






'''simple docstring'''



import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__UpperCamelCase      :       List[str]      			=      logging.getLogger(__name__)
@dataclass
class 							__SCREAMING_SNAKE_CASE       :
      __a  					=42
      __a  					=42
      __a  					=42







@dataclass
class 							__SCREAMING_SNAKE_CASE       :
      __a  					=42
      __a  					=42
      __a  					=None
      __a  					=None







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="train"
      __a  					="dev"
      __a  					="test"







class 							__SCREAMING_SNAKE_CASE       :




      @staticmethod
      def  __UpperCamelCase							(  lowerCamelCase ,   lowerCamelCase      )							->List[InputExample]:



         '''simple docstring'''
         raise NotImplementedError




      @staticmethod
      def  __UpperCamelCase							(  lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         raise NotImplementedError






      @staticmethod
      def  __UpperCamelCase							(  lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase=False ,   lowerCamelCase="[CLS]" ,   lowerCamelCase=1 ,   lowerCamelCase="[SEP]" ,   lowerCamelCase=False ,   lowerCamelCase=False ,   lowerCamelCase=0 ,   lowerCamelCase=0 ,   lowerCamelCase=-100 ,   lowerCamelCase=0 ,   lowerCamelCase=True ,   )							->List[InputFeatures]:



         '''simple docstring'''
         __a				     =    {label: i for i, label in enumerate(lowerCamelCase      )}

         __a				     =    []
         for ex_index, example in enumerate(lowerCamelCase      ):
            if ex_index % 1_0000 == 0:
               logger.info('Writing example %d of %d' ,   lowerCamelCase ,   len(lowerCamelCase      )      )

            __a				     =    []
            __a				     =    []
            for word, label in zip(example.words ,   example.labels      ):
               __a				     =    tokenizer.tokenize(lowerCamelCase      )

               # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
               if len(lowerCamelCase      ) > 0:
                  tokens.extend(lowerCamelCase      )
                  # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                  label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowerCamelCase      ) - 1)      )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
            __a				     =    tokenizer.num_special_tokens_to_add()
            if len(lowerCamelCase      ) > max_seq_length - special_tokens_count:
               __a				     =    tokens[: (max_seq_length - special_tokens_count)]
               __a				     =    label_ids[: (max_seq_length - special_tokens_count)]

            # The convention in BERT is:
            # (a) For sequence pairs:
            #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
            #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
            # (b) For single sequences:
            #  tokens:   [CLS] the dog is hairy . [SEP]
            #  type_ids:   0   0   0   0  0     0   0
            #
            # Where "type_ids" are used to indicate whether this is the first
            # sequence or the second sequence. The embedding vectors for `type=0` and
            # `type=1` were learned during pre-training and are added to the wordpiece
            # embedding vector (and position vector). This is not *strictly* necessary
            # since the [SEP] token unambiguously separates the sequences, but it makes
            # it easier for the model to learn the concept of sequences.
            #
            # For classification tasks, the first vector (corresponding to [CLS]) is
            # used as the "sentence vector". Note that this only makes sense because
            # the entire model is fine-tuned.
            tokens += [sep_token]
            label_ids += [pad_token_label_id]
            if sep_token_extra:
               # roberta uses an extra separator b/w pairs of sentences
               tokens += [sep_token]
               label_ids += [pad_token_label_id]
            __a				     =    [sequence_a_segment_id] * len(lowerCamelCase      )

            if cls_token_at_end:
               tokens += [cls_token]
               label_ids += [pad_token_label_id]
               segment_ids += [cls_token_segment_id]
            else:
               __a				     =    [cls_token] + tokens
               __a				     =    [pad_token_label_id] + label_ids
               __a				     =    [cls_token_segment_id] + segment_ids

            __a				     =    tokenizer.convert_tokens_to_ids(lowerCamelCase      )

            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            __a				     =    [1 if mask_padding_with_zero else 0] * len(lowerCamelCase      )

            # Zero-pad up to the sequence length.
            __a				     =    max_seq_length - len(lowerCamelCase      )
            if pad_on_left:
               __a				     =    ([pad_token] * padding_length) + input_ids
               __a				     =    ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
               __a				     =    ([pad_token_segment_id] * padding_length) + segment_ids
               __a				     =    ([pad_token_label_id] * padding_length) + label_ids
            else:
               input_ids += [pad_token] * padding_length
               input_mask += [0 if mask_padding_with_zero else 1] * padding_length
               segment_ids += [pad_token_segment_id] * padding_length
               label_ids += [pad_token_label_id] * padding_length

            assert len(lowerCamelCase      ) == max_seq_length
            assert len(lowerCamelCase      ) == max_seq_length
            assert len(lowerCamelCase      ) == max_seq_length
            assert len(lowerCamelCase      ) == max_seq_length

