from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase				:		Dict       			=		logging.get_logger(__name__)

_UpperCamelCase				:		Tuple       			=		{
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class   _lowercase(     A_				):



   """simple docstring"""





   __lowerCamelCase    =  '''plbart'''
   __lowerCamelCase    =  ['''past_key_values''']
   __lowerCamelCase    =  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





   def __init__(							self:    List[Any]			,a:    int=50005			,a:    Tuple=1024			,a:    Optional[int]=6			,a:    Any=3072			,a:    Any=12			,a:    List[str]=6			,a:    Any=3072			,a:    Optional[int]=12			,a:    Any=0.0			,a:    Optional[Any]=0.0			,a:    str=True			,a:    List[Any]=True			,a:    List[str]="gelu"			,a:    List[Any]=768			,a:    Tuple=0.1			,a:    Optional[Any]=0.1			,a:    int=0.0			,a:    Any=0.02			,a:    int=0.0			,a:    Dict=True			,a:    Any=1			,a:    Optional[Any]=0			,a:    List[str]=2			,a:    Optional[Any]=2			,**a:    int			,):
    __UpperCAmelCase         =					vocab_size
    __UpperCAmelCase         =					max_position_embeddings
    __UpperCAmelCase         =					d_model
    __UpperCAmelCase         =					encoder_ffn_dim
    __UpperCAmelCase         =					encoder_layers
    __UpperCAmelCase         =					encoder_attention_heads
    __UpperCAmelCase         =					decoder_ffn_dim
    __UpperCAmelCase         =					decoder_layers
    __UpperCAmelCase         =					decoder_attention_heads
    __UpperCAmelCase         =					dropout
    __UpperCAmelCase         =					attention_dropout
    __UpperCAmelCase         =					activation_dropout
    __UpperCAmelCase         =					activation_function
    __UpperCAmelCase         =					init_std
    __UpperCAmelCase         =					encoder_layerdrop
    __UpperCAmelCase         =					decoder_layerdrop
    __UpperCAmelCase         =					classifier_dropout
    __UpperCAmelCase         =					use_cache
    __UpperCAmelCase         =					encoder_layers
    __UpperCAmelCase         =					scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=a			,bos_token_id=a			,eos_token_id=a			,is_encoder_decoder=a			,forced_eos_token_id=a			,**a			,)




class   _lowercase(     A_				):



   """simple docstring"""





   @property
   def 			snake_case	(							self:    Optional[int]	):
    return OrderedDict(
        [
            ('input_ids', {0: 'batch', 1: 'sequence'}),
            ('attention_mask', {0: 'batch', 1: 'sequence'}),
        ]	)





   @property
   def 			snake_case	(							self:    Tuple	):



    if self.use_past:
     return OrderedDict(
         [
             ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
             ('past_keys', {0: 'batch', 2: 'sequence'}),
             ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
         ]	)
    else:
     return OrderedDict(
         [
             ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
             ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
         ]	)




'''simple docstring'''




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,
)


_UpperCamelCase				:		Dict       			=		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[int]       			=		['MarianTokenizer']

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

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['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

     _UpperCamelCase				:		List[Any]       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



