



"""simple docstring"""




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


_lowerCAmelCase    : Optional[Any]							=							logging.get_logger(__name__)

_lowerCAmelCase    : List[str]							=							{
    "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/config.json",
    "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json",
    "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/config.json",
    "funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json",
    "funnel-transformer/intermediate": (
        "https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"
    ),
    "funnel-transformer/intermediate-base": (
        "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"
    ),
    "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/config.json",
    "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json",
    "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json",
    "funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json",
}






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Any								=			'funnel'
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			{
          'hidden_size': 'd_model',
          'num_attention_heads': 'n_head',
      }



      def __init__(					self					:     int					,     A					:     List[Any]=3_0_5_2_2					,     A					:     Optional[Any]=[4, 4, 4]					,     A					:     List[Any]=None					,     A					:     Dict=2					,     A					:     Dict=7_6_8					,     A					:     Union[str, Any]=1_2					,     A					:     Tuple=6_4					,     A					:     Dict=3_0_7_2					,     A					:     Tuple="gelu_new"					,     A					:     Optional[Any]=0.1					,     A					:     Union[str, Any]=0.1					,     A					:     Optional[int]=0.0					,     A					:     int=0.1					,     A					:     Optional[int]=None					,     A					:     str=1e-9					,     A					:     Any="mean"					,     A					:     Optional[int]="relative_shift"					,     A					:     Optional[Any]=True					,     A					:     int=True					,     A					:     Union[str, Any]=True					,     **A					:     Union[str, Any]					,     ):
           _UpperCAmelCase      :     List[str]        =   vocab_size
           _UpperCAmelCase      :     int        =   block_sizes
           _UpperCAmelCase      :     Tuple        =   [1] * len(A						) if block_repeats is None else block_repeats
           assert len(A						) == len(
               self.block_repeats						), "`block_sizes` and `block_repeats` should have the same length."
           _UpperCAmelCase      :     Tuple        =   num_decoder_layers
           _UpperCAmelCase      :     Optional[Any]        =   d_model
           _UpperCAmelCase      :     Dict        =   n_head
           _UpperCAmelCase      :     Optional[int]        =   d_head
           _UpperCAmelCase      :     str        =   d_inner
           _UpperCAmelCase      :     Optional[int]        =   hidden_act
           _UpperCAmelCase      :     Any        =   hidden_dropout
           _UpperCAmelCase      :     Dict        =   attention_dropout
           _UpperCAmelCase      :     Union[str, Any]        =   activation_dropout
           _UpperCAmelCase      :     Optional[int]        =   initializer_range
           _UpperCAmelCase      :     List[Any]        =   initializer_std
           _UpperCAmelCase      :     Dict        =   layer_norm_eps
           assert pooling_type in [
               "mean",
               "max",
           ], f'Got {pooling_type} for `pooling_type` but only \'mean\' and \'max\' are supported.'
           _UpperCAmelCase      :     Optional[Any]        =   pooling_type
           assert attention_type in [
               "relative_shift",
               "factorized",
           ], f'Got {attention_type} for `attention_type` but only \'relative_shift\' and \'factorized\' are supported.'
           _UpperCAmelCase      :     int        =   attention_type
           _UpperCAmelCase      :     int        =   separate_cls
           _UpperCAmelCase      :     Optional[int]        =   truncate_seq
           _UpperCAmelCase      :     Union[str, Any]        =   pool_q_only

           super().__init__(**A						)



      @property
      def 						snake_case_ (					self					:     List[Any]						):
           return sum(self.block_sizes						)



      @num_hidden_layers.setter
      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     List[str]						):
           raise NotImplementedError(
               "This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`."						)



      @property
      def 						snake_case_ (					self					:     Any						):
           return len(self.block_sizes						)



      @num_blocks.setter
      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     int						):
           raise NotImplementedError("This model does not support the setting of `num_blocks`. Please set `block_sizes`."						)









"""simple docstring"""




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


_lowerCAmelCase    : Union[str, Any]							=							get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
 _lowerCAmelCase    : Tuple							=							json.load(f)






@require_torch
class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     str					,     A					:     Union[str, Any]						):
           return FSMTTokenizer.from_pretrained(A						)



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[Any]        =   FSMTForConditionalGeneration.from_pretrained(A						).to(A						)
           if torch_device == "cuda":
                model.half()
           return model



      @parameterized.expand(
          [
              ["en-ru", 26.0],
              ["ru-en", 22.0],
              ["en-de", 22.0],
              ["de-en", 29.0],
          ]						)
      @slow
      def 						snake_case_ (					self					:     Any					,     A					:     Dict					,     A					:     List[str]						):
           # note: this test is not testing the best performance since it only evals a small batch
           # but it should be enough to detect a regression in the output quality
           _UpperCAmelCase      :     Any        =   f'facebook/wmt19-{pair}'
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer(A						)
           _UpperCAmelCase      :     Optional[int]        =   self.get_model(A						)

           _UpperCAmelCase      :     int        =   bleu_data[pair]["src"]
           _UpperCAmelCase      :     Optional[int]        =   bleu_data[pair]["tgt"]

           _UpperCAmelCase      :     List[str]        =   tokenizer(A					,     return_tensors="pt"					,     truncation=A					,     padding="longest"						).to(A						)
           _UpperCAmelCase      :     List[str]        =   model.generate(
               input_ids=batch.input_ids					,     num_beams=8					,     )
           _UpperCAmelCase      :     Any        =   tokenizer.batch_decode(
               A					,     skip_special_tokens=A					,     clean_up_tokenization_spaces=A						)
           _UpperCAmelCase      :     Any        =   calculate_bleu(A					,     A						)
           print(A						)
           self.assertGreaterEqual(scores["bleu"]					,     A						)
