


"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowerCAmelCase							:    Union[str, Any]					   =	logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCAmelCase							:    str					   =	'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase=8       )    ->     Tuple:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   _lowerCamelCase   :				Optional[Any]							=     width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase=512			,		_lowerCamelCase=512       )    ->     int:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     pil_image.resize((w, h)			,		resample=Image.BICUBIC			,		reducing_gap=1       )
   _lowerCamelCase   :				Union[str, Any]							=     np.array(pil_image.convert("RGB"       )       )
   _lowerCamelCase   :				Any							=     arr.astype(np.floataa       ) / 1_2_7.5 - 1
   _lowerCamelCase   :				Optional[Any]							=     np.transpose(_lowerCamelCase			,		[2, 0, 1]       )
   _lowerCamelCase   :				Any							=     torch.from_numpy(_lowerCamelCase       ).unsqueeze(0       )
   return image




class 							A_      (       _a ):


  def __init__(			self:						Any							,__lowerCAmelCase:						UNetaDConditionModel							,__lowerCAmelCase:						DDPMScheduler							,__lowerCAmelCase:						VQModel							,):







     '''simple docstring'''







     super().__init__()

     self.register_modules(
         unet=__lowerCAmelCase							,scheduler=__lowerCAmelCase							,movq=__lowerCAmelCase							,)
     _lowerCamelCase   :				List[str]							=     2 ** (len(self.movq.config.block_out_channels    ) - 1)


  def 							_lowercase					(			self:						Dict							,__lowerCAmelCase:						Union[str, Any]							,__lowerCAmelCase:						List[Any]							,__lowerCAmelCase:						Tuple    ):







     '''simple docstring'''







     _lowerCamelCase   :				int							=     min(int(num_inference_steps * strength    )							,__lowerCAmelCase    )

     _lowerCamelCase   :				Tuple							=     max(num_inference_steps - init_timestep							,0    )
     _lowerCamelCase   :				Optional[int]							=     self.scheduler.timesteps[t_start:]

     return timesteps, num_inference_steps - t_start


  def 							_lowercase					(			self:						Optional[int]							,__lowerCAmelCase:						Any							,__lowerCAmelCase:						Any							,__lowerCAmelCase:						Optional[Any]							,__lowerCAmelCase:						Union[str, Any]							,__lowerCAmelCase:						Optional[int]							,__lowerCAmelCase:						List[Any]							,__lowerCAmelCase:						List[str]=None    ):







     '''simple docstring'''







     if not isinstance(__lowerCAmelCase							,(torch.Tensor, PIL.Image.Image, list)    ):
        raise ValueError(
            F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowerCAmelCase    )}"""    )

     _lowerCamelCase   :				Any							=     image.to(device=__lowerCAmelCase							,dtype=__lowerCAmelCase    )

     _lowerCamelCase   :				Optional[Any]							=     batch_size * num_images_per_prompt

     if image.shape[1] == 4:
        _lowerCamelCase   :				List[Any]							=     image

     else:
        if isinstance(__lowerCAmelCase							,__lowerCAmelCase    ) and len(__lowerCAmelCase    ) != batch_size:
           raise ValueError(
               F"""You have passed a list of generators of length {len(__lowerCAmelCase    )}, but requested an effective batch"""
               F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

        elif isinstance(__lowerCAmelCase							,__lowerCAmelCase    ):
           _lowerCamelCase   :				List[Any]							=     [
               self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(__lowerCAmelCase    )
           ]
           _lowerCamelCase   :				Tuple							=     torch.cat(__lowerCAmelCase							,dim=0    )
        else:
           _lowerCamelCase   :				int							=     self.movq.encode(__lowerCAmelCase    ).latent_dist.sample(__lowerCAmelCase    )

        _lowerCamelCase   :				int							=     self.movq.config.scaling_factor * init_latents

     _lowerCamelCase   :				Tuple							=     torch.cat([init_latents]							,dim=0    )

