



"""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


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







class  __lowercase  (       UpperCamelCase__       ,     UpperCamelCase__):







						"""simple docstring"""







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

											snake_case_       :		int			       =       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_       :		Dict			       =       torch.zeros(__snake_case ,		__snake_case							)
											else:
																snake_case_       :		Tuple			       =       None

											snake_case_       :		int			       =       torch.nn.Parameter(__snake_case							)







class  __lowercase  (       UpperCamelCase__):







						"""simple docstring"""







						_A		:				Optional[int]											=		42
						_A		:				Optional[int]											=		42
						_A		:				Optional[Any]											=		42
						_A		:				Tuple											=		42
						_A		:				int											=		42
						_A		:				Tuple											=		42







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

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







						def     __UpperCamelCase				(self ,		lowercase__ ,		lowercase__ ,		lowercase__							):
											snake_case_       :		List[str]			       =       len(__snake_case							) if isinstance(__snake_case ,		__snake_case							) else 1

											# get prompt text embeddings
											snake_case_       :		str			       =       self.tokenizer(
											    __snake_case ,		padding="""max_length""" ,		max_length=self.tokenizer.model_max_length ,		return_tensors="""pt""" ,		)
											snake_case_       :		str			       =       text_inputs.input_ids

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																snake_case_       :		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}'							)
																snake_case_       :		List[Any]			       =       text_input_ids[:, : self.tokenizer.model_max_length]
											snake_case_       :		Union[str, 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
											snake_case_       :		Optional[Any]			       =       prompt_embeds / prompt_embeds.norm(dim=-1 ,		keepdim=__snake_case							)

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

											if do_classifier_free_guidance:
																if self.learned_classifier_free_sampling_embeddings.learnable:
																					snake_case_       :		Union[str, Any]			       =       self.learned_classifier_free_sampling_embeddings.embeddings
																					snake_case_       :		Optional[Any]			       =       negative_prompt_embeds.unsqueeze(0							).repeat(__snake_case ,		1 ,		1							)
																else:
																					snake_case_       :		Any			       =       [""""""] * batch_size

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

																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																snake_case_       :		Any			       =       negative_prompt_embeds.shape[1]
																snake_case_       :		str			       =       negative_prompt_embeds.repeat(1 ,		__snake_case ,		1							)
																snake_case_       :		Any			       =       negative_prompt_embeds.view(batch_size * num_images_per_prompt ,		__snake_case ,		-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_       :		Optional[int]			       =       torch.cat([negative_prompt_embeds, prompt_embeds]							)

											return prompt_embeds







						@torch.no_grad()
						def __call__(self ,		lowercase__ ,		lowercase__ = 1_00 ,		lowercase__ = 5.0 ,		lowercase__ = 1.0 ,		lowercase__ = 1 ,		lowercase__ = None ,		lowercase__ = None ,		lowercase__ = "pil" ,		lowercase__ = True ,		lowercase__ = None ,		lowercase__ = 1 ,		):

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

											snake_case_       :		List[str]			       =       batch_size * num_images_per_prompt

											snake_case_       :		Dict			       =       guidance_scale > 1.0

											snake_case_       :		Union[str, Any]			       =       self._encode_prompt(__snake_case ,		__snake_case ,		__snake_case							)

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

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

											snake_case_       :		List[Any]			       =       (batch_size, self.transformer.num_latent_pixels)
											if latents is None:
																snake_case_       :		List[Any]			       =       self.transformer.num_vector_embeds - 1
																snake_case_       :		Union[str, Any]			       =       torch.full(__snake_case ,		__snake_case							).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_       :		Dict			       =       latents.to(self.device							)

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

											snake_case_       :		Optional[Any]			       =       self.scheduler.timesteps.to(self.device							)

											snake_case_       :		List[Any]			       =       latents

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

																# predict the un-noised image
																# model_output == `log_p_x_0`
																snake_case_       :		List[str]			       =       self.transformer(__snake_case ,		encoder_hidden_states=__snake_case ,		timestep=__snake_case							).sample

																if do_classifier_free_guidance:
																					snake_case_       :		List[str]			       =       model_output.chunk(2							)
																					snake_case_       :		List[Any]			       =       model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																					model_output -= torch.logsumexp(__snake_case ,		dim=1 ,		keepdim=__snake_case							)

																snake_case_       :		Any			       =       self.truncate(__snake_case ,		__snake_case							)

																# remove `log(0)`'s (`-inf`s)
																snake_case_       :		Union[str, Any]			       =       model_output.clamp(-70							)

																# compute the previous noisy sample x_t -> x_t-1
																snake_case_       :		Union[str, Any]			       =       self.scheduler.step(__snake_case ,		timestep=__snake_case ,		sample=__snake_case ,		generator=__snake_case							).prev_sample

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

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

											snake_case_       :		Any			       =       (image / 2 + 0.5).clamp(0 ,		1							)
											snake_case_       :		Optional[Any]			       =       image.cpu().permute(0 ,		2 ,		3 ,		1							).numpy()

											if output_type == "pil":
																snake_case_       :		Union[str, Any]			       =       self.numpy_to_pil(__snake_case							)

											if not return_dict:
																return (image,)

											return ImagePipelineOutput(images=__snake_case							)







						def     __UpperCamelCase				(self ,		lowercase__ ,		lowercase__							):
											snake_case_       :		int			       =       torch.sort(__snake_case ,		1 ,		descending=__snake_case							)
											snake_case_       :		Any			       =       torch.exp(__snake_case							)
											snake_case_       :		Dict			       =       sorted_p_x_0.cumsum(dim=1							) < truncation_rate

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

											snake_case_       :		Optional[int]			       =       keep_mask.gather(1 ,		indices.argsort(1							)							)

											snake_case_       :		Optional[int]			       =       log_p_x_0.clone()

											snake_case_       :		Union[str, Any]			       =       -torch.inf  # -inf = log(0)

											return rv










import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										debug_launcher(test_script.main			)


						def      A__    (		self :Dict			):
										'''simple docstring'''

										debug_launcher(test_ops.main			)




