from collections import namedtuple

lowerCAmelCase__     :   Union[str, Any]		=   namedtuple('''from_to''', '''from_ to''')

lowerCAmelCase__     :   Dict		=   {
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.0_01, 10_00),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.0_04_54, 2_64.1_72),
    '''cubicyard''': from_to(0.7_64_55, 1.3_07_95),
    '''cubicfoot''': from_to(0.0_28, 35.31_47),
    '''cup''': from_to(0.0_00_23_65_88, 42_26.75),
}
def  UpperCamelCase__						(				A__			, A__			, A__    )   ->					Any:



				if from_type not in METRIC_CONVERSION:
								raise ValueError(
								    F"""Invalid \'from_type\' value: {from_type!r}  Supported values are:\n"""
								    + ', '.join(A__    )    )
				if to_type not in METRIC_CONVERSION:
								raise ValueError(
								    F"""Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n"""
								    + ', '.join(A__    )    )
				return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
				import doctest

				doctest.testmod()








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


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





class     lowerCamelCase__(					__lowerCamelCase ,     __lowerCamelCase):
      @register_to_config
      def __init__(				self: Optional[Any]    ,       UpperCamelCase_: bool    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[int] = None   ):
             super().__init__()

             __lowerCamelCase   =						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   =						torch.zeros(UpperCamelCase_    ,       UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						None

             __lowerCamelCase   =						torch.nn.Parameter(UpperCamelCase_   )





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	VQModel
      UpperCAmelCase__      :	CLIPTextModel
      UpperCAmelCase__      :	CLIPTokenizer
      UpperCAmelCase__      :	TransformeraDModel
      UpperCAmelCase__      :	LearnedClassifierFreeSamplingEmbeddings
      UpperCAmelCase__      :	VQDiffusionScheduler
      def __init__(				self: str    ,       UpperCamelCase_: VQModel    ,       UpperCamelCase_: CLIPTextModel    ,       UpperCamelCase_: CLIPTokenizer    ,       UpperCamelCase_: TransformeraDModel    ,       UpperCamelCase_: VQDiffusionScheduler    ,       UpperCamelCase_: LearnedClassifierFreeSamplingEmbeddings    ,       ):
             super().__init__()

             self.register_modules(
                 vqvae=UpperCamelCase_    ,       transformer=UpperCamelCase_    ,       text_encoder=UpperCamelCase_    ,       tokenizer=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       learned_classifier_free_sampling_embeddings=UpperCamelCase_    ,       )
      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: Any   ):
             __lowerCamelCase   =						len(UpperCamelCase_   ) if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ) else 1

             # get prompt text embeddings
             __lowerCamelCase   =						self.tokenizer(
                 UpperCamelCase_    ,       padding="""max_length"""    ,       max_length=self.tokenizer.model_max_length    ,       return_tensors="""pt"""    ,       )
             __lowerCamelCase   =						text_inputs.input_ids

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

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

             if do_classifier_free_guidance:
                    if self.learned_classifier_free_sampling_embeddings.learnable:
                           __lowerCamelCase   =						self.learned_classifier_free_sampling_embeddings.embeddings
                           __lowerCamelCase   =						negative_prompt_embeds.unsqueeze(0   ).repeat(UpperCamelCase_    ,       1    ,       1   )
                    else:
                           __lowerCamelCase   =						[""""""] * batch_size

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

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

             return prompt_embeds
      @torch.no_grad()
      def __call__(				self: Tuple    ,       UpperCamelCase_: Union[str, List[str]]    ,       UpperCamelCase_: int = 1_00    ,       UpperCamelCase_: float = 5.0    ,       UpperCamelCase_: float = 1.0    ,       UpperCamelCase_: int = 1    ,       UpperCamelCase_: Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,       UpperCamelCase_: Optional[torch.FloatTensor] = None    ,       UpperCamelCase_: Optional[str] = "pil"    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,       UpperCamelCase_: int = 1    ,       ):
             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						1
             elif isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						len(UpperCamelCase_   )
             else:
                    raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase_   )}'   )

             __lowerCamelCase   =						batch_size * num_images_per_prompt

             __lowerCamelCase   =						guidance_scale > 1.0

             __lowerCamelCase   =						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

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

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

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

             __lowerCamelCase   =						latents

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

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

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

                    __lowerCamelCase   =						self.truncate(UpperCamelCase_    ,       UpperCamelCase_   )

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

                    # compute the previous noisy sample x_t -> x_t-1
                    __lowerCamelCase   =						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_   )

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

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

             if output_type == "pil":
                    __lowerCamelCase   =						self.numpy_to_pil(UpperCamelCase_   )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: torch.FloatTensor    ,       UpperCamelCase_: float   ):
             __lowerCamelCase,   __lowerCamelCase   =						torch.sort(UpperCamelCase_    ,       1    ,       descending=UpperCamelCase_   )
             __lowerCamelCase   =						torch.exp(UpperCamelCase_   )
             __lowerCamelCase   =						sorted_p_x_0.cumsum(dim=1   ) < truncation_rate

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

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

             __lowerCamelCase   =						log_p_x_0.clone()

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

             return rv

