






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  _lowercase   :
      def __init__(     self				,			a				,			a=1_3				,			a=7				,			a=True				,			a=True				,			a=True				,			a=True				,			a=9_9				,			a=2_4				,			a=2				,			a=6				,			a=3_7				,			a="gelu"				,			a=0.1				,			a=0.1				,			a=5_1_2				,			a=1_6				,			a=2				,			a=0.02				,			a=3				,			a=None				,			a=1_0_0_0				,			):
       snake_case__  :    str							       =parent
       snake_case__  :    Optional[int]							       =batch_size
       snake_case__  :    str							       =seq_length
       snake_case__  :    List[str]							       =is_training
       snake_case__  :    List[str]							       =use_input_mask
       snake_case__  :    Any							       =use_token_type_ids
       snake_case__  :    Any							       =use_labels
       snake_case__  :    List[str]							       =vocab_size
       snake_case__  :    int							       =hidden_size
       snake_case__  :    Union[str, Any]							       =num_hidden_layers
       snake_case__  :    Any							       =num_attention_heads
       snake_case__  :    Optional[int]							       =intermediate_size
       snake_case__  :    int							       =hidden_act
       snake_case__  :    Tuple							       =hidden_dropout_prob
       snake_case__  :    List[Any]							       =attention_probs_dropout_prob
       snake_case__  :    Union[str, Any]							       =max_position_embeddings
       snake_case__  :    str							       =type_vocab_size
       snake_case__  :    Union[str, Any]							       =type_sequence_label_size
       snake_case__  :    int							       =initializer_range
       snake_case__  :    Any							       =num_labels
       snake_case__  :    Optional[Any]							       =scope
       snake_case__  :    Optional[Any]							       =range_bbox
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size   )

       snake_case__  :    Any							       =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]:
          snake_case__  :    Tuple							       =bbox[i, j, 3]
          snake_case__  :    int							       =bbox[i, j, 1]
          snake_case__  :    Union[str, Any]							       =t
         if bbox[i, j, 2] < bbox[i, j, 0]:
          snake_case__  :    List[Any]							       =bbox[i, j, 2]
          snake_case__  :    List[str]							       =bbox[i, j, 0]
          snake_case__  :    Dict							       =t

       snake_case__  :    Optional[Any]							       =None
       if self.use_input_mask:
        snake_case__  :    Dict							       =ids_tensor([self.batch_size, self.seq_length]				,			vocab_size=2   )

       snake_case__  :    List[str]							       =None
       if self.use_token_type_ids:
        snake_case__  :    Optional[int]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size   )

       snake_case__  :    Union[str, Any]							       =None
       snake_case__  :    List[str]							       =None
       if self.use_labels:
        snake_case__  :    str							       =ids_tensor([self.batch_size]				,			self.type_sequence_label_size   )
        snake_case__  :    Optional[int]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.num_labels   )

       snake_case__  :    List[Any]							       =self.get_config()

       return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
      def 						lowercase__					(     self   ):
       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 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a				,			a				,			a				,			):
       snake_case__  :    Any							       =LiltModel(config=a   )
       model.to(a   )
       model.eval()
       snake_case__  :    Optional[Any]							       =model(a				,			bbox=a				,			attention_mask=a				,			token_type_ids=a   )
       snake_case__  :    Tuple							       =model(a				,			bbox=a				,			token_type_ids=a   )
       snake_case__  :    List[Any]							       =model(a				,			bbox=a   )

       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 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a				,			a				,			a				,			):
       snake_case__  :    Tuple							       =self.num_labels
       snake_case__  :    str							       =LiltForTokenClassification(config=a   )
       model.to(a   )
       model.eval()
       snake_case__  :    int							       =model(
           a				,			bbox=a				,			attention_mask=a				,			token_type_ids=a				,			labels=a   )
       self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.num_labels)   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a				,			a				,			a				,			):
       snake_case__  :    int							       =LiltForQuestionAnswering(config=a   )
       model.to(a   )
       model.eval()
       snake_case__  :    str							       =model(
           a				,			bbox=a				,			attention_mask=a				,			token_type_ids=a				,			start_positions=a				,			end_positions=a				,			)
       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 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.prepare_config_and_inputs()
       (
           (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   (
           snake_case__
       )       ,   
       )  :    Optional[int]							       =config_and_inputs
       snake_case__  :    str							       ={
           """input_ids""": input_ids,
           """bbox""": bbox,
           """token_type_ids""": token_type_ids,
           """attention_mask""": input_mask,
       }
       return config, inputs_dict







@require_torch
class  _lowercase   (   _A						,				_A						,				_A						,				unittest.TestCase						):
      _a							:						Dict										=							(
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      _a							:						int										=							(
          {
              'feature-extraction': LiltModel,
              'question-answering': LiltForQuestionAnswering,
              'text-classification': LiltForSequenceClassification,
              'token-classification': LiltForTokenClassification,
              'zero-shot': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _a							:						Union[str, Any]										=							False
      _a							:						Optional[int]										=							False
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a   ):
       return True
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =LiltModelTester(self   )
       snake_case__  :    str							       =ConfigTester(self				,			config_class=a				,			hidden_size=3_7   )
      def 						lowercase__					(     self   ):
       self.config_tester.run_common_tests()
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
        snake_case__  :    Optional[int]							       =type
        self.model_tester.create_and_check_model(*a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*a   )



      @slow
      def 						lowercase__					(     self   ):
       for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        snake_case__  :    Tuple							       =LiltModel.from_pretrained(a   )
        self.assertIsNotNone(a   )







