






import copy

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


__SCREAMING_SNAKE_CASE						:   Tuple         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    '''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_   (					a__		):
							_lowerCamelCase						=		'deformable_detr'
							_lowerCamelCase						=		{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}



							def __init__(						self				,    lowercase_=True				,    lowercase_=None				,    lowercase_=3				,    lowercase_=300				,    lowercase_=1_024				,    lowercase_=6				,    lowercase_=1_024				,    lowercase_=8				,    lowercase_=6				,    lowercase_=1_024				,    lowercase_=8				,    lowercase_=0.0				,    lowercase_=True				,    lowercase_="relu"				,    lowercase_=256				,    lowercase_=0.1				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=0.02				,    lowercase_=1.0				,    lowercase_=True				,    lowercase_=False				,    lowercase_="sine"				,    lowercase_="resnet50"				,    lowercase_=True				,    lowercase_=False				,    lowercase_=4				,    lowercase_=4				,    lowercase_=4				,    lowercase_=False				,    lowercase_=300				,    lowercase_=False				,    lowercase_=1				,    lowercase_=5				,    lowercase_=2				,    lowercase_=1				,    lowercase_=1				,    lowercase_=5				,    lowercase_=2				,    lowercase_=0.1				,    lowercase_=0.25				,    lowercase_=False				,    **lowercase_				,    ):
													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						:					Union[str, Any]    =   CONFIG_MAPPING['''resnet'''](out_features=["stage4"]    )
																			elif isinstance(_lowerCamelCase				,    _lowerCamelCase    ):
																									_snake_case						:					Union[str, Any]    =   backbone_config.get("model_type"    )
																									_snake_case						:					Optional[Any]    =   CONFIG_MAPPING[backbone_model_type]
																									_snake_case						:					Tuple    =   config_class.from_dict(_lowerCamelCase    )
													_snake_case						:					List[Any]    =   use_timm_backbone
													_snake_case						:					Tuple    =   backbone_config
													_snake_case						:					int    =   num_channels
													_snake_case						:					Optional[int]    =   num_queries
													_snake_case						:					Any    =   max_position_embeddings
													_snake_case						:					Optional[Any]    =   d_model
													_snake_case						:					List[str]    =   encoder_ffn_dim
													_snake_case						:					int    =   encoder_layers
													_snake_case						:					Union[str, Any]    =   encoder_attention_heads
													_snake_case						:					int    =   decoder_ffn_dim
													_snake_case						:					Tuple    =   decoder_layers
													_snake_case						:					Union[str, Any]    =   decoder_attention_heads
													_snake_case						:					int    =   dropout
													_snake_case						:					List[str]    =   attention_dropout
													_snake_case						:					Dict    =   activation_dropout
													_snake_case						:					Union[str, Any]    =   activation_function
													_snake_case						:					Tuple    =   init_std
													_snake_case						:					Dict    =   init_xavier_std
													_snake_case						:					str    =   encoder_layerdrop
													_snake_case						:					List[str]    =   auxiliary_loss
													_snake_case						:					List[str]    =   position_embedding_type
													_snake_case						:					Tuple    =   backbone
													_snake_case						:					Optional[int]    =   use_pretrained_backbone
													_snake_case						:					List[Any]    =   dilation
													# deformable attributes
													_snake_case						:					Union[str, Any]    =   num_feature_levels
													_snake_case						:					Any    =   encoder_n_points
													_snake_case						:					List[Any]    =   decoder_n_points
													_snake_case						:					List[Any]    =   two_stage
													_snake_case						:					str    =   two_stage_num_proposals
													_snake_case						:					str    =   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
													_snake_case						:					List[str]    =   class_cost
													_snake_case						:					Dict    =   bbox_cost
													_snake_case						:					Optional[Any]    =   giou_cost
													# Loss coefficients
													_snake_case						:					List[str]    =   mask_loss_coefficient
													_snake_case						:					List[Any]    =   dice_loss_coefficient
													_snake_case						:					Optional[Any]    =   bbox_loss_coefficient
													_snake_case						:					List[str]    =   giou_loss_coefficient
													_snake_case						:					str    =   eos_coefficient
													_snake_case						:					Dict    =   focal_alpha
													_snake_case						:					str    =   disable_custom_kernels
													super().__init__(is_encoder_decoder=_lowerCamelCase				,    **_lowerCamelCase    )



