



'''simple docstring'''







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    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   A			:  List[Any]					=						["image_processor", "tokenizer"]
   A			:  Optional[Any]					=						"LayoutLMv2ImageProcessor"
   A			:  List[Any]					=						("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")


   def __init__(	self  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."  ,    __UpperCamelCase  ,    )
           UpperCamelCase__	     =      kwargs.pop("feature_extractor"			)

       UpperCamelCase__	     =      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__(__UpperCamelCase  ,    __UpperCamelCase			)


   def __call__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase = None  ,    __UpperCamelCase = None  ,    __UpperCamelCase = None  ,    __UpperCamelCase = None  ,    __UpperCamelCase = True  ,    __UpperCamelCase = False  ,    __UpperCamelCase = False  ,    __UpperCamelCase = None  ,    __UpperCamelCase = 0  ,    __UpperCamelCase = None  ,    __UpperCamelCase = None  ,    __UpperCamelCase = None  ,    __UpperCamelCase = False  ,    __UpperCamelCase = False  ,    __UpperCamelCase = False  ,    __UpperCamelCase = False  ,    __UpperCamelCase = True  ,    __UpperCamelCase = None  ,    **__UpperCamelCase  ,    ):
       # 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
       UpperCamelCase__	     =      self.image_processor(images=__UpperCamelCase  ,    return_tensors=__UpperCamelCase			)

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

       UpperCamelCase__	     =      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=__UpperCamelCase  ,    add_special_tokens=__UpperCamelCase  ,    padding=__UpperCamelCase  ,    truncation=__UpperCamelCase  ,    max_length=__UpperCamelCase  ,    stride=__UpperCamelCase  ,    pad_to_multiple_of=__UpperCamelCase  ,    return_token_type_ids=__UpperCamelCase  ,    return_attention_mask=__UpperCamelCase  ,    return_overflowing_tokens=__UpperCamelCase  ,    return_special_tokens_mask=__UpperCamelCase  ,    return_offsets_mapping=__UpperCamelCase  ,    return_length=__UpperCamelCase  ,    verbose=__UpperCamelCase  ,    return_tensors=__UpperCamelCase  ,    **__UpperCamelCase  ,    )

       # add pixel values
       UpperCamelCase__	     =      features.pop("pixel_values"			)
       if return_overflowing_tokens is True:
           UpperCamelCase__	     =      self.get_overflowing_images(__UpperCamelCase  ,    encoded_inputs["overflow_to_sample_mapping"]			)
       UpperCamelCase__	     =      images

       return encoded_inputs


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       UpperCamelCase__	     =      []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]			)

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

       return images_with_overflow


   def 						_UpperCamelCase							(	self  ,    *__UpperCamelCase  ,    **__UpperCamelCase			):
       return self.tokenizer.batch_decode(*__UpperCamelCase  ,    **__UpperCamelCase			)


   def 						_UpperCamelCase							(	self  ,    *__UpperCamelCase  ,    **__UpperCamelCase			):
       return self.tokenizer.decode(*__UpperCamelCase  ,    **__UpperCamelCase			)


   @property
   def 						_UpperCamelCase							(	self			):
       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


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







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









'''simple docstring'''







import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


lowercase__  :	str            =      logging.get_logger(__name__)

@dataclass
class    lowerCamelCase_				:






   '''simple docstring'''

   A			:  str
   A			:  str
   A			:  Optional[str]					=						None
   A			:  Optional[str]					=						None


   def 						_UpperCamelCase							(	self			):
       return json.dumps(dataclasses.asdict(self			)  ,    indent=2			) + "\n"

@dataclass(frozen=lowerCAmelCase_	)
class    lowerCamelCase_				:






   '''simple docstring'''

   A			:  List[int]
   A			:  Optional[List[int]]					=						None
   A			:  Optional[List[int]]					=						None
   A			:  Optional[Union[int, float]]					=						None


   def 						_UpperCamelCase							(	self			):
       return json.dumps(dataclasses.asdict(self			)			) + "\n"

class    lowerCamelCase_				:






   '''simple docstring'''

   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       if len(self.get_labels()			) > 1:
           UpperCamelCase__	     =      self.get_labels()[int(example.label			)]
       return example







