



'''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


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


class       __SCREAMING_SNAKE_CASE	(						_UpperCamelCase					,    _UpperCamelCase			):



     '''simple docstring'''






     @register_to_config
     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = None	):
       super().__init__()

       _snake_case     		=				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"

         _snake_case     		=				torch.zeros(__a						,   __a	)
       else:
         _snake_case     		=				None

       _snake_case     		=				torch.nn.Parameter(__a	)







class       __SCREAMING_SNAKE_CASE	(						_UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						VQModel
     UpperCAmelCase__       :						CLIPTextModel
     UpperCAmelCase__       :						CLIPTokenizer
     UpperCAmelCase__       :						TransformeraDModel
     UpperCAmelCase__       :						LearnedClassifierFreeSamplingEmbeddings
     UpperCAmelCase__       :						VQDiffusionScheduler

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

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				len(__a	) if isinstance(__a						,   __a	) else 1

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

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
         _snake_case     		=				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}'''	)
         _snake_case     		=				text_input_ids[:, : self.tokenizer.model_max_length]
       _snake_case     		=				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
       _snake_case     		=				prompt_embeds / prompt_embeds.norm(dim=-1						,   keepdim=__a	)

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

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

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

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

       return prompt_embeds

     @torch.no_grad()
     def __call__(					self						,   lowerCamelCase						,   lowerCamelCase = 100						,   lowerCamelCase = 5.0						,   lowerCamelCase = 1.0						,   lowerCamelCase = 1						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = "pil"						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = 1						,   ):
       if isinstance(__a						,   __a	):
         _snake_case     		=				1
       elif isinstance(__a						,   __a	):
         _snake_case     		=				len(__a	)
       else:
         raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__a	)}'''	)

       _snake_case     		=				batch_size * num_images_per_prompt

       _snake_case     		=				guidance_scale > 1.0

       _snake_case     		=				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

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

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

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

       _snake_case     		=				latents

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

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

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

         _snake_case     		=				self.truncate(__a						,   __a	)

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

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

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

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

       if output_type == "pil":
         _snake_case     		=				self.numpy_to_pil(__a	)

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=__a	)






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				torch.sort(__a						,   1						,   descending=__a	)
       _snake_case     		=				torch.exp(__a	)
       _snake_case     		=				sorted_p_x_0.cumsum(dim=1	) < truncation_rate

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

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

       _snake_case     		=				log_p_x_0.clone()

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

       return rv









'''simple docstring'''



from __future__ import annotations

import numpy as np




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return np.maximum(0					,	SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


