

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 				SCREAMING_SNAKE_CASE__ (__snake_case					):
				__lowerCamelCase						:	torch.FloatTensor
				__lowerCamelCase						:	torch.FloatTensor
				__lowerCamelCase						:	Optional[torch.FloatTensor]														=					None



class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	__snake_case					):
				__lowerCamelCase						:	Tuple														=					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
									lowercase__   :       str				=	sigma_max

									# setable values
									lowercase__   :       int				=	None
									lowercase__   :       np.IntTensor				=	None
									lowercase__   :       torch.FloatTensor				=	None  # sigma(t_i)





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





				def 				snake_case_    (				self						,       a						,       a = None):
									lowercase__   :       Dict				=	num_inference_steps
									lowercase__   :       Dict				=	np.arange(0						,       self.num_inference_steps)[::-1].copy()
									lowercase__   :       List[Any]				=	torch.from_numpy(a).to(a)
									lowercase__   :       Optional[int]				=	[
									    (
									        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
									]
									lowercase__   :       List[Any]				=	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:
														lowercase__   :       Optional[int]				=	min(self.config.s_churn / self.num_inference_steps						,       2**0.5 - 1)
									else:
														lowercase__   :       Optional[int]				=	0

									# sample eps ~ N(0, S_noise^2 * I)
									lowercase__   :       List[str]				=	self.config.s_noise * randn_tensor(sample.shape						,       generator=a).to(sample.device)
									lowercase__   :       List[Any]				=	sigma + gamma * sigma
									lowercase__   :       Optional[Any]				=	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat





				def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a = True						,       ):
									lowercase__   :       str				=	sample_hat + sigma_hat * model_output
									lowercase__   :       int				=	(sample_hat - pred_original_sample) / sigma_hat
									lowercase__   :       Optional[Any]				=	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						,       ):
									lowercase__   :       List[str]				=	sample_prev + sigma_prev * model_output
									lowercase__   :       Optional[int]				=	(sample_prev - pred_original_sample) / sigma_prev
									lowercase__   :       str				=	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()




import warnings
from typing import List, Optional, Union

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


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Tuple														=					["""image_processor""", """tokenizer"""]
    __lowerCamelCase						:	int														=					"""ViltImageProcessor"""
    __lowerCamelCase						:	Union[str, Any]														=					("""BertTokenizer""", """BertTokenizerFast""")





    def __init__(				self						,       a=None						,       a=None						,       **a):
         lowercase__   :       Any				=	None
         if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'						,       a						,       )
              lowercase__   :       Union[str, Any]				=	kwargs.pop('feature_extractor')

         lowercase__   :       List[Any]				=	image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
              raise ValueError('You need to specify an `image_processor`.')
         if tokenizer is None:
              raise ValueError('You need to specify a `tokenizer`.')

         super().__init__(a						,       a)
         lowercase__   :       str				=	self.image_processor





    def __call__(				self						,       a						,       a = None						,       a = True						,       a = False						,       a = None						,       a = None						,       a = 0						,       a = None						,       a = None						,       a = None						,       a = False						,       a = False						,       a = False						,       a = False						,       a = True						,       a = None						,       **a						,       ):
         lowercase__   :       Optional[Any]				=	self.tokenizer(
             text=a						,       add_special_tokens=a						,       padding=a						,       truncation=a						,       max_length=a						,       stride=a						,       pad_to_multiple_of=a						,       return_token_type_ids=a						,       return_attention_mask=a						,       return_overflowing_tokens=a						,       return_special_tokens_mask=a						,       return_offsets_mapping=a						,       return_length=a						,       verbose=a						,       return_tensors=a						,       **a						,       )
         # add pixel_values + pixel_mask
         lowercase__   :       str				=	self.image_processor(a						,       return_tensors=a)
         encoding.update(a)

         return encoding





    def 				snake_case_    (				self						,       *a						,       **a):
         return self.tokenizer.batch_decode(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         return self.tokenizer.decode(*a						,       **a)





    @property
    def 				snake_case_    (				self):
         lowercase__   :       int				=	self.tokenizer.model_input_names
         lowercase__   :       List[str]				=	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





    @property
    def 				snake_case_    (				self):
         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,       a						,       )
         return self.image_processor_class





    @property
    def 				snake_case_    (				self):
         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,       a						,       )
         return self.image_processor

