




"""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    UpperCAmelCase_  :
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_=13							,		UpperCamelCase_=7							,		UpperCamelCase_=True							,		UpperCamelCase_=True							,		UpperCamelCase_=True							,		UpperCamelCase_=True							,		UpperCamelCase_=99							,		UpperCamelCase_=24							,		UpperCamelCase_=2							,		UpperCamelCase_=6							,		UpperCamelCase_=37							,		UpperCamelCase_="gelu"							,		UpperCamelCase_=0.1							,		UpperCamelCase_=0.1							,		UpperCamelCase_=5_12							,		UpperCamelCase_=16							,		UpperCamelCase_=2							,		UpperCamelCase_=0.0_2							,		UpperCamelCase_=3							,		UpperCamelCase_=None							,		UpperCamelCase_=10_00							,		)		->    Tuple:
					__lowercase       :   Optional[Any]        =    parent
					__lowercase       :   int        =    batch_size
					__lowercase       :   Optional[int]        =    seq_length
					__lowercase       :   List[Any]        =    is_training
					__lowercase       :   Tuple        =    use_input_mask
					__lowercase       :   Optional[int]        =    use_token_type_ids
					__lowercase       :   List[Any]        =    use_labels
					__lowercase       :   str        =    vocab_size
					__lowercase       :   List[str]        =    hidden_size
					__lowercase       :   List[Any]        =    num_hidden_layers
					__lowercase       :   Tuple        =    num_attention_heads
					__lowercase       :   List[str]        =    intermediate_size
					__lowercase       :   List[Any]        =    hidden_act
					__lowercase       :   Dict        =    hidden_dropout_prob
					__lowercase       :   List[Any]        =    attention_probs_dropout_prob
					__lowercase       :   Dict        =    max_position_embeddings
					__lowercase       :   List[str]        =    type_vocab_size
					__lowercase       :   int        =    type_sequence_label_size
					__lowercase       :   Optional[Any]        =    initializer_range
					__lowercase       :   Dict        =    num_labels
					__lowercase       :   Union[str, Any]        =    scope
					__lowercase       :   Union[str, Any]        =    range_bbox
		def      _lowerCamelCase				(		self    )		->    Dict:
					__lowercase       :   int        =    ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size    )

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

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

					__lowercase       :   Union[str, Any]        =    None
					if self.use_token_type_ids:
								__lowercase       :   List[str]        =    ids_tensor([self.batch_size, self.seq_length]							,		self.type_vocab_size    )

					__lowercase       :   str        =    None
					__lowercase       :   int        =    None
					if self.use_labels:
								__lowercase       :   Tuple        =    ids_tensor([self.batch_size]							,		self.type_sequence_label_size    )
								__lowercase       :   Tuple        =    ids_tensor([self.batch_size, self.seq_length]							,		self.num_labels    )

					__lowercase       :   Any        =    self.get_config()

					return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
		def      _lowerCamelCase				(		self    )		->    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      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		)		->    List[str]:
					__lowercase       :   Optional[Any]        =    LiltModel(config=UpperCamelCase_    )
					model.to(UpperCamelCase_    )
					model.eval()
					__lowercase       :   List[str]        =    model(UpperCamelCase_							,		bbox=UpperCamelCase_							,		attention_mask=UpperCamelCase_							,		token_type_ids=UpperCamelCase_    )
					__lowercase       :   List[str]        =    model(UpperCamelCase_							,		bbox=UpperCamelCase_							,		token_type_ids=UpperCamelCase_    )
					__lowercase       :   List[str]        =    model(UpperCamelCase_							,		bbox=UpperCamelCase_    )

