




'''simple docstring'''

import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


a    :							List[Any]										=    get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
							from transformers.models.plbart.modeling_plbart import shift_tokens_right

a    :							Optional[int]										=    5_0003
a    :							Union[str, Any]										=    5_0002






@require_sentencepiece
@require_tokenizers
class 		a							(  _lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						PLBartTokenizer
				snake_case_           =						None
				snake_case_           =						False


				def     A_					(						self       :   int   ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									snake_case_		     =							PLBartTokenizer(lowercase_			,							language_codes='''base'''			,							keep_accents=lowercase_   )
									tokenizer.save_pretrained(self.tmpdirname   )


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							PLBartTokenizer(lowercase_			,							language_codes='''base'''			,							keep_accents=lowercase_   )

									snake_case_		     =							tokenizer.tokenize('''This is a test'''   )
									self.assertListEqual(lowercase_			,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowercase_   )			,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,							)

									snake_case_		     =							tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
									self.assertListEqual(
									    lowercase_			,							[
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''9''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''é''',
									        '''.''',
									    ]			,							)
									snake_case_		     =							tokenizer.convert_tokens_to_ids(lowercase_   )
									self.assertListEqual(
									    lowercase_			,							[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									    ]			,							)

									snake_case_		     =							tokenizer.convert_ids_to_tokens(lowercase_   )
									self.assertListEqual(
									    lowercase_			,							[
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''<unk>''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''<unk>''',
									        '''.''',
									    ]			,							)

									snake_case_		     =							tokenizer.vocab_size
									snake_case_		     =							[tokenizer.convert_ids_to_tokens(lowercase_   ) for x in range(end - 4			,							lowercase_   )]

									self.assertListEqual(lowercase_			,							['''__java__''', '''__python__''', '''__en_XX__''', '''<mask>''']   )

									snake_case_		     =							'''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
									snake_case_		     =							tokenizer(lowercase_   ).input_ids
									self.assertEqual(
									    tokenizer.decode(lowercase_			,							skip_special_tokens=lowercase_			,							clean_up_tokenization_spaces=lowercase_   )			,							lowercase_			,							)


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							PLBartTokenizer(lowercase_			,							language_codes='''multi'''			,							keep_accents=lowercase_   )

									snake_case_		     =							tokenizer.tokenize('''This is a test'''   )
									self.assertListEqual(lowercase_			,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowercase_   )			,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,							)

									snake_case_		     =							tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
									self.assertListEqual(
									    lowercase_			,							[
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''9''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''é''',
									        '''.''',
									    ]			,							)
									snake_case_		     =							tokenizer.convert_tokens_to_ids(lowercase_   )
									self.assertListEqual(
									    lowercase_			,							[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									    ]			,							)

									snake_case_		     =							tokenizer.convert_ids_to_tokens(lowercase_   )
									self.assertListEqual(
									    lowercase_			,							[
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''<unk>''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''<unk>''',
									        '''.''',
									    ]			,							)
									snake_case_		     =							tokenizer.vocab_size
									snake_case_		     =							[tokenizer.convert_ids_to_tokens(lowercase_   ) for x in range(end - 7			,							lowercase_   )]

									self.assertListEqual(
									    lowercase_			,							['''__java__''', '''__python__''', '''__en_XX__''', '''__javascript__''', '''__php__''', '''__ruby__''', '''__go__''']   )
									snake_case_		     =							'''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
									snake_case_		     =							tokenizer(lowercase_   ).input_ids
									self.assertEqual(
									    tokenizer.decode(lowercase_			,							skip_special_tokens=lowercase_			,							clean_up_tokenization_spaces=lowercase_   )			,							lowercase_			,							)







@require_torch
@require_sentencepiece
@require_tokenizers
class 		a							(  unittest.TestCase	):
				snake_case_           =						"uclanlp/plbart-python-en_XX"
				snake_case_           =						[
				    "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])",
				    "def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])",
				]
				snake_case_           =						[
				    "Returns the maximum value of a b c.",
				    "Sums the values of a b c.",
				]
				snake_case_           =						[
				    134,
				    5_452,
				    33_460,
				    33_441,
				    33_463,
				    33_465,
				    33_463,
				    33_449,
				    988,
				    20,
				    33_456,
				    19,
				    33_456,
				    771,
				    39,
				    4_258,
				    889,
				    3_318,
				    33_441,
				    33_463,
				    33_465,
				    33_463,
				    33_449,
				    2_471,
				    2,
				    PYTHON_CODE,
				]


