




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       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								return FSMTTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								# 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      =			F"facebook/wmt19-{pair}"
								_UpperCAmelCase      =			self.get_tokenizer(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.get_model(_SCREAMING_SNAKE_CASE			)

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

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





import logging

from transformers.configuration_utils import PretrainedConfig


a													=			logging.getLogger(__name__)




class       _A				(		__lowercase     ):
		__a					    =       """masked_bert"""






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3_0522						,		_SCREAMING_SNAKE_CASE=768						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=3072						,		_SCREAMING_SNAKE_CASE="gelu"						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=512						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=0.02						,		_SCREAMING_SNAKE_CASE=1e-12						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE="topK"						,		_SCREAMING_SNAKE_CASE="constant"						,		_SCREAMING_SNAKE_CASE=0.0						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			vocab_size
								_UpperCAmelCase      =			hidden_size
								_UpperCAmelCase      =			num_hidden_layers
								_UpperCAmelCase      =			num_attention_heads
								_UpperCAmelCase      =			hidden_act
								_UpperCAmelCase      =			intermediate_size
								_UpperCAmelCase      =			hidden_dropout_prob
								_UpperCAmelCase      =			attention_probs_dropout_prob
								_UpperCAmelCase      =			max_position_embeddings
								_UpperCAmelCase      =			type_vocab_size
								_UpperCAmelCase      =			initializer_range
								_UpperCAmelCase      =			layer_norm_eps
								_UpperCAmelCase      =			pruning_method
								_UpperCAmelCase      =			mask_init
								_UpperCAmelCase      =			mask_scale