   @classmethod
   def 						_UpperCamelCase							(	cls  ,    __UpperCamelCase  ,    __UpperCamelCase=None			):
       with open(__UpperCamelCase  ,    "r"  ,    encoding="utf-8-sig"			) as f:
           return list(csv.reader(__UpperCamelCase  ,    delimiter="\t"  ,    quotechar=__UpperCamelCase			)			)

class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase="classification"  ,    __UpperCamelCase=False			):
       UpperCamelCase__	     =      [] if labels is None else labels
       UpperCamelCase__	     =      [] if examples is None else examples
       UpperCamelCase__	     =      mode
       UpperCamelCase__	     =      verbose


   def __len__(	self			):
       return len(self.examples			)


   def __getitem__(	self  ,    __UpperCamelCase			):
       if isinstance(__UpperCamelCase  ,    __UpperCamelCase			):
           return SingleSentenceClassificationProcessor(labels=self.labels  ,    examples=self.examples[idx]			)
       return self.examples[idx]


   @classmethod
   def 						_UpperCamelCase							(	cls  ,    __UpperCamelCase  ,    __UpperCamelCase=""  ,    __UpperCamelCase=0  ,    __UpperCamelCase=1  ,    __UpperCamelCase=None  ,    __UpperCamelCase=False  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      cls(**__UpperCamelCase			)
       processor.add_examples_from_csv(
           __UpperCamelCase  ,    split_name=__UpperCamelCase  ,    column_label=__UpperCamelCase  ,    column_text=__UpperCamelCase  ,    column_id=__UpperCamelCase  ,    skip_first_row=__UpperCamelCase  ,    overwrite_labels=__UpperCamelCase  ,    overwrite_examples=__UpperCamelCase  ,    )
       return processor


   @classmethod
   def 						_UpperCamelCase							(	cls  ,    __UpperCamelCase  ,    __UpperCamelCase=None  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      cls(**__UpperCamelCase			)
       processor.add_examples(__UpperCamelCase  ,    labels=__UpperCamelCase			)
       return processor


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=""  ,    __UpperCamelCase=0  ,    __UpperCamelCase=1  ,    __UpperCamelCase=None  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False  ,    ):
       UpperCamelCase__	     =      self._read_tsv(__UpperCamelCase			)
       if skip_first_row:
           UpperCamelCase__	     =      lines[1:]
       UpperCamelCase__	     =      []
       UpperCamelCase__	     =      []
       UpperCamelCase__	     =      []
       for i, line in enumerate(__UpperCamelCase			):
           texts.append(line[column_text]			)
           labels.append(line[column_label]			)
           if column_id is not None:
               ids.append(line[column_id]			)
           else:
               UpperCamelCase__	     =      f'{split_name}-{i}' if split_name else str(__UpperCamelCase			)
               ids.append(__UpperCamelCase			)

       return self.add_examples(
           __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    overwrite_labels=__UpperCamelCase  ,    overwrite_examples=__UpperCamelCase			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False			):
       if labels is not None and len(__UpperCamelCase			) != len(__UpperCamelCase			):
           raise ValueError(
               f'Text and labels have mismatched lengths {len(__UpperCamelCase			)} and {len(__UpperCamelCase			)}'			)
       if ids is not None and len(__UpperCamelCase			) != len(__UpperCamelCase			):
           raise ValueError(f'Text and ids have mismatched lengths {len(__UpperCamelCase			)} and {len(__UpperCamelCase			)}'			)
       if ids is None:
           UpperCamelCase__	     =      [None] * len(__UpperCamelCase			)
       if labels is None:
           UpperCamelCase__	     =      [None] * len(__UpperCamelCase			)
       UpperCamelCase__	     =      []
       UpperCamelCase__	     =      set()
       for text_or_text_and_label, label, guid in zip(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
           if isinstance(__UpperCamelCase  ,    (tuple, list)			) and label is None:
               UpperCamelCase__     ,	UpperCamelCase__	     =      text_or_text_and_label
           else:
               UpperCamelCase__	     =      text_or_text_and_label
           added_labels.add(__UpperCamelCase			)
           examples.append(InputExample(guid=__UpperCamelCase  ,    text_a=__UpperCamelCase  ,    text_b=__UpperCamelCase  ,    label=__UpperCamelCase			)			)

       # Update examples
       if overwrite_examples:
           UpperCamelCase__	     =      examples
       else:
           self.examples.extend(__UpperCamelCase			)

