

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,
)


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

_UpperCAmelCase						:  Optional[int]									=			"""
    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      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=8 ):







							'''simple docstring'''






							snake_case_     							=       height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							snake_case_     							=       width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__=512  ,				UpperCamelCase__=512 ):







							'''simple docstring'''






							snake_case_     							=       pil_image.resize((w, h)  ,				resample=Image.BICUBIC  ,				reducing_gap=1 )
							snake_case_     							=       np.array(pil_image.convert('RGB' ) )
							snake_case_     							=       arr.astype(np.floataa ) / 1_27.5 - 1
							snake_case_     							=       np.transpose(UpperCamelCase__  ,				[2, 0, 1] )
							snake_case_     							=       torch.from_numpy(UpperCamelCase__ ).unsqueeze(0 )
							return image







class 					lowercase							(			lowercase_     ):




				def __init__(   self ,  snake_case ,  snake_case ,  snake_case ,  ):
											super().__init__()

											self.register_modules(
											    unet=snake_case ,  scheduler=snake_case ,  movq=snake_case ,  )
											snake_case_     							=       2 ** (len(self.movq.config.block_out_channels			) - 1)




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											# get the original timestep using init_timestep
											snake_case_     							=       min(int(num_inference_steps * strength			) ,  snake_case			)

											snake_case_     							=       max(num_inference_steps - init_timestep ,  0			)
											snake_case_     							=       self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case=None			):
											if not isinstance(snake_case ,  (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(snake_case			)}'''			)

											snake_case_     							=       image.to(device=snake_case ,  dtype=snake_case			)

											snake_case_     							=       batch_size * num_images_per_prompt

											if image.shape[1] == 4:
																		snake_case_     							=       image

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

																		elif isinstance(snake_case ,  snake_case			):
																									snake_case_     							=       [
																									    self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(snake_case			)
																									]
																									snake_case_     							=       torch.cat(snake_case ,  dim=0			)
																		else:
																									snake_case_     							=       self.movq.encode(snake_case			).latent_dist.sample(snake_case			)

																		snake_case_     							=       self.movq.config.scaling_factor * init_latents

											snake_case_     							=       torch.cat([init_latents] ,  dim=0			)

											snake_case_     							=       init_latents.shape
											snake_case_     							=       randn_tensor(snake_case ,  generator=snake_case ,  device=snake_case ,  dtype=snake_case			)

											# get latents
											snake_case_     							=       self.scheduler.add_noise(snake_case ,  snake_case ,  snake_case			)

											snake_case_     							=       init_latents

											return latents




				def        a				(   self ,  snake_case=0			):
											if is_accelerate_available():
																		from accelerate import cpu_offload
											else:
																		raise ImportError('Please install accelerate via `pip install accelerate`'			)

											snake_case_     							=       torch.device(F'''cuda:{gpu_id}'''			)

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




				def        a				(   self ,  snake_case=0			):
											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.'			)

											snake_case_     							=       torch.device(F'''cuda:{gpu_id}'''			)

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

											snake_case_     							=       None
											for cpu_offloaded_model in [self.unet, self.movq]:
																		snake_case_							,       snake_case_     							=       cpu_offload_with_hook(snake_case ,  snake_case ,  prev_module_hook=snake_case			)

											# We'll offload the last model manually.
											snake_case_     							=       hook




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def        a				(   self			):
											if not hasattr(self.unet ,  '_hf_hook'			):
																		return self.device
											for module in self.unet.modules():
																		if (
																		    hasattr(snake_case ,  '_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(snake_case			)
				def __call__(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case = 512 ,  snake_case = 512 ,  snake_case = 100 ,  snake_case = 4.0 ,  snake_case = 0.3 ,  snake_case = 1 ,  snake_case = None ,  snake_case = "pil" ,  snake_case = True ,  ):
											snake_case_     							=       self._execution_device

											snake_case_     							=       guidance_scale > 1.0

											if isinstance(snake_case ,  snake_case			):
																		snake_case_     							=       torch.cat(snake_case ,  dim=0			)
											snake_case_     							=       image_embeds.shape[0]
											if isinstance(snake_case ,  snake_case			):
																		snake_case_     							=       torch.cat(snake_case ,  dim=0			)

											if do_classifier_free_guidance:
																		snake_case_     							=       image_embeds.repeat_interleave(snake_case ,  dim=0			)
																		snake_case_     							=       negative_image_embeds.repeat_interleave(snake_case ,  dim=0			)

											snake_case_     							=       torch.cat([negative_image_embeds, image_embeds] ,  dim=0			).to(dtype=self.unet.dtype ,  device=snake_case			)

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

											snake_case_     							=       torch.cat([prepare_image(snake_case ,  snake_case ,  snake_case			) for i in image] ,  dim=0			)
											snake_case_     							=       image.to(dtype=image_embeds.dtype ,  device=snake_case			)

											snake_case_     							=       self.movq.encode(snake_case			)['latents']
											snake_case_     							=       latents.repeat_interleave(snake_case ,  dim=0			)
											self.scheduler.set_timesteps(snake_case ,  device=snake_case			)
											snake_case_							,       snake_case_     							=       self.get_timesteps(snake_case ,  snake_case ,  snake_case			)
											snake_case_     							=       timesteps[:1].repeat(batch_size * num_images_per_prompt			)
											snake_case_							,       snake_case_     							=       downscale_height_and_width(snake_case ,  snake_case ,  self.movq_scale_factor			)
											snake_case_     							=       self.prepare_latents(
											    snake_case ,  snake_case ,  snake_case ,  snake_case ,  image_embeds.dtype ,  snake_case ,  snake_case			)
											for i, t in enumerate(self.progress_bar(snake_case			)			):
																		# expand the latents if we are doing classifier free guidance
																		snake_case_     							=       torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

																		snake_case_     							=       {'image_embeds': image_embeds}
																		snake_case_     							=       self.unet(
																		    sample=snake_case ,  timestep=snake_case ,  encoder_hidden_states=snake_case ,  added_cond_kwargs=snake_case ,  return_dict=snake_case ,  )[0]

																		if do_classifier_free_guidance:
																									snake_case_							,       snake_case_     							=       noise_pred.split(latents.shape[1] ,  dim=1			)
																									snake_case_							,       snake_case_     							=       noise_pred.chunk(2			)
																									snake_case_							,       snake_case_     							=       variance_pred.chunk(2			)
																									snake_case_     							=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																									snake_case_     							=       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"]
																		):
																									snake_case_							,       snake_case_     							=       noise_pred.split(latents.shape[1] ,  dim=1			)

																		# compute the previous noisy sample x_t -> x_t-1
																		snake_case_     							=       self.scheduler.step(
																		    snake_case ,  snake_case ,  snake_case ,  generator=snake_case ,  )[0]

											# post-processing
											snake_case_     							=       self.movq.decode(snake_case ,  force_not_quantize=snake_case			)['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"]:
																		snake_case_     							=       image * 0.5 + 0.5
																		snake_case_     							=       image.clamp(0 ,  1			)
																		snake_case_     							=       image.cpu().permute(0 ,  2 ,  3 ,  1			).float().numpy()

											if output_type == "pil":
																		snake_case_     							=       self.numpy_to_pil(snake_case			)

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=snake_case			)







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']			)




