




'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		1



					@register_to_config
					def __init__(	self     :						str      ,       _lowercase     :						int = 1_000      ,       _lowercase     :						Optional[Union[np.ndarray, List[float]]] = None)    ->							List[str]:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(_lowercase)

								# standard deviation of the initial noise distribution
								A_									= 1.0

								# For now we only support F-PNDM, i.e. the runge-kutta method
								# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
								# mainly at formula (9), (12), (13) and the Algorithm 2.
								A_									= 4

								# running values
								A_									= []



					def     __snake_case						(	self     :						int      ,       _lowercase     :						int      ,       _lowercase     :						Union[str, torch.device] = None)    ->							Any:
								A_									= num_inference_steps
								A_									= torch.linspace(1      ,       0      ,       num_inference_steps + 1)[:-1]
								A_									= torch.cat([steps, torch.tensor([0.0])])

								if self.config.trained_betas is not None:
											A_									= torch.tensor(self.config.trained_betas      ,       dtype=torch.floataa)
								else:
											A_									= torch.sin(steps * math.pi / 2) ** 2

								A_									= (1.0 - self.betas**2) ** 0.5

								A_									= (torch.atana(self.betas      ,       self.alphas) / math.pi * 2)[:-1]
								A_									= timesteps.to(_lowercase)

								A_									= []



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						int      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						bool = True      ,       )    ->							Union[SchedulerOutput, Tuple]:
								if self.num_inference_steps is None:
											raise ValueError(
											    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler')

								A_									= (self.timesteps == timestep).nonzero().item()
								A_									= timestep_index + 1

								A_									= sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
								self.ets.append(_lowercase)

								if len(self.ets) == 1:
											A_									= self.ets[-1]
								elif len(self.ets) == 2:
											A_									= (3 * self.ets[-1] - self.ets[-2]) / 2
								elif len(self.ets) == 3:
											A_									= (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
								else:
											A_									= (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

								A_									= self._get_prev_sample(_lowercase      ,       _lowercase      ,       _lowercase      ,       _lowercase)

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowercase)



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						int)    ->							torch.FloatTensor:
								return sample



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Dict      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any])    ->							Union[str, Any]:
								A_									= self.alphas[timestep_index]
								A_									= self.betas[timestep_index]

								A_									= self.alphas[prev_timestep_index]
								A_									= self.betas[prev_timestep_index]

								A_									= (sample - sigma * ets) / max(_lowercase      ,       1E-8)
								A_									= next_alpha * pred + ets * next_sigma

								return prev_sample



					def __len__(	self     :						List[str])    ->							Union[str, Any]:
								return self.config.num_train_timesteps










'''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   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		["""image_processor""", """tokenizer"""]
					_UpperCamelCase							=		"""BlipImageProcessor"""
					_UpperCamelCase							=		"""AutoTokenizer"""



					def __init__(	self     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						List[Any]      ,       _lowercase     :						List[str])    ->							Union[str, Any]:
								super().__init__(_lowercase      ,       _lowercase)

								# add QFormer tokenizer
								A_									= qformer_tokenizer



					def __call__(	self     :						Optional[int]      ,       _lowercase     :						ImageInput = None      ,       _lowercase     :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,       _lowercase     :						bool = True      ,       _lowercase     :						Union[bool, str, PaddingStrategy] = False      ,       _lowercase     :						Union[bool, str, TruncationStrategy] = None      ,       _lowercase     :						Optional[int] = None      ,       _lowercase     :						int = 0      ,       _lowercase     :						Optional[int] = None      ,       _lowercase     :						Optional[bool] = None      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = True      ,       _lowercase     :						Optional[Union[str, TensorType]] = None      ,       **_lowercase     :						List[Any]      ,       )    ->							BatchFeature:
								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=_lowercase      ,       add_special_tokens=_lowercase      ,       padding=_lowercase      ,       truncation=_lowercase      ,       max_length=_lowercase      ,       stride=_lowercase      ,       pad_to_multiple_of=_lowercase      ,       return_attention_mask=_lowercase      ,       return_overflowing_tokens=_lowercase      ,       return_special_tokens_mask=_lowercase      ,       return_offsets_mapping=_lowercase      ,       return_token_type_ids=_lowercase      ,       return_length=_lowercase      ,       verbose=_lowercase      ,       return_tensors=_lowercase      ,       **_lowercase      ,       )
											encoding.update(_lowercase)
											A_									= self.qformer_tokenizer(
											    text=_lowercase      ,       add_special_tokens=_lowercase      ,       padding=_lowercase      ,       truncation=_lowercase      ,       max_length=_lowercase      ,       stride=_lowercase      ,       pad_to_multiple_of=_lowercase      ,       return_attention_mask=_lowercase      ,       return_overflowing_tokens=_lowercase      ,       return_special_tokens_mask=_lowercase      ,       return_offsets_mapping=_lowercase      ,       return_token_type_ids=_lowercase      ,       return_length=_lowercase      ,       verbose=_lowercase      ,       return_tensors=_lowercase      ,       **_lowercase      ,       )
											A_									= qformer_text_encoding.pop('input_ids')
											A_									= qformer_text_encoding.pop('attention_mask')

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

								return encoding



					def     __snake_case						(	self     :						List[Any]      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						List[Any])    ->							Union[str, Any]:
								return self.tokenizer.batch_decode(*_lowercase      ,       **_lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       *_lowercase     :						List[Any]      ,       **_lowercase     :						List[str])    ->							str:
								return self.tokenizer.decode(*_lowercase      ,       **_lowercase)



					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def     __snake_case						(	self     :						Any)    ->							Optional[int]:
								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     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						Optional[int]      ,       **_lowercase     :						int)    ->							Tuple:
								if os.path.isfile(_lowercase):
											raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
								os.makedirs(_lowercase      ,       exist_ok=_lowercase)
								A_									= os.path.join(_lowercase      ,       'qformer_tokenizer')
								self.qformer_tokenizer.save_pretrained(_lowercase)
								return super().save_pretrained(_lowercase      ,       **_lowercase)



					@classmethod
					def     __snake_case						(	cls     :						Dict      ,       _lowercase     :						Dict      ,       **_lowercase     :						int)    ->							Any:
								A_									= AutoTokenizer.from_pretrained(_lowercase      ,       subfolder='qformer_tokenizer')
								A_									= cls._get_arguments_from_pretrained(_lowercase      ,       **_lowercase)
								args.append(_lowercase)
								return cls(*_lowercase)




