

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



def  __lowerCAmelCase (  self    ,       A    ,       A    ,       A    ,       A      ):
	UpperCAmelCase_    			=     image.to(device=A      )
	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    ,       A    ,       1      )
	UpperCAmelCase_    			=     image_embeddings.view(bs_embed * num_images_per_prompt    ,       A    ,       -1      )

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

	return image_embeddings




class   __UpperCamelCase      (   lowercase  ):




	def __init__(						self		:  List[str]						,		lowerCAmelCase		:  PriorTransformer						,		lowerCAmelCase		:  CLIPTokenizer						,		lowerCAmelCase		:  CLIPTextModelWithProjection						,		lowerCAmelCase		:  UnCLIPScheduler						,		lowerCAmelCase		:  Optional[dict] = None						,		):


		'''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"						,		lowerCAmelCase							) or prior.dtype

		UpperCAmelCase_    			=     StableDiffusionImageVariationPipeline.from_pretrained(
		    "lambdalabs/sd-image-variations-diffusers"						,		**lowerCAmelCase							)

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

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




	def  __A   (						self		:  Tuple						,		lowerCAmelCase		:  List[str]						,		lowerCAmelCase		:  str						,		lowerCAmelCase		:  List[str]						,		lowerCAmelCase		:  int						,		lowerCAmelCase		:  Optional[Union[CLIPTextModelOutput, Tuple]] = None						,		lowerCAmelCase		:  Optional[torch.Tensor] = None						,		):


		'''simple docstring'''







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

			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(lowerCAmelCase							)							)

			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(lowerCAmelCase						,		dim=0							)
		UpperCAmelCase_    			=     text_encoder_hidden_states.repeat_interleave(lowerCAmelCase						,		dim=0							)
		UpperCAmelCase_    			=     text_mask.repeat_interleave(lowerCAmelCase						,		dim=0							)

		if do_classifier_free_guidance:
			UpperCAmelCase_    			=     [""] * batch_size

			UpperCAmelCase_    			=     self.tokenizer(
			    lowerCAmelCase						,		padding="max_length"						,		max_length=self.tokenizer.model_max_length						,		truncation=lowerCAmelCase						,		return_tensors="pt"						,		)
			UpperCAmelCase_    			=     uncond_input.attention_mask.bool().to(lowerCAmelCase							)
			UpperCAmelCase_    			=     self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase							)							)

			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						,		lowerCAmelCase							)
			UpperCAmelCase_    			=     uncond_embeddings.view(batch_size * num_images_per_prompt						,		lowerCAmelCase							)

			UpperCAmelCase_    			=     uncond_text_encoder_hidden_states.shape[1]
			UpperCAmelCase_    			=     uncond_text_encoder_hidden_states.repeat(1						,		lowerCAmelCase						,		1							)
			UpperCAmelCase_    			=     uncond_text_encoder_hidden_states.view(
			    batch_size * num_images_per_prompt						,		lowerCAmelCase						,		-1							)
			UpperCAmelCase_    			=     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
			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  __A   (						self		:  int							):


		'''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  __A   (						self		:  int						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  Optional[int]						,		lowerCAmelCase		:  int						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  Tuple						,		lowerCAmelCase		:  Tuple							):


		'''simple docstring'''







		if latents is None:
			UpperCAmelCase_    			=     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}"							)
			UpperCAmelCase_    			=     latents.to(lowerCAmelCase							)

		UpperCAmelCase_    			=     latents * scheduler.init_noise_sigma
		return latents




	def  __A   (						self		:  Any						,		lowerCAmelCase		:  Optional[Union[str, torch.device]] = None							):


		'''simple docstring'''







		self.decoder_pipe.to(lowerCAmelCase							)
		super().to(lowerCAmelCase							)




	@torch.no_grad()
	def __call__(						self		:  List[str]						,		lowerCAmelCase		:  Optional[Union[str, List[str]]] = None						,		lowerCAmelCase		:  Optional[int] = None						,		lowerCAmelCase		:  Optional[int] = None						,		lowerCAmelCase		:  int = 1						,		lowerCAmelCase		:  int = 25						,		lowerCAmelCase		:  Optional[torch.Generator] = None						,		lowerCAmelCase		:  Optional[torch.FloatTensor] = None						,		lowerCAmelCase		:  Optional[Union[CLIPTextModelOutput, Tuple]] = None						,		lowerCAmelCase		:  Optional[torch.Tensor] = None						,		lowerCAmelCase		:  float = 4.0						,		lowerCAmelCase		:  float = 8.0						,		lowerCAmelCase		:  int = 50						,		lowerCAmelCase		:  Optional[int] = 1						,		lowerCAmelCase		:  float = 0.0						,		lowerCAmelCase		:  Optional[str] = "pil"						,		lowerCAmelCase		:  bool = True						,		):


		'''simple docstring'''







		if prompt is not None:
			if isinstance(lowerCAmelCase						,		lowerCAmelCase							):
				UpperCAmelCase_    			=     1
			elif isinstance(lowerCAmelCase						,		lowerCAmelCase							):
				UpperCAmelCase_    			=     len(lowerCAmelCase							)
			else:
				raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase							)}"							)
		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(
		    lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase							)

		# prior

		self.prior_scheduler.set_timesteps(lowerCAmelCase						,		device=lowerCAmelCase							)
		UpperCAmelCase_    			=     self.prior_scheduler.timesteps

		UpperCAmelCase_    			=     self.prior.config.embedding_dim

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

			UpperCAmelCase_    			=     self.prior(
			    lowerCAmelCase						,		timestep=lowerCAmelCase						,		proj_embedding=lowerCAmelCase						,		encoder_hidden_states=lowerCAmelCase						,		attention_mask=lowerCAmelCase						,		).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(
			    lowerCAmelCase						,		timestep=lowerCAmelCase						,		sample=lowerCAmelCase						,		generator=lowerCAmelCase						,		prev_timestep=lowerCAmelCase						,		).prev_sample

		UpperCAmelCase_    			=     self.prior.post_process_latents(lowerCAmelCase							)

		UpperCAmelCase_    			=     prior_latents

		UpperCAmelCase_    			=     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


from typing import TYPE_CHECKING

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


_a:      Dict								=     {
    """configuration_nystromformer""": ["""NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NystromformerConfig"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Any								=     [
				    """NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """NystromformerForMaskedLM""",
				    """NystromformerForMultipleChoice""",
				    """NystromformerForQuestionAnswering""",
				    """NystromformerForSequenceClassification""",
				    """NystromformerForTokenClassification""",
				    """NystromformerLayer""",
				    """NystromformerModel""",
				    """NystromformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nystromformer import (
								    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NystromformerForMaskedLM,
								    NystromformerForMultipleChoice,
								    NystromformerForQuestionAnswering,
								    NystromformerForSequenceClassification,
								    NystromformerForTokenClassification,
								    NystromformerLayer,
								    NystromformerModel,
								    NystromformerPreTrainedModel,
								)


else:
				import sys

				_a:      Optional[Any]								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)