     _lowerCamelCase   :				Optional[int]							=     init_latents.shape
     _lowerCamelCase   :				int							=     randn_tensor(__lowerCAmelCase							,generator=__lowerCAmelCase							,device=__lowerCAmelCase							,dtype=__lowerCAmelCase    )

     # get latents
     _lowerCamelCase   :				Union[str, Any]							=     self.scheduler.add_noise(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase    )

     _lowerCamelCase   :				str							=     init_latents

     return latents


  def 							_lowercase					(			self:						List[Any]							,__lowerCAmelCase:						Optional[int]=0    ):







     '''simple docstring'''







     if is_accelerate_available():
        from accelerate import cpu_offload
     else:
        raise ImportError("Please install accelerate via `pip install accelerate`"    )

     _lowerCamelCase   :				str							=     torch.device(F"""cuda:{gpu_id}"""    )

     _lowerCamelCase   :				Dict							=     [
         self.unet,
         self.movq,
     ]
     for cpu_offloaded_model in models:
        if cpu_offloaded_model is not None:
           cpu_offload(__lowerCAmelCase							,__lowerCAmelCase    )


  def 							_lowercase					(			self:						List[Any]							,__lowerCAmelCase:						int=0    ):







     '''simple docstring'''







     if is_accelerate_available() and is_accelerate_version(">="							,"0.17.0.dev0"    ):
        from accelerate import cpu_offload_with_hook
     else:
        raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."    )

     _lowerCamelCase   :				List[str]							=     torch.device(F"""cuda:{gpu_id}"""    )

     if self.device.type != "cpu":
        self.to("cpu"							,silence_dtype_warnings=__lowerCAmelCase    )
        torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

     _lowerCamelCase   :				str							=     None
     for cpu_offloaded_model in [self.unet, self.movq]:
        _lowerCamelCase,					_lowerCamelCase   :				str							=     cpu_offload_with_hook(__lowerCAmelCase							,__lowerCAmelCase							,prev_module_hook=__lowerCAmelCase    )

     # We'll offload the last model manually.
     _lowerCamelCase   :				int							=     hook


  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 							_lowercase					(			self:						Union[str, Any]    ):







     '''simple docstring'''







     if not hasattr(self.unet							,"_hf_hook"    ):
        return self.device
     for module in self.unet.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






  @torch.no_grad()
  @replace_example_docstring(__lowerCAmelCase    )
  def __call__(			self:						Dict							,__lowerCAmelCase:						Union[torch.FloatTensor, List[torch.FloatTensor]]							,__lowerCAmelCase:						Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]							,__lowerCAmelCase:						Union[torch.FloatTensor, List[torch.FloatTensor]]							,__lowerCAmelCase:						int = 512							,__lowerCAmelCase:						int = 512							,__lowerCAmelCase:						int = 100							,__lowerCAmelCase:						float = 4.0							,__lowerCAmelCase:						float = 0.3							,__lowerCAmelCase:						int = 1							,__lowerCAmelCase:						Optional[Union[torch.Generator, List[torch.Generator]]] = None							,__lowerCAmelCase:						Optional[str] = "pil"							,__lowerCAmelCase:						bool = True							,):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[int]							=     self._execution_device

     _lowerCamelCase   :				Dict							=     guidance_scale > 1.0

     if isinstance(__lowerCAmelCase							,__lowerCAmelCase    ):
        _lowerCamelCase   :				int							=     torch.cat(__lowerCAmelCase							,dim=0    )
     _lowerCamelCase   :				Any							=     image_embeds.shape[0]
     if isinstance(__lowerCAmelCase							,__lowerCAmelCase    ):
        _lowerCamelCase   :				str							=     torch.cat(__lowerCAmelCase							,dim=0    )

     if do_classifier_free_guidance:
        _lowerCamelCase   :				List[str]							=     image_embeds.repeat_interleave(__lowerCAmelCase							,dim=0    )
        _lowerCamelCase   :				Optional[int]							=     negative_image_embeds.repeat_interleave(__lowerCAmelCase							,dim=0    )

