
'''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


__lowercase							:   Dict					=     get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
					__lowercase							:   Optional[int]					=     json.load(f)

@require_torch
class 	__lowercase				(  unittest.TestCase    ):

			def        UpperCAmelCase__ (self			,   A      ):
								return FSMTTokenizer.from_pretrained(A      )

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      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        UpperCAmelCase__ (self			,   A			,   A      ):
								# 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
								lowerCamelCase_					:      Optional[int]     =		F"""facebook/wmt19-{pair}"""
								lowerCamelCase_					:      Dict     =		self.get_tokenizer(A      )
								lowerCamelCase_					:      Tuple     =		self.get_model(A      )

								lowerCamelCase_					:      Optional[int]     =		bleu_data[pair]['''src''']
								lowerCamelCase_					:      str     =		bleu_data[pair]['''tgt''']

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




'''simple docstring'''


import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__lowercase							:   Tuple					=     logging.getLogger()

__lowercase							:   str					=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class 	__lowercase				(  _lowercase    ):

			def        UpperCAmelCase__ (self			,   A      ):
								os.makedirs(A			,   exist_ok=A      )
								lowerCamelCase_					:      Any     =		{'''source''': '''What is love ?''', '''target''': '''life'''}
								lowerCamelCase_					:      List[str]     =		{'''train''': 1_2, '''val''': 2, '''test''': 2}
								for split in ["train", "test", "val"]:
													for field in ["source", "target"]:
																		lowerCamelCase_					:      List[Any]     =		'''\n'''.join([contents[field]] * n_lines[split]      )
																		with open(os.path.join(A			,   F"""{split}.{field}"""      )			,   '''w'''      ) as f:
																							f.write(A      )

			def        UpperCAmelCase__ (self			,   A			,   A = "pytorch"      ):
								lowerCamelCase_					:      List[str]     =		self.get_auto_remove_tmp_dir()
								lowerCamelCase_					:      Tuple     =		os.path.join(A			,   '''output'''      )
								lowerCamelCase_					:      Union[str, Any]     =		os.path.join(A			,   '''data'''      )
								self._create_dummy_data(data_dir=A      )

								lowerCamelCase_					:      Tuple     =		F"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

								if gpus > 0:
													testargs.append(F"""--gpus={gpus}"""      )
													if is_apex_available():
																		testargs.append('''--fp16'''      )
								else:
													testargs.append('''--gpus=0'''      )
													testargs.append('''--distributed_backend=ddp_cpu'''      )
													testargs.append('''--num_processes=2'''      )

								lowerCamelCase_					:      str     =		[sys.executable, str(Path(finetune_rag.__file__      ).resolve()      )] + testargs
								execute_subprocess_async(A			,   env=self.get_env()      )

								lowerCamelCase_					:      List[Any]     =		os.path.join(A			,   '''metrics.json'''      )
								with open(A      ) as f:
													lowerCamelCase_					:      List[str]     =		json.load(A      )
								return result

			@require_torch_gpu
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		self._run_finetune(gpus=1      )
								self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']			,   0.2      )

			@require_torch_multi_gpu
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		self._run_finetune(gpus=2      )
								self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']			,   0.2      )

			@require_torch_gpu
			@require_ray
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		self._run_finetune(gpus=1			,   distributed_retriever='''ray'''      )
								self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']			,   0.2      )






			@require_torch_multi_gpu
			@require_ray
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Union[str, Any]     =		self._run_finetune(gpus=1			,   distributed_retriever='''ray'''      )
								self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']			,   0.2      )