							@property
							def 	UpperCamelCase	(						self    ):
													return self.encoder_attention_heads



							@property
							def 	UpperCamelCase	(						self    ):
													return self.d_model



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   copy.deepcopy(self.__dict__    )
													if self.backbone_config is not None:
																			_snake_case						:					Optional[Any]    =   self.backbone_config.to_dict()
													_snake_case						:					List[Any]    =   self.__class__.model_type
													return output
import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   "laion/clap-htsat-unfused"
													_snake_case						:					Dict    =   tempfile.mkdtemp()



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													return RobertaTokenizer.from_pretrained(self.checkpoint				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													return ClapFeatureExtractor.from_pretrained(self.checkpoint				,    **lowercase_    )



							def 	UpperCamelCase	(						self    ):
													shutil.rmtree(self.tmpdirname    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   self.get_tokenizer()
													_snake_case						:					List[Any]    =   self.get_feature_extractor()

													_snake_case						:					Optional[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													processor.save_pretrained(self.tmpdirname    )
													_snake_case						:					Tuple    =   ClapProcessor.from_pretrained(self.tmpdirname    )

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer.get_vocab()    )
													self.assertIsInstance(processor.tokenizer				,    lowercase_    )

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor.to_json_string()    )
													self.assertIsInstance(processor.feature_extractor				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   ClapProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()    )
													processor.save_pretrained(self.tmpdirname    )

													_snake_case						:					Any    =   self.get_tokenizer(bos_token="(BOS)"				,    eos_token="(EOS)"    )
													_snake_case						:					List[Any]    =   self.get_feature_extractor(do_normalize=lowercase_				,    padding_value=1.0    )

													_snake_case						:					List[Any]    =   ClapProcessor.from_pretrained(
													    self.tmpdirname				,    bos_token="(BOS)"				,    eos_token="(EOS)"				,    do_normalize=lowercase_				,    padding_value=1.0    )

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer_add_kwargs.get_vocab()    )
													self.assertIsInstance(processor.tokenizer				,    lowercase_    )

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor_add_kwargs.to_json_string()    )
													self.assertIsInstance(processor.feature_extractor				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   self.get_feature_extractor()
													_snake_case						:					Union[str, Any]    =   self.get_tokenizer()

													_snake_case						:					Optional[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					List[str]    =   floats_list((3, 1_000)    )

													_snake_case						:					Union[str, Any]    =   feature_extractor(lowercase_				,    return_tensors="np"    )
													_snake_case						:					Any    =   processor(audios=lowercase_				,    return_tensors="np"    )

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()				,    input_processor[key].sum()				,    delta=1e-2    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.get_feature_extractor()
													_snake_case						:					Optional[Any]    =   self.get_tokenizer()

													_snake_case						:					Dict    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					Any    =   "This is a test string"

													_snake_case						:					Optional[Any]    =   processor(text=lowercase_    )

													_snake_case						:					Optional[Any]    =   tokenizer(lowercase_    )

													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]				,    encoded_processor[key]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.get_feature_extractor()
													_snake_case						:					Dict    =   self.get_tokenizer()

													_snake_case						:					List[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					Dict    =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													_snake_case						:					List[Any]    =   processor.batch_decode(lowercase_    )
													_snake_case						:					Optional[int]    =   tokenizer.batch_decode(lowercase_    )

													self.assertListEqual(lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   self.get_feature_extractor()
													_snake_case						:					str    =   self.get_tokenizer()

													_snake_case						:					Optional[int]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													self.assertListEqual(
													    processor.model_input_names[2:]				,    feature_extractor.model_input_names				,    msg="`processor` and `feature_extractor` model input names do not match"				,    )