





import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

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


if is_torch_available():
     import torch

     from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel



class   _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 def __init__(self							:  Any			,					UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  List[Any]=99			,					UpperCAmelCase_							:  Any=13			,					UpperCAmelCase_							:  Dict=7			,					UpperCAmelCase_							:  str=9			,					UpperCAmelCase_							:  int=True			,					UpperCAmelCase_							:  int=True			,					UpperCAmelCase_							:  Dict=False			,					UpperCAmelCase_							:  Optional[Any]=32			,					UpperCAmelCase_							:  Optional[Any]=5			,					UpperCAmelCase_							:  Union[str, Any]=4			,					UpperCAmelCase_							:  List[Any]=37			,					UpperCAmelCase_							:  Tuple=8			,					UpperCAmelCase_							:  Optional[Any]=0.1			,					UpperCAmelCase_							:  Optional[Any]=0.002			,					UpperCAmelCase_							:  Optional[int]=1			,					UpperCAmelCase_							:  Union[str, Any]=0			,					UpperCAmelCase_							:  str=0			,					UpperCAmelCase_							:  Any=None			,					UpperCAmelCase_							:  Union[str, Any]=None			,					)			->int:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =parent
   lowerCamelCase__: List[Any]         =batch_size
   lowerCamelCase__: List[Any]         =encoder_seq_length
   lowerCamelCase__: Union[str, Any]         =decoder_seq_length
   # For common tests
   lowerCamelCase__: Tuple         =self.decoder_seq_length
   lowerCamelCase__: List[Any]         =is_training
   lowerCamelCase__: str         =use_attention_mask
   lowerCamelCase__: Any         =use_labels
   lowerCamelCase__: List[Any]         =vocab_size
   lowerCamelCase__: List[Any]         =hidden_size
   lowerCamelCase__: Optional[int]         =num_hidden_layers
   lowerCamelCase__: List[str]         =num_attention_heads
   lowerCamelCase__: Optional[int]         =d_ff
   lowerCamelCase__: int         =relative_attention_num_buckets
   lowerCamelCase__: Tuple         =dropout_rate
   lowerCamelCase__: Dict         =initializer_factor
   lowerCamelCase__: Any         =eos_token_id
   lowerCamelCase__: Optional[int]         =pad_token_id
   lowerCamelCase__: Dict         =decoder_start_token_id
   lowerCamelCase__: Dict         =None
   lowerCamelCase__: Optional[int]         =decoder_layers

 def 				SCREAMING_SNAKE_CASE_   (self							:  str)			->List[str]:



   '''simple docstring'''



   return TaConfig.from_pretrained("google/umt5-base")

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any]			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  Tuple			,					UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  List[Any]=None			,					UpperCAmelCase_							:  Any=None			,					UpperCAmelCase_							:  int=None			,					UpperCAmelCase_							:  Union[str, Any]=None			,					UpperCAmelCase_							:  List[str]=None			,					)			->Tuple:



   '''simple docstring'''



   if attention_mask is None:
     lowerCamelCase__: int         =input_ids.ne(config.pad_token_id)
   if decoder_attention_mask is None:
     lowerCamelCase__: Tuple         =decoder_input_ids.ne(config.pad_token_id)
   if head_mask is None:
     lowerCamelCase__: List[Any]         =torch.ones(config.num_hidden_layers			,					config.num_attention_heads			,					device=UpperCAmelCase_)
   if decoder_head_mask is None:
     lowerCamelCase__: Union[str, Any]         =torch.ones(config.num_decoder_layers			,					config.num_attention_heads			,					device=UpperCAmelCase_)
   if cross_attn_head_mask is None:
     lowerCamelCase__: List[str]         =torch.ones(
         config.num_decoder_layers			,					config.num_attention_heads			,					device=UpperCAmelCase_)
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Any:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =ids_tensor([self.batch_size, self.encoder_seq_length]			,					self.vocab_size)
   lowerCamelCase__: int         =ids_tensor([self.batch_size, self.decoder_seq_length]			,					self.vocab_size)

   # we need to clamp the input ids here to avoid having pad token in between
   # this is because for NllbMoe the position_ids are prepared such that
   # all pad tokens have pos id = 2 and rest are between 2..seq_length
   # and the seq_length here is seq_length - num_pad_tokens
   # but when using past, there is no way of knowing if the past input ids had
   # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
   # position_ids being off by num_pad_tokens in past input
   lowerCamelCase__: Optional[int]         =input_ids.clamp(self.pad_token_id + 1)
   lowerCamelCase__: Union[str, Any]         =decoder_input_ids.clamp(self.pad_token_id + 1)

   lowerCamelCase__: Tuple         =self.get_config()
   lowerCamelCase__: Dict         =config.num_attention_heads
   lowerCamelCase__: List[str]         =self.prepare_inputs_dict(UpperCAmelCase_			,					UpperCAmelCase_			,					UpperCAmelCase_)
   return config, input_dict

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__    ,   lowerCamelCase__: List[Any]         =self.prepare_config_and_inputs()
   return config, inputs_dict

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Any:



