
"""simple docstring"""





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


_a		       =							logging.get_logger(__name__)

_a		       =							{
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		"""realm"""







  def __init__(		self				,   lowercase_=3_0522				,   lowercase_=768				,   lowercase_=128				,   lowercase_=12				,   lowercase_=12				,   lowercase_=8				,   lowercase_=3072				,   lowercase_="gelu_new"				,   lowercase_=0.1				,   lowercase_=0.1				,   lowercase_=512				,   lowercase_=2				,   lowercase_=0.02				,   lowercase_=1E-1_2				,   lowercase_=256				,   lowercase_=10				,   lowercase_=1E-3				,   lowercase_=5				,   lowercase_=320				,   lowercase_=1335_3718				,   lowercase_=5000				,   lowercase_=1				,   lowercase_=0				,   lowercase_=2				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(pad_token_id=lowercase_				,   bos_token_id=lowercase_				,   eos_token_id=lowercase_				,   **lowercase_  )

     # Common config
     UpperCAmelCase_					:					List[Any]			     =    vocab_size
     UpperCAmelCase_					:					Dict			     =    max_position_embeddings
     UpperCAmelCase_					:					Union[str, Any]			     =    hidden_size
     UpperCAmelCase_					:					List[str]			     =    retriever_proj_size
     UpperCAmelCase_					:					Dict			     =    num_hidden_layers
     UpperCAmelCase_					:					Tuple			     =    num_attention_heads
     UpperCAmelCase_					:					Optional[int]			     =    num_candidates
     UpperCAmelCase_					:					List[str]			     =    intermediate_size
     UpperCAmelCase_					:					List[Any]			     =    hidden_act
     UpperCAmelCase_					:					Optional[int]			     =    hidden_dropout_prob
     UpperCAmelCase_					:					Optional[Any]			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					List[str]			     =    initializer_range
     UpperCAmelCase_					:					Optional[int]			     =    type_vocab_size
     UpperCAmelCase_					:					List[str]			     =    layer_norm_eps

     # Reader config
     UpperCAmelCase_					:					int			     =    span_hidden_size
     UpperCAmelCase_					:					Optional[Any]			     =    max_span_width
     UpperCAmelCase_					:					Dict			     =    reader_layer_norm_eps
     UpperCAmelCase_					:					Optional[int]			     =    reader_beam_size
     UpperCAmelCase_					:					Union[str, Any]			     =    reader_seq_len

     # Retrieval config
     UpperCAmelCase_					:					Union[str, Any]			     =    num_block_records
     UpperCAmelCase_					:					Optional[Any]			     =    searcher_beam_size







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


__UpperCAmelCase          =       get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
    __UpperCAmelCase          =       json.load(f)



@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	FSMTForConditionalGeneration.from_pretrained(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
          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,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Any:
          # 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    :  int							=	F"""facebook/wmt19-{pair}"""
          UpperCamelCase    :  Optional[int]							=	self.get_tokenizer(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	self.get_model(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	bleu_data[pair]['src']
          UpperCamelCase    :  Tuple							=	bleu_data[pair]['tgt']

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



