





import warnings

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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
    """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
      __magic_name__:			Optional[int]       =       "xlnet"
      __magic_name__:			Optional[int]       =       ["mems"]
      __magic_name__:			List[Any]       =       {
          "n_token": "vocab_size",  # Backward compatibility
          "hidden_size": "d_model",
          "num_attention_heads": "n_head",
          "num_hidden_layers": "n_layer",
      }






      def __init__(      self      :      Union[str, Any]		,   _A      :      Any=32000		,   _A      :      str=1024		,   _A      :      List[Any]=24		,   _A      :      Tuple=16		,   _A      :      List[Any]=4096		,   _A      :      Dict="gelu"		,   _A      :      Optional[Any]=True		,   _A      :      str="bi"		,   _A      :      List[str]=0.0_2		,   _A      :      Optional[int]=1E-12		,   _A      :      List[Any]=0.1		,   _A      :      Any=512		,   _A      :      int=None		,   _A      :      Tuple=True		,   _A      :      Any=False		,   _A      :      Tuple=False		,   _A      :      Any=-1		,   _A      :      Optional[int]=False		,   _A      :      List[Any]="last"		,   _A      :      Any=True		,   _A      :      Dict="tanh"		,   _A      :      Union[str, Any]=0.1		,   _A      :      List[str]=5		,   _A      :      List[str]=5		,   _A      :      Dict=5		,   _A      :      Any=1		,   _A      :      Dict=2		,   **_A      :      List[Any]		,   )							->    Optional[Any]:







       """simple docstring"""


       snake_case_		:			List[Any]											=	vocab_size
       snake_case_		:			int											=	d_model
       snake_case_		:			Any											=	n_layer
       snake_case_		:			List[str]											=	n_head
       if d_model % n_head != 0:
        raise ValueError(F"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""	)
       if "d_head" in kwargs:
        if kwargs["d_head"] != d_model // n_head:
         raise ValueError(
             F"""`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"""	)
       snake_case_		:			Any											=	d_model // n_head
       snake_case_		:			List[Any]											=	ff_activation
       snake_case_		:			List[Any]											=	d_inner
       snake_case_		:			Dict											=	untie_r
       snake_case_		:			Optional[Any]											=	attn_type

       snake_case_		:			int											=	initializer_range
       snake_case_		:			Dict											=	layer_norm_eps

       snake_case_		:			Union[str, Any]											=	dropout
       snake_case_		:			Tuple											=	mem_len
       snake_case_		:			str											=	reuse_len
       snake_case_		:			Union[str, Any]											=	bi_data
       snake_case_		:			List[str]											=	clamp_len
       snake_case_		:			Optional[Any]											=	same_length

       snake_case_		:			Union[str, Any]											=	summary_type
       snake_case_		:			Tuple											=	summary_use_proj
       snake_case_		:			Tuple											=	summary_activation
       snake_case_		:			Dict											=	summary_last_dropout
       snake_case_		:			List[str]											=	start_n_top
       snake_case_		:			Tuple											=	end_n_top

       snake_case_		:			Optional[Any]											=	bos_token_id
       snake_case_		:			Union[str, Any]											=	pad_token_id
       snake_case_		:			str											=	eos_token_id

       if "use_cache" in kwargs:
        warnings.warn(
            'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
            ' instead.'		,   _A		,   )
        snake_case_		:			Any											=	kwargs['use_cache']

       snake_case_		:			List[str]											=	use_mems_eval
       snake_case_		:			int											=	use_mems_train
       super().__init__(pad_token_id=_A		,   bos_token_id=_A		,   eos_token_id=_A		,   **_A	)






      @property
      def UpperCAmelCase_				(      self      :      Dict	)							->    Union[str, Any]:







       """simple docstring"""


       logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit."""	)
       return -1






      @max_position_embeddings.setter
      def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[Any]	)							->    Union[str, Any]:







       """simple docstring"""


       raise NotImplementedError(
           F"""The model {self.model_type} is one of the few models that has no sequence length limit."""	)








import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_SCREAMING_SNAKE_CASE      	=		get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
						_SCREAMING_SNAKE_CASE      	=		json.load(f)
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							return FSMTTokenizer.from_pretrained(_A	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Union[str, Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	FSMTForConditionalGeneration.from_pretrained(_A	).to(_A	)
							if torch_device == "cuda":
								model.half()
							return model






						@parameterized.expand(
						    [
						        ['en-ru', 2_6.0],
						        ['ru-en', 2_2.0],
						        ['en-de', 2_2.0],
						        ['de-en', 2_9.0],
						    ]	)
						@slow
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Optional[Any]		,   _A      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	F"""facebook/wmt19-{pair}"""
							snake_case_		:			List[Any]											=	self.get_tokenizer(_A	)
							snake_case_		:			List[str]											=	self.get_model(_A	)

							snake_case_		:			Union[str, Any]											=	bleu_data[pair]['src']
							snake_case_		:			List[str]											=	bleu_data[pair]['tgt']

							snake_case_		:			Optional[int]											=	tokenizer(_A		,   return_tensors='pt'		,   truncation=_A		,   padding='longest'	).to(_A	)
							snake_case_		:			List[Any]											=	model.generate(
							    input_ids=batch.input_ids		,   num_beams=8		,   )
							snake_case_		:			Dict											=	tokenizer.batch_decode(
							    _A		,   skip_special_tokens=_A		,   clean_up_tokenization_spaces=_A	)
							snake_case_		:			Tuple											=	calculate_bleu(_A		,   _A	)
							print(_A	)
							self.assertGreaterEqual(scores['bleu']		,   _A	)

