



'''simple docstring'''





import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class 			_lowercase							(     A__,   unittest.TestCase			):





	"""simple docstring"""
	__A					=							BartTokenizer
	__A					=							BartTokenizerFast
	__A					=							True
	__A					=							filter_roberta_detectors




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						super().setUp()
						a        =     [
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						a        =     dict(zip(_lowerCAmelCase	, range(len(_lowerCAmelCase	)	)	)	)
						a        =     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						a        =     {"unk_token": "<unk>"}

						a        =     os.path.join(self.tmpdirname	, VOCAB_FILES_NAMES["vocab_file"]	)
						a        =     os.path.join(self.tmpdirname	, VOCAB_FILES_NAMES["merges_file"]	)
						with open(self.vocab_file	, "w"	, encoding="utf-8"	) as fp:
											fp.write(json.dumps(_lowerCAmelCase	) + "\n"	)
						with open(self.merges_file	, "w"	, encoding="utf-8"	) as fp:
											fp.write("\n".join(_lowerCAmelCase	)	)




	def   UpperCamelCase_ (self	, **lowerCamelCase_	):
						"""simple docstring"""

						kwargs.update(self.special_tokens_map	)
						return self.tokenizer_class.from_pretrained(self.tmpdirname	, **_lowerCAmelCase	)




	def   UpperCamelCase_ (self	, **lowerCamelCase_	):
						"""simple docstring"""

						kwargs.update(self.special_tokens_map	)
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	, **_lowerCAmelCase	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						return "lower newer", "lower newer"




	@cached_property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return BartTokenizer.from_pretrained("facebook/bart-large"	)




	@cached_property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return BartTokenizerFast.from_pretrained("facebook/bart-large"	)




	@require_torch
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ["A long paragraph for summarization.", "Another paragraph for summarization."]
						a        =     [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											a        =     tokenizer(_lowerCAmelCase	, max_length=len(_lowerCAmelCase	)	, padding=_lowerCAmelCase	, return_tensors="pt"	)
											self.assertIsInstance(_lowerCAmelCase	, _lowerCAmelCase	)

											self.assertEqual((2, 9)	, batch.input_ids.shape	)
											self.assertEqual((2, 9)	, batch.attention_mask.shape	)
											a        =     batch.input_ids.tolist()[0]
											self.assertListEqual(_lowerCAmelCase	, _lowerCAmelCase	)
											# Test that special tokens are reset




	@require_torch
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ["A long paragraph for summarization.", "Another paragraph for summarization."]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											a        =     tokenizer(_lowerCAmelCase	, padding=_lowerCAmelCase	, return_tensors="pt"	)
											# check if input_ids are returned and no labels
											self.assertIn("input_ids"	, _lowerCAmelCase	)
											self.assertIn("attention_mask"	, _lowerCAmelCase	)
											self.assertNotIn("labels"	, _lowerCAmelCase	)
											self.assertNotIn("decoder_attention_mask"	, _lowerCAmelCase	)




	@require_torch
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     [
						    "Summary of the text.",
						    "Another summary.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											a        =     tokenizer(text_target=_lowerCAmelCase	, max_length=32	, padding="max_length"	, return_tensors="pt"	)
											self.assertEqual(32	, targets["input_ids"].shape[1]	)




	@require_torch
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											a        =     tokenizer(
											    ["I am a small frog" * 1024, "I am a small frog"]	, padding=_lowerCAmelCase	, truncation=_lowerCAmelCase	, return_tensors="pt"	)
											self.assertIsInstance(_lowerCAmelCase	, _lowerCAmelCase	)
											self.assertEqual(batch.input_ids.shape	, (2, 1024)	)




	@require_torch
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ["A long paragraph for summarization."]
						a        =     [
						    "Summary of the text.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											a        =     tokenizer(_lowerCAmelCase	, return_tensors="pt"	)
											a        =     tokenizer(text_target=_lowerCAmelCase	, return_tensors="pt"	)
											a        =     inputs["input_ids"]
											a        =     targets["input_ids"]
											self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
											self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
											self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
											self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						pass




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
																a        =     self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase	, **_lowerCAmelCase	)
																a        =     self.tokenizer_class.from_pretrained(_lowerCAmelCase	, **_lowerCAmelCase	)
																a        =     "A, <mask> AllenNLP sentence."
																a        =     tokenizer_r.encode_plus(_lowerCAmelCase	, add_special_tokens=_lowerCAmelCase	, return_token_type_ids=_lowerCAmelCase	)
																a        =     tokenizer_p.encode_plus(_lowerCAmelCase	, add_special_tokens=_lowerCAmelCase	, return_token_type_ids=_lowerCAmelCase	)

																# token_type_ids should put 0 everywhere
																self.assertEqual(sum(tokens_r["token_type_ids"]	)	, sum(tokens_p["token_type_ids"]	)	)

																# attention_mask should put 1 everywhere, so sum over length should be 1
																self.assertEqual(
																    sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)	, sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)	, )

																a        =     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
																a        =     tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)

																# Rust correctly handles the space before the mask while python doesnt
																self.assertSequenceEqual(tokens_p["input_ids"]	, [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)
																self.assertSequenceEqual(tokens_r["input_ids"]	, [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)

																self.assertSequenceEqual(
																    _lowerCAmelCase	, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
																self.assertSequenceEqual(
																    _lowerCAmelCase	, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls




	@slow
	@require_torch_gpu
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.get_auto_remove_tmp_dir()
						a        =     F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						a        =     get_gpu_count()
						a        =     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
						a        =     [sys.executable] + distributed_args + args
						execute_subprocess_async(lowerCamelCase_	, env=self.get_env()	)

						a        =     os.path.join(lowerCamelCase_	, "test_bleu.json"	)
						a        =     load_json(lowerCamelCase_	)
						# print(metrics)
						self.assertGreaterEqual(metrics["bleu"]	, 25	)
