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


@require_torch
class 		__UpperCAmelCase   (      unittest.TestCase		):


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								return FSMTTokenizer.from_pretrained(lowerCAmelCase_						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	FSMTForConditionalGeneration.from_pretrained(lowerCAmelCase_						).to(lowerCAmelCase_						)
								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 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	F'facebook/wmt19-{pair}'
								_snake_case     		=	self.get_tokenizer(lowerCAmelCase_						)
								_snake_case     		=	self.get_model(lowerCAmelCase_						)

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

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



'''simple docstring'''







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel

