
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  :  str   ,							__lowerCAmelCase  :  Dict   ,							__lowerCAmelCase  :  List[Any]   ,							__lowerCAmelCase  :  str   ,							__lowerCAmelCase  :  List[Any]    ):




			"""simple docstring"""


			lowerCAmelCase_      =     image.to(device=__lowerCAmelCase    )
			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_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     image_embeddings.shape
			lowerCAmelCase_      =     image_embeddings.repeat(1   ,							__lowerCAmelCase   ,							1    )
			lowerCAmelCase_      =     image_embeddings.view(bs_embed * num_images_per_prompt   ,							__lowerCAmelCase   ,							-1    )

			if do_classifier_free_guidance:
						lowerCAmelCase_      =     torch.zeros_like(__lowerCAmelCase    )

						# 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      _lowerCAmelCase (	__a	):




				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = None    ,    )    ->				Optional[int]:
							super().__init__()

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

							lowerCAmelCase_      =     decoder_pipe_kwargs.get("torch_dtype"    ,    _UpperCamelCase					) or prior.dtype

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

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

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




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    )    ->				List[Any]:
							if text_model_output is None:
										lowerCAmelCase_      =     len(_UpperCamelCase					) if isinstance(_UpperCamelCase    ,    _UpperCamelCase					) else 1
										# get prompt text embeddings
										lowerCAmelCase_      =     self.tokenizer(
										    _UpperCamelCase    ,    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(_UpperCamelCase					)

										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(_UpperCamelCase					)					)

										lowerCAmelCase_      =     text_encoder_output.text_embeds
										lowerCAmelCase_      =     text_encoder_output.last_hidden_state

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

							lowerCAmelCase_      =     text_embeddings.repeat_interleave(_UpperCamelCase    ,    dim=0					)
							lowerCAmelCase_      =     text_encoder_hidden_states.repeat_interleave(_UpperCamelCase    ,    dim=0					)
							lowerCAmelCase_      =     text_mask.repeat_interleave(_UpperCamelCase    ,    dim=0					)

							if do_classifier_free_guidance:
										lowerCAmelCase_      =     [""] * batch_size

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

										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    ,    _UpperCamelCase					)
										lowerCAmelCase_      =     uncond_embeddings.view(batch_size * num_images_per_prompt    ,    _UpperCamelCase					)

										lowerCAmelCase_      =     uncond_text_encoder_hidden_states.shape[1]
										lowerCAmelCase_      =     uncond_text_encoder_hidden_states.repeat(1    ,    _UpperCamelCase    ,    1					)
										lowerCAmelCase_      =     uncond_text_encoder_hidden_states.view(
										    batch_size * num_images_per_prompt    ,    _UpperCamelCase    ,    -1					)
										lowerCAmelCase_      =     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
										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 					__a						( self					)    ->				Union[str, Any]:
							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 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:
							if latents is None:
										lowerCAmelCase_      =     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}"""					)
										lowerCAmelCase_      =     latents.to(_UpperCamelCase					)

							lowerCAmelCase_      =     latents * scheduler.init_noise_sigma
							return latents




				def 					__a						( self    ,    _UpperCamelCase = None					)    ->				Optional[int]:
							self.decoder_pipe.to(_UpperCamelCase					)
							super().to(_UpperCamelCase					)




				@torch.no_grad()
				def __call__( self    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    _UpperCamelCase = 1    ,    _UpperCamelCase = 25    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    _UpperCamelCase = 4.0    ,    _UpperCamelCase = 8.0    ,    _UpperCamelCase = 50    ,    _UpperCamelCase = 1    ,    _UpperCamelCase = 0.0    ,    _UpperCamelCase = "pil"    ,    _UpperCamelCase = True    ,    )    ->				Any:
							if prompt is not None:
										if isinstance(_UpperCamelCase    ,    _UpperCamelCase					):
													lowerCAmelCase_      =     1
										elif isinstance(_UpperCamelCase    ,    _UpperCamelCase					):
													lowerCAmelCase_      =     len(_UpperCamelCase					)
										else:
													raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase					)}"""					)
							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_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     self._encode_prompt(
							    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)

							# prior

							self.prior_scheduler.set_timesteps(_UpperCamelCase    ,    device=_UpperCamelCase					)
							lowerCAmelCase_      =     self.prior_scheduler.timesteps

							lowerCAmelCase_      =     self.prior.config.embedding_dim

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

										lowerCAmelCase_      =     self.prior(
										    _UpperCamelCase    ,    timestep=_UpperCamelCase    ,    proj_embedding=_UpperCamelCase    ,    encoder_hidden_states=_UpperCamelCase    ,    attention_mask=_UpperCamelCase    ,    ).predicted_image_embedding

										if do_classifier_free_guidance:
													lowerCAmelCase_     ,  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(
										    _UpperCamelCase    ,    timestep=_UpperCamelCase    ,    sample=_UpperCamelCase    ,    generator=_UpperCamelCase    ,    prev_timestep=_UpperCamelCase    ,    ).prev_sample

							lowerCAmelCase_      =     self.prior.post_process_latents(_UpperCamelCase					)

							lowerCAmelCase_      =     prior_latents

							lowerCAmelCase_      =     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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A       =					{"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "NatForImageClassification",
			    "NatModel",
			    "NatPreTrainedModel",
			    "NatBackbone",
			]

if TYPE_CHECKING:
			from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nat import (
						    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NatBackbone,
						    NatForImageClassification,
						    NatModel,
						    NatPreTrainedModel,
						)

else:
			import sys

			_A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



