"""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


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


def    lowerCAmelCase			(self							:    Optional[int]							,					__UpperCamelCase							:    List[Any]							,					__UpperCamelCase							:    Optional[Any]							,					__UpperCamelCase							:    Optional[int]							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


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

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

							if do_classifier_free_guidance:
														__UpperCamelCase							=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
														__UpperCamelCase							=torch.cat([uncond_embeddings, image_embeddings]			)

							return image_embeddings






class 		_lowercase (						SCREAMING_SNAKE_CASE_      ):



						"""simple docstring"""




						def __init__(				self							:    str     , UpperCamelCase__							:    Optional[int]     , UpperCamelCase__							:    Dict     , UpperCamelCase__							:    Tuple     , UpperCamelCase__							:    List[str]     , UpperCamelCase__							:    List[Any] = None     , )				->				Optional[int]:
													'''simple docstring'''



													super().__init__()

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

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

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

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

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





						def   UpperCAmelCase_ (				self							:    Optional[int]     , UpperCamelCase__							:    Optional[int]     , UpperCamelCase__							:    Optional[int]     , UpperCamelCase__							:    str     , UpperCamelCase__							:    Dict     , UpperCamelCase__							:    int = None     , UpperCamelCase__							:    List[str] = None     , )				->				str:
													'''simple docstring'''



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

																				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																											__UpperCamelCase							=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							=text_input_ids[:, : self.tokenizer.model_max_length]

																				__UpperCamelCase							=self.text_encoder(text_input_ids.to(__UpperCAmelCase				)				)

																				__UpperCamelCase							=text_encoder_output.text_embeds
																				__UpperCamelCase							=text_encoder_output.last_hidden_state

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

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

													if do_classifier_free_guidance:
																				__UpperCamelCase							=[''''''] * batch_size

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

																				__UpperCamelCase							=uncond_embeddings_text_encoder_output.text_embeds
																				__UpperCamelCase							=uncond_embeddings_text_encoder_output.last_hidden_state

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

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

																				__UpperCamelCase							=uncond_text_encoder_hidden_states.shape[1]
																				__UpperCamelCase							=uncond_text_encoder_hidden_states.repeat(1     , __UpperCAmelCase     , 1				)
																				__UpperCamelCase							=uncond_text_encoder_hidden_states.view(
																				    batch_size * num_images_per_prompt     , __UpperCAmelCase     , -1				)
																				__UpperCamelCase							=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
																				__UpperCamelCase							=torch.cat([uncond_embeddings, text_embeddings]				)
																				__UpperCamelCase							=torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]				)

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

													return text_embeddings, text_encoder_hidden_states, text_mask





						@property
						def   UpperCAmelCase_ (				self							:    Dict				)				->				Tuple:
													'''simple docstring'''



													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   UpperCAmelCase_ (				self							:    List[str]     , UpperCamelCase__							:    Any     , UpperCamelCase__							:    List[Any]     , UpperCamelCase__							:    int     , UpperCamelCase__							:    Any     , UpperCamelCase__							:    int     , UpperCamelCase__							:    Optional[Any]				)				->				Tuple:
													'''simple docstring'''



													if latents is None:
																				__UpperCamelCase							=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}"""				)
																				__UpperCamelCase							=latents.to(__UpperCAmelCase				)

													__UpperCamelCase							=latents * scheduler.init_noise_sigma
													return latents





						def   UpperCAmelCase_ (				self							:    Optional[int]     , UpperCamelCase__							:    List[Any] = None				)				->				Tuple:
													'''simple docstring'''



													self.decoder_pipe.to(__UpperCAmelCase				)
													super().to(__UpperCAmelCase				)





						@torch.no_grad()
						def __call__(				self							:    Tuple     , UpperCamelCase__							:    Dict = None     , UpperCamelCase__							:    List[Any] = None     , UpperCamelCase__							:    Union[str, Any] = None     , UpperCamelCase__							:    Tuple = 1     , UpperCamelCase__							:    List[Any] = 25     , UpperCamelCase__							:    List[Any] = None     , UpperCamelCase__							:    Optional[int] = None     , UpperCamelCase__							:    Optional[int] = None     , UpperCamelCase__							:    Dict = None     , UpperCamelCase__							:    Dict = 4.0     , UpperCamelCase__							:    List[Any] = 8.0     , UpperCamelCase__							:    Tuple = 50     , UpperCamelCase__							:    Any = 1     , UpperCamelCase__							:    Union[str, Any] = 0.0     , UpperCamelCase__							:    Union[str, Any] = "pil"     , UpperCamelCase__							:    int = True     , )				->				str:
													'''simple docstring'''



													if prompt is not None:
																				if isinstance(__UpperCAmelCase     , __UpperCAmelCase				):
																											__UpperCamelCase							=1
																				elif isinstance(__UpperCAmelCase     , __UpperCAmelCase				):
																											__UpperCamelCase							=len(__UpperCAmelCase				)
																				else:
																											raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCAmelCase				)}"""				)
													else:
																				__UpperCamelCase							=text_model_output[0].shape[0]

													__UpperCamelCase							=self._execution_device

													__UpperCamelCase							=batch_size * num_images_per_prompt

													__UpperCamelCase							=prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

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

													# prior

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

													__UpperCamelCase							=self.prior.config.embedding_dim

													__UpperCamelCase							=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
																				__UpperCamelCase							=torch.cat([prior_latents] * 2				) if do_classifier_free_guidance else prior_latents

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

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

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

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

													__UpperCamelCase							=self.prior.post_process_latents(__UpperCAmelCase				)

													__UpperCamelCase							=prior_latents

													__UpperCamelCase							=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_torch_available,
    is_vision_available,
)


UpperCamelCase_						   =   {
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   [
          'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SamModel',
          'SamPreTrainedModel',
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   [
          'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSamModel',
          'TFSamPreTrainedModel',
      ]
try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   ['SamImageProcessor']


if TYPE_CHECKING:
      from .configuration_sam import (
          SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SamConfig,
          SamMaskDecoderConfig,
          SamPromptEncoderConfig,
          SamVisionConfig,
      )
      from .processing_sam import SamProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_sam import SamImageProcessor

else:
      import sys

      UpperCamelCase_						   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)