				@classmethod
				def     A_					(						cls       :   Tuple   ):
									snake_case_		     =							PLBartTokenizer.from_pretrained(
									    cls.checkpoint_name			,							language_codes='''base'''			,							src_lang='''python'''			,							tgt_lang='''en_XX'''   )
									snake_case_		     =							1
									return cls


				def     A_					(						self       :   Optional[Any]   ):
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__java__''']			,							5_0001   )
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__python__''']			,							5_0002   )
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__en_XX__''']			,							5_0003   )


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
									self.assertListEqual(self.expected_src_tokens			,							lowercase_   )


				def     A_					(						self       :   List[Any]   ):
									self.assertIn(lowercase_			,							self.tokenizer.all_special_ids   )
									snake_case_		     =							[EN_CODE, 9037, 3_3442, 57, 752, 153, 14, 56, 18, 9, 2]
									snake_case_		     =							self.tokenizer.decode(lowercase_			,							skip_special_tokens=lowercase_   )
									snake_case_		     =							self.tokenizer.decode(generated_ids[1:]			,							skip_special_tokens=lowercase_   )
									self.assertEqual(lowercase_			,							lowercase_   )
									self.assertNotIn(self.tokenizer.eos_token			,							lowercase_   )


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							['''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''' * 20]
									self.assertIsInstance(src_text[0]			,							lowercase_   )
									snake_case_		     =							10
									snake_case_		     =							self.tokenizer(lowercase_			,							max_length=lowercase_			,							truncation=lowercase_   ).input_ids[0]
									self.assertEqual(ids[-2]			,							2   )
									self.assertEqual(ids[-1]			,							lowercase_   )
									self.assertEqual(len(lowercase_   )			,							lowercase_   )


				def     A_					(						self       :   List[Any]   ):
									self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''__java__''']   )			,							[5_0004, 5_0001]   )


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							tempfile.mkdtemp()
									snake_case_		     =							self.tokenizer.fairseq_tokens_to_ids
									self.tokenizer.save_pretrained(lowercase_   )
									snake_case_		     =							PLBartTokenizer.from_pretrained(lowercase_   )
									self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,							lowercase_   )


				@require_torch
				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							self.tokenizer(self.src_text			,							text_target=self.tgt_text			,							padding=lowercase_			,							return_tensors='''pt'''   )
									snake_case_		     =							shift_tokens_right(batch['''labels''']			,							self.tokenizer.pad_token_id   )

									# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
									self.assertEqual(batch.input_ids[1][-2:].tolist()			,							[2, PYTHON_CODE]   )
									self.assertEqual(batch.decoder_input_ids[1][0]			,							lowercase_   )
									self.assertEqual(batch.decoder_input_ids[1][-1]			,							2   )
									self.assertEqual(batch.labels[1][-2:].tolist()			,							[2, EN_CODE]   )


				@require_torch
				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							self.tokenizer(
									    self.src_text			,							text_target=self.tgt_text			,							padding=lowercase_			,							truncation=lowercase_			,							max_length=len(self.expected_src_tokens   )			,							return_tensors='''pt'''			,							)
									snake_case_		     =							shift_tokens_right(batch['''labels''']			,							self.tokenizer.pad_token_id   )

									self.assertIsInstance(lowercase_			,							lowercase_   )

									self.assertEqual((2, 26)			,							batch.input_ids.shape   )
									self.assertEqual((2, 26)			,							batch.attention_mask.shape   )
									snake_case_		     =							batch.input_ids.tolist()[0]
									self.assertListEqual(self.expected_src_tokens			,							lowercase_   )
									self.assertEqual(2			,							batch.decoder_input_ids[0, -1]   )  # EOS
									# Test that special tokens are reset
									self.assertEqual(self.tokenizer.prefix_tokens			,							[]   )
									self.assertEqual(self.tokenizer.suffix_tokens			,							[self.tokenizer.eos_token_id, PYTHON_CODE]   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.tokenizer(self.src_text			,							padding=lowercase_			,							truncation=lowercase_			,							max_length=3			,							return_tensors='''pt'''   )
									snake_case_		     =							self.tokenizer(
									    text_target=self.tgt_text			,							padding=lowercase_			,							truncation=lowercase_			,							max_length=10			,							return_tensors='''pt'''   )
									snake_case_		     =							targets['''input_ids''']
									snake_case_		     =							shift_tokens_right(lowercase_			,							self.tokenizer.pad_token_id   )

									self.assertEqual(batch.input_ids.shape[1]			,							3   )
									self.assertEqual(batch.decoder_input_ids.shape[1]			,							10   )


