"""simple docstring"""



import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[int]           =		["""image_processor""", """tokenizer"""]
 _snake_case   :			Dict           =		"""BlipImageProcessor"""
 _snake_case   :			Any           =		"""AutoTokenizer"""


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

        # add QFormer tokenizer
        __a     				=  qformer_tokenizer


 def __call__(				self  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = True  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = 0  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        if images is None and text is None:
               raise ValueError("You have to specify at least images or text."		)

        __a     				=  BatchFeature()

        if text is not None:
               __a     				=  self.tokenizer(
                   text=lowerCamelCase  ,						add_special_tokens=lowerCamelCase  ,						padding=lowerCamelCase  ,						truncation=lowerCamelCase  ,						max_length=lowerCamelCase  ,						stride=lowerCamelCase  ,						pad_to_multiple_of=lowerCamelCase  ,						return_attention_mask=lowerCamelCase  ,						return_overflowing_tokens=lowerCamelCase  ,						return_special_tokens_mask=lowerCamelCase  ,						return_offsets_mapping=lowerCamelCase  ,						return_token_type_ids=lowerCamelCase  ,						return_length=lowerCamelCase  ,						verbose=lowerCamelCase  ,						return_tensors=lowerCamelCase  ,						**lowerCamelCase  ,						)
               encoding.update(lowerCamelCase		)
               __a     				=  self.qformer_tokenizer(
                   text=lowerCamelCase  ,						add_special_tokens=lowerCamelCase  ,						padding=lowerCamelCase  ,						truncation=lowerCamelCase  ,						max_length=lowerCamelCase  ,						stride=lowerCamelCase  ,						pad_to_multiple_of=lowerCamelCase  ,						return_attention_mask=lowerCamelCase  ,						return_overflowing_tokens=lowerCamelCase  ,						return_special_tokens_mask=lowerCamelCase  ,						return_offsets_mapping=lowerCamelCase  ,						return_token_type_ids=lowerCamelCase  ,						return_length=lowerCamelCase  ,						verbose=lowerCamelCase  ,						return_tensors=lowerCamelCase  ,						**lowerCamelCase  ,						)
               __a     				=  qformer_text_encoding.pop("input_ids"		)
               __a     				=  qformer_text_encoding.pop("attention_mask"		)

        if images is not None:
               __a     				=  self.image_processor(lowerCamelCase  ,						return_tensors=lowerCamelCase		)
               encoding.update(lowerCamelCase		)

        return encoding


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.batch_decode(*lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.decode(*lowerCamelCase  ,						**lowerCamelCase		)


 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def 		a__ (				self		):
        __a     				=  self.tokenizer.model_input_names
        __a     				=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)


 def 		a__ (				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        if os.path.isfile(lowerCamelCase		):
               raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file"		)
        os.makedirs(lowerCamelCase  ,						exist_ok=lowerCamelCase		)
        __a     				=  os.path.join(lowerCamelCase  ,						"qformer_tokenizer"		)
        self.qformer_tokenizer.save_pretrained(lowerCamelCase		)
        return super().save_pretrained(lowerCamelCase  ,						**lowerCamelCase		)






 @classmethod
 def 		a__ (				cls  ,						lowerCamelCase  ,						**lowerCamelCase		):
        __a     				=  AutoTokenizer.from_pretrained(lowerCamelCase  ,						subfolder="qformer_tokenizer"		)
        __a     				=  cls._get_arguments_from_pretrained(lowerCamelCase  ,						**lowerCamelCase		)
        args.append(lowerCamelCase		)
        return cls(*lowerCamelCase		)

"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			UNetaDModel
 _snake_case   :			KarrasVeScheduler


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__()
        self.register_modules(unet=lowerCamelCase  ,						scheduler=lowerCamelCase		)






 @torch.no_grad()
 def __call__(				self  ,						lowerCamelCase = 1  ,						lowerCamelCase = 50  ,						lowerCamelCase = None  ,						lowerCamelCase = "pil"  ,						lowerCamelCase = True  ,						**lowerCamelCase  ,						):
        __a     				=  self.unet.config.sample_size
        __a     				=  (batch_size, 3, img_size, img_size)

        __a     				=  self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        __a     				=  randn_tensor(lowerCamelCase  ,						generator=lowerCamelCase  ,						device=self.device		) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(lowerCamelCase		)

        for t in self.progress_bar(self.scheduler.timesteps		):
               # here sigma_t == t_i from the paper
               __a     				=  self.scheduler.schedule[t]
               __a     				=  self.scheduler.schedule[t - 1] if t > 0 else 0

               # 1. Select temporarily increased noise level sigma_hat
               # 2. Add new noise to move from sample_i to sample_hat
               __a				, __a     				=  self.scheduler.add_noise_to_input(lowerCamelCase  ,						lowerCamelCase  ,						generator=lowerCamelCase		)

               # 3. Predict the noise residual given the noise magnitude `sigma_hat`
               # The model inputs and output are adjusted by following eq. (213) in [1].
               __a     				=  (sigma_hat / 2) * model((sample_hat + 1) / 2  ,						sigma_hat / 2		).sample

               # 4. Evaluate dx/dt at sigma_hat
               # 5. Take Euler step from sigma to sigma_prev
               __a     				=  self.scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

               if sigma_prev != 0:
                      # 6. Apply 2nd order correction
                      # The model inputs and output are adjusted by following eq. (213) in [1].
                      __a     				=  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2  ,						sigma_prev / 2		).sample
                      __a     				=  self.scheduler.step_correct(
                          lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						step_output.prev_sample  ,						step_output["derivative"]  ,						)
               __a     				=  step_output.prev_sample

        __a     				=  (sample / 2 + 0.5).clamp(0  ,						1		)
        __a     				=  sample.cpu().permute(0  ,						2  ,						3  ,						1		).numpy()
        if output_type == "pil":
               __a     				=  self.numpy_to_pil(lowerCamelCase		)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCamelCase		)
