




"""simple docstring"""

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


_lowerCamelCase  :			Tuple        =       logging.get_logger(__name__)

_lowerCamelCase  :			List[str]        =       {
    '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        lowercase      (    __UpperCAmelCase		):
				__lowerCAmelCase    :    Tuple         =   """detr"""
				__lowerCAmelCase    :    List[Any]         =   ["""past_key_values"""]
				__lowerCAmelCase    :    Optional[Any]         =   {
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				}







				def __init__(							self   :					Union[str, Any]      ,     _lowerCamelCase   :					Optional[Any]=True      ,     _lowerCamelCase   :					List[str]=None      ,     _lowerCamelCase   :					Union[str, Any]=3      ,     _lowerCamelCase   :					Optional[Any]=1_00      ,     _lowerCamelCase   :					Dict=6      ,     _lowerCamelCase   :					Optional[Any]=20_48      ,     _lowerCamelCase   :					List[str]=8      ,     _lowerCamelCase   :					Optional[int]=6      ,     _lowerCamelCase   :					Tuple=20_48      ,     _lowerCamelCase   :					Optional[int]=8      ,     _lowerCamelCase   :					Tuple=0.0      ,     _lowerCamelCase   :					List[str]=0.0      ,     _lowerCamelCase   :					Tuple=True      ,     _lowerCamelCase   :					Dict="relu"      ,     _lowerCamelCase   :					Union[str, Any]=2_56      ,     _lowerCamelCase   :					str=0.1      ,     _lowerCamelCase   :					Optional[int]=0.0      ,     _lowerCamelCase   :					int=0.0      ,     _lowerCamelCase   :					Optional[int]=0.02      ,     _lowerCamelCase   :					List[str]=1.0      ,     _lowerCamelCase   :					Optional[int]=False      ,     _lowerCamelCase   :					Tuple="sine"      ,     _lowerCamelCase   :					Union[str, Any]="resnet50"      ,     _lowerCamelCase   :					Tuple=True      ,     _lowerCamelCase   :					List[Any]=False      ,     _lowerCamelCase   :					Optional[int]=1      ,     _lowerCamelCase   :					List[str]=5      ,     _lowerCamelCase   :					Union[str, Any]=2      ,     _lowerCamelCase   :					str=1      ,     _lowerCamelCase   :					Union[str, Any]=1      ,     _lowerCamelCase   :					List[Any]=5      ,     _lowerCamelCase   :					Any=2      ,     _lowerCamelCase   :					Dict=0.1      ,     **_lowerCamelCase   :					int      ,     ):



					"""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_  :				Dict	   =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']       )
						elif isinstance(_lowerCamelCase      ,     _lowerCamelCase       ):
							A_  :				List[Any]	   =		backbone_config.get('''model_type'''       )
							A_  :				Union[str, Any]	   =		CONFIG_MAPPING[backbone_model_type]
							A_  :				int	   =		config_class.from_dict(_lowerCamelCase       )
						# set timm attributes to None
						A_  :				List[str]	   =		None, None, None

					A_  :				Any	   =		use_timm_backbone
					A_  :				int	   =		backbone_config
					A_  :				Optional[Any]	   =		num_channels
					A_  :				Any	   =		num_queries
					A_  :				Tuple	   =		d_model
					A_  :				Optional[Any]	   =		encoder_ffn_dim
					A_  :				int	   =		encoder_layers
					A_  :				Optional[int]	   =		encoder_attention_heads
					A_  :				Dict	   =		decoder_ffn_dim
					A_  :				List[str]	   =		decoder_layers
					A_  :				List[str]	   =		decoder_attention_heads
					A_  :				List[Any]	   =		dropout
					A_  :				Any	   =		attention_dropout
					A_  :				str	   =		activation_dropout
					A_  :				str	   =		activation_function
					A_  :				Tuple	   =		init_std
					A_  :				Any	   =		init_xavier_std
					A_  :				Union[str, Any]	   =		encoder_layerdrop
					A_  :				Tuple	   =		decoder_layerdrop
					A_  :				Optional[Any]	   =		encoder_layers
					A_  :				Union[str, Any]	   =		auxiliary_loss
					A_  :				List[Any]	   =		position_embedding_type
					A_  :				Optional[Any]	   =		backbone
					A_  :				Union[str, Any]	   =		use_pretrained_backbone
					A_  :				List[Any]	   =		dilation
					# Hungarian matcher
					A_  :				Any	   =		class_cost
					A_  :				Optional[int]	   =		bbox_cost
					A_  :				str	   =		giou_cost
					# Loss coefficients
					A_  :				List[Any]	   =		mask_loss_coefficient
					A_  :				Tuple	   =		dice_loss_coefficient
					A_  :				Any	   =		bbox_loss_coefficient
					A_  :				List[str]	   =		giou_loss_coefficient
					A_  :				Union[str, Any]	   =		eos_coefficient
					super().__init__(is_encoder_decoder=_lowerCamelCase      ,     **_lowerCamelCase       )







