



'''simple docstring'''

import copy

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


__lowerCAmelCase		    =     logging.get_logger(__name__)

__lowerCAmelCase		    =     {}

class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					Any            =      'fsmt_decoder'







							def __init__(							self      : Union[str, Any]			,_UpperCAmelCase      : List[Any]=0			,_UpperCAmelCase      : List[Any]=0	):
									super().__init__()
									_a  :   Dict				 =							vocab_size
									_a  :   Dict				 =							bos_token_id

class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					str            =      'fsmt'
							lowerCAmelCase :					Union[str, Any]            =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







							def __init__(							self      : int			,_UpperCAmelCase      : Union[str, Any]=["en", "de"]			,_UpperCAmelCase      : Tuple=42024			,_UpperCAmelCase      : Optional[Any]=42024			,_UpperCAmelCase      : Dict="relu"			,_UpperCAmelCase      : Dict=1024			,_UpperCAmelCase      : Union[str, Any]=200			,_UpperCAmelCase      : str=1024			,_UpperCAmelCase      : Optional[int]=4096			,_UpperCAmelCase      : List[str]=12			,_UpperCAmelCase      : str=16			,_UpperCAmelCase      : str=0.0			,_UpperCAmelCase      : Any=4096			,_UpperCAmelCase      : Optional[int]=12			,_UpperCAmelCase      : List[Any]=16			,_UpperCAmelCase      : List[Any]=0.0			,_UpperCAmelCase      : List[str]=0.0			,_UpperCAmelCase      : Union[str, Any]=0.1			,_UpperCAmelCase      : str=0.0			,_UpperCAmelCase      : List[Any]=0.02			,_UpperCAmelCase      : Union[str, Any]=2			,_UpperCAmelCase      : List[Any]=True			,_UpperCAmelCase      : Union[str, Any]=True			,_UpperCAmelCase      : Optional[Any]=False			,_UpperCAmelCase      : int=5			,_UpperCAmelCase      : Dict=1.0			,_UpperCAmelCase      : List[str]=False			,_UpperCAmelCase      : Optional[Any]=True			,_UpperCAmelCase      : List[Any]=1			,_UpperCAmelCase      : str=0			,_UpperCAmelCase      : Dict=2			,_UpperCAmelCase      : Optional[int]=2			,**_UpperCAmelCase      : Tuple			,):
									_a  :   Any				 =							langs
									_a  :   Dict				 =							src_vocab_size
									_a  :   Tuple				 =							tgt_vocab_size
									_a  :   int				 =							d_model  # encoder_embed_dim and decoder_embed_dim

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

									_a  :   List[Any]				 =							DecoderConfig(vocab_size=_UpperCAmelCase			,bos_token_id=_UpperCAmelCase	)
									if "decoder" in common_kwargs:
											del common_kwargs["decoder"]

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

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

									_a  :   List[str]				 =							use_cache
									super().__init__(
									    pad_token_id=_UpperCAmelCase			,bos_token_id=_UpperCAmelCase			,eos_token_id=_UpperCAmelCase			,decoder_start_token_id=_UpperCAmelCase			,is_encoder_decoder=_UpperCAmelCase			,tie_word_embeddings=_UpperCAmelCase			,forced_eos_token_id=_UpperCAmelCase			,max_length=_UpperCAmelCase			,num_beams=_UpperCAmelCase			,length_penalty=_UpperCAmelCase			,early_stopping=_UpperCAmelCase			,**_UpperCAmelCase			,)







							def 							__lowercase    (							self      : Optional[int]	):
									_a  :   List[Any]				 =							copy.deepcopy(self.__dict__	)
									_a  :   Dict				 =							self.decoder.to_dict()
									_a  :   List[Any]				 =							self.__class__.model_type
									return output









'''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,
)


__lowerCAmelCase		    =     {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     ['''T5Tokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     ['''T5TokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''T5EncoderModel''',
							    '''T5ForConditionalGeneration''',
							    '''T5Model''',
							    '''T5PreTrainedModel''',
							    '''load_tf_weights_in_t5''',
							    '''T5ForQuestionAnswering''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFT5EncoderModel''',
							    '''TFT5ForConditionalGeneration''',
							    '''TFT5Model''',
							    '''TFT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''FlaxT5EncoderModel''',
							    '''FlaxT5ForConditionalGeneration''',
							    '''FlaxT5Model''',
							    '''FlaxT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta import TaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta_fast import TaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ta import (
														    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TaEncoderModel,
														    TaForConditionalGeneration,
														    TaForQuestionAnswering,
														    TaModel,
														    TaPreTrainedModel,
														    load_tf_weights_in_ta,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ta import (
														    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTaEncoderModel,
														    TFTaForConditionalGeneration,
														    TFTaModel,
														    TFTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_ta import (
														    FlaxTaEncoderModel,
														    FlaxTaForConditionalGeneration,
														    FlaxTaModel,
														    FlaxTaPreTrainedModel,
														)


else:
							import sys

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