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


@require_torch
class    UpperCAmelCase_  (       lowerCAmelCase_					, lowerCAmelCase_					, lowerCAmelCase_					, unittest.TestCase						):
		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      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_    )		->    Union[str, Any]:
					return True
		def      _lowerCamelCase				(		self    )		->    Dict:
					__lowercase       :   Optional[Any]        =    LiltModelTester(self    )
					__lowercase       :   List[Any]        =    ConfigTester(self							,		config_class=UpperCamelCase_							,		hidden_size=37    )
		def      _lowerCamelCase				(		self    )		->    Dict:
					self.config_tester.run_common_tests()
		def      _lowerCamelCase				(		self    )		->    Any:
					__lowercase       :   int        =    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase_    )
		def      _lowerCamelCase				(		self    )		->    List[str]:
					__lowercase       :   str        =    self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
								__lowercase       :   Any        =    type
								self.model_tester.create_and_check_model(*UpperCamelCase_    )
		def      _lowerCamelCase				(		self    )		->    List[str]:
					__lowercase       :   str        =    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_    )
		def      _lowerCamelCase				(		self    )		->    List[Any]:
					__lowercase       :   Optional[Any]        =    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_    )
		@slow
		def      _lowerCamelCase				(		self    )		->    str:
					for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowercase       :   int        =    LiltModel.from_pretrained(UpperCamelCase_    )
								self.assertIsNotNone(UpperCamelCase_    )


@require_torch
@slow
class    UpperCAmelCase_  (       unittest.TestCase						):
		def      _lowerCamelCase				(		self    )		->    List[Any]:
					__lowercase       :   str        =    LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''    ).to(UpperCamelCase_    )

					__lowercase       :   List[Any]        =    torch.tensor([[1, 2]]							,		device=UpperCamelCase_    )
					__lowercase       :   Optional[int]        =    torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]							,		device=UpperCamelCase_    )

					# forward pass
					with torch.no_grad():
								__lowercase       :   List[Any]        =    model(input_ids=UpperCamelCase_							,		bbox=UpperCamelCase_    )

					__lowercase       :   Optional[Any]        =    torch.Size([1, 2, 7_68]    )
					__lowercase       :   int        =    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=UpperCamelCase_							,		)

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








"""simple docstring"""






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

import sentencepiece as spm

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


_lowercase : Optional[int]      						=						"▁"

_lowercase : Optional[Any]      						=						{"vocab_file": "spiece.model"}

_lowercase : Optional[Any]      						=						{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_lowercase : Tuple      						=						{
    "google/pegasus-xsum": 5_1_2,
}


_lowercase : Optional[int]      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							VOCAB_FILES_NAMES

					_a   			=							VOCAB_FILES_NAMES
					_a   			=							PRETRAINED_VOCAB_FILES_MAP
					_a   			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a   			=							['input_ids', 'attention_mask']



					def __init__(       self							:  Any,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Any="<pad>",  lowerCamelCase							:  Optional[Any]="</s>",  lowerCamelCase							:  Any="<unk>",  lowerCamelCase							:  Tuple="<mask_2>",  lowerCamelCase							:  int="<mask_1>",  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Dict=103,  lowerCamelCase							:  Optional[Dict[str, Any]] = None,  **lowerCamelCase							:  Optional[int],  )->					None:
								lowerCamelCase__		:		Union[str, Any]			=offset
								if additional_special_tokens is not None:
											if not isinstance(lowerCamelCase,  lowerCamelCase     ):
														raise TypeError(
														    F'''additional_special_tokens should be of type {type(lowerCamelCase     )}, but is'''
														    F''' {type(lowerCamelCase     )}'''     )

											lowerCamelCase__		:		Any			=(
											    ([mask_token_sent] + additional_special_tokens)
											    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
											    else additional_special_tokens
											)
											# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
											additional_special_tokens_extended += [
											    F'''<unk_{i}>''' for i in range(len(lowerCamelCase     ),  self.offset - 1     )
											]

											if len(set(lowerCamelCase     )     ) != len(lowerCamelCase     ):
														raise ValueError(
														    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
														    F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''     )
											lowerCamelCase__		:		Optional[Any]			=additional_special_tokens_extended
								else:
											lowerCamelCase__		:		Tuple			=[mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [F'''<unk_{i}>''' for i in range(2,  self.offset     )]

								lowerCamelCase__		:		Tuple			={} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=lowerCamelCase,  unk_token=lowerCamelCase,  mask_token=lowerCamelCase,  pad_token=lowerCamelCase,  mask_token_sent=lowerCamelCase,  offset=lowerCamelCase,  additional_special_tokens=lowerCamelCase,  sp_model_kwargs=self.sp_model_kwargs,  **lowerCamelCase,  )
								lowerCamelCase__		:		Optional[int]			=mask_token_sent
								lowerCamelCase__		:		Optional[Any]			=vocab_file
								lowerCamelCase__		:		List[str]			=spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(lowerCamelCase     )