            if ex_index < 5:
               logger.info('*** Example ***'      )
               logger.info('guid: %s' ,   example.guid      )
               logger.info('tokens: %s' ,   ' '.join([str(lowerCamelCase      ) for x in tokens]      )      )
               logger.info('input_ids: %s' ,   ' '.join([str(lowerCamelCase      ) for x in input_ids]      )      )
               logger.info('input_mask: %s' ,   ' '.join([str(lowerCamelCase      ) for x in input_mask]      )      )
               logger.info('segment_ids: %s' ,   ' '.join([str(lowerCamelCase      ) for x in segment_ids]      )      )
               logger.info('label_ids: %s' ,   ' '.join([str(lowerCamelCase      ) for x in label_ids]      )      )

            if "token_type_ids" not in tokenizer.model_input_names:
               __a				     =    None

            features.append(
                InputFeatures(
                    input_ids=lowerCamelCase ,   attention_mask=lowerCamelCase ,   token_type_ids=lowerCamelCase ,   label_ids=lowerCamelCase      )      )
         return features


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset
 class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
       __a  					=42
       __a  					=nn.CrossEntropyLoss().ignore_index




       def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase=False ,   lowerCamelCase = Split.train ,   )							->str:



          '''simple docstring'''
          # Load data features from cache or dataset file
          __a				     =    os.path.join(
              lowerCamelCase ,   'cached_{}_{}_{}'.format(mode.value ,   tokenizer.__class__.__name__ ,   str(lowerCamelCase      )      ) ,   )

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          __a				     =    cached_features_file + '.lock'
          with FileLock(lowerCamelCase      ):
             if os.path.exists(lowerCamelCase      ) and not overwrite_cache:
                logger.info(F"""Loading features from cached file {cached_features_file}"""      )
                __a				     =    torch.load(lowerCamelCase      )
             else:
                logger.info(F"""Creating features from dataset file at {data_dir}"""      )
                __a				     =    token_classification_task.read_examples_from_file(lowerCamelCase ,   lowerCamelCase      )
                # TODO clean up all this to leverage built-in features of tokenizers
                __a				     =    token_classification_task.convert_examples_to_features(
                    lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   cls_token_at_end=bool(model_type in ['xlnet']      ) ,   cls_token=tokenizer.cls_token ,   cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,   sep_token=tokenizer.sep_token ,   sep_token_extra=lowerCamelCase ,   pad_on_left=bool(tokenizer.padding_side == 'left'      ) ,   pad_token=tokenizer.pad_token_id ,   pad_token_segment_id=tokenizer.pad_token_type_id ,   pad_token_label_id=self.pad_token_label_id ,   )
                logger.info(F"""Saving features into cached file {cached_features_file}"""      )
                torch.save(self.features ,   lowerCamelCase      )




       def __len__(  self      )							->Union[str, Any]:



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






       def __getitem__(  self ,   lowerCamelCase      )							->InputFeatures:



          '''simple docstring'''
          return self.features[i]


if is_tf_available():
 import tensorflow as tf
 class 							__SCREAMING_SNAKE_CASE       :
       __a  					=42
       __a  					=-100




       def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase=False ,   lowerCamelCase = Split.train ,   )							->int:



          '''simple docstring'''
          __a				     =    token_classification_task.read_examples_from_file(lowerCamelCase ,   lowerCamelCase      )
          # TODO clean up all this to leverage built-in features of tokenizers
          __a				     =    token_classification_task.convert_examples_to_features(
              lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   cls_token_at_end=bool(model_type in ['xlnet']      ) ,   cls_token=tokenizer.cls_token ,   cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,   sep_token=tokenizer.sep_token ,   sep_token_extra=lowerCamelCase ,   pad_on_left=bool(tokenizer.padding_side == 'left'      ) ,   pad_token=tokenizer.pad_token_id ,   pad_token_segment_id=tokenizer.pad_token_type_id ,   pad_token_label_id=self.pad_token_label_id ,   )

          def gen():
             for ex in self.features:
                if ex.token_type_ids is None:
                   yield (
                       {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                       ex.label_ids,
                   )
                else:
                   yield (
                       {
                           "input_ids": ex.input_ids,
                           "attention_mask": ex.attention_mask,
                           "token_type_ids": ex.token_type_ids,
                       },
                       ex.label_ids,
                   )

          if "token_type_ids" not in tokenizer.model_input_names:
             __a				     =    tf.data.Dataset.from_generator(
                 lowerCamelCase ,   ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa) ,   (
                     {'input_ids': tf.TensorShape([None]      ), 'attention_mask': tf.TensorShape([None]      )},
                     tf.TensorShape([None]      ),
                 ) ,   )
          else:
             __a				     =    tf.data.Dataset.from_generator(
                 lowerCamelCase ,   ({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa) ,   (
                     {
                         'input_ids': tf.TensorShape([None]      ),
                         'attention_mask': tf.TensorShape([None]      ),
                         'token_type_ids': tf.TensorShape([None]      ),
                     },
                     tf.TensorShape([None]      ),
                 ) ,   )




       def  __UpperCamelCase							(  self      )							->int:



