



import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       LiltForQuestionAnswering,
       LiltForSequenceClassification,
       LiltForTokenClassification,
       LiltModel,
   )
   from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST


class 	lowerCamelCase__	:



 """simple docstring"""

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					Optional[Any]=13			,						__lowerCAmelCase					:					int=7			,						__lowerCAmelCase					:					Any=True			,						__lowerCAmelCase					:					Tuple=True			,						__lowerCAmelCase					:					Dict=True			,						__lowerCAmelCase					:					Optional[Any]=True			,						__lowerCAmelCase					:					Tuple=99			,						__lowerCAmelCase					:					Any=24			,						__lowerCAmelCase					:					Tuple=2			,						__lowerCAmelCase					:					str=6			,						__lowerCAmelCase					:					Optional[Any]=37			,						__lowerCAmelCase					:					Optional[Any]="gelu"			,						__lowerCAmelCase					:					int=0.1			,						__lowerCAmelCase					:					Tuple=0.1			,						__lowerCAmelCase					:					int=5_12			,						__lowerCAmelCase					:					List[Any]=16			,						__lowerCAmelCase					:					Any=2			,						__lowerCAmelCase					:					List[Any]=0.02			,						__lowerCAmelCase					:					Optional[Any]=3			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					Any=10_00			,						)   ->	Tuple:
    _A      =       parent
    _A      =       batch_size
    _A      =       seq_length
    _A      =       is_training
    _A      =       use_input_mask
    _A      =       use_token_type_ids
    _A      =       use_labels
    _A      =       vocab_size
    _A      =       hidden_size
    _A      =       num_hidden_layers
    _A      =       num_attention_heads
    _A      =       intermediate_size
    _A      =       hidden_act
    _A      =       hidden_dropout_prob
    _A      =       attention_probs_dropout_prob
    _A      =       max_position_embeddings
    _A      =       type_vocab_size
    _A      =       type_sequence_label_size
    _A      =       initializer_range
    _A      =       num_labels
    _A      =       scope
    _A      =       range_bbox

 def     snake_case_      (    self					:					int      )   ->	str:
    _A      =       ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size      )

    _A      =       ids_tensor([self.batch_size, self.seq_length, 4]			,						self.range_bbox      )
    # Ensure that bbox is legal
    for i in range(bbox.shape[0]      ):
       for j in range(bbox.shape[1]      ):
          if bbox[i, j, 3] < bbox[i, j, 1]:
             _A      =       bbox[i, j, 3]
             _A      =       bbox[i, j, 1]
             _A      =       t
          if bbox[i, j, 2] < bbox[i, j, 0]:
             _A      =       bbox[i, j, 2]
             _A      =       bbox[i, j, 0]
             _A      =       t

    _A      =       None
    if self.use_input_mask:
       _A      =       ids_tensor([self.batch_size, self.seq_length]			,						vocab_size=2      )

    _A      =       None
    if self.use_token_type_ids:
       _A      =       ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size      )

    _A      =       None
    _A      =       None
    if self.use_labels:
       _A      =       ids_tensor([self.batch_size]			,						self.type_sequence_label_size      )
       _A      =       ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels      )

    _A      =       self.get_config()

    return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels

 def     snake_case_      (    self					:					str      )   ->	Union[str, Any]:
    return LiltConfig(
        vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						initializer_range=self.initializer_range			,						)

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					List[str]			,						)   ->	Dict:
    _A      =       LiltModel(config=__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(__lowerCAmelCase			,						bbox=__lowerCAmelCase			,						attention_mask=__lowerCAmelCase			,						token_type_ids=__lowerCAmelCase      )
    _A      =       model(__lowerCAmelCase			,						bbox=__lowerCAmelCase			,						token_type_ids=__lowerCAmelCase      )
    _A      =       model(__lowerCAmelCase			,						bbox=__lowerCAmelCase      )

    self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)      )
    self.parent.assertEqual(result.pooler_output.shape			,						(self.batch_size, self.hidden_size)      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					str			,						)   ->	Tuple:
    _A      =       self.num_labels
    _A      =       LiltForTokenClassification(config=__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(
        __lowerCAmelCase			,						bbox=__lowerCAmelCase			,						attention_mask=__lowerCAmelCase			,						token_type_ids=__lowerCAmelCase			,						labels=__lowerCAmelCase      )
    self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)      )

