

'''simple docstring'''


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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__						:       str										=						logging.get_logger(__name__)

lowerCAmelCase__						:       List[str]										=						"""▁"""

lowerCAmelCase__						:       Optional[Any]										=						{"""vocab_file""": """prophetnet.tokenizer"""}

lowerCAmelCase__						:       Dict										=						{
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

lowerCAmelCase__						:       Union[str, Any]										=						{
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

lowerCAmelCase__						:       Tuple										=						{
    """microsoft/xprophetnet-large-wiki100-cased""": 512,
}




def 	_a       (				__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  collections.OrderedDict()
		with open(__lowerCAmelCase   ,							'''r'''   ,							encoding='''utf-8'''     ) as reader:
				snake_case__						:       Any       						=  reader.readlines()
		for index, token in enumerate(__lowerCAmelCase     ):
				snake_case__						:       Union[str, Any]       						=  token.rstrip('''\n'''     )
				snake_case__						:       List[str]       						=  index
		return vocab





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    VOCAB_FILES_NAMES
						__UpperCAmelCase			     =    PRETRAINED_VOCAB_FILES_MAP
						__UpperCAmelCase			     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCAmelCase			     =    ["""input_ids""", """attention_mask"""]







						def __init__(							self					:      int			,			snake_case_					:      str			,			snake_case_					:      Any="[SEP]"			,			snake_case_					:      Optional[Any]="[SEP]"			,			snake_case_					:      Union[str, Any]="[SEP]"			,			snake_case_					:      Union[str, Any]="[UNK]"			,			snake_case_					:      Tuple="[PAD]"			,			snake_case_					:      Optional[Any]="[CLS]"			,			snake_case_					:      Optional[Any]="[MASK]"			,			snake_case_					:      Optional[Dict[str, Any]] = None			,			**snake_case_					:      List[Any]			,			):



								'''simple docstring'''



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

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

								try:
										import sentencepiece as spm
								except ImportError:
										logger.warning(
										    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
										    ''' pip install sentencepiece'''  )
										raise

								snake_case__						:       Tuple       						=  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(str(snake_case_  )  )
								snake_case__						:       Any       						=  vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# put special tokens and [unused] tokens into the vocab
								snake_case__						:       List[str]       						=  {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

								for i in range(1_0  ):
										snake_case__						:       Union[str, Any]       						=  F"""[unused{i}]"""
										snake_case__						:       Optional[int]       						=  5 + i

								# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
								snake_case__						:       str       						=  1_2
								snake_case__						:       Union[str, Any]       						=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
								for k in self.fairseq_tokens_to_ids.keys():
										self.unique_no_split_tokens.append(snake_case_  )







						def __getstate__(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.__dict__.copy()
								snake_case__						:       int       						=  None
								return state







						def __setstate__(							self					:      Optional[Any]			,			snake_case_					:      str  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  d
								try:
										import sentencepiece as spm
								except ImportError:
										logger.warning(
										    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
										    ''' pip install sentencepiece'''  )
										raise

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

								snake_case__						:       List[Any]       						=  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(self.vocab_file  )







						def       __magic_name__							(							self					:      Dict			,			snake_case_					:      List[int]			,			snake_case_					:      Optional[List[int]] = None			,			snake_case_					:      bool = False  ):



								'''simple docstring'''



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

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







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      List[int]			,			snake_case_					:      Optional[List[int]] = None  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  [self.sep_token_id]

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







						@property
						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								return len(self.sp_model  ) + self.fairseq_offset







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  {self.convert_ids_to_tokens(snake_case_  ): i for i in range(self.vocab_size  )}
								vocab.update(self.added_tokens_encoder  )
								return vocab







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      str  ):



								'''simple docstring'''



								return self.sp_model.encode(snake_case_			,			out_type=snake_case_  )







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
								snake_case__						:       Union[str, Any]       						=  self.sp_model.PieceToId(snake_case_  )

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								if index in self.fairseq_ids_to_tokens:
										return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset  )







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  ''''''.join(snake_case_  ).replace(snake_case_			,			''' '''  ).strip()
								return out_string







						def       __magic_name__							(							self					:      int			,			snake_case_					:      str			,			snake_case_					:      Optional[str] = None  ):



								'''simple docstring'''



								if not os.path.isdir(snake_case_  ):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
										return
								snake_case__						:       str       						=  os.path.join(
								    snake_case_			,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

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

								return (out_vocab_file,)







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      List[int]			,			snake_case_					:      Optional[List[int]] = None  ):



								'''simple docstring'''



								if token_ids_a is None:
										return token_ids_a + [self.sep_token_id]
								snake_case__						:       Union[str, Any]       						=  [self.sep_token_id]
								return token_ids_a + sep + token_ids_a + sep






'''simple docstring'''


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     a			:






						"""simple docstring"""




						def __init__(							self					:      Dict			,			snake_case_					:      Tuple			,			snake_case_					:      List[str]=1_3			,			snake_case_					:      Dict=7			,			snake_case_					:      int=True			,			snake_case_					:      Tuple=True			,			snake_case_					:      int=True			,			snake_case_					:      Any=True			,			snake_case_					:      Union[str, Any]=9_9			,			snake_case_					:      List[Any]=2_4			,			snake_case_					:      Optional[Any]=2			,			snake_case_					:      Union[str, Any]=6			,			snake_case_					:      Any=3_7			,			snake_case_					:      Any="gelu"			,			snake_case_					:      Dict=0.1			,			snake_case_					:      List[str]=0.1			,			snake_case_					:      Dict=5_1_2			,			snake_case_					:      Any=1_6			,			snake_case_					:      Optional[int]=2			,			snake_case_					:      Union[str, Any]=0.0_2			,			snake_case_					:      int=3			,			snake_case_					:      int=None			,			snake_case_					:      Union[str, Any]=1_0_0_0			,			):