				@property
				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					return self.encoder_attention_heads







				@property
				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					return self.d_model







				@classmethod
				def 					a_  (							cls   :					Optional[int]      ,     _lowerCamelCase   :					PretrainedConfig      ,     **_lowerCamelCase   :					Dict       ):



					"""simple docstring"""

					return cls(backbone_config=_lowerCamelCase      ,     **_lowerCamelCase       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		copy.deepcopy(self.__dict__       )
					if output["backbone_config"] is not None:
						A_  :				Optional[Any]	   =		self.backbone_config.to_dict()
					A_  :				str	   =		self.__class__.model_type
					return output

class        lowercase      (    __UpperCAmelCase		):
				__lowerCAmelCase    :    Union[str, Any]         =   version.parse("""1.11"""		)







				@property
				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

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







				@property
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					return 1E-5







				@property
				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					return 12











"""simple docstring"""

import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase  :			Tuple        =       get_tests_dir('fixtures/test_sentencepiece.model')

@require_sentencepiece
@require_tokenizers
class        lowercase      (    __UpperCAmelCase     ,	unittest.TestCase):
				__lowerCAmelCase    :    Tuple         =   XLNetTokenizer
				__lowerCAmelCase    :    Optional[int]         =   XLNetTokenizerFast
				__lowerCAmelCase    :    Union[str, Any]         =   True
				__lowerCAmelCase    :    Any         =   True







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					super().setUp()

					# We have a SentencePiece fixture for testing
					A_  :				Optional[Any]	   =		XLNetTokenizer(_lowerCamelCase      ,     keep_accents=_lowerCamelCase       )
					tokenizer.sanitize_special_tokens()
					tokenizer.save_pretrained(self.tmpdirname       )







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		'''<s>'''
					A_  :				int	   =		1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase       )      ,     _lowerCamelCase       )
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase       )      ,     _lowerCamelCase       )







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				Any	   =		list(self.get_tokenizer().get_vocab().keys()       )

					self.assertEqual(vocab_keys[0]      ,     '''<unk>'''       )
					self.assertEqual(vocab_keys[1]      ,     '''<s>'''       )
					self.assertEqual(vocab_keys[-1]      ,     '''<eod>'''       )
					self.assertEqual(len(_lowerCamelCase       )      ,     10_06       )







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					self.assertEqual(self.get_tokenizer().vocab_size      ,     10_00       )







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		XLNetTokenizer(_lowerCamelCase      ,     keep_accents=_lowerCamelCase       )

					A_  :				Union[str, Any]	   =		tokenizer.tokenize('''This is a test'''       )
					self.assertListEqual(_lowerCamelCase      ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

					self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase       )      ,     [2_85, 46, 10, 1_70, 3_82]       )

					A_  :				List[str]	   =		tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        SPIECE_UNDERLINE + '''I''',
					        SPIECE_UNDERLINE + '''was''',
					        SPIECE_UNDERLINE + '''b''',
					        '''or''',
					        '''n''',
					        SPIECE_UNDERLINE + '''in''',
					        SPIECE_UNDERLINE + '''''',
					        '''9''',
					        '''2''',
					        '''0''',
					        '''0''',
					        '''0''',
					        ''',''',
					        SPIECE_UNDERLINE + '''and''',
					        SPIECE_UNDERLINE + '''this''',
					        SPIECE_UNDERLINE + '''is''',
					        SPIECE_UNDERLINE + '''f''',
					        '''al''',
					        '''s''',
					        '''é''',
					        '''.''',
					    ]      ,     )
					A_  :				int	   =		tokenizer.convert_tokens_to_ids(_lowerCamelCase       )
					self.assertListEqual(_lowerCamelCase      ,     [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]       )

					A_  :				Union[str, Any]	   =		tokenizer.convert_ids_to_tokens(_lowerCamelCase       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        SPIECE_UNDERLINE + '''I''',
					        SPIECE_UNDERLINE + '''was''',
					        SPIECE_UNDERLINE + '''b''',
					        '''or''',
					        '''n''',
					        SPIECE_UNDERLINE + '''in''',
					        SPIECE_UNDERLINE + '''''',
					        '''<unk>''',
					        '''2''',
					        '''0''',
					        '''0''',
					        '''0''',
					        ''',''',
					        SPIECE_UNDERLINE + '''and''',
					        SPIECE_UNDERLINE + '''this''',
					        SPIECE_UNDERLINE + '''is''',
					        SPIECE_UNDERLINE + '''f''',
					        '''al''',
					        '''s''',
					        '''<unk>''',
					        '''.''',
					    ]      ,     )