								# add special tokens to encoder dict
								lowerCamelCase__		:		Dict[int, str]			={
								    0: self.pad_token,
								    1: self.eos_token,
								}

								if self.mask_token_sent is not None:
											self.encoder.update(
											    {
											        2: self.mask_token_sent,
											        3: self.mask_token,
											    }     )

								if self.offset > 0:
											# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
											# mask_token_sent is already added to list -> so start at 1
											self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1,  self.offset - 1     )}     )

								lowerCamelCase__		:		Dict[str, int]			={v: k for k, v in self.encoder.items()}



					@property
					def 		snake_case       (       self							:  Union[str, Any]     )->					int:
								return len(self.sp_model     ) + self.offset



					def 		snake_case       (       self							:  Optional[Any]     )->					Dict[str, int]:
								lowerCamelCase__		:		List[Any]			={self.convert_ids_to_tokens(lowerCamelCase     ): i for i in range(self.vocab_size     )}
								vocab.update(self.added_tokens_encoder     )
								return vocab



					def __getstate__(       self							:  str     )->					List[Any]:
								lowerCamelCase__		:		Optional[Any]			=self.__dict__.copy()
								lowerCamelCase__		:		Optional[int]			=None
								return state



					def __setstate__(       self							:  Dict,  lowerCamelCase							:  int     )->					Optional[Any]:
								lowerCamelCase__		:		Optional[int]			=d

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

								lowerCamelCase__		:		Union[str, Any]			=spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(self.vocab_file     )



					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  str     )->					List[str]:
								return self.sp_model.encode(lowerCamelCase,  out_type=lowerCamelCase     )



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  str     )->					int:
								if token in self.decoder:
											return self.decoder[token]
								elif token in self.added_tokens_decoder:
											return self.added_tokens_decoder[token]
								lowerCamelCase__		:		Any			=self.sp_model.piece_to_id(lowerCamelCase     )
								return sp_id + self.offset



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  int     )->					str:
								if index in self.encoder:
											return self.encoder[index]
								elif index in self.added_tokens_encoder:
											return self.added_tokens_encoder[index]
								else:
											lowerCamelCase__		:		Any			=self.sp_model.IdToPiece(index - self.offset     )
								return token



					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  Optional[int]     )->					Any:
								lowerCamelCase__		:		Optional[int]			=[]
								lowerCamelCase__		:		Tuple			=''''''
								for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
														out_string += self.sp_model.decode(lowerCamelCase     ) + token
														lowerCamelCase__		:		str			=[]
											else:
														current_sub_tokens.append(lowerCamelCase     )
								out_string += self.sp_model.decode(lowerCamelCase     )
								return out_string.strip()



					def 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any]=False     )->					List[str]:
								return 1



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  Optional[int]     )->					Tuple:
								lowerCamelCase__		:		Tuple			=set(self.all_special_ids     )  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id     )  # <unk> is only sometimes special

								return [1 if x in all_special_ids else 0 for x in seq]



					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  List,  lowerCamelCase							:  Optional[List] = None,  lowerCamelCase							:  bool = False     )->					List[int]:
								if already_has_special_tokens:
											return self._special_token_mask(lowerCamelCase     )
								elif token_ids_a is None:
											return self._special_token_mask(lowerCamelCase     ) + [1]
								else:
											return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Optional[int]=None     )->					List[int]:
								if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]




					def 		snake_case       (       self							:  Optional[int],  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
								lowerCamelCase__		:		List[str]			=os.path.join(
								    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''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:
														lowerCamelCase__		:		int			=self.sp_model.serialized_model_proto()
														fi.write(lowerCamelCase     )

								return (out_vocab_file,)




