





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


_lowercase							:      Any      							=get_tests_dir('''fixtures/test_sentencepiece.model''')


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

_lowercase							:      List[str]      							=5_0_0_0_3
_lowercase							:      Optional[Any]      							=5_0_0_0_2



@require_sentencepiece
@require_tokenizers
class  SCREAMING_SNAKE_CASE_ (					UpperCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     Union[str, Any]            =				PLBartTokenizer
  lowercase		:     Optional[Any]            =				None
  lowercase		:     Tuple            =				False







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				List[Any]:
         super().setUp()

         # We have a SentencePiece fixture for testing
         A						:     Optional[Any]         =PLBartTokenizer(_snake_case      ,	language_codes='base'      ,	keep_accents=_snake_case     )
         tokenizer.save_pretrained(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Optional[Any]:
         A						:     Optional[Any]         =PLBartTokenizer(_snake_case      ,	language_codes='base'      ,	keep_accents=_snake_case     )

         A						:     Dict         =tokenizer.tokenize('This is a test'     )
         self.assertListEqual(_snake_case      ,	['▁This', '▁is', '▁a', '▁t', 'est']     )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(_snake_case     )      ,	[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      ,	)

         A						:     str         =tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
         self.assertListEqual(
             _snake_case      ,	[
                 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',
                 '.',
             ]      ,	)
         A						:     Union[str, Any]         =tokenizer.convert_tokens_to_ids(_snake_case     )
         self.assertListEqual(
             _snake_case      ,	[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]      ,	)

         A						:     int         =tokenizer.convert_ids_to_tokens(_snake_case     )
         self.assertListEqual(
             _snake_case      ,	[
                 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>',
                 '.',
             ]      ,	)

         A						:     int         =tokenizer.vocab_size
         A						:     Optional[int]         =[tokenizer.convert_ids_to_tokens(_snake_case     ) for x in range(end - 4      ,	_snake_case     )]

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

         A						:     Tuple         ='java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'
         A						:     List[Any]         =tokenizer(_snake_case     ).input_ids
         self.assertEqual(
             tokenizer.decode(_snake_case      ,	skip_special_tokens=_snake_case      ,	clean_up_tokenization_spaces=_snake_case     )      ,	_snake_case      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Optional[int]:
         A						:     Tuple         =PLBartTokenizer(_snake_case      ,	language_codes='multi'      ,	keep_accents=_snake_case     )

         A						:     List[Any]         =tokenizer.tokenize('This is a test'     )
         self.assertListEqual(_snake_case      ,	['▁This', '▁is', '▁a', '▁t', 'est']     )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(_snake_case     )      ,	[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      ,	)

         A						:     List[str]         =tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
         self.assertListEqual(
             _snake_case      ,	[
                 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',
                 '.',
             ]      ,	)
         A						:     List[str]         =tokenizer.convert_tokens_to_ids(_snake_case     )
         self.assertListEqual(
             _snake_case      ,	[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]      ,	)

         A						:     List[Any]         =tokenizer.convert_ids_to_tokens(_snake_case     )
         self.assertListEqual(
             _snake_case      ,	[
                 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>',
                 '.',
             ]      ,	)
         A						:     Dict         =tokenizer.vocab_size
         A						:     Optional[int]         =[tokenizer.convert_ids_to_tokens(_snake_case     ) for x in range(end - 7      ,	_snake_case     )]

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







@require_torch
@require_sentencepiece
@require_tokenizers
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     List[str]            =				"uclanlp/plbart-python-en_XX"
  lowercase		:     Optional[Any]            =				[
      "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])",
      "def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])",
  ]
  lowercase		:     Dict            =				[
      "Returns the maximum value of a b c.",
      "Sums the values of a b c.",
  ]
  lowercase		:     Tuple            =				[
      134,
      5452,
      33460,
      33441,
      33463,
      33465,
      33463,
      33449,
      988,
      20,
      33456,
      19,
      33456,
      771,
      39,
      4258,
      889,
      3318,
      33441,
      33463,
      33465,
      33463,
      33449,
      2471,
      2,
      PYTHON_CODE,
  ]







  @classmethod
  def 		SCREAMING_SNAKE_CASE_		(				cls    :					Dict     )    ->				Tuple:
         A						:     int         =PLBartTokenizer.from_pretrained(
             cls.checkpoint_name      ,	language_codes='base'      ,	src_lang='python'      ,	tgt_lang='en_XX'     )
         A						:     str         =1
         return cls