       # Update labels
       if overwrite_labels:
           UpperCamelCase__	     =      list(__UpperCamelCase			)
       else:
           UpperCamelCase__	     =      list(set(self.labels			).union(__UpperCamelCase			)			)

       return self.examples







   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=None  ,    __UpperCamelCase=False  ,    __UpperCamelCase=0  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    ):
       if max_length is None:
           UpperCamelCase__	     =      tokenizer.max_len

       UpperCamelCase__	     =      {label: i for i, label in enumerate(self.labels			)}

       UpperCamelCase__	     =      []
       for ex_index, example in enumerate(self.examples			):
           if ex_index % 1_00_00 == 0:
               logger.info(f'Tokenizing example {ex_index}'			)

           UpperCamelCase__	     =      tokenizer.encode(
               example.text_a  ,    add_special_tokens=__UpperCamelCase  ,    max_length=min(__UpperCamelCase  ,    tokenizer.max_len			)  ,    )
           all_input_ids.append(__UpperCamelCase			)

       UpperCamelCase__	     =      max(len(__UpperCamelCase			) for input_ids in all_input_ids			)

       UpperCamelCase__	     =      []
       for ex_index, (input_ids, example) in enumerate(zip(__UpperCamelCase  ,    self.examples			)			):
           if ex_index % 1_00_00 == 0:
               logger.info(f'Writing example {ex_index}/{len(self.examples			)}'			)
           # The mask has 1 for real tokens and 0 for padding tokens. Only real
           # tokens are attended to.
           UpperCamelCase__	     =      [1 if mask_padding_with_zero else 0] * len(__UpperCamelCase			)

           # Zero-pad up to the sequence length.
           UpperCamelCase__	     =      batch_length - len(__UpperCamelCase			)
           if pad_on_left:
               UpperCamelCase__	     =      ([pad_token] * padding_length) + input_ids
               UpperCamelCase__	     =      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
           else:
               UpperCamelCase__	     =      input_ids + ([pad_token] * padding_length)
               UpperCamelCase__	     =      attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

           if len(__UpperCamelCase			) != batch_length:
               raise ValueError(f'Error with input length {len(__UpperCamelCase			)} vs {batch_length}'			)
           if len(__UpperCamelCase			) != batch_length:
               raise ValueError(f'Error with input length {len(__UpperCamelCase			)} vs {batch_length}'			)

           if self.mode == "classification":
               UpperCamelCase__	     =      label_map[example.label]
           elif self.mode == "regression":
               UpperCamelCase__	     =      float(example.label			)
           else:
               raise ValueError(self.mode			)

           if ex_index < 5 and self.verbose:
               logger.info("*** Example ***"			)
               logger.info(f'guid: {example.guid}'			)
               logger.info(f'input_ids: {" ".join([str(__UpperCamelCase			) for x in input_ids]			)}'			)
               logger.info(f'attention_mask: {" ".join([str(__UpperCamelCase			) for x in attention_mask]			)}'			)
               logger.info(f'label: {example.label} (id = {label})'			)

           features.append(InputFeatures(input_ids=__UpperCamelCase  ,    attention_mask=__UpperCamelCase  ,    label=__UpperCamelCase			)			)

       if return_tensors is None:
           return features
       elif return_tensors == "tf":
           if not is_tf_available():
               raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"			)
           import tensorflow as tf

           def gen():
               for ex in features:
                   yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

           UpperCamelCase__	     =      tf.data.Dataset.from_generator(
               __UpperCamelCase  ,    ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)  ,    ({"input_ids": tf.TensorShape([None]			), "attention_mask": tf.TensorShape([None]			)}, tf.TensorShape([]			))  ,    )
           return dataset
       elif return_tensors == "pt":
           if not is_torch_available():
               raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"			)
           import torch
           from torch.utils.data import TensorDataset

           UpperCamelCase__	     =      torch.tensor([f.input_ids for f in features]  ,    dtype=torch.long			)
           UpperCamelCase__	     =      torch.tensor([f.attention_mask for f in features]  ,    dtype=torch.long			)
           if self.mode == "classification":
               UpperCamelCase__	     =      torch.tensor([f.label for f in features]  ,    dtype=torch.long			)
           elif self.mode == "regression":
               UpperCamelCase__	     =      torch.tensor([f.label for f in features]  ,    dtype=torch.float			)

           UpperCamelCase__	     =      TensorDataset(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
           return dataset
       else:
           raise ValueError("return_tensors should be one of 'tf' or 'pt'"			)




