



'''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


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






@require_torch
class 							lowercase__     (							unittest.TestCase       ):





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    Optional[int]			):







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





	def      UpperCamelCase_  (			self :    int			,lowerCamelCase__ :    str			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       FSMTForConditionalGeneration.from_pretrained(lowerCamelCase__			).to(lowerCamelCase__			)
							if torch_device == "cuda":
													model.half()
							return model





	@parameterized.expand(
	    [
	        ['en-ru', 2_6.0],
	        ['ru-en', 2_2.0],
	        ['en-de', 2_2.0],
	        ['de-en', 2_9.0],
	    ]			)
	@slow
	def      UpperCamelCase_  (			self :    List[Any]			,lowerCamelCase__ :    Optional[int]			,lowerCamelCase__ :    Any			):







							'''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
							_UpperCamelCase :		Optional[int]			    =       F'facebook/wmt19-{pair}'
							_UpperCamelCase :		str			    =       self.get_tokenizer(lowerCamelCase__			)
							_UpperCamelCase :		Optional[int]			    =       self.get_model(lowerCamelCase__			)

							_UpperCamelCase :		Optional[Any]			    =       bleu_data[pair]['src']
							_UpperCamelCase :		int			    =       bleu_data[pair]['tgt']

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







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_			:     Optional[int]  	=  {
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     int  	=  ['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Dict  	=  _LazyModule(__name__, globals()['__file__'], _import_structure)


