


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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


@dataclass
class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      42
     _lowerCamelCase   =      42
     _lowerCamelCase   =      None







class     __UpperCAmelCase							(       __A ,					__A							):


     """simple docstring"""
     _lowerCamelCase   =      2


     @register_to_config
     def __init__(	self  ,       __A = 0.02  ,       __A = 100  ,       __A = 1.007  ,       __A = 80  ,       __A = 0.05  ,       __A = 50  ,       ):
        # standard deviation of the initial noise distribution
        __a					=  sigma_max

        # setable values
        __a					=  None
        __a					=  None
        __a					=  None  # sigma(t_i)


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        return sample


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  num_inference_steps
        __a					=  np.arange(0  ,       self.num_inference_steps    )[::-1].copy()
        __a					=  torch.from_numpy(__A    ).to(__A    )
        __a					=  [
            (
                self.config.sigma_max**2
                * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
            )
            for i in self.timesteps
        ]
        __a					=  torch.tensor(__A  ,       dtype=torch.floataa  ,       device=__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = None    ):
        if self.config.s_min <= sigma <= self.config.s_max:
           __a					=  min(self.config.s_churn / self.num_inference_steps  ,       2**0.5 - 1    )
        else:
           __a					=  0

        # sample eps ~ N(0, S_noise^2 * I)
        __a					=  self.config.s_noise * randn_tensor(sample.shape  ,       generator=__A    ).to(sample.device    )
        __a					=  sigma + gamma * sigma
        __a					=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

        return sample_hat, sigma_hat


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A = True  ,       ):
        __a					=  sample_hat + sigma_hat * model_output
        __a					=  (sample_hat - pred_original_sample) / sigma_hat
        __a					=  sample_hat + (sigma_prev - sigma_hat) * derivative

        if not return_dict:
           return (sample_prev, derivative)

        return KarrasVeOutput(
            prev_sample=__A  ,       derivative=__A  ,       pred_original_sample=__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A = True  ,       ):
        __a					=  sample_prev + sigma_prev * model_output
        __a					=  (sample_prev - pred_original_sample) / sigma_prev
        __a					=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

        if not return_dict:
           return (sample_prev, derivative)

        return KarrasVeOutput(
            prev_sample=__A  ,       derivative=__A  ,       pred_original_sample=__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        raise NotImplementedError()











from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Dict					=['''image_processor''', '''tokenizer''']
       __lowerCAmelCase    :		List[Any]					='''BlipImageProcessor'''
       __lowerCAmelCase    :		Union[str, Any]					=('''BertTokenizer''', '''BertTokenizerFast''')





       def __init__(				self	:Optional[int],      snake_case	:int,      snake_case	:Optional[int]):



          """simple docstring"""

          _lowercase              =False
          super().__init__(snake_case,      snake_case)
          _lowercase              =self.image_processor





       def __call__(				self	:Dict,      snake_case	:ImageInput = None,      snake_case	:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,      snake_case	:bool = True,      snake_case	:Union[bool, str, PaddingStrategy] = False,      snake_case	:Union[bool, str, TruncationStrategy] = None,      snake_case	:Optional[int] = None,      snake_case	:int = 0,      snake_case	:Optional[int] = None,      snake_case	:Optional[bool] = None,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = True,      snake_case	:Optional[Union[str, TensorType]] = None,      **snake_case	:str,      ):



          """simple docstring"""

          if images is None and text is None:
             raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
             _lowercase              =self.tokenizer
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
             return text_encoding

          # add pixel_values
          _lowercase              =self.image_processor(snake_case,      return_tensors=snake_case)

          if text is not None:
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
          else:
             _lowercase              =None

          if text_encoding is not None:
             encoding_image_processor.update(snake_case)

          return encoding_image_processor





       def 				UpperCamelCase__						(				self	:List[Any],      *snake_case	:int,      **snake_case	:str):



          """simple docstring"""

          return self.tokenizer.batch_decode(*snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Tuple,      *snake_case	:List[str],      **snake_case	:str):



          """simple docstring"""

          return self.tokenizer.decode(*snake_case,      **snake_case)





       @property
       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          _lowercase              =self.tokenizer.model_input_names
          _lowercase              =self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


