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







@require_torch
class      A__							(     unittest.TestCase	):






				def 							_lowerCamelCase		(						self					:      List[str]    ,     a					:      Union[str, Any]   ):






							'''simple docstring'''







							return FSMTTokenizer.from_pretrained(a   )






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		FSMTForConditionalGeneration.from_pretrained(a   ).to(a   )
							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 							_lowerCamelCase		(						self					:      int    ,     a					:      List[str]    ,     a					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		f'''facebook/wmt19-{pair}'''
							lowerCAmelCase__      :      Tuple         =		self.get_tokenizer(a   )
							lowerCAmelCase__      :      Any         =		self.get_model(a   )

							lowerCAmelCase__      :      List[str]         =		bleu_data[pair]['src']
							lowerCAmelCase__      :      int         =		bleu_data[pair]['tgt']

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

import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
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      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  MvpTokenizer
				lowercase			    =  MvpTokenizerFast
				lowercase			    =  True
				lowercase			    =  filter_roberta_detectors






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							super().setUp()
							lowerCAmelCase__      :      Dict         =		[
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							lowerCAmelCase__      :      Any         =		dict(zip(a    ,     range(len(a   )   )   )   )
							lowerCAmelCase__      :      Union[str, Any]         =		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							lowerCAmelCase__      :      Any         =		{'unk_token': '<unk>'}

							lowerCAmelCase__      :      int         =		os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['vocab_file']   )
							lowerCAmelCase__      :      Optional[int]         =		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(a   ) + '\n'   )
							with open(self.merges_file    ,     'w'    ,     encoding='utf-8'   ) as fp:
										fp.write('\n'.join(a   )   )






				def 							_lowerCamelCase		(						self					:      str    ,     **a					:      Tuple   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Dict    ,     **a					:      Optional[int]   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Dict   ):






							'''simple docstring'''







							return "lower newer", "lower newer"






				@cached_property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return MvpTokenizer.from_pretrained('RUCAIBox/mvp'   )






				@cached_property
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
							lowerCAmelCase__      :      List[Any]         =		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

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






				@require_torch
				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







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






				@require_torch
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







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






				@require_torch
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      str         =		tokenizer(
										    ['I am a small frog' * 1_024, 'I am a small frog']    ,     padding=a    ,     truncation=a    ,     return_tensors='pt'   )
										self.assertIsInstance(a    ,     a   )
										self.assertEqual(batch.input_ids.shape    ,     (2, 1_024)   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		['A long paragraph for summarization.']
							lowerCAmelCase__      :      Any         =		[
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      List[Any]         =		tokenizer(a    ,     text_target=a    ,     return_tensors='pt'   )
										lowerCAmelCase__      :      Optional[int]         =		inputs['input_ids']
										lowerCAmelCase__      :      str         =		inputs['labels']
										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 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							pass






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
													lowerCAmelCase__      :      str         =		self.rust_tokenizer_class.from_pretrained(a    ,     **a   )
													lowerCAmelCase__      :      List[str]         =		self.tokenizer_class.from_pretrained(a    ,     **a   )
													lowerCAmelCase__      :      Optional[int]         =		'A, <mask> AllenNLP sentence.'
													lowerCAmelCase__      :      int         =		tokenizer_r.encode_plus(a    ,     add_special_tokens=a    ,     return_token_type_ids=a   )
													lowerCAmelCase__      :      Optional[int]         =		tokenizer_p.encode_plus(a    ,     add_special_tokens=a    ,     return_token_type_ids=a   )

													# 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']   )    ,     )

													lowerCAmelCase__      :      List[Any]         =		tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']   )
													lowerCAmelCase__      :      Dict         =		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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]   )
													self.assertSequenceEqual(tokens_r['input_ids']    ,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]   )

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