





from typing import Mapping

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


lowerCamelCase_  						=       logging.get_logger(__name__)

lowerCamelCase_  						=       {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   str     =  "longt5"
     _A    :   List[Any]     =  ["past_key_values"]
     _A    :   Tuple     =  {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




     def __init__(     self      :			int    ,_UpperCamelCase      :			Any=3_2_1_2_8    ,_UpperCamelCase      :			Optional[Any]=5_1_2    ,_UpperCamelCase      :			List[str]=6_4    ,_UpperCamelCase      :			Any=2_0_4_8    ,_UpperCamelCase      :			List[str]=6    ,_UpperCamelCase      :			List[str]=None    ,_UpperCamelCase      :			Union[str, Any]=8    ,_UpperCamelCase      :			Tuple=1_2_7    ,_UpperCamelCase      :			Dict=1_6    ,_UpperCamelCase      :			str=3_2    ,_UpperCamelCase      :			Union[str, Any]=1_2_8    ,_UpperCamelCase      :			Optional[int]=0.1    ,_UpperCamelCase      :			List[Any]=1e-6    ,_UpperCamelCase      :			List[Any]=1.0    ,_UpperCamelCase      :			str="relu"    ,_UpperCamelCase      :			Dict=True    ,_UpperCamelCase      :			Any="local"    ,_UpperCamelCase      :			Optional[int]=True    ,_UpperCamelCase      :			Any=0    ,_UpperCamelCase      :			Tuple=1    ,**_UpperCamelCase      :			List[Any]    ,)		->       Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =vocab_size
         SCREAMING_SNAKE_CASE__       =d_model
         SCREAMING_SNAKE_CASE__       =d_kv
         SCREAMING_SNAKE_CASE__       =d_ff
         SCREAMING_SNAKE_CASE__       =num_layers
         # default = symmetry
         SCREAMING_SNAKE_CASE__       =num_decoder_layers if num_decoder_layers is not None else self.num_layers
         SCREAMING_SNAKE_CASE__       =num_heads
         SCREAMING_SNAKE_CASE__       =local_radius
         SCREAMING_SNAKE_CASE__       =global_block_size
         SCREAMING_SNAKE_CASE__       =relative_attention_num_buckets
         SCREAMING_SNAKE_CASE__       =relative_attention_max_distance
         SCREAMING_SNAKE_CASE__       =dropout_rate
         SCREAMING_SNAKE_CASE__       =layer_norm_epsilon
         SCREAMING_SNAKE_CASE__       =initializer_factor
         SCREAMING_SNAKE_CASE__       =feed_forward_proj
         SCREAMING_SNAKE_CASE__       =encoder_attention_type
         SCREAMING_SNAKE_CASE__       =use_cache

         SCREAMING_SNAKE_CASE__       =self.feed_forward_proj.split("""-"""						)
         SCREAMING_SNAKE_CASE__       =act_info[-1]
         SCREAMING_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":
             SCREAMING_SNAKE_CASE__       ="""gelu_new"""

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






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     @property
     def   __A    (     self      :			List[Any]						)		->       Mapping[str, Mapping[int, str]]:


         '''simple docstring'''





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

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

         return common_inputs




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


         '''simple docstring'''





         return 1_3












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_  						=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       ["MarianTokenizer"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       [
        "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MarianForCausalLM",
        "MarianModel",
        "MarianMTModel",
        "MarianPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
    from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_marian import MarianTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_marian import (
            MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
            MarianForCausalLM,
            MarianModel,
            MarianMTModel,
            MarianPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
    import sys

    lowerCamelCase_  						=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






