

import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


UpperCamelCase													=		logging.get_logger(__name__)



class 						snake_case_ (	nn.Module ):






   def __init__(   self       :  str						)      ->		Optional[int]:
       super().__init__()
       if version.parse(torch.__version__						) < version.parse("1.12.0"						):
           raise ImportError(
               F'''You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '''
               "PytorchGELUTanh. Please upgrade torch."						)







   def    __UpperCamelCase  (   self       :  List[Any]			,		lowercase_       :  Optional[int]						)      ->		Tensor:
       return nn.functional.gelu(lowercase_			,		approximate="tanh"						)



class 						snake_case_ (	nn.Module ):






   def    __UpperCamelCase  (   self       :  Any			,		lowercase_       :  Optional[int]						)      ->		Tensor:
       return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi						) * (input + 0.04_47_15 * torch.pow(lowercase_			,		3.0						))						))



class 						snake_case_ (	nn.Module ):






   def __init__(   self       :  Tuple			,		lowercase_       :  Optional[int] = False						)      ->		List[Any]:
       super().__init__()
       if use_gelu_python:
           lowercase__				:     Optional[int]					      =       self._gelu_python
       else:
           lowercase__				:     List[str]					      =       nn.functional.gelu






   def    __UpperCamelCase  (   self       :  Any			,		lowercase_       :  List[str]						)      ->		Tensor:
       return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0						)						))







   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  Optional[int]						)      ->		Tensor:
       return self.act(lowercase_						)



class 						snake_case_ (	nn.Module ):






   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  str						)      ->		Tensor:
       return 0.5 * input * (1.0 + torch.tanh(input * 0.79_78_84_56_08 * (1.0 + 0.04_47_15 * input * input)						))



class 						snake_case_ (	nn.Module ):






   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]						)      ->		Tensor:
       return input * torch.sigmoid(1.7_02 * input						)



class 						snake_case_ (	nn.Module ):






   def __init__(   self       :  Optional[int]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Tuple						)      ->		Union[str, Any]:
       if min > max:
           raise ValueError(F'''min should be < max (got min: {min}, max: {max})'''						)

       super().__init__()
       lowercase__				:     List[str]					      =       min
       lowercase__				:     Optional[int]					      =       max







   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  Optional[int]						)      ->		Tensor:
       return torch.clip(gelu(lowercase_						)			,		self.min			,		self.max						)



class 						snake_case_ (	nn.Module ):






   def __init__(   self       :  str						)      ->		Optional[Any]:
       super().__init__()
       lowercase__				:     List[Any]					      =       math.sqrt(2 / math.pi						)







   def    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  int						)      ->		Tensor:
       return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_47_15 * torch.pow(lowercase_			,		3						))						))



class 						snake_case_ (	nn.Module ):






   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  Any						)      ->		Tensor:
       return nn.functional.silu(lowercase_						)



class 						snake_case_ (	nn.Module ):






   def __init__(   self       :  Union[str, Any]						)      ->		Tuple:
       super().__init__()
       if version.parse(torch.__version__						) < version.parse("1.9.0"						):
           lowercase__				:     Any					      =       self._mish_python
       else:
           lowercase__				:     List[str]					      =       nn.functional.mish






   def    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  Dict						)      ->		Tensor:
       return input * torch.tanh(nn.functional.softplus(lowercase_						)						)







   def    __UpperCamelCase  (   self       :  Any			,		lowercase_       :  int						)      ->		Tensor:
       return self.act(lowercase_						)



class 						snake_case_ (	nn.Module ):






   def    __UpperCamelCase  (   self       :  Any			,		lowercase_       :  Dict						)      ->		Tensor:
       return input



class 						snake_case_ (	nn.Module ):






   def    __UpperCamelCase  (   self       :  List[Any]			,		lowercase_       :  Union[str, Any]			,		lowercase_       :  str=0.70_71_07			,		lowercase_       :  List[str]=0.28_20_95						)      ->		Optional[int]:
       lowercase__				:     Dict					      =       (input - mu).div(sigma * math.sqrt(2.0						)						)
       return 0.5 * (1.0 + torch.erf(lowercase_						))



class 						snake_case_ (	nn.Module ):






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  Optional[int]						)      ->		Tuple:
       lowercase__				:     List[str]					      =       nn.functional.relu(lowercase_						)
       lowercase__				:     Optional[Any]					      =       torch.square(lowercase_						)
       return squared



class 						snake_case_ (	snake_case_ ):






   def __getitem__(   self       :  Any			,		lowercase_       :  Union[str, Any]						)      ->		Any:
       lowercase__				:     int					      =       super().__getitem__(lowercase_						)
       lowercase__      ,     lowercase__				:     List[str]					      =       content if isinstance(lowercase_			,		lowercase_						) else (content, {})
       return cls(**lowercase_						)



