
"""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


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




class 				lowercase__(				UpperCAmelCase__		,						UpperCAmelCase__   ):
	'''simple docstring'''

	@register_to_config
	def __init__(						self    :str		, lowerCamelCase_    :bool		, lowerCamelCase_    :Optional[int] = None		, lowerCamelCase_    :Optional[int] = None					)							->			Optional[int]:







			'''simple docstring'''




			super().__init__()

			SCREAMING_SNAKE_CASE       :     Optional[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"

					SCREAMING_SNAKE_CASE       :     str				= torch.zeros(lowerCamelCase__		, lowerCamelCase__					)
			else:
					SCREAMING_SNAKE_CASE       :     str				= None

			SCREAMING_SNAKE_CASE       :     str				= torch.nn.Parameter(lowerCamelCase__					)





class 				lowercase__(				UpperCAmelCase__   ):
	'''simple docstring'''

	UpperCamelCase				=  42
	UpperCamelCase				=  42
	UpperCamelCase				=  42
	UpperCamelCase				=  42
	UpperCamelCase				=  42
	UpperCamelCase				=  42





	def __init__(						self    :int		, lowerCamelCase_    :VQModel		, lowerCamelCase_    :CLIPTextModel		, lowerCamelCase_    :CLIPTokenizer		, lowerCamelCase_    :TransformeraDModel		, lowerCamelCase_    :VQDiffusionScheduler		, lowerCamelCase_    :LearnedClassifierFreeSamplingEmbeddings		, )							->			Dict:







			'''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   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :List[Any]					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= len(lowerCamelCase__					) if isinstance(lowerCamelCase__		, lowerCamelCase__					) else 1

			# get prompt text embeddings
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.tokenizer(
			    lowerCamelCase__		, padding='''max_length'''		, max_length=self.tokenizer.model_max_length		, return_tensors='''pt'''		, )
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= text_inputs.input_ids

			if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
					SCREAMING_SNAKE_CASE       :     Tuple				= 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}"					)
					SCREAMING_SNAKE_CASE       :     List[Any]				= text_input_ids[:, : self.tokenizer.model_max_length]
			SCREAMING_SNAKE_CASE       :     int				= 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
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= prompt_embeds / prompt_embeds.norm(dim=-1		, keepdim=lowerCamelCase__					)

			# duplicate text embeddings for each generation per prompt
			SCREAMING_SNAKE_CASE       :     Tuple				= prompt_embeds.repeat_interleave(lowerCamelCase__		, dim=0					)

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

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

					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= negative_prompt_embeds.shape[1]
					SCREAMING_SNAKE_CASE       :     str				= negative_prompt_embeds.repeat(1		, lowerCamelCase__		, 1					)
					SCREAMING_SNAKE_CASE       :     Any				= 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
					SCREAMING_SNAKE_CASE       :     str				= torch.cat([negative_prompt_embeds, prompt_embeds]					)

			return prompt_embeds





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







			'''simple docstring'''




			if isinstance(lowerCamelCase__		, lowerCamelCase__					):
					SCREAMING_SNAKE_CASE       :     Tuple				= 1
			elif isinstance(lowerCamelCase__		, lowerCamelCase__					):
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= len(lowerCamelCase__					)
			else:
					raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase__					)}"					)

			SCREAMING_SNAKE_CASE       :     List[str]				= batch_size * num_images_per_prompt

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= guidance_scale > 1.0

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 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

			SCREAMING_SNAKE_CASE       :     int				= (batch_size, self.transformer.num_latent_pixels)
			if latents is None:
					SCREAMING_SNAKE_CASE       :     Optional[int]				= self.transformer.num_vector_embeds - 1
					SCREAMING_SNAKE_CASE       :     Optional[int]				= 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)."					)
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= latents.to(self.device					)

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

			SCREAMING_SNAKE_CASE       :     str				= self.scheduler.timesteps.to(self.device					)

			SCREAMING_SNAKE_CASE       :     Any				= latents

			for i, t in enumerate(self.progress_bar(lowerCamelCase__					)					):
					# expand the sample if we are doing classifier free guidance
					SCREAMING_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`
					SCREAMING_SNAKE_CASE       :     Any				= self.transformer(lowerCamelCase__		, encoder_hidden_states=lowerCamelCase__		, timestep=lowerCamelCase__					).sample

