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


@require_torch
class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self					,						UpperCamelCase      ):
								return FSMTTokenizer.from_pretrained(UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= FSMTForConditionalGeneration.from_pretrained(UpperCamelCase      ).to(UpperCamelCase      )
								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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								# 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
								_SCREAMING_SNAKE_CASE				= F'facebook/wmt19-{pair}'
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.get_model(UpperCamelCase      )

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

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

'''simple docstring'''







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					int       =					LayoutLMTokenizer
		a							:					Optional[int]       =					LayoutLMTokenizerFast
		a							:					Optional[int]       =					True
		a							:					Any       =					True




		def     lowercase       (				self      ):
								super().setUp()

								_SCREAMING_SNAKE_CASE				= [
								    "[UNK]",
								    "[CLS]",
								    "[SEP]",
								    "want",
								    "##want",
								    "##ed",
								    "wa",
								    "un",
								    "runn",
								    "##ing",
								    ",",
								    "low",
								    "lowest",
								]
								_SCREAMING_SNAKE_CASE				= os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES["vocab_file"]      )
								with open(self.vocab_file					,						"w"					,						encoding="utf-8"      ) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )




		def     lowercase       (				self					,						**UpperCamelCase      ):
								return LayoutLMTokenizer.from_pretrained(self.tmpdirname					,						**UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= "UNwant\u00E9d,running"
								_SCREAMING_SNAKE_CASE				= "unwanted, running"
								return input_text, output_text




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer_class(self.vocab_file      )

								_SCREAMING_SNAKE_CASE				= tokenizer.tokenize("UNwant\u00E9d,running"      )
								self.assertListEqual(UpperCamelCase					,						["un", "##want", "##ed", ",", "runn", "##ing"]      )
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase      )					,						[7, 4, 5, 10, 8, 9]      )




		def     lowercase       (				self      ):
								pass