UpperCamelCase													=		{
    '''gelu''': GELUActivation,
    '''gelu_10''': (ClippedGELUActivation, {'''min''': -10, '''max''': 10}),
    '''gelu_fast''': FastGELUActivation,
    '''gelu_new''': NewGELUActivation,
    '''gelu_python''': (GELUActivation, {'''use_gelu_python''': True}),
    '''gelu_pytorch_tanh''': PytorchGELUTanh,
    '''gelu_accurate''': AccurateGELUActivation,
    '''laplace''': LaplaceActivation,
    '''linear''': LinearActivation,
    '''mish''': MishActivation,
    '''quick_gelu''': QuickGELUActivation,
    '''relu''': nn.ReLU,
    '''relu2''': ReLUSquaredActivation,
    '''relu6''': nn.ReLUa,
    '''sigmoid''': nn.Sigmoid,
    '''silu''': SiLUActivation,
    '''swish''': SiLUActivation,
    '''tanh''': nn.Tanh,
}
UpperCamelCase													=		ClassInstantier(ACTaCLS)

def     lowercase_       (    _lowerCamelCase							: int):
    if activation_string in ACTaFN:
        return ACTaFN[activation_string]
    else:
        raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys())}''')


# For backwards compatibility with: from activations import gelu_python
UpperCamelCase													=		get_activation('''gelu_python''')
UpperCamelCase													=		get_activation('''gelu_new''')
UpperCamelCase													=		get_activation('''gelu''')
UpperCamelCase													=		get_activation('''gelu_fast''')
UpperCamelCase													=		get_activation('''quick_gelu''')
UpperCamelCase													=		get_activation('''silu''')
UpperCamelCase													=		get_activation('''mish''')

UpperCamelCase													=		get_activation('''linear''')




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 						snake_case_ (	__A ):
   __A			:	Optional[Any]        =       ["image_processor", "tokenizer"]
   __A			:	Tuple        =       "LayoutLMv2ImageProcessor"
   __A			:	Optional[int]        =       ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






   def __init__(   self       :  Tuple			,		lowercase_       :  List[str]=None			,		lowercase_       :  Dict=None			,		**lowercase_       :  List[str]						)      ->		int:
       lowercase__				:     Optional[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."			,		lowercase_			,		)
           lowercase__				:     Any					      =       kwargs.pop("feature_extractor"						)

       lowercase__				:     List[str]					      =       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__(lowercase_			,		lowercase_						)






   def __call__(   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,		lowercase_       :  Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,		lowercase_       :  Union[List[List[int]], List[List[List[int]]]] = None			,		lowercase_       :  Optional[Union[List[int], List[List[int]]]] = None			,		lowercase_       :  bool = True			,		lowercase_       :  Union[bool, str, PaddingStrategy] = False			,		lowercase_       :  Union[bool, str, TruncationStrategy] = False			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  int = 0			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = True			,		lowercase_       :  Optional[Union[str, TensorType]] = None			,		**lowercase_       :  List[str]			,		)      ->		BatchEncoding:
       # verify input
       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."						)

       # first, apply the image processor
       lowercase__				:     List[Any]					      =       self.image_processor(images=lowercase_			,		return_tensors=lowercase_						)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowercase_			,		lowercase_						):
               lowercase__				:     List[Any]					      =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase__				:     Union[str, Any]					      =       features["words"]

       lowercase__				:     List[str]					      =       self.tokenizer(
           text=text if text is not None else features["words"]			,		text_pair=text_pair if text_pair is not None else None			,		boxes=boxes if boxes is not None else features["boxes"]			,		word_labels=lowercase_			,		add_special_tokens=lowercase_			,		padding=lowercase_			,		truncation=lowercase_			,		max_length=lowercase_			,		stride=lowercase_			,		pad_to_multiple_of=lowercase_			,		return_token_type_ids=lowercase_			,		return_attention_mask=lowercase_			,		return_overflowing_tokens=lowercase_			,		return_special_tokens_mask=lowercase_			,		return_offsets_mapping=lowercase_			,		return_length=lowercase_			,		verbose=lowercase_			,		return_tensors=lowercase_			,		**lowercase_			,		)

       # add pixel values
       lowercase__				:     Optional[int]					      =       features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           lowercase__				:     Tuple					      =       self.get_overflowing_images(lowercase_			,		encoded_inputs["overflow_to_sample_mapping"]						)
       lowercase__				:     str					      =       images

       return encoded_inputs






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  Optional[int]			,		lowercase_       :  Union[str, Any]						)      ->		Any:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowercase__				:     str					      =       []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

       if len(lowercase_						) != len(lowercase_						):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F''' {len(lowercase_						)} and {len(lowercase_						)}'''						)

       return images_with_overflow






   def    __UpperCamelCase  (   self       :  Any			,		*lowercase_       :  Tuple			,		**lowercase_       :  List[Any]						)      ->		Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[Any]			,		*lowercase_       :  Tuple			,		**lowercase_       :  Union[str, Any]						)      ->		List[Any]:
       return self.tokenizer.decode(*lowercase_			,		**lowercase_						)






   @property
   def    __UpperCamelCase  (   self       :  int						)      ->		Optional[Any]:
       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






   @property
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Tuple:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,		lowercase_			,		)
       return self.image_processor_class







   @property
   def    __UpperCamelCase  (   self       :  Dict						)      ->		List[Any]:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,		lowercase_			,		)
       return self.image_processor



