


"""simple docstring"""







import copy

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


lowerCAmelCase__				    =       logging.get_logger(__name__)

lowerCAmelCase__				    =       {
    '''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 				SCREAMING_SNAKE_CASE__    (		__lowerCAmelCase    ):
							"""simple docstring"""


							a  :    Optional[Any]       ='''deformable_detr'''
							a  :    Dict       ={
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}

							def __init__(						self		,					snake_case__=True		,					snake_case__=None		,					snake_case__=3		,					snake_case__=300		,					snake_case__=1_024		,					snake_case__=6		,					snake_case__=1_024		,					snake_case__=8		,					snake_case__=6		,					snake_case__=1_024		,					snake_case__=8		,					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__=True		,					snake_case__=False		,					snake_case__="sine"		,					snake_case__="resnet50"		,					snake_case__=True		,					snake_case__=False		,					snake_case__=4		,					snake_case__=4		,					snake_case__=4		,					snake_case__=False		,					snake_case__=300		,					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__=0.25		,					snake_case__=False		,					**snake_case__		,					):






									"""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."						)
													lowerCAmelCase					:					List[str]								=							CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
											elif isinstance(lowerCAmelCase_		,					lowerCAmelCase_						):
													lowerCAmelCase					:					Optional[Any]								=							backbone_config.get("model_type"						)
													lowerCAmelCase					:					Optional[int]								=							CONFIG_MAPPING[backbone_model_type]
													lowerCAmelCase					:					Tuple								=							config_class.from_dict(lowerCAmelCase_						)
									lowerCAmelCase					:					Tuple								=							use_timm_backbone
									lowerCAmelCase					:					Optional[int]								=							backbone_config
									lowerCAmelCase					:					List[str]								=							num_channels
									lowerCAmelCase					:					Any								=							num_queries
									lowerCAmelCase					:					Optional[Any]								=							max_position_embeddings
									lowerCAmelCase					:					Optional[int]								=							d_model
									lowerCAmelCase					:					List[str]								=							encoder_ffn_dim
									lowerCAmelCase					:					int								=							encoder_layers
									lowerCAmelCase					:					Dict								=							encoder_attention_heads
									lowerCAmelCase					:					List[str]								=							decoder_ffn_dim
									lowerCAmelCase					:					Tuple								=							decoder_layers
									lowerCAmelCase					:					Optional[Any]								=							decoder_attention_heads
									lowerCAmelCase					:					Optional[int]								=							dropout
									lowerCAmelCase					:					List[str]								=							attention_dropout
									lowerCAmelCase					:					List[Any]								=							activation_dropout
									lowerCAmelCase					:					List[str]								=							activation_function
									lowerCAmelCase					:					Optional[int]								=							init_std
									lowerCAmelCase					:					List[str]								=							init_xavier_std
									lowerCAmelCase					:					Any								=							encoder_layerdrop
									lowerCAmelCase					:					Tuple								=							auxiliary_loss
									lowerCAmelCase					:					List[Any]								=							position_embedding_type
									lowerCAmelCase					:					int								=							backbone
									lowerCAmelCase					:					str								=							use_pretrained_backbone
									lowerCAmelCase					:					Any								=							dilation
									# deformable attributes
									lowerCAmelCase					:					Dict								=							num_feature_levels
									lowerCAmelCase					:					Dict								=							encoder_n_points
									lowerCAmelCase					:					int								=							decoder_n_points
									lowerCAmelCase					:					int								=							two_stage
									lowerCAmelCase					:					Any								=							two_stage_num_proposals
									lowerCAmelCase					:					Dict								=							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
									lowerCAmelCase					:					Tuple								=							class_cost
									lowerCAmelCase					:					int								=							bbox_cost
									lowerCAmelCase					:					Dict								=							giou_cost
									# Loss coefficients
									lowerCAmelCase					:					Tuple								=							mask_loss_coefficient
									lowerCAmelCase					:					Tuple								=							dice_loss_coefficient
									lowerCAmelCase					:					Optional[int]								=							bbox_loss_coefficient
									lowerCAmelCase					:					Optional[int]								=							giou_loss_coefficient
									lowerCAmelCase					:					Any								=							eos_coefficient
									lowerCAmelCase					:					Union[str, Any]								=							focal_alpha
									lowerCAmelCase					:					int								=							disable_custom_kernels
									super().__init__(is_encoder_decoder=lowerCAmelCase_		,					**lowerCAmelCase_						)

							@property
							def 	lowercase__							(						self						):






