


from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class   A__ (   _lowerCamelCase     ):







    """simple docstring"""

    __magic_name__        =		DistilBertTokenizer
    __magic_name__        =		DistilBertTokenizerFast
    __magic_name__        =		True
    @slow
    def 		a_      (	self   ):
         snake_case					=    DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''   )

         snake_case					=    tokenizer.encode('''sequence builders'''      , add_special_tokens=__snake_case   )
         snake_case					=    tokenizer.encode('''multi-sequence build'''      , add_special_tokens=__snake_case   )

         snake_case					=    tokenizer.build_inputs_with_special_tokens(__snake_case   )
         snake_case					=    tokenizer.build_inputs_with_special_tokens(__snake_case      , __snake_case   )

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]








import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Any      				=   {
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'detr'
    __magic_name__        =		['past_key_values']
    __magic_name__        =		{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }
    def __init__(	self      , __snake_case=True      , __snake_case=None      , __snake_case=3      , __snake_case=1_0_0      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=True      , __snake_case="relu"      , __snake_case=2_5_6      , __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      , ):
         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.'''   )
                   snake_case					=    CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
              elif isinstance(__snake_case      , __snake_case   ):
                   snake_case					=    backbone_config.get('''model_type'''   )
                   snake_case					=    CONFIG_MAPPING[backbone_model_type]
                   snake_case					=    config_class.from_dict(__snake_case   )
              # set timm attributes to None
              snake_case			,  snake_case			,  snake_case					=    None, None, None

         snake_case					=    use_timm_backbone
         snake_case					=    backbone_config
         snake_case					=    num_channels
         snake_case					=    num_queries
         snake_case					=    d_model
         snake_case					=    encoder_ffn_dim
         snake_case					=    encoder_layers
         snake_case					=    encoder_attention_heads
         snake_case					=    decoder_ffn_dim
         snake_case					=    decoder_layers
         snake_case					=    decoder_attention_heads
         snake_case					=    dropout
         snake_case					=    attention_dropout
         snake_case					=    activation_dropout
         snake_case					=    activation_function
         snake_case					=    init_std
         snake_case					=    init_xavier_std
         snake_case					=    encoder_layerdrop
         snake_case					=    decoder_layerdrop
         snake_case					=    encoder_layers
         snake_case					=    auxiliary_loss
         snake_case					=    position_embedding_type
         snake_case					=    backbone
         snake_case					=    use_pretrained_backbone
         snake_case					=    dilation
         # Hungarian matcher
         snake_case					=    class_cost
         snake_case					=    bbox_cost
         snake_case					=    giou_cost
         # Loss coefficients
         snake_case					=    mask_loss_coefficient
         snake_case					=    dice_loss_coefficient
         snake_case					=    bbox_loss_coefficient
         snake_case					=    giou_loss_coefficient
         snake_case					=    eos_coefficient
         super().__init__(is_encoder_decoder=__snake_case      , **__snake_case   )
    @property
    def 		a_      (	self   ):
         return self.encoder_attention_heads
    @property
    def 		a_      (	self   ):
         return self.d_model
    @classmethod
    def 		a_      (	cls      , __snake_case      , **__snake_case   ):
         return cls(backbone_config=__snake_case      , **__snake_case   )




    def 		a_      (	self   ):
         snake_case					=    copy.deepcopy(self.__dict__   )
         if output["backbone_config"] is not None:
              snake_case					=    self.backbone_config.to_dict()
         snake_case					=    self.__class__.model_type
         return output






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]   )
    @property
    def 		a_      (	self   ):
         return 1E-5




    @property
    def 		a_      (	self   ):
         return 1_2




