





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


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





def lowerCAmelCase__(self	,__snake_case	,__snake_case	,__snake_case	,__snake_case							)    ->				Dict:







	'''simple docstring'''







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

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

	if do_classifier_free_guidance:
		lowerCamelCase__        =		torch.zeros_like(_a							)

		# 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
		lowerCamelCase__        =		torch.cat([uncond_embeddings, image_embeddings]							)

	return image_embeddings






class  __A   (   __A			):

			'''simple docstring'''







			def __init__(   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				super().__init__()

				lowerCamelCase__        =		{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

				lowerCamelCase__        =		decoder_pipe_kwargs.get('''torch_dtype'''	,  __lowerCAmelCase							) or prior.dtype

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

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

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







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

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						lowerCamelCase__        =		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}'							)
						lowerCamelCase__        =		text_input_ids[:, : self.tokenizer.model_max_length]

					lowerCamelCase__        =		self.text_encoder(text_input_ids.to(__lowerCAmelCase							)							)

					lowerCamelCase__        =		text_encoder_output.text_embeds
					lowerCamelCase__        =		text_encoder_output.last_hidden_state

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

				lowerCamelCase__        =		text_embeddings.repeat_interleave(__lowerCAmelCase	,  dim=0							)
				lowerCamelCase__        =		text_encoder_hidden_states.repeat_interleave(__lowerCAmelCase	,  dim=0							)
				lowerCamelCase__        =		text_mask.repeat_interleave(__lowerCAmelCase	,  dim=0							)

				if do_classifier_free_guidance:
					lowerCamelCase__        =		[""] * batch_size

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

					lowerCamelCase__        =		uncond_embeddings_text_encoder_output.text_embeds
					lowerCamelCase__        =		uncond_embeddings_text_encoder_output.last_hidden_state

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

					lowerCamelCase__        =		uncond_embeddings.shape[1]
					lowerCamelCase__        =		uncond_embeddings.repeat(1	,  __lowerCAmelCase							)
					lowerCamelCase__        =		uncond_embeddings.view(batch_size * num_images_per_prompt	,  __lowerCAmelCase							)

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

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

				return text_embeddings, text_encoder_hidden_states, text_mask





			@property
			def 		__lowerCamelCase    (   self							):



				'''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(__lowerCAmelCase	,  '''_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	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







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

				lowerCamelCase__        =		latents * scheduler.init_noise_sigma
				return latents





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase = None							):



				'''simple docstring'''







				self.decoder_pipe.to(__lowerCAmelCase							)
				super().to(__lowerCAmelCase							)





			@torch.no_grad()
			def __call__(   self	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = 1	,  __lowerCAmelCase = 2_5	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = 4.0	,  __lowerCAmelCase = 8.0	,  __lowerCAmelCase = 5_0	,  __lowerCAmelCase = 1	,  __lowerCAmelCase = 0.0	,  __lowerCAmelCase = "pil"	,  __lowerCAmelCase = True	,  ):



				'''simple docstring'''







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

				lowerCamelCase__        =		self._execution_device

				lowerCamelCase__        =		batch_size * num_images_per_prompt

				lowerCamelCase__        =		prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

				lowerCamelCase__        =		self._encode_prompt(
				    __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)

				# prior

				self.prior_scheduler.set_timesteps(__lowerCAmelCase	,  device=__lowerCAmelCase							)
				lowerCamelCase__        =		self.prior_scheduler.timesteps

				lowerCamelCase__        =		self.prior.config.embedding_dim

				lowerCamelCase__        =		self.prepare_latents(
				    (batch_size, embedding_dim)	,  text_embeddings.dtype	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  self.prior_scheduler	,  )

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

					lowerCamelCase__        =		self.prior(
					    __lowerCAmelCase	,  timestep=__lowerCAmelCase	,  proj_embedding=__lowerCAmelCase	,  encoder_hidden_states=__lowerCAmelCase	,  attention_mask=__lowerCAmelCase	,  ).predicted_image_embedding

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

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

					lowerCamelCase__        =		self.prior_scheduler.step(
					    __lowerCAmelCase	,  timestep=__lowerCAmelCase	,  sample=__lowerCAmelCase	,  generator=__lowerCAmelCase	,  prev_timestep=__lowerCAmelCase	,  ).prev_sample

				lowerCamelCase__        =		self.prior.post_process_latents(__lowerCAmelCase							)

				lowerCamelCase__        =		prior_latents

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













import warnings

from .generation import FlaxGenerationMixin






class  __A   (   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      ,    )



