
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


A     :	Tuple    = logging.get_logger(__name__)

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







class 			__A( a			):
				snake_case_						   =    '''table-transformer'''
				snake_case_						   =    ['''past_key_values''']
				snake_case_						   =    {
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}




				def __init__(     self		,     _snake_case=True		,     _snake_case=None		,     _snake_case=3		,     _snake_case=100		,     _snake_case=6		,     _snake_case=2_048		,     _snake_case=8		,     _snake_case=6		,     _snake_case=2_048		,     _snake_case=8		,     _snake_case=0.0		,     _snake_case=0.0		,     _snake_case=True		,     _snake_case="relu"		,     _snake_case=256		,     _snake_case=0.1		,     _snake_case=0.0		,     _snake_case=0.0		,     _snake_case=0.02		,     _snake_case=1.0		,     _snake_case=False		,     _snake_case="sine"		,     _snake_case="resnet50"		,     _snake_case=True		,     _snake_case=False		,     _snake_case=1		,     _snake_case=5		,     _snake_case=2		,     _snake_case=1		,     _snake_case=1		,     _snake_case=5		,     _snake_case=2		,     _snake_case=0.1		,     **_snake_case		,     )    ->      List[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(_snake_case		,     _snake_case				):
																__a										=    backbone_config.get('''model_type'''				)
																__a										=    CONFIG_MAPPING[backbone_model_type]
																__a										=    config_class.from_dict(_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=_snake_case		,     **_snake_case				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return self.encoder_attention_heads




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return self.d_model







class 			__A( a			):
				snake_case_						   =    version.parse('''1.11'''			)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 1E-5




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return 12







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase  							=					{
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

lowercase  							=					{
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


lowercase  							=					{
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= VOCAB_FILES_NAMES
	lowerCAmelCase													= PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase													= PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase													= SqueezeBertTokenizer




	def __init__(       self       ,			a=None       ,			a=None       ,			a=True       ,			a="[UNK]"       ,			a="[SEP]"       ,			a="[PAD]"       ,			a="[CLS]"       ,			a="[MASK]"       ,			a=True       ,			a=None       ,			**a       ,			)       ->    Tuple:
								super().__init__(
								    a       ,			tokenizer_file=a       ,			do_lower_case=a       ,			unk_token=a       ,			sep_token=a       ,			pad_token=a       ,			cls_token=a       ,			mask_token=a       ,			tokenize_chinese_chars=a       ,			strip_accents=a       ,			**a       ,			)

								snake_case_				=							json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
								if (
								    normalizer_state.get('lowercase'       ,			a					) != do_lower_case
								    or normalizer_state.get('strip_accents'       ,			a					) != strip_accents
								    or normalizer_state.get('handle_chinese_chars'       ,			a					) != tokenize_chinese_chars
								):
															snake_case_				=							getattr(a       ,			normalizer_state.pop('type'					)					)
															snake_case_				=							do_lower_case
															snake_case_				=							strip_accents
															snake_case_				=							tokenize_chinese_chars
															snake_case_				=							normalizer_class(**a					)

								snake_case_				=							do_lower_case




	def 							_UpperCamelCase			(       self       ,			a       ,			a=None					)       ->    Tuple:
								snake_case_				=							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

								return output




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								snake_case_				=							[self.sep_token_id]
								snake_case_				=							[self.cls_token_id]
								if token_ids_a is None:
															return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    Tuple[str]:
								snake_case_				=							self._tokenizer.model.save(a       ,			name=a					)
								return tuple(a					)