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		XLNetTokenizer(_lowerCamelCase      ,     do_lower_case=_lowerCamelCase       )
					A_  :				str	   =		tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        SPIECE_UNDERLINE + '''''',
					        '''i''',
					        SPIECE_UNDERLINE + '''was''',
					        SPIECE_UNDERLINE + '''b''',
					        '''or''',
					        '''n''',
					        SPIECE_UNDERLINE + '''in''',
					        SPIECE_UNDERLINE + '''''',
					        '''9''',
					        '''2''',
					        '''0''',
					        '''0''',
					        '''0''',
					        ''',''',
					        SPIECE_UNDERLINE + '''and''',
					        SPIECE_UNDERLINE + '''this''',
					        SPIECE_UNDERLINE + '''is''',
					        SPIECE_UNDERLINE + '''f''',
					        '''al''',
					        '''se''',
					        '''.''',
					    ]      ,     )
					self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''       )      ,     ['''▁he''', '''ll''', '''o''']       )







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		XLNetTokenizer(_lowerCamelCase      ,     do_lower_case=_lowerCamelCase       )
					A_  :				Tuple	   =		tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
					self.assertListEqual(
					    _lowerCamelCase      ,     [
					        SPIECE_UNDERLINE + '''I''',
					        SPIECE_UNDERLINE + '''was''',
					        SPIECE_UNDERLINE + '''b''',
					        '''or''',
					        '''n''',
					        SPIECE_UNDERLINE + '''in''',
					        SPIECE_UNDERLINE + '''''',
					        '''9''',
					        '''2''',
					        '''0''',
					        '''0''',
					        '''0''',
					        ''',''',
					        SPIECE_UNDERLINE + '''and''',
					        SPIECE_UNDERLINE + '''this''',
					        SPIECE_UNDERLINE + '''is''',
					        SPIECE_UNDERLINE + '''f''',
					        '''al''',
					        '''se''',
					        '''.''',
					    ]      ,     )







				@slow
				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				int	   =		XLNetTokenizer.from_pretrained('''xlnet-base-cased'''       )

					A_  :				Optional[int]	   =		tokenizer.encode('''sequence builders'''      ,     add_special_tokens=_lowerCamelCase       )
					A_  :				Optional[int]	   =		tokenizer.encode('''multi-sequence build'''      ,     add_special_tokens=_lowerCamelCase       )

					A_  :				Tuple	   =		tokenizer.build_inputs_with_special_tokens(_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		tokenizer.build_inputs_with_special_tokens(_lowerCamelCase      ,     _lowerCamelCase       )

					assert encoded_sentence == text + [4, 3]
					assert encoded_pair == text + [4] + text_a + [4, 3]







				@slow
				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Dict	   =		{'''input_ids''': [[17, 2_14_42, 2_70, 17, 10, 1_46_45, 3_18, 34, 17, 45_46, 31_45, 7_87, 13, 77_52, 2_20_18, 23, 21, 17, 45_46, 31_45, 7_87, 13, 33_52, 1_44_31, 13, 55_00, 11, 11_76, 5_80, 13, 1_68_19, 47_97, 23, 17, 10, 1_71_35, 6_58, 19, 4_57, 79_32, 13, 1_84, 19, 31_54, 1_71_35, 64_68, 19, 14_04, 1_22_69, 19, 42_29, 53_56, 1_62_64, 46, 19, 17, 2_05_45, 1_03_95, 9, 9, 9, 11, 28, 64_21, 95_31, 2_07_29, 17, 10, 3_53, 1_70_22, 11, 21, 64_21, 95_31, 1_69_49, 17, 10, 1_15_09, 7_53, 11, 33, 95, 24_21, 73_85, 9_56, 1_44_31, 26_26, 25, 8_42, 73_85, 48_36, 21, 14_29, 22_72, 98_55, 31_20, 1_61, 2_47_38, 19, 1_32_03, 6_58, 2_18, 7_87, 21, 4_30, 1_84_82, 8_47, 26_37, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_22, 2_21_78, 27, 10_64, 22, 9_56, 13, 1_11_01, 14_29, 58_54, 2_43_13, 1_89_53, 40, 4_22, 2_43_66, 68, 17_58, 37, 1_04_83, 1_42_57, 31, 2_07, 2_63, 21, 2_03, 37_73, 25, 71, 97_35, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 20_49, 34_42, 17, 1_38_94, 33_80, 23, 95, 18, 1_76_34, 22_88, 9, 4, 3]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=_lowerCamelCase      ,     model_name='''xlnet-base-cased'''      ,     revision='''c841166438c31ec7ca9a106dee7bb312b73ae511'''      ,     )





