"""simple docstring"""




import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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






def 	UpperCAmelCase       (						self							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				image.to(device=UpperCamelCase__					)
							A__						  =				image  # take image as image_embeddings
							A__						  =				image_embeddings.unsqueeze(1					)

							# duplicate image embeddings for each generation per prompt, using mps friendly method
							A__  ,	A__  ,	A__						  =				image_embeddings.shape
							A__						  =				image_embeddings.repeat(1							,     UpperCamelCase__							,     1					)
							A__						  =				image_embeddings.view(bs_embed * num_images_per_prompt							,     UpperCamelCase__							,     -1					)

							if do_classifier_free_guidance:
														A__						  =				torch.zeros_like(UpperCamelCase__					)

														# 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([uncond_embeddings, image_embeddings]					)

							return image_embeddings


class      UpperCamelCase__(      __A			):





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,)       ->					List[str]:
													super().__init__()

													A__						  =				{'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

													A__						  =				decoder_pipe_kwargs.get('torch_dtype'		,__UpperCAmelCase  ) or prior.dtype

													A__						  =				StableDiffusionImageVariationPipeline.from_pretrained(
													    'lambdalabs/sd-image-variations-diffusers'		,**__UpperCAmelCase  )

													# replace `_encode_image` method
													A__						  =				types.MethodType(_encode_image		,self.decoder_pipe  )

													self.register_modules(
													    prior=__UpperCAmelCase		,tokenizer=__UpperCAmelCase		,text_encoder=__UpperCAmelCase		,prior_scheduler=__UpperCAmelCase		,)





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,)       ->					Dict:
													if text_model_output is None:
																				A__						  =				len(__UpperCAmelCase  ) if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ) else 1
																				# get prompt text embeddings
																				A__						  =				self.tokenizer(
																				    __UpperCAmelCase		,padding='max_length'		,max_length=self.tokenizer.model_max_length		,return_tensors='pt'		,)
																				A__						  =				text_inputs.input_ids
																				A__						  =				text_inputs.attention_mask.bool().to(__UpperCAmelCase  )

																				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(__UpperCAmelCase  )  )

																				A__						  =				text_encoder_output.text_embeds
																				A__						  =				text_encoder_output.last_hidden_state

													else:
																				A__						  =				text_model_output[0].shape[0]
																				A__  ,	A__						  =				text_model_output[0], text_model_output[1]
																				A__						  =				text_attention_mask

													A__						  =				text_embeddings.repeat_interleave(__UpperCAmelCase		,dim=0  )
													A__						  =				text_encoder_hidden_states.repeat_interleave(__UpperCAmelCase		,dim=0  )
													A__						  =				text_mask.repeat_interleave(__UpperCAmelCase		,dim=0  )

													if do_classifier_free_guidance:
																				A__						  =				[''] * batch_size

																				A__						  =				self.tokenizer(
																				    __UpperCAmelCase		,padding='max_length'		,max_length=self.tokenizer.model_max_length		,truncation=__UpperCAmelCase		,return_tensors='pt'		,)
																				A__						  =				uncond_input.attention_mask.bool().to(__UpperCAmelCase  )
																				A__						  =				self.text_encoder(uncond_input.input_ids.to(__UpperCAmelCase  )  )

																				A__						  =				uncond_embeddings_text_encoder_output.text_embeds
																				A__						  =				uncond_embeddings_text_encoder_output.last_hidden_state

																				# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

																				A__						  =				uncond_embeddings.shape[1]
																				A__						  =				uncond_embeddings.repeat(1		,__UpperCAmelCase  )
																				A__						  =				uncond_embeddings.view(batch_size * num_images_per_prompt		,__UpperCAmelCase  )

																				A__						  =				uncond_text_encoder_hidden_states.shape[1]
																				A__						  =				uncond_text_encoder_hidden_states.repeat(1		,__UpperCAmelCase		,1  )
																				A__						  =				uncond_text_encoder_hidden_states.view(
																				    batch_size * num_images_per_prompt		,__UpperCAmelCase		,-1  )
																				A__						  =				uncond_text_mask.repeat_interleave(__UpperCAmelCase		,dim=0  )

																				# done duplicates

																				# 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([uncond_embeddings, text_embeddings]  )
																				A__						  =				torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]  )

																				A__						  =				torch.cat([uncond_text_mask, text_mask]  )

													return text_embeddings, text_encoder_hidden_states, text_mask





						@property
						def 					snake_case__						(							self  )       ->					str:
													if self.device != torch.device('meta'  ) or not hasattr(self.prior		,'_hf_hook'  ):
																				return self.device
													for module in self.prior.modules():
																				if (
																				    hasattr(__UpperCAmelCase		,'_hf_hook'  )
																				    and hasattr(module._hf_hook		,'execution_device'  )
																				    and module._hf_hook.execution_device is not None
																				):
																											return torch.device(module._hf_hook.execution_device  )
													return self.device





