


import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
     from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
     snake_case							:       Optional[int]										=	get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
     from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

snake_case							:       Dict										=	12_80_22
snake_case							:       Optional[Any]										=	12_80_28







@require_sentencepiece
class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			MaMaaaTokenizer
 SCREAMING_SNAKE_CASE__ =			False
 SCREAMING_SNAKE_CASE__ =			False
 SCREAMING_SNAKE_CASE__ =			True




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

       a		:Dict									=			['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
       a		:Any									=			dict(zip(_lowerCamelCase   ,    range(len(_lowerCamelCase       )       )       )       )
       a		:Any									=			Path(self.tmpdirname       )
       save_json(_lowerCamelCase   ,    save_dir / VOCAB_FILES_NAMES['''vocab_file''']       )
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
             copyfile(_lowerCamelCase   ,    save_dir / VOCAB_FILES_NAMES['''spm_file''']       )

       a		:Optional[Any]									=			MaMaaaTokenizer.from_pretrained(self.tmpdirname       )
       tokenizer.save_pretrained(self.tmpdirname       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    **_lowerCamelCase       ):
       return MaMaaaTokenizer.from_pretrained(self.tmpdirname   ,    **_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       return (
           "This is a test",
           "This is a test",
       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			'''</s>'''
       a		:int									=			0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase       )   ,    _lowerCamelCase       )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase       )   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			self.get_tokenizer()
       a		:List[str]									=			list(tokenizer.get_vocab().keys()       )

       self.assertEqual(vocab_keys[0]   ,    '''</s>'''       )
       self.assertEqual(vocab_keys[1]   ,    '''<unk>'''       )
       self.assertEqual(vocab_keys[-1]   ,    '''<s>'''       )
       self.assertEqual(len(_lowerCamelCase       )   ,    tokenizer.vocab_size + len(tokenizer.get_added_vocab()       )       )




 @unittest.skip('''Skip this test while all models are still to be uploaded.'''       )
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			self.get_tokenizer()

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

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(_lowerCamelCase       )   ,    [2, 3, 4, 5, 6]   ,    )

       a		:Optional[int]									=			tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]       )
       self.assertListEqual(_lowerCamelCase   ,    ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

       a		:List[str]									=			tokenizer.convert_tokens_to_string(_lowerCamelCase       )
       self.assertEqual(_lowerCamelCase   ,    '''This is a test'''       )




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # fmt: off
       a		:Any									=			{'''input_ids''': [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_lowerCamelCase   ,    model_name='''facebook/m2m100_418M'''   ,    revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''   ,    )







@require_torch
@require_sentencepiece
@require_tokenizers
class 						_snake_case    ( unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			'facebook/m2m100_418M'
 SCREAMING_SNAKE_CASE__ =			[
     'In my opinion, there are two levels of response from the French government.',
     'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
 ]
 SCREAMING_SNAKE_CASE__ =			[
     'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
     'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
 ]

 # fmt: off
 SCREAMING_SNAKE_CASE__ =			[EN_CODE, 593, 1949, 11_5781, 4, 7_1586, 4234, 6_0633, 12_6233, 432, 12_3808, 1_5592, 1197, 11_7132, 12_0618, 5, 2]




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						cls       ):
       a		:MaMaaaTokenizer									=			MaMaaaTokenizer.from_pretrained(
           cls.checkpoint_name   ,    src_lang='''en'''   ,    tgt_lang='''fr'''       )
       a		:Any									=			1
       return cls




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       self.assertEqual(self.tokenizer.get_lang_id('''ar'''       )   ,    12_8006       )
       self.assertEqual(self.tokenizer.get_lang_id('''en'''       )   ,    12_8022       )
       self.assertEqual(self.tokenizer.get_lang_id('''ro'''       )   ,    12_8076       )
       self.assertEqual(self.tokenizer.get_lang_id('''mr'''       )   ,    12_8063       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			self.tokenizer.get_vocab()
       self.assertEqual(len(_lowerCamelCase       )   ,    self.tokenizer.vocab_size       )
       self.assertEqual(vocab['''<unk>''']   ,    3       )
       self.assertIn(self.tokenizer.get_lang_token('''en'''       )   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			'''en'''
       a		:Union[str, Any]									=			self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       self.assertIn(_lowerCamelCase   ,    self.tokenizer.all_special_ids       )
       # fmt: off
       a		:Tuple									=			[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
       # fmt: on
       a		:List[Any]									=			self.tokenizer.decode(_lowerCamelCase   ,    skip_special_tokens=_lowerCamelCase       )
       a		:List[Any]									=			self.tokenizer.decode(generated_ids[1:]   ,    skip_special_tokens=_lowerCamelCase       )
       self.assertEqual(_lowerCamelCase   ,    _lowerCamelCase       )
       self.assertNotIn(self.tokenizer.eos_token   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:str									=			tempfile.mkdtemp()
       a		:Dict									=			self.tokenizer.lang_token_to_id
       self.tokenizer.save_pretrained(_lowerCamelCase       )
       a		:List[str]									=			MaMaaaTokenizer.from_pretrained(_lowerCamelCase       )
       self.assertDictEqual(new_tok.lang_token_to_id   ,    _lowerCamelCase       )




