






import copy

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


UpperCAmelCase__      			=						logging.get_logger(__name__)

UpperCAmelCase__      			=						{
    '''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 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """deformable_detr"""
						snake_case__        =      {
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}



						def __init__(self:					Dict ,					__UpperCAmelCase:					Dict=True ,					__UpperCAmelCase:					Optional[Any]=None ,					__UpperCAmelCase:					Optional[Any]=3 ,					__UpperCAmelCase:					str=300 ,					__UpperCAmelCase:					Optional[Any]=1024 ,					__UpperCAmelCase:					Tuple=6 ,					__UpperCAmelCase:					Optional[Any]=1024 ,					__UpperCAmelCase:					List[Any]=8 ,					__UpperCAmelCase:					Tuple=6 ,					__UpperCAmelCase:					Any=1024 ,					__UpperCAmelCase:					str=8 ,					__UpperCAmelCase:					Optional[int]=0.0 ,					__UpperCAmelCase:					Tuple=True ,					__UpperCAmelCase:					List[Any]="relu" ,					__UpperCAmelCase:					List[str]=256 ,					__UpperCAmelCase:					Optional[int]=0.1 ,					__UpperCAmelCase:					Union[str, Any]=0.0 ,					__UpperCAmelCase:					Dict=0.0 ,					__UpperCAmelCase:					Dict=0.02 ,					__UpperCAmelCase:					Dict=1.0 ,					__UpperCAmelCase:					List[str]=True ,					__UpperCAmelCase:					Optional[int]=False ,					__UpperCAmelCase:					Tuple="sine" ,					__UpperCAmelCase:					List[str]="resnet50" ,					__UpperCAmelCase:					Optional[Any]=True ,					__UpperCAmelCase:					str=False ,					__UpperCAmelCase:					Tuple=4 ,					__UpperCAmelCase:					Dict=4 ,					__UpperCAmelCase:					Optional[int]=4 ,					__UpperCAmelCase:					Tuple=False ,					__UpperCAmelCase:					int=300 ,					__UpperCAmelCase:					Tuple=False ,					__UpperCAmelCase:					List[str]=1 ,					__UpperCAmelCase:					List[str]=5 ,					__UpperCAmelCase:					List[str]=2 ,					__UpperCAmelCase:					str=1 ,					__UpperCAmelCase:					List[str]=1 ,					__UpperCAmelCase:					str=5 ,					__UpperCAmelCase:					List[Any]=2 ,					__UpperCAmelCase:					Dict=0.1 ,					__UpperCAmelCase:					str=0.25 ,					__UpperCAmelCase:					Any=False ,					**__UpperCAmelCase:					Any ,					)    ->     Optional[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					:						Tuple      =					CONFIG_MAPPING["resnet"](out_features=["stage4"]   )
																elif isinstance(__UpperCAmelCase ,					__UpperCAmelCase   ):
																					__a					:						List[str]      =					backbone_config.get("model_type"   )
																					__a					:						List[Any]      =					CONFIG_MAPPING[backbone_model_type]
																					__a					:						Any      =					config_class.from_dict(__UpperCAmelCase   )
											__a					:						Optional[int]      =					use_timm_backbone
											__a					:						Tuple      =					backbone_config
											__a					:						Dict      =					num_channels
											__a					:						Tuple      =					num_queries
											__a					:						Dict      =					max_position_embeddings
											__a					:						int      =					d_model
											__a					:						int      =					encoder_ffn_dim
											__a					:						List[Any]      =					encoder_layers
											__a					:						Optional[Any]      =					encoder_attention_heads
											__a					:						int      =					decoder_ffn_dim
											__a					:						List[str]      =					decoder_layers
											__a					:						Dict      =					decoder_attention_heads
											__a					:						Union[str, Any]      =					dropout
											__a					:						List[str]      =					attention_dropout
											__a					:						Tuple      =					activation_dropout
											__a					:						Optional[Any]      =					activation_function
											__a					:						int      =					init_std
											__a					:						Optional[Any]      =					init_xavier_std
											__a					:						str      =					encoder_layerdrop
											__a					:						Optional[Any]      =					auxiliary_loss
											__a					:						str      =					position_embedding_type
											__a					:						Union[str, Any]      =					backbone
											__a					:						List[Any]      =					use_pretrained_backbone
											__a					:						str      =					dilation
											# deformable attributes
											__a					:						List[str]      =					num_feature_levels
											__a					:						Any      =					encoder_n_points
											__a					:						Optional[int]      =					decoder_n_points
											__a					:						Dict      =					two_stage
											__a					:						Union[str, Any]      =					two_stage_num_proposals
											__a					:						Optional[Any]      =					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
											__a					:						List[Any]      =					class_cost
											__a					:						Optional[int]      =					bbox_cost
											__a					:						Optional[int]      =					giou_cost
											# Loss coefficients
											__a					:						int      =					mask_loss_coefficient
											__a					:						List[str]      =					dice_loss_coefficient
											__a					:						List[str]      =					bbox_loss_coefficient
											__a					:						Dict      =					giou_loss_coefficient
											__a					:						str      =					eos_coefficient
											__a					:						Optional[Any]      =					focal_alpha
											__a					:						List[str]      =					disable_custom_kernels
											super().__init__(is_encoder_decoder=__UpperCAmelCase ,					**__UpperCAmelCase   )



						@property
						def        UpperCAmelCase__					(self:					List[str]   )    ->     int:




											'''simple docstring'''



											return self.encoder_attention_heads



						@property
						def        UpperCAmelCase__					(self:					Any   )    ->     int:




											'''simple docstring'''



											return self.d_model



						def        UpperCAmelCase__					(self:					Any   )    ->     List[str]:




											'''simple docstring'''



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













import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
			import optax
			from flax.training.common_utils import onehot

			from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
			from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						@slow
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[str]:




											'''simple docstring'''



											__a					:						Optional[Any]      =					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"   )
											__a					:						Dict      =					AutoTokenizer.from_pretrained("google/mt5-small"   )

											__a					:						Any      =					tokenizer("Hello there" ,					return_tensors="np"   ).input_ids
											__a					:						Union[str, Any]      =					tokenizer("Hi I am" ,					return_tensors="np"   ).input_ids

											__a					:						Union[str, Any]      =					shift_tokens_right(__UpperCAmelCase ,					model.config.pad_token_id ,					model.config.decoder_start_token_id   )

											__a					:						Tuple      =					model(__UpperCAmelCase ,					decoder_input_ids=__UpperCAmelCase   ).logits
											__a					:						str      =					optax.softmax_cross_entropy(__UpperCAmelCase ,					onehot(__UpperCAmelCase ,					logits.shape[-1]   )   ).mean()

											__a					:						List[str]      =					-(labels.shape[-1] * loss.item())

											__a					:						Optional[int]      =					-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )





