



'''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


UpperCamelCase__:     List[Any]										=    logging.get_logger(__name__)  # pylint: disable=invalid-name

class        SCREAMING_SNAKE_CASE(							UpperCamelCase__							,	UpperCamelCase__    ):







	"""simple docstring"""






	@register_to_config
	def __init__(		self    :  Any	,    __snake_case    :  bool	,    __snake_case    :  Optional[int] = None	,    __snake_case    :  Optional[int] = None	)    ->	str:
								super().__init__()

								UpperCAmelCase				: Optional[Any]											=	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"

															UpperCAmelCase				: Union[str, Any]											=	torch.zeros(__A	,    __A	)
								else:
															UpperCAmelCase				: str											=	None

								UpperCAmelCase				: Any											=	torch.nn.Parameter(__A	)

class        SCREAMING_SNAKE_CASE(							UpperCamelCase__    ):







	"""simple docstring"""






	lowerCamelCase__    				= 42
	lowerCamelCase__    				= 42
	lowerCamelCase__    				= 42
	lowerCamelCase__    				= 42
	lowerCamelCase__    				= 42
	lowerCamelCase__    				= 42







	def __init__(		self    :  List[Any]	,    __snake_case    :  VQModel	,    __snake_case    :  CLIPTextModel	,    __snake_case    :  CLIPTokenizer	,    __snake_case    :  TransformeraDModel	,    __snake_case    :  VQDiffusionScheduler	,    __snake_case    :  LearnedClassifierFreeSamplingEmbeddings	,    )    ->	Tuple:
								super().__init__()

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







	def   A			(		self    :  int	,    __snake_case    :  int	,    __snake_case    :  Dict	,    __snake_case    :  Tuple	)    ->	Dict:
								UpperCAmelCase				: List[Any]											=	len(__A	) if isinstance(__A	,    __A	) else 1

								# get prompt text embeddings
								UpperCAmelCase				: str											=	self.tokenizer(
								    __A	,    padding='''max_length'''	,    max_length=self.tokenizer.model_max_length	,    return_tensors='''pt'''	,    )
								UpperCAmelCase				: Dict											=	text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
															UpperCAmelCase				: Dict											=	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}"""	)
															UpperCAmelCase				: str											=	text_input_ids[:, : self.tokenizer.model_max_length]
								UpperCAmelCase				: Dict											=	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
								UpperCAmelCase				: List[str]											=	prompt_embeds / prompt_embeds.norm(dim=-1	,    keepdim=__A	)

								# duplicate text embeddings for each generation per prompt
								UpperCAmelCase				: List[Any]											=	prompt_embeds.repeat_interleave(__A	,    dim=0	)

								if do_classifier_free_guidance:
															if self.learned_classifier_free_sampling_embeddings.learnable:
																						UpperCAmelCase				: Optional[int]											=	self.learned_classifier_free_sampling_embeddings.embeddings
																						UpperCAmelCase				: Tuple											=	negative_prompt_embeds.unsqueeze(0	).repeat(__A	,    1	,    1	)
															else:
																						UpperCAmelCase				: Dict											=	[''''''] * batch_size

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

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

								return prompt_embeds







	@torch.no_grad()
	def __call__(		self    :  Tuple	,    __snake_case    :  Union[str, List[str]]	,    __snake_case    :  int = 100	,    __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	,    )    ->	Union[ImagePipelineOutput, Tuple]:



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

								UpperCAmelCase				: Optional[int]											=	batch_size * num_images_per_prompt

								UpperCAmelCase				: List[str]											=	guidance_scale > 1.0

								UpperCAmelCase				: List[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

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

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

								UpperCAmelCase				: Union[str, Any]											=	self.scheduler.timesteps.to(self.device	)

								UpperCAmelCase				: Tuple											=	latents

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

															# predict the un-noised image
															# model_output == `log_p_x_0`
															UpperCAmelCase				: Any											=	self.transformer(__A	,    encoder_hidden_states=__A	,    timestep=__A	).sample

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

															UpperCAmelCase				: int											=	self.truncate(__A	,    __A	)

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

															# compute the previous noisy sample x_t -> x_t-1
															UpperCAmelCase				: Any											=	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	)

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

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

								if output_type == "pil":
															UpperCAmelCase				: str											=	self.numpy_to_pil(__A	)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=__A	)







	def   A			(		self    :  Any	,    __snake_case    :  torch.FloatTensor	,    __snake_case    :  float	)    ->	torch.FloatTensor:
								UpperCAmelCase							,	UpperCAmelCase				: int											=	torch.sort(__A	,    1	,    descending=__A	)
								UpperCAmelCase				: str											=	torch.exp(__A	)
								UpperCAmelCase				: Dict											=	sorted_p_x_0.cumsum(dim=1	) < truncation_rate

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

								UpperCAmelCase				: Tuple											=	keep_mask.gather(1	,    indices.argsort(1	)	)

								UpperCAmelCase				: Optional[Any]											=	log_p_x_0.clone()

								UpperCAmelCase				: List[Any]											=	-torch.inf  # -inf = log(0)

								return rv










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__:     Union[str, Any]										=    {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:     List[str]										=    [
						    "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LiltForQuestionAnswering",
						    "LiltForSequenceClassification",
						    "LiltForTokenClassification",
						    "LiltModel",
						    "LiltPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__:     Optional[Any]										=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





