


import copy

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


A      = logging.get_logger(__name__)

A      = {
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}


class     lowercase__  (					SCREAMING_SNAKE_CASE_		):
		A__=  'deformable_detr'
		A__=  {
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		}






		def __init__(					self      :					Dict		,						_lowercase      :					Optional[int]=True		,						_lowercase      :					Any=None		,						_lowercase      :					List[str]=3		,						_lowercase      :					Optional[int]=3_00		,						_lowercase      :					Union[str, Any]=10_24		,						_lowercase      :					Union[str, Any]=6		,						_lowercase      :					Any=10_24		,						_lowercase      :					List[str]=8		,						_lowercase      :					Tuple=6		,						_lowercase      :					Optional[int]=10_24		,						_lowercase      :					int=8		,						_lowercase      :					Tuple=0.0		,						_lowercase      :					List[str]=True		,						_lowercase      :					Tuple="relu"		,						_lowercase      :					Tuple=2_56		,						_lowercase      :					Optional[Any]=0.1		,						_lowercase      :					int=0.0		,						_lowercase      :					Dict=0.0		,						_lowercase      :					Optional[Any]=0.0_2		,						_lowercase      :					Optional[Any]=1.0		,						_lowercase      :					Tuple=True		,						_lowercase      :					Any=False		,						_lowercase      :					Union[str, Any]="sine"		,						_lowercase      :					List[str]="resnet50"		,						_lowercase      :					Optional[Any]=True		,						_lowercase      :					Any=False		,						_lowercase      :					List[Any]=4		,						_lowercase      :					List[str]=4		,						_lowercase      :					Tuple=4		,						_lowercase      :					Any=False		,						_lowercase      :					Tuple=3_00		,						_lowercase      :					Tuple=False		,						_lowercase      :					Optional[Any]=1		,						_lowercase      :					Tuple=5		,						_lowercase      :					List[Any]=2		,						_lowercase      :					str=1		,						_lowercase      :					str=1		,						_lowercase      :					List[Any]=5		,						_lowercase      :					str=2		,						_lowercase      :					List[str]=0.1		,						_lowercase      :					List[str]=0.2_5		,						_lowercase      :					Optional[Any]=False		,						**_lowercase      :					Dict		,						):




									"""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." )
																							UpperCAmelCase__										=     CONFIG_MAPPING['''resnet'''](out_features=["stage4"] )
																elif isinstance(UpperCamelCase__		,						UpperCamelCase__ ):
																							UpperCAmelCase__										=     backbone_config.get("model_type" )
																							UpperCAmelCase__										=     CONFIG_MAPPING[backbone_model_type]
																							UpperCAmelCase__										=     config_class.from_dict(UpperCamelCase__ )
									UpperCAmelCase__										=     use_timm_backbone
									UpperCAmelCase__										=     backbone_config
									UpperCAmelCase__										=     num_channels
									UpperCAmelCase__										=     num_queries
									UpperCAmelCase__										=     max_position_embeddings
									UpperCAmelCase__										=     d_model
									UpperCAmelCase__										=     encoder_ffn_dim
									UpperCAmelCase__										=     encoder_layers
									UpperCAmelCase__										=     encoder_attention_heads
									UpperCAmelCase__										=     decoder_ffn_dim
									UpperCAmelCase__										=     decoder_layers
									UpperCAmelCase__										=     decoder_attention_heads
									UpperCAmelCase__										=     dropout
									UpperCAmelCase__										=     attention_dropout
									UpperCAmelCase__										=     activation_dropout
									UpperCAmelCase__										=     activation_function
									UpperCAmelCase__										=     init_std
									UpperCAmelCase__										=     init_xavier_std
									UpperCAmelCase__										=     encoder_layerdrop
									UpperCAmelCase__										=     auxiliary_loss
									UpperCAmelCase__										=     position_embedding_type
									UpperCAmelCase__										=     backbone
									UpperCAmelCase__										=     use_pretrained_backbone
									UpperCAmelCase__										=     dilation
									# deformable attributes
									UpperCAmelCase__										=     num_feature_levels
									UpperCAmelCase__										=     encoder_n_points
									UpperCAmelCase__										=     decoder_n_points
									UpperCAmelCase__										=     two_stage
									UpperCAmelCase__										=     two_stage_num_proposals
									UpperCAmelCase__										=     with_box_refine
									if two_stage is True and with_box_refine is False:
																raise ValueError("If two_stage is True, with_box_refine must be True." )
									# Hungarian matcher
									UpperCAmelCase__										=     class_cost
									UpperCAmelCase__										=     bbox_cost
									UpperCAmelCase__										=     giou_cost
									# Loss coefficients
									UpperCAmelCase__										=     mask_loss_coefficient
									UpperCAmelCase__										=     dice_loss_coefficient
									UpperCAmelCase__										=     bbox_loss_coefficient
									UpperCAmelCase__										=     giou_loss_coefficient
									UpperCAmelCase__										=     eos_coefficient
									UpperCAmelCase__										=     focal_alpha
									UpperCAmelCase__										=     disable_custom_kernels
									super().__init__(is_encoder_decoder=UpperCamelCase__		,						**UpperCamelCase__ )