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

					SCREAMING_SNAKE_CASE       :     Dict				= self.truncate(lowerCamelCase__		, lowerCamelCase__					)

					# remove `log(0)`'s (`-inf`s)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= model_output.clamp(-70					)

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

			SCREAMING_SNAKE_CASE       :     Any				= self.vqvae.config.vq_embed_dim
			SCREAMING_SNAKE_CASE       :     Dict				= (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.vqvae.quantize.get_codebook_entry(lowerCamelCase__		, shape=lowerCamelCase__					)
			SCREAMING_SNAKE_CASE       :     int				= self.vqvae.decode(lowerCamelCase__		, force_not_quantize=lowerCamelCase__					).sample

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

			if output_type == "pil":
					SCREAMING_SNAKE_CASE       :     List[str]				= self.numpy_to_pil(lowerCamelCase__					)

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=lowerCamelCase__					)







	def   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :float					)							->			torch.FloatTensor:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= torch.sort(lowerCamelCase__		, 1		, descending=lowerCamelCase__					)
			SCREAMING_SNAKE_CASE       :     Any				= torch.exp(lowerCamelCase__					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= sorted_p_x_0.cumsum(dim=1					) < truncation_rate

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

			SCREAMING_SNAKE_CASE       :     str				= keep_mask.gather(1		, indices.argsort(1					)					)

			SCREAMING_SNAKE_CASE       :     int				= log_p_x_0.clone()

			SCREAMING_SNAKE_CASE       :     int				= -torch.inf  # -inf = log(0)

			return rv







"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__ :       int						     =			logging.get_logger(__name__)




class 				lowercase__(				_UpperCAmelCase		,						_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """maskformer-swin"""

	UpperCamelCase				=  {
	    """num_attention_heads""": """num_heads""",
	    """num_hidden_layers""": """num_layers""",
	}





	def __init__(						self    :Optional[int]		, lowerCamelCase_    :List[Any]=2_24		, lowerCamelCase_    :Tuple=4		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :List[str]=96		, lowerCamelCase_    :int=[2, 2, 6, 2]		, lowerCamelCase_    :Union[str, Any]=[3, 6, 12, 24]		, lowerCamelCase_    :Optional[int]=7		, lowerCamelCase_    :Tuple=4.0		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Dict=0.0		, lowerCamelCase_    :Any=0.0		, lowerCamelCase_    :List[Any]=0.1		, lowerCamelCase_    :Dict="gelu"		, lowerCamelCase_    :Optional[int]=False		, lowerCamelCase_    :List[str]=0.0_2		, lowerCamelCase_    :Any=1E-5		, lowerCamelCase_    :Union[str, Any]=None		, lowerCamelCase_    :List[str]=None		, **lowerCamelCase_    :Union[str, Any]		, )							->			Dict:







			'''simple docstring'''




			super().__init__(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     int				= image_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= patch_size
			SCREAMING_SNAKE_CASE       :     str				= num_channels
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= embed_dim
			SCREAMING_SNAKE_CASE       :     List[Any]				= depths
			SCREAMING_SNAKE_CASE       :     List[str]				= len(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     str				= num_heads
			SCREAMING_SNAKE_CASE       :     Any				= window_size
			SCREAMING_SNAKE_CASE       :     List[str]				= mlp_ratio
			SCREAMING_SNAKE_CASE       :     str				= qkv_bias
			SCREAMING_SNAKE_CASE       :     Tuple				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     int				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     int				= drop_path_rate
			SCREAMING_SNAKE_CASE       :     Tuple				= hidden_act
			SCREAMING_SNAKE_CASE       :     Any				= use_absolute_embeddings
			SCREAMING_SNAKE_CASE       :     List[str]				= layer_norm_eps
			SCREAMING_SNAKE_CASE       :     List[str]				= initializer_range
			# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
			# this indicates the channel dimension after the last stage of the model
			SCREAMING_SNAKE_CASE       :     int				= int(embed_dim * 2 ** (len(lowerCamelCase_					) - 1)					)
			SCREAMING_SNAKE_CASE       :     Dict				= ['''stem'''] + [f"stage{idx}" for idx in range(1		, len(lowerCamelCase_					) + 1					)]
			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     int				= get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_		, out_indices=lowerCamelCase_		, stage_names=self.stage_names					)