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					List[str]:
													if latents is None:
																				A__						  =				randn_tensor(__UpperCAmelCase		,generator=__UpperCAmelCase		,device=__UpperCAmelCase		,dtype=__UpperCAmelCase  )
													else:
																				if latents.shape != shape:
																											raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''  )
																				A__						  =				latents.to(__UpperCAmelCase  )

													A__						  =				latents * scheduler.init_noise_sigma
													return latents





						def 					snake_case__						(							self		,__UpperCAmelCase = None  )       ->					int:
													self.decoder_pipe.to(__UpperCAmelCase  )
													super().to(__UpperCAmelCase  )





						@torch.no_grad()
						def __call__(							self		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = 1		,__UpperCAmelCase = 25		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = 4.0		,__UpperCAmelCase = 8.0		,__UpperCAmelCase = 50		,__UpperCAmelCase = 1		,__UpperCAmelCase = 0.0		,__UpperCAmelCase = "pil"		,__UpperCAmelCase = True		,)       ->					Any:
													if prompt is not None:
																				if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																											A__						  =				1
																				elif isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																											A__						  =				len(__UpperCAmelCase  )
																				else:
																											raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__UpperCAmelCase  )}'''  )
													else:
																				A__						  =				text_model_output[0].shape[0]

													A__						  =				self._execution_device

													A__						  =				batch_size * num_images_per_prompt

													A__						  =				prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

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

													# prior

													self.prior_scheduler.set_timesteps(__UpperCAmelCase		,device=__UpperCAmelCase  )
													A__						  =				self.prior_scheduler.timesteps

													A__						  =				self.prior.config.embedding_dim

													A__						  =				self.prepare_latents(
													    (batch_size, embedding_dim)		,text_embeddings.dtype		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,self.prior_scheduler		,)

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

																				A__						  =				self.prior(
																				    __UpperCAmelCase		,timestep=__UpperCAmelCase		,proj_embedding=__UpperCAmelCase		,encoder_hidden_states=__UpperCAmelCase		,attention_mask=__UpperCAmelCase		,).predicted_image_embedding

																				if do_classifier_free_guidance:
																											A__  ,	A__						  =				predicted_image_embedding.chunk(2  )
																											A__						  =				predicted_image_embedding_uncond + prior_guidance_scale * (
																											    predicted_image_embedding_text - predicted_image_embedding_uncond
																											)

																				if i + 1 == prior_timesteps_tensor.shape[0]:
																											A__						  =				None
																				else:
																											A__						  =				prior_timesteps_tensor[i + 1]

																				A__						  =				self.prior_scheduler.step(
																				    __UpperCAmelCase		,timestep=__UpperCAmelCase		,sample=__UpperCAmelCase		,generator=__UpperCAmelCase		,prev_timestep=__UpperCAmelCase		,).prev_sample

													A__						  =				self.prior.post_process_latents(__UpperCAmelCase  )

													A__						  =				prior_latents

													A__						  =				self.decoder_pipe(
													    image=__UpperCAmelCase		,height=__UpperCAmelCase		,width=__UpperCAmelCase		,num_inference_steps=__UpperCAmelCase		,guidance_scale=__UpperCAmelCase		,generator=__UpperCAmelCase		,output_type=__UpperCAmelCase		,return_dict=__UpperCAmelCase		,num_images_per_prompt=__UpperCAmelCase		,eta=__UpperCAmelCase		,)
													return output






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase									=	{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	["LayoutLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "LayoutLMForMaskedLM",
				    "LayoutLMForSequenceClassification",
				    "LayoutLMForTokenClassification",
				    "LayoutLMForQuestionAnswering",
				    "LayoutLMModel",
				    "LayoutLMPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFLayoutLMForMaskedLM",
				    "TFLayoutLMForSequenceClassification",
				    "TFLayoutLMForTokenClassification",
				    "TFLayoutLMForQuestionAnswering",
				    "TFLayoutLMMainLayer",
				    "TFLayoutLMModel",
				    "TFLayoutLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
				from .tokenization_layoutlm import LayoutLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlm import (
								    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMForMaskedLM,
								    LayoutLMForQuestionAnswering,
								    LayoutLMForSequenceClassification,
								    LayoutLMForTokenClassification,
								    LayoutLMModel,
								    LayoutLMPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlm import (
								    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMForMaskedLM,
								    TFLayoutLMForQuestionAnswering,
								    TFLayoutLMForSequenceClassification,
								    TFLayoutLMForTokenClassification,
								    TFLayoutLMMainLayer,
								    TFLayoutLMModel,
								    TFLayoutLMPreTrainedModel,
								)

else:
				import sys

				__lowerCamelCase									=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





