
'''simple docstring'''





import copy

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


SCREAMING_SNAKE_CASE__     :    List[Any]      							=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :    List[str]      							=					{}

class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		Tuple   						=	"""fsmt_decoder"""






		def __init__(	self	:							int							,    SCREAMING_SNAKE_CASE__	:							List[Any]=0							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=0						)  ->							Union[str, Any]:
						super().__init__()
						__lowerCamelCase					      =			vocab_size
						__lowerCamelCase					      =			bos_token_id





class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		int   						=	"""fsmt"""
		a__ :		Tuple   						=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






		def __init__(	self	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							Any=["en", "de"]							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=4_20_24							,    SCREAMING_SNAKE_CASE__	:							Any=4_20_24							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]="relu"							,    SCREAMING_SNAKE_CASE__	:							List[str]=10_24							,    SCREAMING_SNAKE_CASE__	:							Dict=2_00							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=10_24							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=40_96							,    SCREAMING_SNAKE_CASE__	:							Any=12							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=16							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=0.0							,    SCREAMING_SNAKE_CASE__	:							int=40_96							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=12							,    SCREAMING_SNAKE_CASE__	:							Any=16							,    SCREAMING_SNAKE_CASE__	:							List[Any]=0.0							,    SCREAMING_SNAKE_CASE__	:							Dict=0.0							,    SCREAMING_SNAKE_CASE__	:							int=0.1							,    SCREAMING_SNAKE_CASE__	:							Tuple=0.0							,    SCREAMING_SNAKE_CASE__	:							str=0.02							,    SCREAMING_SNAKE_CASE__	:							List[Any]=2							,    SCREAMING_SNAKE_CASE__	:							List[str]=True							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=True							,    SCREAMING_SNAKE_CASE__	:							str=False							,    SCREAMING_SNAKE_CASE__	:							str=5							,    SCREAMING_SNAKE_CASE__	:							List[str]=1.0							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=False							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=True							,    SCREAMING_SNAKE_CASE__	:							Tuple=1							,    SCREAMING_SNAKE_CASE__	:							Tuple=0							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=2							,    SCREAMING_SNAKE_CASE__	:							List[Any]=2							,    **SCREAMING_SNAKE_CASE__	:							List[str]							,    )  ->							Any:
						__lowerCamelCase					      =			langs
						__lowerCamelCase					      =			src_vocab_size
						__lowerCamelCase					      =			tgt_vocab_size
						__lowerCamelCase					      =			d_model  # encoder_embed_dim and decoder_embed_dim

						__lowerCamelCase					      =			encoder_ffn_dim
						__lowerCamelCase					      =			__lowerCamelCase					      =			encoder_layers
						__lowerCamelCase					      =			encoder_attention_heads
						__lowerCamelCase					      =			encoder_layerdrop
						__lowerCamelCase					      =			decoder_layerdrop
						__lowerCamelCase					      =			decoder_ffn_dim
						__lowerCamelCase					      =			decoder_layers
						__lowerCamelCase					      =			decoder_attention_heads
						__lowerCamelCase					      =			max_position_embeddings
						__lowerCamelCase					      =			init_std  # Normal(0, this parameter)
						__lowerCamelCase					      =			activation_function

						__lowerCamelCase					      =			DecoderConfig(vocab_size=SCREAMING_SNAKE_CASE__							,    bos_token_id=SCREAMING_SNAKE_CASE__						)
						if "decoder" in common_kwargs:
										del common_kwargs["decoder"]

						__lowerCamelCase					      =			scale_embedding  # scale factor will be sqrt(d_model) if True

						# 3 Types of Dropout
						__lowerCamelCase					      =			attention_dropout
						__lowerCamelCase					      =			activation_dropout
						__lowerCamelCase					      =			dropout

						__lowerCamelCase					      =			use_cache
						super().__init__(
						    pad_token_id=SCREAMING_SNAKE_CASE__							,    bos_token_id=SCREAMING_SNAKE_CASE__							,    eos_token_id=SCREAMING_SNAKE_CASE__							,    decoder_start_token_id=SCREAMING_SNAKE_CASE__							,    is_encoder_decoder=SCREAMING_SNAKE_CASE__							,    tie_word_embeddings=SCREAMING_SNAKE_CASE__							,    forced_eos_token_id=SCREAMING_SNAKE_CASE__							,    max_length=SCREAMING_SNAKE_CASE__							,    num_beams=SCREAMING_SNAKE_CASE__							,    length_penalty=SCREAMING_SNAKE_CASE__							,    early_stopping=SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__							,    )







		def 				__A (	self	:							List[Any]						)  ->							Tuple:
						__lowerCamelCase					      =			copy.deepcopy(self.__dict__						)
						__lowerCamelCase					      =			self.decoder.to_dict()
						__lowerCamelCase					      =			self.__class__.model_type
						return output





from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		Any   						=	SqueezeBertTokenizer
		a__ :		Union[str, Any]   						=	SqueezeBertTokenizerFast
		a__ :		int   						=	True






		def 				__A (	self	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							int:
						return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **SCREAMING_SNAKE_CASE__						)







		@slow
		def 				__A (	self	:							int						)  ->							Optional[int]:
						__lowerCamelCase					      =			SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''						)

						__lowerCamelCase					      =			tokenizer.encode('''sequence builders'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.encode('''multi-sequence build'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

						assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
						assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
						    tokenizer.sep_token_id
						]