          '''simple docstring'''
          __a				     =    self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

          return self.dataset




       def __len__(  self      )							->Optional[int]:



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






       def __getitem__(  self ,   lowerCamelCase      )							->InputFeatures:



          '''simple docstring'''
          return self.features[i]







'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	str    )  ->			List[List[ImageInput]]:

   """simple docstring"""
   if isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and isinstance(videos[0],					(list, tuple)    ) and is_valid_image(videos[0][0]    ):
      return videos

   elif isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and is_valid_image(videos[0]    ):
      return [videos]

   elif is_valid_image(SCREAMING_SNAKE_CASE__    ):
      return [[videos]]

   raise ValueError(f"""Could not make batched video from {videos}"""    )



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=["pixel_values"]




      def __init__(  self ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = True ,   lowerCamelCase = 1 / 255 ,   lowerCamelCase = True ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->None:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )
         __a				     =    size if size is not None else {'shortest_edge': 256}
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

         __a				     =    do_resize
         __a				     =    size
         __a				     =    do_center_crop
         __a				     =    crop_size
         __a				     =    resample
         __a				     =    do_rescale
         __a				     =    rescale_factor
         __a				     =    offset
         __a				     =    do_normalize
         __a				     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __a				     =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         if "shortest_edge" in size:
            __a				     =    get_resize_output_image_size(lowerCamelCase ,   size['shortest_edge'] ,   default_to_square=lowerCamelCase      )
         elif "height" in size and "width" in size:
            __a				     =    (size['height'], size['width'])
         else:
            raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
         return resize(lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase      )
         if "height" not in size or "width" not in size:
            raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""      )
         return center_crop(lowerCamelCase ,   size=(size['height'], size['width']) ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = True ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    image.astype(np.floataa      )
         if offset:
            __a				     =    image - (scale / 2)
         return rescale(lowerCamelCase ,   scale=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         return normalize(lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   )							->np.ndarray:



         '''simple docstring'''
         if do_resize and size is None or resample is None:
            raise ValueError('Size and resample must be specified if do_resize is True.'      )

         if do_center_crop and crop_size is None:
            raise ValueError('Crop size must be specified if do_center_crop is True.'      )

         if do_rescale and rescale_factor is None:
            raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

         if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

         if offset and not do_rescale:
            raise ValueError('For offset, do_rescale must also be set to True.'      )

         # All transformations expect numpy arrays.
         __a				     =    to_numpy_array(lowerCamelCase      )

         if do_resize:
            __a				     =    self.resize(image=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase      )

         if do_center_crop:
            __a				     =    self.center_crop(lowerCamelCase ,   size=lowerCamelCase      )

         if do_rescale:
            __a				     =    self.rescale(image=lowerCamelCase ,   scale=lowerCamelCase ,   offset=lowerCamelCase      )

         if do_normalize:
            __a				     =    self.normalize(image=lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase      )

         __a				     =    to_channel_dimension_format(lowerCamelCase ,   lowerCamelCase      )
         return image






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   **lowerCamelCase ,   )							->PIL.Image.Image:



         '''simple docstring'''
         __a				     =    do_resize if do_resize is not None else self.do_resize
         __a				     =    resample if resample is not None else self.resample
         __a				     =    do_center_crop if do_center_crop is not None else self.do_center_crop
         __a				     =    do_rescale if do_rescale is not None else self.do_rescale
         __a				     =    rescale_factor if rescale_factor is not None else self.rescale_factor
         __a				     =    offset if offset is not None else self.offset
         __a				     =    do_normalize if do_normalize is not None else self.do_normalize
         __a				     =    image_mean if image_mean is not None else self.image_mean
         __a				     =    image_std if image_std is not None else self.image_std

         __a				     =    size if size is not None else self.size
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else self.crop_size
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

         if not valid_images(lowerCamelCase      ):
            raise ValueError(
                'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                'torch.Tensor, tf.Tensor or jax.ndarray.'      )

         __a				     =    make_batched(lowerCamelCase      )

         __a				     =    [
             [
                 self._preprocess_image(
                     image=lowerCamelCase ,   do_resize=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase ,   do_center_crop=lowerCamelCase ,   crop_size=lowerCamelCase ,   do_rescale=lowerCamelCase ,   rescale_factor=lowerCamelCase ,   offset=lowerCamelCase ,   do_normalize=lowerCamelCase ,   image_mean=lowerCamelCase ,   image_std=lowerCamelCase ,   data_format=lowerCamelCase ,   )
                 for img in video
             ]
             for video in videos
         ]

         __a				     =    {'pixel_values': videos}
         return BatchFeature(data=lowerCamelCase ,   tensor_type=lowerCamelCase      )