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Optional[Any]			,						)   ->	int:
    _A      =       LiltForQuestionAnswering(config=__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(
        __lowerCAmelCase			,						bbox=__lowerCAmelCase			,						attention_mask=__lowerCAmelCase			,						token_type_ids=__lowerCAmelCase			,						start_positions=__lowerCAmelCase			,						end_positions=__lowerCAmelCase			,						)
    self.parent.assertEqual(result.start_logits.shape			,						(self.batch_size, self.seq_length)      )
    self.parent.assertEqual(result.end_logits.shape			,						(self.batch_size, self.seq_length)      )

 def     snake_case_      (    self					:					Dict      )   ->	Dict:
    _A      =       self.prepare_config_and_inputs()
    (
        (
        _A
    )  ,    (
        _A
    )  ,    (
        _A
    )  ,    (
        _A
    )  ,    (
        _A
    )  ,    (
        _A
    )  ,    (
        _A
    )  ,    
    )      =       config_and_inputs
    _A      =       {
        '''input_ids''': input_ids,
        '''bbox''': bbox,
        '''token_type_ids''': token_type_ids,
        '''attention_mask''': input_mask,
    }
    return config, inputs_dict


@require_torch
class 	lowerCamelCase__	(       _A							,  _A							,  _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Tuple =  (
     (
         LiltModel,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 a__					: Optional[Any] =  (
     {
         "feature-extraction": LiltModel,
         "question-answering": LiltForQuestionAnswering,
         "text-classification": LiltForSequenceClassification,
         "token-classification": LiltForTokenClassification,
         "zero-shot": LiltForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 a__					: Optional[int] =  False
 a__					: Any =  False

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					str      )   ->	Optional[int]:
    return True

 def     snake_case_      (    self					:					Tuple      )   ->	Optional[int]:
    _A      =       LiltModelTester(self      )
    _A      =       ConfigTester(self			,						config_class=__lowerCAmelCase			,						hidden_size=37      )

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	str:
    self.config_tester.run_common_tests()

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Dict:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase      )

 def     snake_case_      (    self					:					Any      )   ->	Tuple:
    _A      =       self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
       _A      =       type
       self.model_tester.create_and_check_model(*__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[int]      )   ->	Optional[Any]:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase      )

 def     snake_case_      (    self					:					int      )   ->	Any:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase      )

 @slow
 def     snake_case_      (    self					:					Optional[int]      )   ->	List[Any]:
    for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _A      =       LiltModel.from_pretrained(__lowerCAmelCase      )
       self.assertIsNotNone(__lowerCAmelCase      )


@require_torch
@slow
class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def     snake_case_      (    self					:					str      )   ->	Tuple:
    _A      =       LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''      ).to(__lowerCAmelCase      )

    _A      =       torch.tensor([[1, 2]]			,						device=__lowerCAmelCase      )
    _A      =       torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]			,						device=__lowerCAmelCase      )

    # forward pass
    with torch.no_grad():
       _A      =       model(input_ids=__lowerCAmelCase			,						bbox=__lowerCAmelCase      )

    _A      =       torch.Size([1, 2, 7_68]      )
    _A      =       torch.tensor(
        [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]			,						device=__lowerCAmelCase			,						)

    self.assertTrue(outputs.last_hidden_state.shape			,						__lowerCAmelCase      )
    self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]			,						__lowerCAmelCase			,						atol=1E-3      )      )








import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					"""▁"""

UpperCAmelCase_		 =					{"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

UpperCAmelCase_		 =					{
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

UpperCAmelCase_		 =					{"""vinai/bartpho-syllable""": 1_0_2_4}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: int =  VOCAB_FILES_NAMES
 a__					: Tuple =  PRETRAINED_VOCAB_FILES_MAP
 a__					: Optional[int] =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a__					: Tuple =  ["input_ids", "attention_mask"]

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Union[str, Any]="<s>"			,						__lowerCAmelCase					:					Dict="</s>"			,						__lowerCAmelCase					:					List[Any]="</s>"			,						__lowerCAmelCase					:					Optional[Any]="<s>"			,						__lowerCAmelCase					:					Tuple="<unk>"			,						__lowerCAmelCase					:					int="<pad>"			,						__lowerCAmelCase					:					Optional[Any]="<mask>"			,						__lowerCAmelCase					:					Optional[Dict[str, Any]] = None			,						**__lowerCAmelCase					:					Tuple			,						)   ->	None:
    # Mask token behave like a normal word, i.e. include the space before it
    _A      =       AddedToken(__lowerCAmelCase			,						lstrip=__lowerCAmelCase			,						rstrip=__lowerCAmelCase      ) if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ) else mask_token

