



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


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


class      __lowerCAmelCase     (		_snake_case							,     _snake_case      ):
					"""simple docstring"""






					@register_to_config
					def __init__(     self					:       Optional[Any]				,					_snake_case					:       bool				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       Optional[int] = None					):






								"""simple docstring"""


								super().__init__()

								A__       =    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"

											A__       =    torch.zeros(lowerCAmelCase__				,					lowerCAmelCase__					)
								else:
											A__       =    None

								A__       =    torch.nn.Parameter(lowerCAmelCase__					)







class      __lowerCAmelCase     (		_snake_case      ):
					"""simple docstring"""






					A__				:		str			       =    42
					A__				:		Optional[Any]			       =    42
					A__				:		Tuple			       =    42
					A__				:		Optional[int]			       =    42
					A__				:		Tuple			       =    42
					A__				:		Dict			       =    42



					def __init__(     self					:       str				,					_snake_case					:       VQModel				,					_snake_case					:       CLIPTextModel				,					_snake_case					:       CLIPTokenizer				,					_snake_case					:       TransformeraDModel				,					_snake_case					:       VQDiffusionScheduler				,					_snake_case					:       LearnedClassifierFreeSamplingEmbeddings				,					):






								"""simple docstring"""


								super().__init__()

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



					def     _a							(     self					:       List[Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[int]				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    len(lowerCAmelCase__					) if isinstance(lowerCAmelCase__				,					lowerCAmelCase__					) else 1

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

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

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

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

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

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

								return prompt_embeds



					@torch.no_grad()
					def __call__(     self					:       Optional[Any]				,					_snake_case					:       Union[str, List[str]]				,					_snake_case					:       int = 1_00				,					_snake_case					:       float = 5.0				,					_snake_case					:       float = 1.0				,					_snake_case					:       int = 1				,					_snake_case					:       Optional[Union[torch.Generator, List[torch.Generator]]] = None				,					_snake_case					:       Optional[torch.FloatTensor] = None				,					_snake_case					:       Optional[str] = "pil"				,					_snake_case					:       bool = True				,					_snake_case					:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,					_snake_case					:       int = 1				,					):






								"""simple docstring"""


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

								A__       =    batch_size * num_images_per_prompt

								A__       =    guidance_scale > 1.0

								A__       =    self._encode_prompt(lowerCAmelCase__				,					lowerCAmelCase__				,					lowerCAmelCase__					)

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

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

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

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

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

								A__       =    latents

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

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

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

											A__       =    self.truncate(lowerCAmelCase__				,					lowerCAmelCase__					)

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

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

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

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

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

								if output_type == "pil":
											A__       =    self.numpy_to_pil(lowerCAmelCase__					)

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase__					)



					def     _a							(     self					:       Union[str, Any]				,					_snake_case					:       torch.FloatTensor				,					_snake_case					:       float					):






								"""simple docstring"""


								A__     , A__       =    torch.sort(lowerCAmelCase__				,					1				,					descending=lowerCAmelCase__					)
								A__       =    torch.exp(lowerCAmelCase__					)
								A__       =    sorted_p_x_0.cumsum(dim=1					) < truncation_rate

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

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

								A__       =    log_p_x_0.clone()

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

								return rv









from typing import Dict

from .base import GenericTensor, Pipeline


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       Any				,					_snake_case					:       str=None				,					_snake_case					:       Dict=None				,					_snake_case					:       Any=None				,					**_snake_case					:       str					):






								"""simple docstring"""


								if tokenize_kwargs is None:
											A__       =    {}

								if truncation is not None:
											if "truncation" in tokenize_kwargs:
														raise ValueError(
														    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'					)
											A__       =    truncation

								A__       =    tokenize_kwargs

								A__       =    {}
								if return_tensors is not None:
											A__       =    return_tensors

								return preprocess_params, {}, postprocess_params



					def     _a							(     self					:       Any				,					_snake_case					:       Dict				,					**_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.framework
								A__       =    self.tokenizer(_snake_case				,					return_tensors=_snake_case				,					**_snake_case					)
								return model_inputs



					def     _a							(     self					:       List[Any]				,					_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    self.model(**_snake_case					)
								return model_outputs



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       List[Any]				,					_snake_case					:       str=False					):






								"""simple docstring"""


								if return_tensors:
											return model_outputs[0]
								if self.framework == "pt":
											return model_outputs[0].tolist()
								elif self.framework == "tf":
											return model_outputs[0].numpy().tolist()



					def __call__(     self					:       Dict				,					*_snake_case					:       int				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								return super().__call__(*_snake_case				,					**_snake_case					)




