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

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

             if do_classifier_free_guidance:
                lowerCamelCase__   ,							lowerCamelCase__   :						List[Any]					   =							model_output.chunk(2			)
                lowerCamelCase__   :						Optional[int]					   =							model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                model_output -= torch.logsumexp(A		,					dim=1		,					keepdim=A			)

             lowerCamelCase__   :						str					   =							self.truncate(A		,					A			)

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

             # compute the previous noisy sample x_t -> x_t-1
             lowerCamelCase__   :						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			)

          lowerCamelCase__   :						List[str]					   =							self.vqvae.config.vq_embed_dim
          lowerCamelCase__   :						Tuple					   =							(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
          lowerCamelCase__   :						Any					   =							self.vqvae.quantize.get_codebook_entry(A		,					shape=A			)
          lowerCamelCase__   :						int					   =							self.vqvae.decode(A		,					force_not_quantize=A			).sample

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

          if output_type == "pil":
             lowerCamelCase__   :						List[Any]					   =							self.numpy_to_pil(A			)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=A			)







       def 						__lowerCamelCase    (      self     :	str		,					A     :	str		,					A     :	Any			)   ->torch.FloatTensor:
          lowerCamelCase__   ,							lowerCamelCase__   :						Optional[Any]					   =							torch.sort(A		,					1		,					descending=A			)
          lowerCamelCase__   :						Any					   =							torch.exp(A			)
          lowerCamelCase__   :						str					   =							sorted_p_x_0.cumsum(dim=1			) < truncation_rate

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

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

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

          lowerCamelCase__   :						Dict					   =							-torch.inf  # -inf = log(0)

          return rv







from typing import Any
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			list:







 """simple docstring"""


 _validation(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)
 # Creates data structures and fill initial step
 _lowercase							      ={}
 _lowercase							      ={}
 for state in states_space:
  _lowercase							      =observations_space[0]
  _lowercase							      =(
      initial_probabilities[state] * emission_probabilities[state][observation]
  )
  _lowercase							      =None

 # Fills the data structure with the probabilities of
 # different transitions and pointers to previous states
 for o in range(1     ,						len(__snake_case						)						):
  _lowercase							      =observations_space[o]
  _lowercase							      =observations_space[o - 1]
  for state in states_space:
   # Calculates the argmax for probability function
   _lowercase							      =''''''
   _lowercase							      =-1
   for k_state in states_space:
    _lowercase							      =(
        probabilities[(k_state, prior_observation)]
        * transition_probabilities[k_state][state]
        * emission_probabilities[state][observation]
    )
    if probability > max_probability:
     _lowercase							      =probability
     _lowercase							      =k_state

            # Update probabilities and pointers dicts
   _lowercase							      =(
       probabilities[(arg_max, prior_observation)]
       * transition_probabilities[arg_max][state]
       * emission_probabilities[state][observation]
   )

   _lowercase							      =arg_max

    # The final observation
 _lowercase							      =observations_space[len(__snake_case						) - 1]

 # argmax for given final observation
 _lowercase							      =''''''
 _lowercase							      =-1
 for k_state in states_space:
  _lowercase							      =probabilities[(k_state, final_observation)]
  if probability > max_probability:
   _lowercase							      =probability
   _lowercase							      =k_state
 _lowercase							      =arg_max

 # Process pointers backwards
 _lowercase							      =last_state
 _lowercase							      =[]
 for o in range(len(__snake_case						) - 1     ,						-1     ,						-1						):
  result.append(__snake_case						)
  _lowercase							      =pointers[previous, observations_space[o]]
 result.reverse()

 return result
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 _validate_not_empty(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)
 _validate_lists(__snake_case     ,						__snake_case						)
 _validate_dicts(
     __snake_case     ,						__snake_case     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 if not all(
     [
         observations_space,
         states_space,
         initial_probabilities,
         transition_probabilities,
         emission_probabilities,
     ]						):
  raise ValueError('''There\'s an empty parameter'''						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 _validate_list(__snake_case     ,						'''observations_space'''						)
 _validate_list(__snake_case     ,						'''states_space'''						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 if not isinstance(_object     ,						__snake_case						):
  _lowercase							      =F"{var_name} must be a list"
  raise ValueError(__snake_case						)
 else:
  for x in _object:
   if not isinstance(__snake_case     ,						__snake_case						):
    _lowercase							      =F"{var_name} must be a list of strings"
    raise ValueError(__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 _validate_dict(__snake_case     ,						'''initial_probabilities'''     ,						__snake_case						)
 _validate_nested_dict(__snake_case     ,						'''transition_probabilities'''						)
 _validate_nested_dict(__snake_case     ,						'''emission_probabilities'''						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 _validate_dict(_object     ,						__snake_case     ,						__snake_case						)
 for x in _object.values():
  _validate_dict(__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case = False						)     ->			None:







 """simple docstring"""


 if not isinstance(_object     ,						__snake_case						):
  _lowercase							      =F"{var_name} must be a dict"
  raise ValueError(__snake_case						)
 if not all(isinstance(__snake_case     ,						__snake_case						) for x in _object						):
  _lowercase							      =F"{var_name} all keys must be strings"
  raise ValueError(__snake_case						)
 if not all(isinstance(__snake_case     ,						__snake_case						) for x in _object.values()						):
  _lowercase							      ='''nested dictionary ''' if nested else ''''''
  _lowercase							      =F"{var_name} {nested_text}all values must be {value_type.__name__}"
  raise ValueError(__snake_case						)


if __name__ == "__main__":
  from doctest import testmod

  testmod()

