





'''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


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






@require_torch
class       UpperCamelCase__  (		unittest.TestCase     ):







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   Optional[Any]		):




				'''simple docstring'''
				return FSMTTokenizer.from_pretrained(_lowercase		)







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				a__     = 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			:   int , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   str		):




				'''simple docstring'''
				# 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
				a__     = F'''facebook/wmt19-{pair}'''
				a__     = self.get_tokenizer(_lowercase		)
				a__     = self.get_model(_lowercase		)

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

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







'''simple docstring'''


import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    List[str]:
			a__     = test_file.split(os.path.sep     )
			if components[0:2] != ["tests", "models"]:
						raise ValueError(
						    "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
						    f'''{test_file} instead.'''     )
			a__     = components[-1]
			if not test_fn.endswith("py"     ):
						raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.'''     )
			if not test_fn.startswith("test_modeling_"     ):
						raise ValueError(
						    f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''     )

			a__     = components[:-1] + [test_fn.replace(".py"					,  ""     )]
			a__     = ".".join(__lowerCamelCase     )

			return test_module_path







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]     )   ->    Optional[Any]:
			a__     = get_module_path(__lowerCamelCase     )
			a__     = importlib.import_module(__lowerCamelCase     )

			return test_module







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[int]     )   ->    Union[str, Any]:
			a__     = []
			a__     = get_test_module(__lowerCamelCase     )
			for attr in dir(__lowerCamelCase     ):
						if attr.endswith("ModelTester"     ):
									tester_classes.append(getattr(__lowerCamelCase					,  __lowerCamelCase     )     )

    # sort with class names
			return sorted(__lowerCamelCase					,  key=lambda __lowerCamelCase     : x.__name__     )







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple     )   ->    Tuple:
			a__     = []
			a__     = get_test_module(__lowerCamelCase     )
			for attr in dir(__lowerCamelCase     ):
						a__     = getattr(__lowerCamelCase					,  __lowerCamelCase     )
						# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
						# `all_model_classes` is not empty (which also excludes other special classes).
						a__     = getattr(__lowerCamelCase					,  "all_model_classes"					,  []     )
						if len(__lowerCamelCase     ) > 0:
									test_classes.append(__lowerCamelCase     )

    # sort with class names
			return sorted(__lowerCamelCase					,  key=lambda __lowerCamelCase     : x.__name__     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    List[Any]:
			a__     = get_test_classes(__lowerCamelCase     )
			a__     = set()
			for test_class in test_classes:
						model_classes.update(test_class.all_model_classes     )

			# sort with class names
			return sorted(__lowerCamelCase					,  key=lambda __lowerCamelCase     : x.__name__     )







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    Dict:
			a__     = test_class()
			if hasattr(__lowerCamelCase					,  "setUp"     ):
						test.setUp()

			a__     = None
			if hasattr(__lowerCamelCase					,  "model_tester"     ):
						# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
						if test.model_tester is not None:
									a__     = test.model_tester.__class__

			return model_tester







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     Optional[int]     )   ->    List[Any]:
			a__     = get_test_classes(__lowerCamelCase     )

			a__     = []
			for test_class in test_classes:
						if model_class in test_class.all_model_classes:
									target_test_classes.append(__lowerCamelCase     )

    # sort with class names
			return sorted(__lowerCamelCase					,  key=lambda __lowerCamelCase     : x.__name__     )







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     Tuple     )   ->    Dict:
			a__     = get_test_classes_for_model(__lowerCamelCase					,  __lowerCamelCase     )

			a__     = []
			for test_class in test_classes:
						a__     = get_model_tester_from_test_class(__lowerCamelCase     )
						if tester_class is not None:
									tester_classes.append(__lowerCamelCase     )

    # sort with class names
			return sorted(__lowerCamelCase					,  key=lambda __lowerCamelCase     : x.__name__     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    int:
			a__     = get_test_classes(__lowerCamelCase     )
			a__     = {test_class: get_model_tester_from_test_class(__lowerCamelCase     ) for test_class in test_classes}
			return test_tester_mapping







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    List[Any]:
			a__     = get_model_classes(__lowerCamelCase     )
			a__     = {
			    model_class: get_test_classes_for_model(__lowerCamelCase					,  __lowerCamelCase     ) for model_class in model_classes
			}
			return model_test_mapping







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple     )   ->    Tuple:
			a__     = get_model_classes(__lowerCamelCase     )
			a__     = {
			    model_class: get_tester_classes_for_model(__lowerCamelCase					,  __lowerCamelCase     ) for model_class in model_classes
			}
			return model_to_tester_mapping







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    Dict:
			if isinstance(__lowerCamelCase					,  __lowerCamelCase     ):
						return o
			elif isinstance(__lowerCamelCase					,  __lowerCamelCase     ):
						return o.__name__
			elif isinstance(__lowerCamelCase					,  (list, tuple)     ):
						return [to_json(__lowerCamelCase     ) for x in o]
			elif isinstance(__lowerCamelCase					,  __lowerCamelCase     ):
						return {to_json(__lowerCamelCase     ): to_json(__lowerCamelCase     ) for k, v in o.items()}
			else:
						return o



