
"""simple docstring"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a									=				logging.get_logger(__name__)

_a									=				{}


class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Tuple    	=						"""fsmt_decoder"""







  def __init__(						self							,  lowercase_=0							,  lowercase_=0    ):

     """simple docstring"""
     super().__init__()
     UpperCAmelCase_   :   List[Any]     	=       vocab_size
     UpperCAmelCase_   :   int     	=       bos_token_id




class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		List[str]    	=						"""fsmt"""
  SCREAMING_SNAKE_CASE__     :		int    	=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







  def __init__(						self							,  lowercase_=["en", "de"]							,  lowercase_=4_2024							,  lowercase_=4_2024							,  lowercase_="relu"							,  lowercase_=1024							,  lowercase_=200							,  lowercase_=1024							,  lowercase_=4096							,  lowercase_=12							,  lowercase_=16							,  lowercase_=0.0							,  lowercase_=4096							,  lowercase_=12							,  lowercase_=16							,  lowercase_=0.0							,  lowercase_=0.0							,  lowercase_=0.1							,  lowercase_=0.0							,  lowercase_=0.02							,  lowercase_=2							,  lowercase_=True							,  lowercase_=True							,  lowercase_=False							,  lowercase_=5							,  lowercase_=1.0							,  lowercase_=False							,  lowercase_=True							,  lowercase_=1							,  lowercase_=0							,  lowercase_=2							,  lowercase_=2							,  **lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       langs
     UpperCAmelCase_   :   Optional[Any]     	=       src_vocab_size
     UpperCAmelCase_   :   int     	=       tgt_vocab_size
     UpperCAmelCase_   :   List[Any]     	=       d_model  # encoder_embed_dim and decoder_embed_dim

     UpperCAmelCase_   :   Optional[int]     	=       encoder_ffn_dim
     UpperCAmelCase_   :   int     	=       encoder_layers
     UpperCAmelCase_   :   int     	=       encoder_attention_heads
     UpperCAmelCase_   :   List[str]     	=       encoder_layerdrop
     UpperCAmelCase_   :   List[Any]     	=       decoder_layerdrop
     UpperCAmelCase_   :   str     	=       decoder_ffn_dim
     UpperCAmelCase_   :   Dict     	=       decoder_layers
     UpperCAmelCase_   :   Dict     	=       decoder_attention_heads
     UpperCAmelCase_   :   Union[str, Any]     	=       max_position_embeddings
     UpperCAmelCase_   :   List[Any]     	=       init_std  # Normal(0, this parameter)
     UpperCAmelCase_   :   Optional[int]     	=       activation_function

     UpperCAmelCase_   :   Optional[int]     	=       DecoderConfig(vocab_size=lowercase_							,  bos_token_id=lowercase_    )
     if "decoder" in common_kwargs:
        del common_kwargs["decoder"]

     UpperCAmelCase_   :   int     	=       scale_embedding  # scale factor will be sqrt(d_model) if True

     # 3 Types of Dropout
     UpperCAmelCase_   :   Optional[Any]     	=       attention_dropout
     UpperCAmelCase_   :   List[Any]     	=       activation_dropout
     UpperCAmelCase_   :   Optional[Any]     	=       dropout

     UpperCAmelCase_   :   Union[str, Any]     	=       use_cache
     super().__init__(
         pad_token_id=lowercase_							,  bos_token_id=lowercase_							,  eos_token_id=lowercase_							,  decoder_start_token_id=lowercase_							,  is_encoder_decoder=lowercase_							,  tie_word_embeddings=lowercase_							,  forced_eos_token_id=lowercase_							,  max_length=lowercase_							,  num_beams=lowercase_							,  length_penalty=lowercase_							,  early_stopping=lowercase_							,  **lowercase_							,  )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       copy.deepcopy(self.__dict__    )
     UpperCAmelCase_   :   Optional[int]     	=       self.decoder.to_dict()
     UpperCAmelCase_   :   Dict     	=       self.__class__.model_type
     return output




from typing import TYPE_CHECKING

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


_UpperCAmelCase   :					int    =			{"configuration_levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig", "LevitOnnxConfig"]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					Optional[Any]    =			["LevitFeatureExtractor"]
					_UpperCAmelCase   :					Optional[int]    =			["LevitImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					Optional[int]    =			[
					    "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "LevitForImageClassification",
					    "LevitForImageClassificationWithTeacher",
					    "LevitModel",
					    "LevitPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_levit import LevitFeatureExtractor
										from .image_processing_levit import LevitImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_levit import (
										    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LevitForImageClassification,
										    LevitForImageClassificationWithTeacher,
										    LevitModel,
										    LevitPreTrainedModel,
										)
else:
					import sys

					_UpperCAmelCase   :					Optional[Any]    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
