



'''simple docstring'''





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			(UpperCamelCase ):
       lowerCAmelCase__	:Tuple     =       ["image_processor", "tokenizer"]
       lowerCAmelCase__	:List[Any]     =       "Pix2StructImageProcessor"
       lowerCAmelCase__	:List[str]     =       ("T5Tokenizer", "T5TokenizerFast")



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Union[str, Any]:
           lowercase__									=    False
           super().__init__(UpperCAmelCase_  ,UpperCAmelCase_   )



       def __call__(					self  ,UpperCAmelCase_=None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = True  ,UpperCAmelCase_ = False  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = 2_048  ,UpperCAmelCase_ = 0  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = False  ,UpperCAmelCase_ = False  ,UpperCAmelCase_ = False  ,UpperCAmelCase_ = False  ,UpperCAmelCase_ = False  ,UpperCAmelCase_ = True  ,UpperCAmelCase_ = None  ,**UpperCAmelCase_  ,)	->				BatchEncoding:
           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 and not self.image_processor.is_vqa:
               lowercase__									=    self.tokenizer
               lowercase__									=    self.tokenizer(
                   text=UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_  ,padding=UpperCAmelCase_  ,truncation=UpperCAmelCase_  ,max_length=UpperCAmelCase_  ,stride=UpperCAmelCase_  ,pad_to_multiple_of=UpperCAmelCase_  ,return_attention_mask=UpperCAmelCase_  ,return_overflowing_tokens=UpperCAmelCase_  ,return_special_tokens_mask=UpperCAmelCase_  ,return_offsets_mapping=UpperCAmelCase_  ,return_token_type_ids=UpperCAmelCase_  ,return_length=UpperCAmelCase_  ,verbose=UpperCAmelCase_  ,return_tensors=UpperCAmelCase_  ,**UpperCAmelCase_  ,)
               return text_encoding

           if not self.image_processor.is_vqa:
               # add pixel_values
               lowercase__									=    self.image_processor(
                   UpperCAmelCase_  ,return_tensors=UpperCAmelCase_  ,max_patches=UpperCAmelCase_  ,**UpperCAmelCase_   )
           else:
               # add pixel_values and bbox
               lowercase__									=    self.image_processor(
                   UpperCAmelCase_  ,return_tensors=UpperCAmelCase_  ,max_patches=UpperCAmelCase_  ,header_text=UpperCAmelCase_  ,**UpperCAmelCase_   )

           if text is not None and not self.image_processor.is_vqa:
               lowercase__									=    self.tokenizer(
                   text=UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_  ,padding=UpperCAmelCase_  ,truncation=UpperCAmelCase_  ,max_length=UpperCAmelCase_  ,stride=UpperCAmelCase_  ,pad_to_multiple_of=UpperCAmelCase_  ,return_attention_mask=UpperCAmelCase_  ,return_overflowing_tokens=UpperCAmelCase_  ,return_special_tokens_mask=UpperCAmelCase_  ,return_offsets_mapping=UpperCAmelCase_  ,return_token_type_ids=UpperCAmelCase_  ,return_length=UpperCAmelCase_  ,verbose=UpperCAmelCase_  ,return_tensors=UpperCAmelCase_  ,**UpperCAmelCase_  ,)

               if "attention_mask" in text_encoding:
                   lowercase__									=    text_encoding.pop("attention_mask"   )
               if "input_ids" in text_encoding:
                   lowercase__									=    text_encoding.pop("input_ids"   )
           else:
               lowercase__									=    None

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

           return encoding_image_processor



       def 				_a   (					self  ,*UpperCAmelCase_  ,**UpperCAmelCase_   )	->				List[str]:
           return self.tokenizer.batch_decode(*UpperCAmelCase_  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,*UpperCAmelCase_  ,**UpperCAmelCase_   )	->				List[str]:
           return self.tokenizer.decode(*UpperCAmelCase_  ,**UpperCAmelCase_   )







       @property
       def 				_a   (					self   )	->				List[Any]:
           lowercase__									=    self.tokenizer.model_input_names
           lowercase__									=    self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )









'''simple docstring'''





from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin


class 					snake_case			(UpperCamelCase		,   UpperCamelCase ):



       @register_to_config
       def __init__(					self  ,UpperCAmelCase_ = 768  ,)	->				List[Any]:
           super().__init__()

           lowercase__									=    nn.Parameter(torch.zeros(1  ,UpperCAmelCase_   )   )
           lowercase__									=    nn.Parameter(torch.ones(1  ,UpperCAmelCase_   )   )



       def 				_a   (					self  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,)	->				Any:
           lowercase__									=    nn.Parameter(self.mean.to(UpperCAmelCase_   ).to(UpperCAmelCase_   )   )
           lowercase__									=    nn.Parameter(self.std.to(UpperCAmelCase_   ).to(UpperCAmelCase_   )   )
           return self



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Tuple:
           lowercase__									=    (embeds - self.mean) * 1.0 / self.std
           return embeds







       def 				_a   (					self  ,UpperCAmelCase_   )	->				List[str]:
           lowercase__									=    (embeds * self.std) + self.mean
           return embeds




