



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


__snake_case						:Optional[int]      		=     logging.get_logger(__name__)

__snake_case						:Optional[Any]      		=     {
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[int]											=						'''table-transformer'''
       UpperCamelCase__  :    Tuple											=						['''past_key_values''']
       UpperCamelCase__  :    List[str]											=						{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
       }



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=None     ,		__SCREAMING_SNAKE_CASE							:     Tuple=3     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=100     ,		__SCREAMING_SNAKE_CASE							:     Tuple=6     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2_048     ,		__SCREAMING_SNAKE_CASE							:     Any=8     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=6     ,		__SCREAMING_SNAKE_CASE							:     str=2_048     ,		__SCREAMING_SNAKE_CASE							:     Tuple=8     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.0     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=0.0     ,		__SCREAMING_SNAKE_CASE							:     int=True     ,		__SCREAMING_SNAKE_CASE							:     Dict="relu"     ,		__SCREAMING_SNAKE_CASE							:     int=256     ,		__SCREAMING_SNAKE_CASE							:     Tuple=0.1     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.0     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0.0     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.02     ,		__SCREAMING_SNAKE_CASE							:     Any=1.0     ,		__SCREAMING_SNAKE_CASE							:     Tuple=False     ,		__SCREAMING_SNAKE_CASE							:     Dict="sine"     ,		__SCREAMING_SNAKE_CASE							:     str="resnet50"     ,		__SCREAMING_SNAKE_CASE							:     Tuple=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=False     ,		__SCREAMING_SNAKE_CASE							:     str=1     ,		__SCREAMING_SNAKE_CASE							:     Dict=5     ,		__SCREAMING_SNAKE_CASE							:     str=2     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=5     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=2     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.1     ,		**__SCREAMING_SNAKE_CASE							:     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(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
                   __a						       =				backbone_config.get('''model_type''')
                   __a						       =				CONFIG_MAPPING[backbone_model_type]
                   __a						       =				config_class.from_dict(__SCREAMING_SNAKE_CASE)
               # 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=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)



       @property
       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           return self.encoder_attention_heads





       @property
       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           return self.d_model


class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Union[str, Any]											=						version.parse('''1.11'''    )



       @property
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

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



       @property
       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           return 1E-5





       @property
       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           return 12










import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length, 2)   , _UpperCAmelCase				)
    else:
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length)   , _UpperCAmelCase				)

    for i, tensor in enumerate(_UpperCAmelCase				):
        if padding_side == "right":
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]
        else:
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]

    return out_tensor.tolist()





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				ord(_UpperCAmelCase				)
    if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
        return True
    __a						       =				unicodedata.category(_UpperCAmelCase				)
    if cat.startswith('''P'''				):
        return True
    return False

@dataclass
class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    PreTrainedTokenizerBase
       UpperCamelCase__  :    Union[bool, str, PaddingStrategy]											=						True
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    int											=						-100
       UpperCamelCase__  :    str											=						"pt"



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]):


           '''simple docstring'''

           import torch

           __a						       =				'''label''' if '''label''' in features[0].keys() else '''labels'''
           __a						       =				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
           __a						       =				self.tokenizer.pad(
               __SCREAMING_SNAKE_CASE     ,		padding=self.padding     ,		max_length=self.max_length     ,		pad_to_multiple_of=self.pad_to_multiple_of     ,		return_tensors='''pt''' if labels is None else None     ,		)

           if labels is None:
               return batch

           __a						       =				torch.tensor(batch['''entity_ids''']).shape[1]
           __a						       =				self.tokenizer.padding_side
           if padding_side == "right":
               __a						       =				[
                   list(__SCREAMING_SNAKE_CASE) + [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) for label in labels
               ]
           else:
               __a						       =				[
                   [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) + list(__SCREAMING_SNAKE_CASE) for label in labels
               ]

           __a						       =				[feature['''ner_tags'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		-1     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				[feature['''original_entity_spans'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		(-1, -1)     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				{k: torch.tensor(__SCREAMING_SNAKE_CASE     ,		dtype=torch.intaa) for k, v in batch.items()}

           return batch