									"""simple docstring"""





									return self.encoder_attention_heads

							@property
							def 	lowercase__							(						self						):






									"""simple docstring"""





									return self.d_model







							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Optional[int]								=							copy.deepcopy(self.__dict__						)
									if self.backbone_config is not None:
											lowerCAmelCase					:					Any								=							self.backbone_config.to_dict()
									lowerCAmelCase					:					List[str]								=							self.__class__.model_type
									return output









"""simple docstring"""







import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   unittest.TestCase    ):
       """simple docstring"""


       a  :    List[str]       =None
       a  :    List[Any]       =BloomTokenizerFast
       a  :    Optional[int]       =BloomTokenizerFast
       a  :    Optional[Any]       =True
       a  :    Dict       =False
       a  :    Optional[Any]       ="tokenizer_file"
       a  :    Optional[int]       ={"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

       def 	lowercase__							(						self						):






         """simple docstring"""





         super().setUp()
         lowerCAmelCase					:					Tuple								=							BloomTokenizerFast.from_pretrained("bigscience/tokenizer"						)
         tokenizer.save_pretrained(self.tmpdirname						)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





         kwargs.update(self.special_tokens_map						)
         return BloomTokenizerFast.from_pretrained(self.tmpdirname		,					**snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_rust_tokenizer()

         lowerCAmelCase					:					List[Any]								=							["The quick brown fox</s>", "jumps over the lazy dog</s>"]
         lowerCAmelCase					:					str								=							[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

         lowerCAmelCase					:					Optional[int]								=							tokenizer.batch_encode_plus(snake_case__						)["input_ids"]
         self.assertListEqual(snake_case__		,					snake_case__						)

         lowerCAmelCase					:					Optional[int]								=							tokenizer.batch_decode(snake_case__						)
         self.assertListEqual(snake_case__		,					snake_case__						)

       def 	lowercase__							(						self		,					snake_case__=6						):






         """simple docstring"""





         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
             lowerCAmelCase					:					Tuple								=							self.rust_tokenizer_class.from_pretrained(snake_case__		,					**snake_case__						)
             # tokenizer_r.pad_token = None # Hotfixing padding = None
             # Simple input
             lowerCAmelCase					:					str								=							"This is a simple input"
             lowerCAmelCase					:					Tuple								=							["This is a simple input 1", "This is a simple input 2"]
             lowerCAmelCase					:					Any								=							("This is a simple input", "This is a pair")
             lowerCAmelCase					:					Tuple								=							[
                 ("This is a simple input 1", "This is a simple input 2"),
                 ("This is a simple pair 1", "This is a simple pair 2"),
             ]

             # Simple input tests
             try:
               tokenizer_r.encode(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.encode_plus(snake_case__		,					max_length=snake_case__						)

               tokenizer_r.batch_encode_plus(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.encode(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.batch_encode_plus(snake_case__		,					max_length=snake_case__						)
             except ValueError:
               self.fail("Bloom Tokenizer should be able to deal with padding"						)

             lowerCAmelCase					:					Tuple								=							None  # Hotfixing padding = None
             self.assertRaises(snake_case__		,					tokenizer_r.encode		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Simple input
             self.assertRaises(snake_case__		,					tokenizer_r.encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Simple input
             self.assertRaises(
                 snake_case__		,					tokenizer_r.batch_encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"		,					)

             # Pair input
             self.assertRaises(snake_case__		,					tokenizer_r.encode		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Pair input
             self.assertRaises(snake_case__		,					tokenizer_r.encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Pair input
             self.assertRaises(
                 snake_case__		,					tokenizer_r.batch_encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"		,					)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_rust_tokenizer()
         lowerCAmelCase					:					int								=							load_dataset("xnli"		,					"all_languages"		,					split="test"		,					streaming=snake_case__						)

         lowerCAmelCase					:					Tuple								=							next(iter(snake_case__						)						)["premise"]  # pick up one data
         lowerCAmelCase					:					Optional[Any]								=							list(sample_data.values()						)

         lowerCAmelCase					:					int								=							list(map(tokenizer.encode		,					snake_case__						)						)
         lowerCAmelCase					:					List[Any]								=							[tokenizer.decode(snake_case__		,					clean_up_tokenization_spaces=snake_case__						) for x in output_tokens]
         self.assertListEqual(snake_case__		,					snake_case__						)







       def 	lowercase__							(						self						):






         """simple docstring"""





         self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map						)		,					1						)
         self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()						)[0]						)		,					1						)





