






from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Any,  lowerCamelCase						:    Distribution,  lowerCamelCase						:    Union[str, Any]=None,  lowerCamelCase						:    Tuple=None,  lowerCamelCase						:    Optional[Any]=0  ):

								'''simple docstring'''
								lowercase__    =			1.0 if scale is None else scale
								lowercase__    =			0.0 if loc is None else loc

								super().__init__(lowerCamelCase,  [AffineTransform(loc=self.loc,  scale=self.scale,  event_dim=lowerCamelCase  )]  )




				@property
				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								return self.base_dist.mean * self.scale + self.loc




				@property
				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								return self.base_dist.variance * self.scale**2




				@property
				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								return self.variance.sqrt()



class       _UpperCAmelCase					(       nn.Module	):

				"""simple docstring"""




				def __init__(     self						:    str,  lowerCamelCase						:    int,  lowerCamelCase						:    Dict[str, int],  lowerCamelCase						:    Callable[..., Tuple[torch.Tensor]],  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								super().__init__(**lowerCamelCase  )
								lowercase__    =			args_dim
								lowercase__    =			nn.ModuleList([nn.Linear(lowerCamelCase,  lowerCamelCase  ) for dim in args_dim.values()]  )
								lowercase__    =			domain_map




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    torch.Tensor  ):

								'''simple docstring'''
								lowercase__    =			[proj(lowerCamelCase  ) for proj in self.proj]

								return self.domain_map(*lowerCamelCase  )



class       _UpperCAmelCase					(       nn.Module	):

				"""simple docstring"""




				def __init__(     self						:    int,  lowerCamelCase						:    Any  ):

								'''simple docstring'''
								super().__init__()
								lowercase__    =			function




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Dict,  *lowerCamelCase						:    Union[str, Any]  ):

								'''simple docstring'''
								return self.function(lowerCamelCase,  *lowerCamelCase  )



class       _UpperCAmelCase					:

				"""simple docstring"""




				lowercase__											=       42
				lowercase__											=       42
				lowercase__											=       42




				def __init__(     self						:    Any,  lowerCamelCase						:    int = 1  ):

								'''simple docstring'''
								lowercase__    =			dim
								lowercase__    =			{k: dim * self.args_dim[k] for k in self.args_dim}




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    str  ):

								'''simple docstring'''
								if self.dim == 1:
												return self.distribution_class(*lowerCamelCase  )
								else:
												return Independent(self.distribution_class(*lowerCamelCase  ),  1  )




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    int,  lowerCamelCase						:    Optional[torch.Tensor] = None,  lowerCamelCase						:    Optional[torch.Tensor] = None,  ):

								'''simple docstring'''
								lowercase__    =			self._base_distribution(lowerCamelCase  )
								if loc is None and scale is None:
												return distr
								else:
												return AffineTransformed(lowerCamelCase,  loc=lowerCamelCase,  scale=lowerCamelCase,  event_dim=self.event_dim  )




				@property
				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								return () if self.dim == 1 else (self.dim,)




				@property
				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								return len(self.event_shape  )




				@property
				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								return 0.0




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    int  ):

								'''simple docstring'''
								return ParameterProjection(
								    in_features=lowerCamelCase,  args_dim=self.args_dim,  domain_map=LambdaLayer(self.domain_map  ),  )




				def 				lowercase__   (     self						:    Optional[Any],  *lowerCamelCase						:    torch.Tensor  ):

								'''simple docstring'''
								raise NotImplementedError()




				@staticmethod
				def 				lowercase__   (     lowerCamelCase						:    torch.Tensor  ):

								'''simple docstring'''
								return (x + torch.sqrt(torch.square(lowerCamelCase  ) + 4.0  )) / 2.0



class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       {"df": 1, "loc": 1, "scale": 1}
				lowercase__											=       StudentT




				@classmethod
				def 				lowercase__   (     cls						:    Dict,  lowerCamelCase						:    torch.Tensor,  lowerCamelCase						:    torch.Tensor,  lowerCamelCase						:    torch.Tensor  ):

								'''simple docstring'''
								lowercase__    =			cls.squareplus(lowerCamelCase  ).clamp_min(torch.finfo(scale.dtype  ).eps  )
								lowercase__    =			2.0 + cls.squareplus(lowerCamelCase  )
								return df.squeeze(-1  ), loc.squeeze(-1  ), scale.squeeze(-1  )



class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       {"loc": 1, "scale": 1}
				lowercase__											=       Normal




				@classmethod
				def 				lowercase__   (     cls						:    Optional[Any],  lowerCamelCase						:    torch.Tensor,  lowerCamelCase						:    torch.Tensor  ):

								'''simple docstring'''
								lowercase__    =			cls.squareplus(lowerCamelCase  ).clamp_min(torch.finfo(scale.dtype  ).eps  )
								return loc.squeeze(-1  ), scale.squeeze(-1  )



class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       {"total_count": 1, "logits": 1}
				lowercase__											=       NegativeBinomial




				@classmethod
				def 				lowercase__   (     cls						:    Optional[int],  lowerCamelCase						:    torch.Tensor,  lowerCamelCase						:    torch.Tensor  ):

								'''simple docstring'''
								lowercase__    =			cls.squareplus(lowerCamelCase  )
								return total_count.squeeze(-1  ), logits.squeeze(-1  )




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			distr_args
								if self.dim == 1:
												return self.distribution_class(total_count=lowerCamelCase,  logits=lowerCamelCase  )
								else:
												return Independent(self.distribution_class(total_count=lowerCamelCase,  logits=lowerCamelCase  ),  1  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Any,  lowerCamelCase						:    Optional[torch.Tensor] = None,  lowerCamelCase						:    Optional[torch.Tensor] = None  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			distr_args

								if scale is not None:
												# See scaling property of Gamma.
												logits += scale.log()

								return self._base_distribution((total_count, logits)  )












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       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       ["""image_processor""", """tokenizer"""]
				lowercase__											=       """BlipImageProcessor"""
				lowercase__											=       ("""BertTokenizer""", """BertTokenizerFast""")




				def __init__(     self						:    Dict,  lowerCamelCase						:    Dict,  lowerCamelCase						:    str  ):

								'''simple docstring'''
								lowercase__    =			False
								super().__init__(lowerCamelCase,  lowerCamelCase  )
								lowercase__    =			self.image_processor




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

								'''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=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,  )
												return text_encoding

								# add pixel_values
								lowercase__    =			self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase  )

								if text is not None:
												lowercase__    =			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,  )
								else:
												lowercase__    =			None

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

								return encoding_image_processor




				def 				lowercase__   (     self						:    Tuple,  *lowerCamelCase						:    Union[str, Any],  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str],  *lowerCamelCase						:    int,  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase  )




				@property
				def 				lowercase__   (     self						:    List[str]  ):

								'''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  )  )




