






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase__							  =						logging.get_logger(__name__)

UpperCAmelCase__							  =						{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    '''table-transformer'''
      __snake_case	    =    ['''past_key_values''']
      __snake_case	    =    {
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''encoder_attention_heads''',
      }







      def __init__( self :		Optional[Any]					,   __UpperCAmelCase :		Tuple=True					,   __UpperCAmelCase :		Optional[Any]=None					,   __UpperCAmelCase :		Union[str, Any]=3					,   __UpperCAmelCase :		Dict=100					,   __UpperCAmelCase :		Optional[Any]=6					,   __UpperCAmelCase :		Tuple=2_048					,   __UpperCAmelCase :		List[Any]=8					,   __UpperCAmelCase :		Tuple=6					,   __UpperCAmelCase :		Dict=2_048					,   __UpperCAmelCase :		str=8					,   __UpperCAmelCase :		int=0.0					,   __UpperCAmelCase :		List[Any]=0.0					,   __UpperCAmelCase :		int=True					,   __UpperCAmelCase :		str="relu"					,   __UpperCAmelCase :		Tuple=256					,   __UpperCAmelCase :		Optional[int]=0.1					,   __UpperCAmelCase :		int=0.0					,   __UpperCAmelCase :		Tuple=0.0					,   __UpperCAmelCase :		Tuple=0.02					,   __UpperCAmelCase :		List[Any]=1.0					,   __UpperCAmelCase :		List[Any]=False					,   __UpperCAmelCase :		int="sine"					,   __UpperCAmelCase :		List[str]="resnet50"					,   __UpperCAmelCase :		Union[str, Any]=True					,   __UpperCAmelCase :		Dict=False					,   __UpperCAmelCase :		str=1					,   __UpperCAmelCase :		Union[str, Any]=5					,   __UpperCAmelCase :		Dict=2					,   __UpperCAmelCase :		List[Any]=1					,   __UpperCAmelCase :		Any=1					,   __UpperCAmelCase :		Optional[Any]=5					,   __UpperCAmelCase :		List[str]=2					,   __UpperCAmelCase :		List[Any]=0.1					,   **__UpperCAmelCase :		Any					,   )       ->str:




       """simple docstring"""


       if backbone_config is not None and use_timm_backbone:
        raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''    )

       if not use_timm_backbone:
        if backbone_config is None:
         logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''    )
         a						    = CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']    )
        elif isinstance(__UpperCAmelCase					,   __UpperCAmelCase    ):
         a						    = backbone_config.get('''model_type'''    )
         a						    = CONFIG_MAPPING[backbone_model_type]
         a						    = config_class.from_dict(__UpperCAmelCase    )
        # set timm attributes to None
        a    ,	a    ,	a						    = None, None, None

       a						    = use_timm_backbone
       a						    = backbone_config
       a						    = num_channels
       a						    = num_queries
       a						    = d_model
       a						    = encoder_ffn_dim
       a						    = encoder_layers
       a						    = encoder_attention_heads
       a						    = decoder_ffn_dim
       a						    = decoder_layers
       a						    = decoder_attention_heads
       a						    = dropout
       a						    = attention_dropout
       a						    = activation_dropout
       a						    = activation_function
       a						    = init_std
       a						    = init_xavier_std
       a						    = encoder_layerdrop
       a						    = decoder_layerdrop
       a						    = encoder_layers
       a						    = auxiliary_loss
       a						    = position_embedding_type
       a						    = backbone
       a						    = use_pretrained_backbone
       a						    = dilation
       # Hungarian matcher
       a						    = class_cost
       a						    = bbox_cost
       a						    = giou_cost
       # Loss coefficients
       a						    = mask_loss_coefficient
       a						    = dice_loss_coefficient
       a						    = bbox_loss_coefficient
       a						    = giou_loss_coefficient
       a						    = eos_coefficient
       super().__init__(is_encoder_decoder=__UpperCAmelCase					,   **__UpperCAmelCase    )







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->int:




       """simple docstring"""


       return self.encoder_attention_heads







      @property
      def  __lowerCAmelCase   ( self :		List[Any]    )       ->int:




       """simple docstring"""


       return self.d_model






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    version.parse('''1.11'''  )







      @property
      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->Mapping[str, Mapping[int, str]]:




       """simple docstring"""


       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ]    )







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->float:




       """simple docstring"""


       return 1e-5







      @property
      def  __lowerCAmelCase   ( self :		int    )       ->int:




       """simple docstring"""


       return 12










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__							  =						logging.getLogger(__name__)






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    42
      __snake_case	    =    42
      __snake_case	    =    42






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    42
      __snake_case	    =    42
      __snake_case	    =    None
      __snake_case	    =    None






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    '''train'''
      __snake_case	    =    '''dev'''
      __snake_case	    =    '''test'''






class 						lowercase_   :






      '''simple docstring'''




      @staticmethod
      def  __lowerCAmelCase   ( __UpperCAmelCase :		int					,   __UpperCAmelCase :		Union[Split, str]    )       ->List[InputExample]:




