




'''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


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







def 	_UpperCamelCase       (self							:   Dict      ,		_lowerCamelCase							:   int      ,		_lowerCamelCase							:   Union[str, Any]      ,		_lowerCamelCase							:   Any      ,		_lowerCamelCase							:   Optional[int]		)->      Union[str, Any]:




     '''simple docstring'''





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

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

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

     return image_embeddings







class 			lowerCAmelCase      (       _snake_case):


       def __init__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = None			,     )					->     Dict:



            '''simple docstring'''




            super().__init__()

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

            __snake_case			    =     decoder_pipe_kwargs.get('''torch_dtype'''			,     _lowercase     ) or prior.dtype

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

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

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


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     )					->     List[str]:



            '''simple docstring'''




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

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

                 __snake_case			    =     self.text_encoder(text_input_ids.to(_lowercase     )     )

                 __snake_case			    =     text_encoder_output.text_embeds
                 __snake_case			    =     text_encoder_output.last_hidden_state

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

            __snake_case			    =     text_embeddings.repeat_interleave(_lowercase			,     dim=0     )
            __snake_case			    =     text_encoder_hidden_states.repeat_interleave(_lowercase			,     dim=0     )
            __snake_case			    =     text_mask.repeat_interleave(_lowercase			,     dim=0     )

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

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

                 __snake_case			    =     uncond_embeddings_text_encoder_output.text_embeds
                 __snake_case			    =     uncond_embeddings_text_encoder_output.last_hidden_state

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

                 __snake_case			    =     uncond_embeddings.shape[1]
                 __snake_case			    =     uncond_embeddings.repeat(1			,     _lowercase     )
                 __snake_case			    =     uncond_embeddings.view(batch_size * num_images_per_prompt			,     _lowercase     )

                 __snake_case			    =     uncond_text_encoder_hidden_states.shape[1]
                 __snake_case			    =     uncond_text_encoder_hidden_states.repeat(1			,     _lowercase			,     1     )
                 __snake_case			    =     uncond_text_encoder_hidden_states.view(
                     batch_size * num_images_per_prompt			,     _lowercase			,     -1     )
                 __snake_case			    =     uncond_text_mask.repeat_interleave(_lowercase			,     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
                 __snake_case			    =     torch.cat([uncond_embeddings, text_embeddings]     )
                 __snake_case			    =     torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

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

            return text_embeddings, text_encoder_hidden_states, text_mask


       @property
       def 					lowerCAmelCase					(      self     )					->     Dict:



            '''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(_lowercase			,     '''_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 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     List[Any]:



            '''simple docstring'''




            if latents is None:
                 __snake_case			    =     randn_tensor(_lowercase			,     generator=_lowercase			,     device=_lowercase			,     dtype=_lowercase     )
            else:
                 if latents.shape != shape:
                      raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''     )
                 __snake_case			    =     latents.to(_lowercase     )

            __snake_case			    =     latents * scheduler.init_noise_sigma
            return latents


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE = None     )					->     List[str]:



            '''simple docstring'''




            self.decoder_pipe.to(_lowercase     )
            super().to(_lowercase     )




       @torch.no_grad()
       def __call__(      self			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = 1			,     __SCREAMING_SNAKE_CASE = 25			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = 4.0			,     __SCREAMING_SNAKE_CASE = 8.0			,     __SCREAMING_SNAKE_CASE = 50			,     __SCREAMING_SNAKE_CASE = 1			,     __SCREAMING_SNAKE_CASE = 0.0			,     __SCREAMING_SNAKE_CASE = "pil"			,     __SCREAMING_SNAKE_CASE = True			,     )					->     Dict:



            '''simple docstring'''




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

            __snake_case			    =     self._execution_device

            __snake_case			    =     batch_size * num_images_per_prompt

            __snake_case			    =     prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

            __snake_case    ,			__snake_case    ,			__snake_case			    =     self._encode_prompt(
                _lowercase			,     _lowercase			,     _lowercase			,     _lowercase			,     _lowercase			,     _lowercase     )

            # prior

            self.prior_scheduler.set_timesteps(_lowercase			,     device=_lowercase     )
            __snake_case			    =     self.prior_scheduler.timesteps

            __snake_case			    =     self.prior.config.embedding_dim

            __snake_case			    =     self.prepare_latents(
                (batch_size, embedding_dim)			,     text_embeddings.dtype			,     _lowercase			,     _lowercase			,     _lowercase			,     self.prior_scheduler			,     )

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

                 __snake_case			    =     self.prior(
                     _lowercase			,     timestep=_lowercase			,     proj_embedding=_lowercase			,     encoder_hidden_states=_lowercase			,     attention_mask=_lowercase			,     ).predicted_image_embedding

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

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

                 __snake_case			    =     self.prior_scheduler.step(
                     _lowercase			,     timestep=_lowercase			,     sample=_lowercase			,     generator=_lowercase			,     prev_timestep=_lowercase			,     ).prev_sample

            __snake_case			    =     self.prior.post_process_latents(_lowercase     )

            __snake_case			    =     prior_latents

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_	       =   {
    "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
    "tokenization_esm": ["EsmTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "EsmForMaskedLM",
							    "EsmForSequenceClassification",
							    "EsmForTokenClassification",
							    "EsmModel",
							    "EsmPreTrainedModel",
							]
							UpperCamelCase_	       =   ["EsmForProteinFolding", "EsmFoldPreTrainedModel"]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFEsmForMaskedLM",
							    "TFEsmForSequenceClassification",
							    "TFEsmForTokenClassification",
							    "TFEsmModel",
							    "TFEsmPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
							from .tokenization_esm import EsmTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_esm import (
														    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EsmForMaskedLM,
														    EsmForSequenceClassification,
														    EsmForTokenClassification,
														    EsmModel,
														    EsmPreTrainedModel,
														)
														from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_esm import (
														    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFEsmForMaskedLM,
														    TFEsmForSequenceClassification,
														    TFEsmForTokenClassification,
														    TFEsmModel,
														    TFEsmPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_	       =   _LazyModule(__name__, globals()["__file__"], _import_structure)