     _lowerCamelCase   :				Optional[Any]							=     torch.cat([negative_image_embeds, image_embeds]							,dim=0    ).to(dtype=self.unet.dtype							,device=__lowerCAmelCase    )

     if not isinstance(__lowerCAmelCase							,__lowerCAmelCase    ):
        _lowerCamelCase   :				Tuple							=     [image]
     if not all(isinstance(__lowerCAmelCase							,(PIL.Image.Image, torch.Tensor)    ) for i in image    ):
        raise ValueError(
            F"""Input is in incorrect format: {[type(__lowerCAmelCase    ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""    )

     _lowerCamelCase   :				Union[str, Any]							=     torch.cat([prepare_image(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase    ) for i in image]							,dim=0    )
     _lowerCamelCase   :				str							=     image.to(dtype=image_embeds.dtype							,device=__lowerCAmelCase    )

     _lowerCamelCase   :				Tuple							=     self.movq.encode(__lowerCAmelCase    )["latents"]
     _lowerCamelCase   :				List[str]							=     latents.repeat_interleave(__lowerCAmelCase							,dim=0    )
     self.scheduler.set_timesteps(__lowerCAmelCase							,device=__lowerCAmelCase    )
     _lowerCamelCase,					_lowerCamelCase   :				Optional[Any]							=     self.get_timesteps(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase    )
     _lowerCamelCase   :				Any							=     timesteps[:1].repeat(batch_size * num_images_per_prompt    )
     _lowerCamelCase,					_lowerCamelCase   :				Tuple							=     downscale_height_and_width(__lowerCAmelCase							,__lowerCAmelCase							,self.movq_scale_factor    )
     _lowerCamelCase   :				List[Any]							=     self.prepare_latents(
         __lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase							,image_embeds.dtype							,__lowerCAmelCase							,__lowerCAmelCase    )
     for i, t in enumerate(self.progress_bar(__lowerCAmelCase    )    ):
        # expand the latents if we are doing classifier free guidance
        _lowerCamelCase   :				Union[str, Any]							=     torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

        _lowerCamelCase   :				List[str]							=     {"image_embeds": image_embeds}
        _lowerCamelCase   :				Tuple							=     self.unet(
            sample=__lowerCAmelCase							,timestep=__lowerCAmelCase							,encoder_hidden_states=__lowerCAmelCase							,added_cond_kwargs=__lowerCAmelCase							,return_dict=__lowerCAmelCase							,)[0]

        if do_classifier_free_guidance:
           _lowerCamelCase,					_lowerCamelCase   :				Tuple							=     noise_pred.split(latents.shape[1]							,dim=1    )
           _lowerCamelCase,					_lowerCamelCase   :				Dict							=     noise_pred.chunk(2    )
           _lowerCamelCase,					_lowerCamelCase   :				str							=     variance_pred.chunk(2    )
           _lowerCamelCase   :				Tuple							=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
           _lowerCamelCase   :				Any							=     torch.cat([noise_pred, variance_pred_text]							,dim=1    )

        if not (
            hasattr(self.scheduler.config							,"variance_type"    )
            and self.scheduler.config.variance_type in ["learned", "learned_range"]
        ):
           _lowerCamelCase,					_lowerCamelCase   :				Union[str, Any]							=     noise_pred.split(latents.shape[1]							,dim=1    )

        # compute the previous noisy sample x_t -> x_t-1
        _lowerCamelCase   :				Optional[int]							=     self.scheduler.step(
            __lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase							,generator=__lowerCAmelCase							,)[0]

     # post-processing
     _lowerCamelCase   :				Optional[int]							=     self.movq.decode(__lowerCAmelCase							,force_not_quantize=__lowerCAmelCase    )["sample"]

     if output_type not in ["pt", "np", "pil"]:
        raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""    )

     if output_type in ["np", "pil"]:
        _lowerCamelCase   :				Optional[int]							=     image * 0.5 + 0.5
        _lowerCamelCase   :				str							=     image.clamp(0							,1    )
        _lowerCamelCase   :				Optional[int]							=     image.cpu().permute(0							,2							,3							,1    ).float().numpy()

     if output_type == "pil":
        _lowerCamelCase   :				str							=     self.numpy_to_pil(__lowerCAmelCase    )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=__lowerCAmelCase    )