								'''simple docstring'''



								snake_case__						:       Dict       						=  parent
								snake_case__						:       Optional[int]       						=  batch_size
								snake_case__						:       List[str]       						=  seq_length
								snake_case__						:       List[Any]       						=  is_training
								snake_case__						:       Optional[Any]       						=  use_input_mask
								snake_case__						:       Optional[Any]       						=  use_token_type_ids
								snake_case__						:       Tuple       						=  use_labels
								snake_case__						:       List[Any]       						=  vocab_size
								snake_case__						:       Union[str, Any]       						=  hidden_size
								snake_case__						:       Optional[int]       						=  num_hidden_layers
								snake_case__						:       Any       						=  num_attention_heads
								snake_case__						:       str       						=  intermediate_size
								snake_case__						:       Optional[int]       						=  hidden_act
								snake_case__						:       List[Any]       						=  hidden_dropout_prob
								snake_case__						:       Union[str, Any]       						=  attention_probs_dropout_prob
								snake_case__						:       Dict       						=  max_position_embeddings
								snake_case__						:       int       						=  type_vocab_size
								snake_case__						:       Tuple       						=  type_sequence_label_size
								snake_case__						:       Optional[Any]       						=  initializer_range
								snake_case__						:       Union[str, Any]       						=  num_labels
								snake_case__						:       Dict       						=  scope
								snake_case__						:       Tuple       						=  range_bbox







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size  )

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

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

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

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



								'''simple docstring'''



								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       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Any			,			snake_case_					:      List[Any]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      str			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Tuple			,			):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  LiltModel(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Dict       						=  model(snake_case_			,			bbox=snake_case_			,			attention_mask=snake_case_			,			token_type_ids=snake_case_  )
								snake_case__						:       Optional[Any]       						=  model(snake_case_			,			bbox=snake_case_			,			token_type_ids=snake_case_  )
								snake_case__						:       Union[str, Any]       						=  model(snake_case_			,			bbox=snake_case_  )

								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       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      Any			,			snake_case_					:      int			,			snake_case_					:      int			,			snake_case_					:      Tuple			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  self.num_labels
								snake_case__						:       str       						=  LiltForTokenClassification(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       List[Any]       						=  model(
								    snake_case_			,			bbox=snake_case_			,			attention_mask=snake_case_			,			token_type_ids=snake_case_			,			labels=snake_case_  )
								self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.num_labels)  )







						def       __magic_name__							(							self					:      Dict			,			snake_case_					:      int			,			snake_case_					:      str			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Any			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Tuple			,			snake_case_					:      int			,			):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  LiltForQuestionAnswering(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Optional[int]       						=  model(
								    snake_case_			,			bbox=snake_case_			,			attention_mask=snake_case_			,			token_type_ids=snake_case_			,			start_positions=snake_case_			,			end_positions=snake_case_			,			)
								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       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.prepare_config_and_inputs()
								(
								    (
								    snake_case__
								)						,       (
								    snake_case__
								)						,       (
								    snake_case__
								)						,       (
								    snake_case__
								)						,       (
								    snake_case__
								)						,       (
								    snake_case__
								)						,       (
								    snake_case__
								)						,       
								)						:       Tuple       						=  config_and_inputs
								snake_case__						:       List[Any]       						=  {
								    '''input_ids''': input_ids,
								    '''bbox''': bbox,
								    '''token_type_ids''': token_type_ids,
								    '''attention_mask''': input_mask,
								}
								return config, inputs_dict





@require_torch
class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    (
						    (
						        LiltModel,
						        LiltForSequenceClassification,
						        LiltForTokenClassification,
						        LiltForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						__UpperCAmelCase			     =    (
						    {
						        """feature-extraction""": LiltModel,
						        """question-answering""": LiltForQuestionAnswering,
						        """text-classification""": LiltForSequenceClassification,
						        """token-classification""": LiltForTokenClassification,
						        """zero-shot""": LiltForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    False







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Dict			,			snake_case_					:      Dict			,			snake_case_					:      str			,			snake_case_					:      Any  ):



								'''simple docstring'''



								return True







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  LiltModelTester(self  )
								snake_case__						:       Dict       						=  ConfigTester(self			,			config_class=snake_case_			,			hidden_size=3_7  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								self.config_tester.run_common_tests()







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case_  )







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
										snake_case__						:       int       						=  type
										self.model_tester.create_and_check_model(*snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*snake_case_  )







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*snake_case_  )







						@slow
						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										snake_case__						:       List[str]       						=  LiltModel.from_pretrained(snake_case_  )
										self.assertIsNotNone(snake_case_  )





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






						"""simple docstring"""




						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''  ).to(snake_case_  )

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

								# forward pass
								with torch.no_grad():
										snake_case__						:       Optional[int]       						=  model(input_ids=snake_case_			,			bbox=snake_case_  )

								snake_case__						:       Tuple       						=  torch.Size([1, 2, 7_6_8]  )
								snake_case__						:       Dict       						=  torch.tensor(
								    [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]			,			device=snake_case_			,			)

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