@require_torch
@slow
class  _lowercase   (   unittest.TestCase						):
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""   ).to(a   )

       snake_case__  :    Optional[int]							       =torch.tensor([[1, 2]]				,			device=a   )
       snake_case__  :    List[str]							       =torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]				,			device=a   )

       # forward pass
       with torch.no_grad():
        snake_case__  :    List[str]							       =model(input_ids=a				,			bbox=a   )

       snake_case__  :    Tuple							       =torch.Size([1, 2, 7_6_8]   )
       snake_case__  :    int							       =torch.tensor(
           [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]				,			device=a				,			)

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













import os
import re
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


__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)

__lowerCamelCase	:		Any										=   {"""vocab_file""": """spiece.model"""}

__lowerCamelCase	:		Optional[Any]										=   {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

__lowerCamelCase	:		List[str]										=   {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}







class  _lowercase   (   _A						):
      _a							:						int										=							VOCAB_FILES_NAMES
      _a							:						List[str]										=							PRETRAINED_VOCAB_FILES_MAP
      _a							:						Any										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a							:						Tuple										=							['input_ids', 'attention_mask']
      _a							:						List[int]										=							[]
      def __init__(     self				,			a				,			a="<unk>"				,			a="<s>"				,			a="</s>"				,			a="<pad>"				,			a="[SEP]"				,			a="[MASK]"				,			a="[CLS]"				,			a = None				,			**a				,			):
       snake_case__  :    str							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else bos_token
       snake_case__  :    Optional[int]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else eos_token
       snake_case__  :    Any							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else unk_token
       snake_case__  :    List[str]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else pad_token
       snake_case__  :    int							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else cls_token
       snake_case__  :    Tuple							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else sep_token

       # Mask token behave like a normal word, i.e. include the space before it
       snake_case__  :    Union[str, Any]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else mask_token

       snake_case__  :    Union[str, Any]							       ={} if sp_model_kwargs is None else sp_model_kwargs

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

       snake_case__  :    Optional[int]							       =vocab_file

       snake_case__  :    List[str]							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(a   )
      @property
      def 						lowercase__					(     self   ):
       return self.sp_model.get_piece_size()
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       ={self.convert_ids_to_tokens(a   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab
      def __getstate__(     self   ):
       snake_case__  :    List[Any]							       =self.__dict__.copy()
       snake_case__  :    Optional[int]							       =None
       return state
      def __setstate__(     self				,			a   ):
       snake_case__  :    Optional[Any]							       =d

       # for backward compatibility
       if not hasattr(self				,			"""sp_model_kwargs"""   ):
        snake_case__  :    Optional[Any]							       ={}

       snake_case__  :    Tuple							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.encode(a				,			out_type=a   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.piece_to_id(a   )
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Dict							       =self.sp_model.IdToPiece(a   )
       return token
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Optional[int]							       =[]
       snake_case__  :    Dict							       =""""""
       snake_case__  :    Dict							       =False
       for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
         if not prev_is_special:
          out_string += " "
         out_string += self.sp_model.decode(a   ) + token
         snake_case__  :    Optional[Any]							       =True
         snake_case__  :    Dict							       =[]
        else:
         current_sub_tokens.append(a   )
         snake_case__  :    List[Any]							       =False
       out_string += self.sp_model.decode(a   )
       return out_string.strip()
      def 						lowercase__					(     self				,			a				,			a = False				,			a = None				,			a = True				,			**a				,			):
       snake_case__  :    List[str]							       =kwargs.pop("""use_source_tokenizer"""				,			a   )

       snake_case__  :    int							       =self.convert_ids_to_tokens(a				,			skip_special_tokens=a   )

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       snake_case__  :    List[Any]							       =[]
       snake_case__  :    int							       =[]
       for token in filtered_tokens:
        if skip_special_tokens and token in self.all_special_ids:
         continue
        if token in self.added_tokens_encoder:
         if current_sub_text:
          sub_texts.append(self.convert_tokens_to_string(a   )   )
          snake_case__  :    List[Any]							       =[]
         sub_texts.append(a   )
        else:
         current_sub_text.append(a   )
       if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(a   )   )

       # Mimic the behavior of the Rust tokenizer:
       # No space before [MASK] and [SEP]
       if spaces_between_special_tokens:
        snake_case__  :    Dict							       =re.sub(R""" (\[(MASK|SEP)\])"""				,			R"""\1"""				,			""" """.join(a   )   )
       else:
        snake_case__  :    Dict							       ="""""".join(a   )

       snake_case__  :    str							       =(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
        snake_case__  :    Any							       =self.clean_up_tokenization(a   )
        return clean_text
       else:
        return text
      def 						lowercase__					(     self				,			a				,			a = None   ):
       if not os.path.isdir(a   ):
        logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
        return
       snake_case__  :    Any							       =os.path.join(
           a				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(a   ) and os.path.isfile(self.vocab_file   ):
        copyfile(self.vocab_file				,			a   )
       elif not os.path.isfile(self.vocab_file   ):
        with open(a				,			"""wb"""   ) as fi:
         snake_case__  :    Any							       =self.sp_model.serialized_model_proto()
         fi.write(a   )

       return (out_vocab_file,)
      def 						lowercase__					(     self				,			a				,			a = None   ):
       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       snake_case__  :    Union[str, Any]							       =[self.cls_token_id]
       snake_case__  :    Dict							       =[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep
      def 						lowercase__					(     self				,			a				,			a = None				,			a = False   ):
       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=a				,			token_ids_a=a				,			already_has_special_tokens=a   )

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



      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    List[str]							       =[self.sep_token_id]
       snake_case__  :    List[Any]							       =[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   ) * [0] + len(token_ids_a + sep   ) * [1]





