
import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


def 		UpperCAmelCase     (      a_		)						->      Dict:







				"""simple docstring"""



				__A     ,				__A       =					image.size
				__A     ,				__A       =					(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
				__A       =					image.resize((w, h)						,  resample=PIL_INTERPOLATION["lanczos"]		)
				__A       =					np.array(a_		).astype(np.floataa		) / 255.0
				__A       =					image[None].transpose(0						,  3						,  1						,  2		)
				__A       =					torch.from_numpy(a_		)
				return 2.0 * image - 1.0





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	def __init__(			self   :					Tuple				,A   :					VQModel				,A   :					UNetaDModel				,A   :					Union[
	        DDIMScheduler,
	        PNDMScheduler,
	        LMSDiscreteScheduler,
	        EulerDiscreteScheduler,
	        EulerAncestralDiscreteScheduler,
	        DPMSolverMultistepScheduler,
	    ]				,):
					super().__init__()
					self.register_modules(vqvae=A				,unet=A				,scheduler=A					)







	@torch.no_grad()
	def __call__(			self   :					Optional[Any]				,A   :					Union[torch.Tensor, PIL.Image.Image] = None				,A   :					Optional[int] = 1				,A   :					Optional[int] = 1_00				,A   :					Optional[float] = 0.0				,A   :					Optional[Union[torch.Generator, List[torch.Generator]]] = None				,A   :					Optional[str] = "pil"				,A   :					bool = True				,):
					if isinstance(A				,PIL.Image.Image					):
									__A       =					1
					elif isinstance(A				,torch.Tensor					):
									__A       =					image.shape[0]
					else:
									raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(A					)}'''					)

					if isinstance(A				,PIL.Image.Image					):
									__A       =					preprocess(A					)

					__A     ,				__A       =					image.shape[-2:]

					# in_channels should be 6: 3 for latents, 3 for low resolution image
					__A       =					(batch_size, self.unet.config.in_channels // 2, height, width)
					__A       =					next(self.unet.parameters()					).dtype

					__A       =					randn_tensor(A				,generator=A				,device=self.device				,dtype=A					)

					__A       =					image.to(device=self.device				,dtype=A					)

					# set timesteps and move to the correct device
					self.scheduler.set_timesteps(A				,device=self.device					)
					__A       =					self.scheduler.timesteps

					# scale the initial noise by the standard deviation required by the scheduler
					__A       =					latents * self.scheduler.init_noise_sigma

					# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
					# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
					# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
					# and should be between [0, 1]
					__A       =					"eta" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
					__A       =					{}
					if accepts_eta:
									__A       =					eta

					for t in self.progress_bar(A					):
									# concat latents and low resolution image in the channel dimension.
									__A       =					torch.cat([latents, image]				,dim=1					)
									__A       =					self.scheduler.scale_model_input(A				,A					)
									# predict the noise residual
									__A       =					self.unet(A				,A					).sample
									# compute the previous noisy sample x_t -> x_t-1
									__A       =					self.scheduler.step(A				,A				,A				,**A					).prev_sample

					# decode the image latents with the VQVAE
					__A       =					self.vqvae.decode(A					).sample
					__A       =					torch.clamp(A				,-1.0				,1.0					)
					__A       =					image / 2 + 0.5
					__A       =					image.cpu().permute(0				,2				,3				,1					).numpy()

					if output_type == "pil":
									__A       =					self.numpy_to_pil(A					)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A					)








from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Dict					=['''image_processor''', '''tokenizer''']
       __lowerCAmelCase    :		List[Any]					='''BlipImageProcessor'''
       __lowerCAmelCase    :		Union[str, Any]					=('''BertTokenizer''', '''BertTokenizerFast''')





       def __init__(				self	:Optional[int],      snake_case	:int,      snake_case	:Optional[int]):



          """simple docstring"""

          _lowercase              =False
          super().__init__(snake_case,      snake_case)
          _lowercase              =self.image_processor





       def __call__(				self	:Dict,      snake_case	:ImageInput = None,      snake_case	:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,      snake_case	:bool = True,      snake_case	:Union[bool, str, PaddingStrategy] = False,      snake_case	:Union[bool, str, TruncationStrategy] = None,      snake_case	:Optional[int] = None,      snake_case	:int = 0,      snake_case	:Optional[int] = None,      snake_case	:Optional[bool] = None,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = True,      snake_case	:Optional[Union[str, TensorType]] = None,      **snake_case	:str,      ):



          """simple docstring"""

          if images is None and text is None:
             raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
             _lowercase              =self.tokenizer
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
             return text_encoding

          # add pixel_values
          _lowercase              =self.image_processor(snake_case,      return_tensors=snake_case)

          if text is not None:
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
          else:
             _lowercase              =None

          if text_encoding is not None:
             encoding_image_processor.update(snake_case)

          return encoding_image_processor





       def 				UpperCamelCase__						(				self	:List[Any],      *snake_case	:int,      **snake_case	:str):



          """simple docstring"""

          return self.tokenizer.batch_decode(*snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Tuple,      *snake_case	:List[str],      **snake_case	:str):



          """simple docstring"""

          return self.tokenizer.decode(*snake_case,      **snake_case)





       @property
       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          _lowercase              =self.tokenizer.model_input_names
          _lowercase              =self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


