



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     _lowerCamelCase       (						self   :  Union[str, Any] ,		A   :  Union[str, Any])   ->    Any:



						"""simple docstring"""






						return FSMTTokenizer.from_pretrained(__lowerCAmelCase)



			def     _lowerCamelCase       (						self   :  List[str] ,		A   :  str)   ->    Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   FSMTForConditionalGeneration.from_pretrained(__lowerCAmelCase).to(__lowerCAmelCase)
						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     _lowerCamelCase       (						self   :  List[str] ,		A   :  Optional[int] ,		A   :  Any)   ->    Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   F"facebook/wmt19-{pair}"
						_UpperCAmelCase		       =   self.get_tokenizer(__lowerCAmelCase)
						_UpperCAmelCase		       =   self.get_model(__lowerCAmelCase)

						_UpperCAmelCase		       =   bleu_data[pair]['''src''']
						_UpperCAmelCase		       =   bleu_data[pair]['''tgt''']

						_UpperCAmelCase		       =   tokenizer(__lowerCAmelCase ,		return_tensors='pt' ,		truncation=__lowerCAmelCase ,		padding='longest').to(__lowerCAmelCase)
						_UpperCAmelCase		       =   model.generate(
						    input_ids=batch.input_ids ,		num_beams=8 ,		)
						_UpperCAmelCase		       =   tokenizer.batch_decode(
						    __lowerCAmelCase ,		skip_special_tokens=__lowerCAmelCase ,		clean_up_tokenization_spaces=__lowerCAmelCase)
						_UpperCAmelCase		       =   calculate_bleu(__lowerCAmelCase ,		__lowerCAmelCase)
						print(__lowerCAmelCase)
						self.assertGreaterEqual(scores['bleu'] ,		__lowerCAmelCase)

'''simple docstring'''

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__a: Optional[int]          =   4
__a: Optional[Any]          =   3



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       pass


def 			__UpperCamelCase       (   UpperCAmelCase     ):
 for shard in shards:
  for i in range(UpperCAmelCase     ):
   yield {"i": i, "shard": shard}
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					int(os.environ['''RANK''']     )
 lowercase__				:    List[str]  =					int(os.environ['''WORLD_SIZE''']     )

 lowercase__				:    Optional[Any]  =					ArgumentParser()
 parser.add_argument('''--streaming'''		,     type=UpperCAmelCase     )
 parser.add_argument('''--local_rank'''		,     type=UpperCAmelCase     )
 parser.add_argument('''--num_workers'''		,     type=UpperCAmelCase		,     default=0     )
 lowercase__				:    List[Any]  =					parser.parse_args()
 lowercase__				:    List[str]  =					args.streaming
 lowercase__				:    str  =					args.num_workers

 lowercase__				:    Optional[int]  =					{'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(UpperCAmelCase     )]}
 lowercase__				:    Tuple  =					IterableDataset.from_generator(UpperCAmelCase		,     gen_kwargs=UpperCAmelCase     )
 if not streaming:
  lowercase__				:    int  =					Dataset.from_list(list(UpperCAmelCase     )     )

 lowercase__				:    str  =					split_dataset_by_node(UpperCAmelCase		,     rank=UpperCAmelCase		,     world_size=UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					torch.utils.data.DataLoader(UpperCAmelCase		,     num_workers=UpperCAmelCase     )

 lowercase__				:    Dict  =					NUM_SHARDS * NUM_ITEMS_PER_SHARD
 lowercase__				:    Optional[Any]  =					full_size // world_size
 expected_local_size += int(rank < (full_size % world_size)     )

 lowercase__				:    Union[str, Any]  =					sum(1 for _ in dataloader     )
 if local_size != expected_local_size:
  raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
  main()