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				Optional[int]:
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__java__']      ,	5_00_01     )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__python__']      ,	5_00_02     )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__en_XX__']      ,	5_00_03     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				List[str]:
         A						:     Any         =self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens      ,	_snake_case     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				int:
         self.assertIn(_snake_case      ,	self.tokenizer.all_special_ids     )
         A						:     List[str]         =[EN_CODE, 90_37, 3_34_42, 57, 7_52, 1_53, 14, 56, 18, 9, 2]
         A						:     int         =self.tokenizer.decode(_snake_case      ,	skip_special_tokens=_snake_case     )
         A						:     int         =self.tokenizer.decode(generated_ids[1:]      ,	skip_special_tokens=_snake_case     )
         self.assertEqual(_snake_case      ,	_snake_case     )
         self.assertNotIn(self.tokenizer.eos_token      ,	_snake_case     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Optional[int]:
         A						:     Any         =['def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])' * 20]
         self.assertIsInstance(src_text[0]      ,	_snake_case     )
         A						:     List[str]         =10
         A						:     Any         =self.tokenizer(_snake_case      ,	max_length=_snake_case      ,	truncation=_snake_case     ).input_ids[0]
         self.assertEqual(ids[-2]      ,	2     )
         self.assertEqual(ids[-1]      ,	_snake_case     )
         self.assertEqual(len(_snake_case     )      ,	_snake_case     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Dict:
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', '__java__']     )      ,	[5_00_04, 5_00_01]     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Union[str, Any]:
         A						:     int         =tempfile.mkdtemp()
         A						:     Optional[int]         =self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(_snake_case     )
         A						:     Union[str, Any]         =PLBartTokenizer.from_pretrained(_snake_case     )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,	_snake_case     )







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Union[str, Any]:
         A						:     Tuple         =self.tokenizer(self.src_text      ,	text_target=self.tgt_text      ,	padding=_snake_case      ,	return_tensors='pt'     )
         A						:     Any         =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]      ,	_snake_case     )
         self.assertEqual(batch.decoder_input_ids[1][-1]      ,	2     )
         self.assertEqual(batch.labels[1][-2:].tolist()      ,	[2, EN_CODE]     )







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Tuple:
         A						:     Dict         =self.tokenizer(
             self.src_text      ,	text_target=self.tgt_text      ,	padding=_snake_case      ,	truncation=_snake_case      ,	max_length=len(self.expected_src_tokens     )      ,	return_tensors='pt'      ,	)
         A						:     List[str]         =shift_tokens_right(batch['labels']      ,	self.tokenizer.pad_token_id     )

         self.assertIsInstance(_snake_case      ,	_snake_case     )

         self.assertEqual((2, 26)      ,	batch.input_ids.shape     )
         self.assertEqual((2, 26)      ,	batch.attention_mask.shape     )
         A						:     Any         =batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens      ,	_snake_case     )
         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 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Tuple:
         A						:     Union[str, Any]         =self.tokenizer(self.src_text      ,	padding=_snake_case      ,	truncation=_snake_case      ,	max_length=3      ,	return_tensors='pt'     )
         A						:     Tuple         =self.tokenizer(
             text_target=self.tgt_text      ,	padding=_snake_case      ,	truncation=_snake_case      ,	max_length=10      ,	return_tensors='pt'     )
         A						:     Optional[Any]         =targets['input_ids']
         A						:     Dict         =shift_tokens_right(_snake_case      ,	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 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[str]:
         A						:     int         =self.tokenizer._build_translation_inputs(
             'A test'      ,	return_tensors='pt'      ,	src_lang='en_XX'      ,	tgt_lang='java'     )

         self.assertEqual(
             nested_simplify(_snake_case     )      ,	{
                 # A, test, EOS, en_XX
                 'input_ids': [[1_50, 2_42, 2, 5_00_03]],
                 'attention_mask': [[1, 1, 1, 1]],
                 # java
                 'forced_bos_token_id': 5_00_01,
             }      ,	)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:      int      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				"xglm"
  lowercase		:     Any            =				["past_key_values"]

  lowercase		:     Dict            =				{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=25_60_08      ,	SCREAMING_SNAKE_CASE__    :					Dict=20_48      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					str=40_96      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					Any="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					str=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				int:
         A						:     str         =vocab_size
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Optional[Any]         =d_model
         A						:     Optional[int]         =ffn_dim
         A						:     int         =num_layers
         A						:     Any         =attention_heads
         A						:     Dict         =activation_function
         A						:     List[Any]         =dropout
         A						:     str         =attention_dropout
         A						:     List[Any]         =activation_dropout
         A						:     List[Any]         =layerdrop
         A						:     List[Any]         =init_std
         A						:     Union[str, Any]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     List[str]         =use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)






