



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  ,			__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)				->	Optional[int]:
		_A          =      image.to(device=SCREAMING_SNAKE_CASE_							)
		_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  ,			SCREAMING_SNAKE_CASE_  ,			1							)
		_A          =      image_embeddings.view(bs_embed * num_images_per_prompt  ,			SCREAMING_SNAKE_CASE_  ,			-1							)

		if do_classifier_free_guidance:
				_A          =      torch.zeros_like(SCREAMING_SNAKE_CASE_							)

				# 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 						_snake_case (     lowerCamelCase			):





	"""simple docstring"""



	def __init__(  self		,      a		,      a		,      a		,      a		,      a = None		,      )		->							List[str]:




			"""simple docstring"""







			super().__init__()

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

			_A          =      decoder_pipe_kwargs.get('''torch_dtype'''		,      a   ) or prior.dtype

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

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

			self.register_modules(
			    prior=a		,      tokenizer=a		,      text_encoder=a		,      prior_scheduler=a		,      )
	def lowercase_						(  self		,      a		,      a		,      a		,      a		,      a = None		,      a = None		,      )		->							Any:




			"""simple docstring"""







			if text_model_output is None:
					_A          =      len(a   ) if isinstance(a		,      a   ) else 1
					# get prompt text embeddings
					_A          =      self.tokenizer(
					    a		,      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(a   )

					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(a   )   )

					_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(a		,      dim=0   )
			_A          =      text_encoder_hidden_states.repeat_interleave(a		,      dim=0   )
			_A          =      text_mask.repeat_interleave(a		,      dim=0   )

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

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

					_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		,      a   )
					_A          =      uncond_embeddings.view(batch_size * num_images_per_prompt		,      a   )

					_A          =      uncond_text_encoder_hidden_states.shape[1]
					_A          =      uncond_text_encoder_hidden_states.repeat(1		,      a		,      1   )
					_A          =      uncond_text_encoder_hidden_states.view(
					    batch_size * num_images_per_prompt		,      a		,      -1   )
					_A          =      uncond_text_mask.repeat_interleave(a		,      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 lowercase_						(  self   )		->							Any:




			"""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(a		,      '''_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 lowercase_						(  self		,      a		,      a		,      a		,      a		,      a		,      a   )		->							Union[str, Any]:




			"""simple docstring"""







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

			_A          =      latents * scheduler.init_noise_sigma
			return latents
	def lowercase_						(  self		,      a = None   )		->							str:




			"""simple docstring"""







			self.decoder_pipe.to(a   )
			super().to(a   )



	@torch.no_grad()
	def __call__(  self		,      a = None		,      a = None		,      a = None		,      a = 1		,      a = 2_5		,      a = None		,      a = None		,      a = None		,      a = None		,      a = 4.0		,      a = 8.0		,      a = 5_0		,      a = 1		,      a = 0.0		,      a = "pil"		,      a = True		,      )		->							Optional[Any]:




			"""simple docstring"""







			if prompt is not None:
					if isinstance(a		,      a   ):
							_A          =      1
					elif isinstance(a		,      a   ):
							_A          =      len(a   )
					else:
							raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(a   )}'''   )
			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(
			    a		,      a		,      a		,      a		,      a		,      a   )

			# prior

			self.prior_scheduler.set_timesteps(a		,      device=a   )
			_A          =      self.prior_scheduler.timesteps

			_A          =      self.prior.config.embedding_dim

			_A          =      self.prepare_latents(
			    (batch_size, embedding_dim)		,      text_embeddings.dtype		,      a		,      a		,      a		,      self.prior_scheduler		,      )

			for i, t in enumerate(self.progress_bar(a   )   ):
					# 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(
					    a		,      timestep=a		,      proj_embedding=a		,      encoder_hidden_states=a		,      attention_mask=a		,      ).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(
					    a		,      timestep=a		,      sample=a		,      generator=a		,      prev_timestep=a		,      ).prev_sample

			_A          =      self.prior.post_process_latents(a   )

			_A          =      prior_latents

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




import warnings

from .generation import FlaxGenerationMixin



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 warnings.warn(
     '''Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '''
     '''be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'''   ,lowerCamelCase   ,)