





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class      lowerCamelCase(						__snake_case			, __snake_case	):
     '''simple docstring'''


     __magic_name__   =			1




     @register_to_config
     def __init__(    self				,     snake_case_=2000				,     snake_case_=0.1				,     snake_case_=20				,     snake_case_=1E-3							):
         _A     =							None
         _A     =							None
         _A     =							None




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):
         _A     =							torch.linspace(1				,     self.config.sampling_eps				,     snake_case_				,     device=snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_=None							):
         if self.timesteps is None:
             raise ValueError(
                 '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'							)

         # TODO(Patrick) better comments + non-PyTorch
         # postprocess model score
         _A     =							(
             -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
         )
         _A     =							torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
         _A     =							std.flatten()
         while len(std.shape							) < len(score.shape							):
             _A     =							std.unsqueeze(-1							)
         _A     =							-score / std

         # compute
         _A     =							-1.0 / len(self.timesteps							)

         _A     =							self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
         _A     =							beta_t.flatten()
         while len(beta_t.shape							) < len(x.shape							):
             _A     =							beta_t.unsqueeze(-1							)
         _A     =							-0.5 * beta_t * x

         _A     =							torch.sqrt(snake_case_							)
         _A     =							drift - diffusion**2 * score
         _A     =							x + drift * dt

         # add noise
         _A     =							randn_tensor(x.shape				,     layout=x.layout				,     generator=snake_case_				,     device=x.device				,     dtype=x.dtype							)
         _A     =							x_mean + diffusion * math.sqrt(-dt							) * noise

         return x, x_mean




     def __len__(    self							):
         return self.config.num_train_timesteps












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_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      ['image_processor', 'tokenizer']
 __UpperCamelCase       =      'BlipImageProcessor'
 __UpperCamelCase       =      'AutoTokenizer'

 def __init__(  self,		A_,		A_,		A_		)	->       str:
    super().__init__(A_,		A_		)

    # add QFormer tokenizer
    UpperCAmelCase__						=qformer_tokenizer

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

    UpperCAmelCase__						=BatchFeature()

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

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

    return encoding

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       List[Any]:
    return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)

 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.tokenizer.model_input_names
    UpperCAmelCase__						=self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)

 def  __UpperCAmelCase						(  self,		A_,		**A_		)	->       str:
    if os.path.isfile(A_		):
       raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
    os.makedirs(A_,		exist_ok=A_		)
    UpperCAmelCase__						=os.path.join(A_,		"qformer_tokenizer"		)
    self.qformer_tokenizer.save_pretrained(A_		)
    return super().save_pretrained(A_,		**A_		)






 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		**A_		)	->       int:
    UpperCAmelCase__						=AutoTokenizer.from_pretrained(A_,		subfolder="qformer_tokenizer"		)
    UpperCAmelCase__						=cls._get_arguments_from_pretrained(A_,		**A_		)
    args.append(A_		)
    return cls(*A_		)