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											snake_case_     							=       tempfile.mkdtemp()

											snake_case_     							=       BlipImageProcessor()
											snake_case_     							=       GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'			)

											snake_case_     							=       BlipaProcessor(snake_case ,  snake_case			)

											processor.save_pretrained(self.tmpdirname			)




				def        a				(   self ,  **snake_case			):
											return AutoProcessor.from_pretrained(self.tmpdirname ,  **snake_case			).tokenizer




				def        a				(   self ,  **snake_case			):
											return AutoProcessor.from_pretrained(self.tmpdirname ,  **snake_case			).image_processor




				def        a				(   self			):
											shutil.rmtree(self.tmpdirname			)




				def        a				(   self			):
											snake_case_     							=       [np.random.randint(255 ,  size=(3, 30, 400) ,  dtype=np.uinta			)]

											snake_case_     							=       [Image.fromarray(np.moveaxis(snake_case ,  0 ,  -1			)			) for x in image_inputs]

											return image_inputs




				def        a				(   self			):
											snake_case_     							=       BlipaProcessor(tokenizer=self.get_tokenizer() ,  image_processor=self.get_image_processor()			)
											processor.save_pretrained(self.tmpdirname			)

											snake_case_     							=       self.get_tokenizer(bos_token='(BOS)' ,  eos_token='(EOS)'			)
											snake_case_     							=       self.get_image_processor(do_normalize=snake_case ,  padding_value=1.0			)

											snake_case_     							=       BlipaProcessor.from_pretrained(
											    self.tmpdirname ,  bos_token='(BOS)' ,  eos_token='(EOS)' ,  do_normalize=snake_case ,  padding_value=1.0			)

											self.assertEqual(processor.tokenizer.get_vocab() ,  tokenizer_add_kwargs.get_vocab()			)
											self.assertIsInstance(processor.tokenizer ,  snake_case			)

											self.assertEqual(processor.image_processor.to_json_string() ,  image_processor_add_kwargs.to_json_string()			)
											self.assertIsInstance(processor.image_processor ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       self.prepare_image_inputs()

											snake_case_     							=       image_processor(snake_case ,  return_tensors='np'			)
											snake_case_     							=       processor(images=snake_case ,  return_tensors='np'			)

											for key in input_feat_extract.keys():
																		self.assertAlmostEqual(input_feat_extract[key].sum() ,  input_processor[key].sum() ,  delta=1e-2			)




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       'lower newer'

											snake_case_     							=       processor(text=snake_case			)

											snake_case_     							=       tokenizer(snake_case ,  return_token_type_ids=snake_case			)

											for key in encoded_tok.keys():
																		self.assertListEqual(encoded_tok[key] ,  encoded_processor[key]			)




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       'lower newer'
											snake_case_     							=       self.prepare_image_inputs()

											snake_case_     							=       processor(text=snake_case ,  images=snake_case			)

											self.assertListEqual(list(inputs.keys()			) ,  ['pixel_values', 'input_ids', 'attention_mask']			)

											# test if it raises when no input is passed
											with pytest.raises(snake_case			):
																		processor()




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											snake_case_     							=       processor.batch_decode(snake_case			)
											snake_case_     							=       tokenizer.batch_decode(snake_case			)

											self.assertListEqual(snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.get_image_processor()
											snake_case_     							=       self.get_tokenizer()

											snake_case_     							=       BlipaProcessor(tokenizer=snake_case ,  image_processor=snake_case			)

											snake_case_     							=       'lower newer'
											snake_case_     							=       self.prepare_image_inputs()

											snake_case_     							=       processor(text=snake_case ,  images=snake_case			)

											# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
											self.assertListEqual(list(inputs.keys()			) ,  ['pixel_values', 'input_ids', 'attention_mask']			)