				@require_torch
				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							self.tokenizer._build_translation_inputs(
									    '''A test'''			,							return_tensors='''pt'''			,							src_lang='''en_XX'''			,							tgt_lang='''java'''   )

									self.assertEqual(
									    nested_simplify(lowercase_   )			,							{
									        # A, test, EOS, en_XX
									        '''input_ids''': [[150, 242, 2, 5_0003]],
									        '''attention_mask''': [[1, 1, 1, 1]],
									        # java
									        '''forced_bos_token_id''': 5_0001,
									    }			,							)







'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class 		a							(  unittest.TestCase	):
				snake_case_           =						ViTImageProcessor if is_vision_available() else None


				@property
				def     A_					(						self       :   List[str]   ):
									return self.image_processor_tester.prepare_image_processor_dict()


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							(3, 32, 128)
									snake_case_		     =							tempfile.mkdtemp()

									# fmt: off
									snake_case_		     =							['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
									# fmt: on
									snake_case_		     =							dict(zip(lowercase_			,							range(len(lowercase_   )   )   )   )

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

									snake_case_		     =							{
									    '''do_normalize''': False,
									    '''do_resize''': True,
									    '''image_processor_type''': '''ViTImageProcessor''',
									    '''resample''': 3,
									    '''size''': {'''height''': 32, '''width''': 128},
									}
									snake_case_		     =							os.path.join(self.tmpdirname			,							lowercase_   )
									with open(self.image_processor_file			,							'''w'''			,							encoding='''utf-8'''   ) as fp:
														json.dump(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any			,							**lowercase_       :   str   ):
									return MgpstrTokenizer.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   List[str]			,							**lowercase_       :   List[Any]   ):
									return ViTImageProcessor.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   Union[str, Any]   ):
									shutil.rmtree(self.tmpdirname   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta   )

									snake_case_		     =							Image.fromarray(np.moveaxis(lowercase_			,							0			,							-1   )   )

									return image_input


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor.save_pretrained(self.tmpdirname   )
									snake_case_		     =							MgpstrProcessor.from_pretrained(self.tmpdirname			,							use_fast=lowercase_   )

									self.assertEqual(processor.char_tokenizer.get_vocab()			,							tokenizer.get_vocab()   )
									self.assertIsInstance(processor.char_tokenizer			,							lowercase_   )

									self.assertEqual(processor.image_processor.to_json_string()			,							image_processor.to_json_string()   )
									self.assertIsInstance(processor.image_processor			,							lowercase_   )


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor.save_pretrained(self.tmpdirname   )

									snake_case_		     =							self.get_tokenizer(bos_token='''(BOS)'''			,							eos_token='''(EOS)'''   )
									snake_case_		     =							self.get_image_processor(do_normalize=lowercase_			,							padding_value=1.0   )

									snake_case_		     =							MgpstrProcessor.from_pretrained(
									    self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=lowercase_			,							padding_value=1.0   )

									self.assertEqual(processor.char_tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab()   )
									self.assertIsInstance(processor.char_tokenizer			,							lowercase_   )

									self.assertEqual(processor.image_processor.to_json_string()			,							image_processor_add_kwargs.to_json_string()   )
									self.assertIsInstance(processor.image_processor			,							lowercase_   )


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							image_processor(lowercase_			,							return_tensors='''np'''   )
									snake_case_		     =							processor(images=lowercase_			,							return_tensors='''np'''   )

									for key in input_image_proc.keys():
														self.assertAlmostEqual(input_image_proc[key].sum()			,							input_processor[key].sum()			,							delta=1e-2   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'''test'''

									snake_case_		     =							processor(text=lowercase_   )

									snake_case_		     =							tokenizer(lowercase_   )
									for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'''test'''
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

									self.assertListEqual(list(inputs.keys()   )			,							['''pixel_values''', '''labels''']   )

									# test if it raises when no input is passed
									with pytest.raises(lowercase_   ):
														processor()


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

									snake_case_		     =							processor.char_decode(lowercase_   )
									snake_case_		     =							tokenizer.batch_decode(lowercase_   )
									snake_case_		     =							[seq.replace(''' '''			,							''''''   ) for seq in decoded_tok]

									self.assertListEqual(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							None
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

									self.assertListEqual(list(inputs.keys()   )			,							processor.model_input_names   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							torch.randn(1			,							27			,							38   )
									snake_case_		     =							torch.randn(1			,							27			,							5_0257   )
									snake_case_		     =							torch.randn(1			,							27			,							3_0522   )

									snake_case_		     =							processor.batch_decode([char_input, bpe_input, wp_input]   )

									self.assertListEqual(list(results.keys()   )			,							['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']   )