		@property
		def 			_UpperCAmelCase  (					self      :					int ):




									"""simple docstring"""







									return self.encoder_attention_heads






		@property
		def 			_UpperCAmelCase  (					self      :					int ):




									"""simple docstring"""







									return self.d_model






		def 			_UpperCAmelCase  (					self      :					Dict ):




									"""simple docstring"""







									UpperCAmelCase__										=     copy.deepcopy(self.__dict__ )
									if self.backbone_config is not None:
																UpperCAmelCase__										=     self.backbone_config.to_dict()
									UpperCAmelCase__										=     self.__class__.model_type
									return output









import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


A      = get_tests_dir("fixtures/test_sentencepiece_bpe.model")


class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  BartphoTokenizer
  A__=  False
  A__=  True






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         super().setUp()

         UpperCAmelCase__										=     ["▁This", "▁is", "▁a", "▁t", "est"]
         UpperCAmelCase__										=     dict(zip(_lowercase		,						range(len(_lowercase ) ) ) )
         UpperCAmelCase__										=     {"unk_token": "<unk>"}

         UpperCAmelCase__										=     os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["monolingual_vocab_file"] )
         with open(self.monolingual_vocab_file		,						"w"		,						encoding="utf-8" ) as fp:
                for token in vocab_tokens:
                       fp.write(F"""{token} {vocab_tokens[token]}\n""" )

         UpperCAmelCase__										=     BartphoTokenizer(_lowercase		,						self.monolingual_vocab_file		,						**self.special_tokens_map )
         tokenizer.save_pretrained(self.tmpdirname )






  def 			_UpperCAmelCase  (					self      :					Dict		,						**_lowercase      :					Optional[Any] ):




         """simple docstring"""







         kwargs.update(self.special_tokens_map )
         return BartphoTokenizer.from_pretrained(self.tmpdirname		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     "This is a là test"
         UpperCAmelCase__										=     "This is a<unk><unk> test"
         return input_text, output_text






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     BartphoTokenizer(_lowercase		,						self.monolingual_vocab_file		,						**self.special_tokens_map )
         UpperCAmelCase__										=     "This is a là test"
         UpperCAmelCase__										=     "▁This ▁is ▁a ▁l à ▁t est".split()
         UpperCAmelCase__										=     tokenizer.tokenize(_lowercase )
         self.assertListEqual(_lowercase		,						_lowercase )

         UpperCAmelCase__										=     tokens + [tokenizer.unk_token]
         UpperCAmelCase__										=     [4, 5, 6, 3, 3, 7, 8, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase )		,						_lowercase )





