




'''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


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






@require_torch
class 						a				(   unittest.TestCase     ):


				def   A_ (		self    :					int       ,				lowercase_    :					int					):
									return FSMTTokenizer.from_pretrained(lowercase_					)


				def   A_ (		self    :					Tuple       ,				lowercase_    :					List[Any]					):
									snake_case_									=				FSMTForConditionalGeneration.from_pretrained(lowercase_					).to(lowercase_					)
									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   A_ (		self    :					Optional[Any]       ,				lowercase_    :					List[Any]       ,				lowercase_    :					List[str]					):
									# 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
									snake_case_									=				F"facebook/wmt19-{pair}"
									snake_case_									=				self.get_tokenizer(lowercase_					)
									snake_case_									=				self.get_model(lowercase_					)

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

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





from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase           =		{'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		['''GPTNeoXTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		[
	    '''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTNeoXForCausalLM''',
	    '''GPTNeoXForQuestionAnswering''',
	    '''GPTNeoXForSequenceClassification''',
	    '''GPTNeoXForTokenClassification''',
	    '''GPTNeoXLayer''',
	    '''GPTNeoXModel''',
	    '''GPTNeoXPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCAmelCase           =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






