





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


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



@require_torch
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       Tuple)	->str:



   '''simple docstring'''



   return FSMTTokenizer.from_pretrained(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       Optional[int])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				int        =FSMTForConditionalGeneration.from_pretrained(UpperCAmelCase_).to(UpperCAmelCase_)
   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 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       Tuple)	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =F"""facebook/wmt19-{pair}"""
   lowerCamelCase__:				str        =self.get_tokenizer(UpperCAmelCase_)
   lowerCamelCase__:				Any        =self.get_model(UpperCAmelCase_)

   lowerCamelCase__:				str        =bleu_data[pair]["src"]
   lowerCamelCase__:				int        =bleu_data[pair]["tgt"]

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








import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__A     =    get_logger(__name__)



class        _SCREAMING_SNAKE_CASE						(     enum.Enum   ):







 '''simple docstring'''
 lowercase_		=      "all_checks"
 lowercase_		=      "basic_checks"
 lowercase_		=      "no_checks"



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a=None		)					->		Optional[int]:







  """simple docstring"""





  if expected_checksums is None:
    logger.info("Unable to verify checksums."		)
    return
  if len(set(__a		) - set(__a		)		) > 0:
    raise ExpectedMoreDownloadedFiles(str(set(__a		) - set(__a		)		)		)
  if len(set(__a		) - set(__a		)		) > 0:
    raise UnexpectedDownloadedFile(str(set(__a		) - set(__a		)		)		)
  lowerCamelCase__:				List[Any]        =[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
  lowerCamelCase__:				Union[str, Any]        =" for " + verification_name if verification_name is not None else ""
  if len(__a		) > 0:
    raise NonMatchingChecksumError(
        F"""Checksums didn't match{for_verification_name}:\n"""
        F"""{bad_urls}\n"""
        "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"		)
  logger.info("All the checksums matched successfully" + for_verification_name		)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Any:







  """simple docstring"""





  if expected_splits is None:
    logger.info("Unable to verify splits sizes."		)
    return
  if len(set(__a		) - set(__a		)		) > 0:
    raise ExpectedMoreSplits(str(set(__a		) - set(__a		)		)		)
  if len(set(__a		) - set(__a		)		) > 0:
    raise UnexpectedSplits(str(set(__a		) - set(__a		)		)		)
  lowerCamelCase__:				Optional[int]        =[
      {"expected": expected_splits[name], "recorded": recorded_splits[name]}
      for name in expected_splits
      if expected_splits[name].num_examples != recorded_splits[name].num_examples
  ]
  if len(__a		) > 0:
    raise NonMatchingSplitsSizesError(str(__a		)		)
  logger.info("All the splits matched successfully."		)





def 					lowerCAmelCase_			(	__a     ,   __a = True		)					->		dict:







  """simple docstring"""





  if record_checksum:
    lowerCamelCase__:				str        =shaaaa()
    with open(__a     ,   "rb"		) as f:
      for chunk in iter(lambda: f.read(1 << 20		)     ,   b""		):
        m.update(__a		)
      lowerCamelCase__:				Dict        =m.hexdigest()
  else:
    lowerCamelCase__:				List[str]        =None
  return {"num_bytes": os.path.getsize(__a		), "checksum": checksum}






def 					lowerCAmelCase_			(	__a		)					->		int:







  """simple docstring"""





  if dataset_size and config.IN_MEMORY_MAX_SIZE:
    return dataset_size < config.IN_MEMORY_MAX_SIZE
  else:
    return False

