



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE_:Any					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:List[Any]					=						{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Optional[Any] =				'''t5'''
    __lowerCamelCase      :			Optional[int] =				['''past_key_values''']
    __lowerCamelCase      :			List[str] =				{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






    def __init__(	self,		lowerCamelCase__=3_2128,		lowerCamelCase__=512,		lowerCamelCase__=64,		lowerCamelCase__=2048,		lowerCamelCase__=6,		lowerCamelCase__=None,		lowerCamelCase__=8,		lowerCamelCase__=32,		lowerCamelCase__=128,		lowerCamelCase__=0.1,		lowerCamelCase__=1e-6,		lowerCamelCase__=1.0,		lowerCamelCase__="relu",		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=0,		lowerCamelCase__=1,		**lowerCamelCase__,		):
        A			:	List[Any]										=       vocab_size
        A			:	List[Any]										=       d_model
        A			:	int										=       d_kv
        A			:	List[Any]										=       d_ff
        A			:	List[Any]										=       num_layers
        A			:	List[Any]										=       (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A			:	List[str]										=       num_heads
        A			:	Any										=       relative_attention_num_buckets
        A			:	int										=       relative_attention_max_distance
        A			:	str										=       dropout_rate
        A			:	Tuple										=       layer_norm_epsilon
        A			:	Optional[Any]										=       initializer_factor
        A			:	str										=       feed_forward_proj
        A			:	Dict										=       use_cache

        A			:	Dict										=       self.feed_forward_proj.split("""-"""		)
        A			:	Union[str, Any]										=       act_info[-1]
        A			:	Any										=       act_info[0] == """gated"""

        if len(_snake_case		) > 1 and act_info[0] != "gated" or len(_snake_case		) > 2:
            raise ValueError(
                f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                """\'gated-gelu\' or \'relu\'"""		)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            A			:	Optional[int]										=       """gelu_new"""

        super().__init__(
            pad_token_id=_snake_case,		eos_token_id=_snake_case,		is_encoder_decoder=_snake_case,		**_snake_case,		)






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @property
    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       {
            """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
            """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
        }
        if self.use_past:
            A			:	Tuple										=       """past_encoder_sequence + sequence"""
            A			:	Optional[Any]										=       {0: """batch"""}
            A			:	List[str]										=       {0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
            A			:	Optional[Any]										=       {0: """batch""", 1: """decoder_sequence"""}
            A			:	Tuple										=       {0: """batch""", 1: """decoder_sequence"""}

        if self.use_past:
            self.fill_with_past_key_values_(_snake_case,		direction="""inputs"""		)

        return common_inputs






    @property
    def       _lowerCAmelCase (	self		):
        return 13







import os
from pathlib import Path




def      __UpperCamelCase     (  )	->  Any:



    """simple docstring"""






    from torch.utils.cpp_extension import load

    A			:	Any										=       Path(_lowerCAmelCase		).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
    A			:	int										=       [
        root / filename
        for filename in [
            """vision.cpp""",
            os.path.join("""cpu"""				,					"""ms_deform_attn_cpu.cpp"""		),
            os.path.join("""cuda"""				,					"""ms_deform_attn_cuda.cu"""		),
        ]
    ]

    load(
        """MultiScaleDeformableAttention"""				,					_lowerCAmelCase				,					with_cuda=_lowerCAmelCase				,					extra_include_paths=[str(_lowerCAmelCase		)]				,					extra_cflags=["""-DWITH_CUDA=1"""]				,					extra_cuda_cflags=[
            """-DCUDA_HAS_FP16=1""",
            """-D__CUDA_NO_HALF_OPERATORS__""",
            """-D__CUDA_NO_HALF_CONVERSIONS__""",
            """-D__CUDA_NO_HALF2_OPERATORS__""",
        ]				,					)

    import MultiScaleDeformableAttention as MSDA

    return MSDA