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			'''en'''
       a		:str									=			'''fr'''

       a		:Dict									=			self.tokenizer(self.src_text   ,    text_target=self.tgt_text   ,    padding=_lowerCamelCase   ,    return_tensors='''pt'''       )

       a		:Dict									=			shift_tokens_right(
           batch['''labels''']   ,    self.tokenizer.pad_token_id   ,    self.tokenizer.eos_token_id       )

       for k in batch:
             a		:Tuple									=			batch[k].tolist()
       # batch = {k: v.tolist() for k,v in batch.items()}
       # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
       # batch.decoder_inputs_ids[0][0] ==
       assert batch.input_ids[1][0] == EN_CODE
       assert batch.input_ids[1][-1] == 2
       assert batch.labels[1][0] == FR_CODE
       assert batch.labels[1][-1] == 2
       assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[Any]									=			'''mr'''
       self.assertListEqual(self.tokenizer.prefix_tokens   ,    [self.tokenizer.get_lang_id('''mr'''       )]       )
       self.assertListEqual(self.tokenizer.suffix_tokens   ,    [self.tokenizer.eos_token_id]       )

       a		:List[str]									=			'''zh'''
       self.assertListEqual(self.tokenizer.prefix_tokens   ,    [self.tokenizer.get_lang_id('''zh'''       )]       )
       self.assertListEqual(self.tokenizer.suffix_tokens   ,    [self.tokenizer.eos_token_id]       )




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			'''mr'''
       self.tokenizer._switch_to_target_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens   ,    [self.tokenizer.get_lang_id('''mr'''       )]       )
       self.assertListEqual(self.tokenizer.suffix_tokens   ,    [self.tokenizer.eos_token_id]       )
       self.tokenizer._switch_to_input_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens   ,    [self.tokenizer.get_lang_id(self.tokenizer.src_lang       )]       )

       a		:Dict									=			'''zh'''
       self.tokenizer._switch_to_target_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens   ,    [self.tokenizer.get_lang_id('''zh'''       )]       )
       self.assertListEqual(self.tokenizer.suffix_tokens   ,    [self.tokenizer.eos_token_id]       )
       self.tokenizer._switch_to_input_mode()
       self.assertListEqual(self.tokenizer.prefix_tokens   ,    [self.tokenizer.get_lang_id(self.tokenizer.src_lang       )]       )




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			self.tokenizer._build_translation_inputs('''A test'''   ,    return_tensors='''pt'''   ,    src_lang='''en'''   ,    tgt_lang='''ar'''       )

       self.assertEqual(
           nested_simplify(_lowerCamelCase       )   ,    {
               # en_XX, A, test, EOS
               '''input_ids''': [[12_8022, 58, 4183, 2]],
               '''attention_mask''': [[1, 1, 1, 1]],
               # ar_AR
               '''forced_bos_token_id''': 12_8006,
           }   ,    )









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None







class 						_snake_case    ( _snake_case				):




 def __init__(						self   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    _lowerCamelCase=2   ,    _lowerCamelCase=512   ,    _lowerCamelCase="cls"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    **_lowerCamelCase   ,    ):
       super().__init__(pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase   ,    **_lowerCamelCase       )
       a		:Tuple									=			project_dim
       a		:Optional[int]									=			pooler_fn
       a		:int									=			learn_encoder
       a		:int									=			use_attention_mask







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			[r'pooler', r'logit_scale']
 SCREAMING_SNAKE_CASE__ =			[r'position_ids', r'predictions.decoder.bias']
 SCREAMING_SNAKE_CASE__ =			'roberta'
 SCREAMING_SNAKE_CASE__ =			RobertaSeriesConfig




 def __init__(						self   ,    _lowerCamelCase       ):
       super().__init__(_lowerCamelCase       )
       a		:Tuple									=			XLMRobertaModel(_lowerCamelCase       )
       a		:Optional[Any]									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
       a		:Optional[int]									=			getattr(_lowerCamelCase   ,    '''has_pre_transformation'''   ,    _lowerCamelCase       )
       if self.has_pre_transformation:
             a		:Tuple									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
             a		:Union[str, Any]									=			nn.LayerNorm(config.hidden_size   ,    eps=config.layer_norm_eps       )
       self.post_init()




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    ):
       a		:Optional[int]									=			return_dict if return_dict is not None else self.config.use_return_dict

       a		:int									=			self.base_model(
           input_ids=_lowerCamelCase   ,    attention_mask=_lowerCamelCase   ,    token_type_ids=_lowerCamelCase   ,    position_ids=_lowerCamelCase   ,    head_mask=_lowerCamelCase   ,    inputs_embeds=_lowerCamelCase   ,    encoder_hidden_states=_lowerCamelCase   ,    encoder_attention_mask=_lowerCamelCase   ,    output_attentions=_lowerCamelCase   ,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states   ,    return_dict=_lowerCamelCase   ,    )

       if self.has_pre_transformation:
             a		:Optional[int]									=			outputs['''hidden_states'''][-2]
             a		:List[Any]									=			self.pre_LN(_lowerCamelCase       )
             a		:Optional[Any]									=			self.transformation_pre(_lowerCamelCase       )

             return TransformationModelOutput(
                 projection_state=_lowerCamelCase   ,    last_hidden_state=outputs.last_hidden_state   ,    hidden_states=outputs.hidden_states   ,    attentions=outputs.attentions   ,    )
       else:
             a		:List[str]									=			self.transformation(outputs.last_hidden_state       )
             return TransformationModelOutput(
                 projection_state=_lowerCamelCase   ,    last_hidden_state=outputs.last_hidden_state   ,    hidden_states=outputs.hidden_states   ,    attentions=outputs.attentions   ,    )