       """simple docstring"""


       raise NotImplementedError







      @staticmethod
      def  __lowerCAmelCase   ( __UpperCAmelCase :		str    )       ->List[str]:




       """simple docstring"""


       raise NotImplementedError







      @staticmethod
      def  __lowerCAmelCase   ( __UpperCAmelCase :		List[InputExample]					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		PreTrainedTokenizer					,   __UpperCAmelCase :		Tuple=False					,   __UpperCAmelCase :		str="[CLS]"					,   __UpperCAmelCase :		str=1					,   __UpperCAmelCase :		List[str]="[SEP]"					,   __UpperCAmelCase :		Union[str, Any]=False					,   __UpperCAmelCase :		Tuple=False					,   __UpperCAmelCase :		List[str]=0					,   __UpperCAmelCase :		int=0					,   __UpperCAmelCase :		Tuple=-100					,   __UpperCAmelCase :		Optional[int]=0					,   __UpperCAmelCase :		Any=True					,   )       ->List[InputFeatures]:




       """simple docstring"""


       a						    = {label: i for i, label in enumerate(__UpperCAmelCase    )}

       a						    = []
       for ex_index, example in enumerate(__UpperCAmelCase    ):
        if ex_index % 10_000 == 0:
         logger.info('''Writing example %d of %d'''					,   __UpperCAmelCase					,   len(__UpperCAmelCase    )    )

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

         # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
         if len(__UpperCAmelCase    ) > 0:
          tokens.extend(__UpperCAmelCase    )
          # 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(__UpperCAmelCase    ) - 1)    )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
        a						    = tokenizer.num_special_tokens_to_add()
        if len(__UpperCAmelCase    ) > 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(__UpperCAmelCase    )

        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(__UpperCAmelCase    )

        # 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(__UpperCAmelCase    )

        # Zero-pad up to the sequence length.
        a						    = max_seq_length - len(__UpperCAmelCase    )
        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(__UpperCAmelCase    ) == max_seq_length
        assert len(__UpperCAmelCase    ) == max_seq_length
        assert len(__UpperCAmelCase    ) == max_seq_length
        assert len(__UpperCAmelCase    ) == max_seq_length

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

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

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


if is_torch_available():
     import torch
     from torch import nn
     from torch.utils.data import Dataset






     class 						lowercase_   (      lowercase  ):






           '''simple docstring'''




           __snake_case	    =    42
           __snake_case	    =    nn.CrossEntropyLoss().ignore_index







           def __init__( self :		str					,   __UpperCAmelCase :		TokenClassificationTask					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		PreTrainedTokenizer					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[int] = None					,   __UpperCAmelCase :		Any=False					,   __UpperCAmelCase :		Split = Split.train					,   )       ->List[str]:




            """simple docstring"""


            a						    = os.path.join(
                __UpperCAmelCase					,   '''cached_{}_{}_{}'''.format(mode.value					,   tokenizer.__class__.__name__					,   str(__UpperCAmelCase    )    )					,   )

            # 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(__UpperCAmelCase    ):
             if os.path.exists(__UpperCAmelCase    ) and not overwrite_cache:
              logger.info(F"""Loading features from cached file {cached_features_file}"""    )
              a						    = torch.load(__UpperCAmelCase    )
             else:
              logger.info(F"""Creating features from dataset file at {data_dir}"""    )
              a						    = token_classification_task.read_examples_from_file(__UpperCAmelCase					,   __UpperCAmelCase    )
              # TODO clean up all this to leverage built-in features of tokenizers
              a						    = token_classification_task.convert_examples_to_features(
                  __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   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=__UpperCAmelCase					,   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					,   __UpperCAmelCase    )







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




            """simple docstring"""


            return len(self.features    )







           def __getitem__( self :		List[Any]					,   __UpperCAmelCase :		Dict    )       ->InputFeatures:




            """simple docstring"""


            return self.features[i]


if is_tf_available():
     import tensorflow as tf






     class 						lowercase_   :






           '''simple docstring'''




           __snake_case	    =    42
           __snake_case	    =    -1_00







           def __init__( self :		Any					,   __UpperCAmelCase :		TokenClassificationTask					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		PreTrainedTokenizer					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[int] = None					,   __UpperCAmelCase :		List[Any]=False					,   __UpperCAmelCase :		Split = Split.train					,   )       ->Optional[int]:




            """simple docstring"""


            a						    = token_classification_task.read_examples_from_file(__UpperCAmelCase					,   __UpperCAmelCase    )
            # TODO clean up all this to leverage built-in features of tokenizers
            a						    = token_classification_task.convert_examples_to_features(
                __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   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=__UpperCAmelCase					,   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(
                 __UpperCAmelCase					,   ({'''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(
                 __UpperCAmelCase					,   ({'''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  __lowerCAmelCase   ( self :		str    )       ->Optional[int]:




            """simple docstring"""


            a						    = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features    )    )    )

            return self.dataset







           def __len__( self :		List[str]    )       ->Dict:




            """simple docstring"""


            return len(self.features    )







           def __getitem__( self :		Tuple					,   __UpperCAmelCase :		List[Any]    )       ->InputFeatures:




            """simple docstring"""


            return self.features[i]


