

from typing import Mapping

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


UpperCamelCase__		   =    logging.get_logger(__name__)

class 						__snake_case						(							snake_case__     ):
							"""simple docstring"""







							__SCREAMING_SNAKE_CASE													=       "mt5"
							__SCREAMING_SNAKE_CASE													=       ["past_key_values"]


							def __init__(		self			,   _UpperCamelCase=25_01_12			,   _UpperCamelCase=5_12			,   _UpperCamelCase=64			,   _UpperCamelCase=10_24			,   _UpperCamelCase=8			,   _UpperCamelCase=None			,   _UpperCamelCase=6			,   _UpperCamelCase=32			,   _UpperCamelCase=1_28			,   _UpperCamelCase=0.1			,   _UpperCamelCase=1E-6			,   _UpperCamelCase=1.0			,   _UpperCamelCase="gated-gelu"			,   _UpperCamelCase=True			,   _UpperCamelCase=True			,   _UpperCamelCase="T5Tokenizer"			,   _UpperCamelCase=False			,   _UpperCamelCase=0			,   _UpperCamelCase=1			,   _UpperCamelCase=0			,   **_UpperCamelCase			,   )			->       Any:






														"""simple docstring"""


														super().__init__(
														    is_encoder_decoder=_UpperCamelCase			,   tokenizer_class=_UpperCamelCase			,   tie_word_embeddings=_UpperCamelCase			,   pad_token_id=_UpperCamelCase			,   eos_token_id=_UpperCamelCase			,   decoder_start_token_id=_UpperCamelCase			,   **_UpperCamelCase			,   )
														__snake_case					  =       vocab_size
														__snake_case					  =       d_model
														__snake_case					  =       d_kv
														__snake_case					  =       d_ff
														__snake_case					  =       num_layers
														__snake_case					  =       (
														    num_decoder_layers if num_decoder_layers is not None else self.num_layers
														)  # default = symmetry
														__snake_case					  =       num_heads
														__snake_case					  =       relative_attention_num_buckets
														__snake_case					  =       relative_attention_max_distance
														__snake_case					  =       dropout_rate
														__snake_case					  =       layer_norm_epsilon
														__snake_case					  =       initializer_factor
														__snake_case					  =       feed_forward_proj
														__snake_case					  =       use_cache

														__snake_case					  =       self.feed_forward_proj.split("""-"""      )
														__snake_case					  =       act_info[-1]
														__snake_case					  =       act_info[0] == """gated"""

														if len(_UpperCamelCase      ) > 1 and act_info[0] != "gated" or len(_UpperCamelCase      ) > 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":
																					__snake_case					  =       """gelu_new"""


							@property
							def  a		(		self      )			->       Union[str, Any]:






														"""simple docstring"""


														return self.d_model


							@property
							def  a		(		self      )			->       str:






														"""simple docstring"""


														return self.num_heads






							@property
							def  a		(		self      )			->       Optional[int]:






														"""simple docstring"""


														return self.num_layers






class 						__snake_case						(							snake_case__     ):
							"""simple docstring"""







							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
							def  a		(		self      )			->       Mapping[str, Mapping[int, str]]:






														"""simple docstring"""


														__snake_case					  =       {
														    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
														    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
														}
														if self.use_past:
																					__snake_case					  =       """past_encoder_sequence + sequence"""
																					__snake_case					  =       {0: """batch"""}
																					__snake_case					  =       {0: """batch""", 1: """past_decoder_sequence + sequence"""}
														else:
																					__snake_case					  =       {0: """batch""", 1: """decoder_sequence"""}
																					__snake_case					  =       {0: """batch""", 1: """decoder_sequence"""}

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

														return common_inputs


							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
							def  a		(		self      )			->       int:






														"""simple docstring"""


														return 13






							@property
							def  a		(		self      )			->       float:






														"""simple docstring"""


														return 5E-4




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__		   =    {
    '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
    '''tokenization_splinter''': ['''SplinterTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		   =    ['''SplinterTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		   =    [
        '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''SplinterForQuestionAnswering''',
        '''SplinterForPreTraining''',
        '''SplinterLayer''',
        '''SplinterModel''',
        '''SplinterPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
    from .tokenization_splinter import SplinterTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_splinter_fast import SplinterTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_splinter import (
            SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
            SplinterForPreTraining,
            SplinterForQuestionAnswering,
            SplinterLayer,
            SplinterModel,
            SplinterPreTrainedModel,
        )


else:
    import sys

    UpperCamelCase__		   =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