    _A      =       {} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=__lowerCAmelCase			,						eos_token=__lowerCAmelCase			,						unk_token=__lowerCAmelCase			,						sep_token=__lowerCAmelCase			,						cls_token=__lowerCAmelCase			,						pad_token=__lowerCAmelCase			,						mask_token=__lowerCAmelCase			,						sp_model_kwargs=self.sp_model_kwargs			,						**__lowerCAmelCase			,						)

    _A      =       vocab_file
    _A      =       monolingual_vocab_file
    _A      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(str(__lowerCAmelCase      )      )

    # Load the reduced vocab

    # Keep order of special tokens for backward compatibility
    _A      =       {}
    _A      =       0
    for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
       if str(__lowerCAmelCase      ) not in self.fairseq_tokens_to_ids:
          _A      =       cnt
          cnt += 1
    with open(__lowerCAmelCase			,						'''r'''			,						encoding='''utf-8'''      ) as f:
       for line in f.readlines():
          _A      =       line.strip().split()[0]
          _A      =       len(self.fairseq_tokens_to_ids      )
    if str(__lowerCAmelCase      ) not in self.fairseq_tokens_to_ids:
       _A      =       len(self.fairseq_tokens_to_ids      )

    _A      =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

 def __getstate__(    self					:					Any      )   ->	List[Any]:
    _A      =       self.__dict__.copy()
    _A      =       None
    _A      =       self.sp_model.serialized_model_proto()
    return state

 def __setstate__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Dict      )   ->	List[Any]:
    _A      =       d

    # for backward compatibility
    if not hasattr(self			,						'''sp_model_kwargs'''      ):
       _A      =       {}

    _A      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None      )   ->	List[int]:

    if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    _A      =       [self.cls_token_id]
    _A      =       [self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None			,						__lowerCAmelCase					:					bool = False      )   ->	List[int]:

    if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__lowerCAmelCase			,						token_ids_a=__lowerCAmelCase			,						already_has_special_tokens=__lowerCAmelCase      )

    if token_ids_a is None:
       return [1] + ([0] * len(__lowerCAmelCase      )) + [1]
    return [1] + ([0] * len(__lowerCAmelCase      )) + [1, 1] + ([0] * len(__lowerCAmelCase      )) + [1]

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None      )   ->	List[int]:
    _A      =       [self.sep_token_id]
    _A      =       [self.cls_token_id]

    if token_ids_a is None:
       return len(cls + token_ids_a + sep      ) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]

 @property
 def     snake_case_      (    self					:					Optional[int]      )   ->	Union[str, Any]:
    return len(self.fairseq_ids_to_tokens      )

 def     snake_case_      (    self					:					Dict      )   ->	Optional[Any]:
    _A      =       {self.convert_ids_to_tokens(__lowerCAmelCase      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					str      )   ->	List[str]:
    return self.sp_model.encode(__lowerCAmelCase			,						out_type=__lowerCAmelCase      )

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Dict:
    if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
    else:
       return self.unk_token_id

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					Optional[int]      )   ->	List[str]:
    return self.fairseq_ids_to_tokens[index]

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Tuple:
    _A      =       ''''''.join(__lowerCAmelCase      ).replace(__lowerCAmelCase			,						''' '''      ).strip()
    return out_string

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[str] = None      )   ->	Tuple[str]:
    if not os.path.isdir(__lowerCAmelCase      ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
       return
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']			,						)

    if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCAmelCase      ) and os.path.isfile(self.vocab_file      ):
       copyfile(self.vocab_file			,						__lowerCAmelCase      )
    elif not os.path.isfile(self.vocab_file      ):
       with open(__lowerCAmelCase			,						'''wb'''      ) as fi:
          _A      =       self.sp_model.serialized_model_proto()
          fi.write(__lowerCAmelCase      )

    if os.path.abspath(self.monolingual_vocab_file      ) != os.path.abspath(
        __lowerCAmelCase      ) and os.path.isfile(self.monolingual_vocab_file      ):
       copyfile(self.monolingual_vocab_file			,						__lowerCAmelCase      )
    elif not os.path.isfile(self.monolingual_vocab_file      ):
       with open(__lowerCAmelCase			,						'''w'''			,						encoding='''utf-8'''      ) as fp:
          for token in self.fairseq_tokens_to_ids:
             if token not in self.all_special_tokens:
                fp.write(f'''{str(__lowerCAmelCase      )} \n'''      )

    return out_vocab_file, out_monolingual_vocab_file