   '''simple docstring'''



   return TaConfig(
       vocab_size=166			,					d_model=self.hidden_size			,					d_ff=self.d_ff			,					d_kv=self.hidden_size // self.num_attention_heads			,					num_layers=self.num_hidden_layers			,					num_decoder_layers=self.decoder_layers			,					num_heads=self.num_attention_heads			,					relative_attention_num_buckets=self.relative_attention_num_buckets			,					dropout_rate=self.dropout_rate			,					initializer_factor=self.initializer_factor			,					eos_token_id=self.eos_token_id			,					bos_token_id=self.pad_token_id			,					pad_token_id=self.pad_token_id			,					decoder_start_token_id=self.decoder_start_token_id			,					)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Dict:



   '''simple docstring'''



   return TaConfig(
       vocab_size=self.vocab_size			,					d_model=self.hidden_size			,					d_ff=self.d_ff			,					d_kv=self.hidden_size // self.num_attention_heads			,					num_layers=self.num_hidden_layers			,					num_decoder_layers=self.decoder_layers			,					num_heads=self.num_attention_heads			,					relative_attention_num_buckets=self.relative_attention_num_buckets			,					dropout_rate=self.dropout_rate			,					initializer_factor=self.initializer_factor			,					eos_token_id=self.eos_token_id			,					bos_token_id=self.pad_token_id			,					pad_token_id=self.pad_token_id			,					decoder_start_token_id=self.decoder_start_token_id			,					)

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any]			,					UpperCAmelCase_							:  Dict			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  Optional[int]			,					)			->Dict:



   '''simple docstring'''



   lowerCamelCase__: Dict         =UMTaModel(config=UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__: Any         =model(
       input_ids=UpperCAmelCase_			,					decoder_input_ids=UpperCAmelCase_			,					attention_mask=UpperCAmelCase_			,					decoder_attention_mask=UpperCAmelCase_			,					)
   lowerCamelCase__: Optional[int]         =model(input_ids=UpperCAmelCase_			,					decoder_input_ids=UpperCAmelCase_)
   lowerCamelCase__: List[str]         =result.last_hidden_state
   lowerCamelCase__: Union[str, Any]         =result.past_key_values
   lowerCamelCase__: str         =result.encoder_last_hidden_state

   self.parent.assertEqual(encoder_output.size()			,					(self.batch_size, self.encoder_seq_length, self.hidden_size))
   self.parent.assertEqual(decoder_output.size()			,					(self.batch_size, self.decoder_seq_length, self.hidden_size))
   # There should be `num_layers` key value embeddings stored in decoder_past
   self.parent.assertEqual(len(UpperCAmelCase_)			,					config.num_layers)
   # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
   self.parent.assertEqual(len(decoder_past[0])			,					4)

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str]			,					UpperCAmelCase_							:  Any			,					UpperCAmelCase_							:  Tuple			,					UpperCAmelCase_							:  Optional[Any]			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  Tuple			,					UpperCAmelCase_							:  Optional[Any]			,					)			->Any:



   '''simple docstring'''



   lowerCamelCase__: Dict         =UMTaModel(config=UpperCAmelCase_).get_decoder().to(UpperCAmelCase_).eval()
   # first forward pass
   lowerCamelCase__: List[Any]         =model(UpperCAmelCase_			,					use_cache=UpperCAmelCase_)
   lowerCamelCase__: Union[str, Any]         =model(UpperCAmelCase_)
   lowerCamelCase__: Optional[int]         =model(UpperCAmelCase_			,					use_cache=UpperCAmelCase_)

   self.parent.assertTrue(len(UpperCAmelCase_) == len(UpperCAmelCase_))
   self.parent.assertTrue(len(UpperCAmelCase_) == len(UpperCAmelCase_) + 1)

   lowerCamelCase__    ,   lowerCamelCase__: Dict         =outputs.to_tuple()

   # create hypothetical next token and extent to next_input_ids
   lowerCamelCase__: List[Any]         =ids_tensor((self.batch_size, 1)			,					config.vocab_size)

   # append to next input_ids and
   lowerCamelCase__: Dict         =torch.cat([input_ids, next_tokens]			,					dim=-1)

   lowerCamelCase__: List[str]         =model(UpperCAmelCase_)["last_hidden_state"]
   lowerCamelCase__: List[str]         =model(UpperCAmelCase_			,					past_key_values=UpperCAmelCase_)["last_hidden_state"]

   # select random slice
   lowerCamelCase__: Optional[Any]         =ids_tensor((1,)			,					output_from_past.shape[-1]).item()
   lowerCamelCase__: Optional[Any]         =output_from_no_past[:, -1, random_slice_idx].detach()
   lowerCamelCase__: List[str]         =output_from_past[:, 0, random_slice_idx].detach()

   # test that outputs are equal for slice
   self.parent.assertTrue(torch.allclose(UpperCAmelCase_			,					UpperCAmelCase_			,					atol=1E-3))







 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple			,					UpperCAmelCase_							:  Optional[Any]			,					UpperCAmelCase_							:  Dict			,					)			->Dict:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =UMTaModel(config=UpperCAmelCase_).to(UpperCAmelCase_).half().eval()
   lowerCamelCase__: str         =model(**UpperCAmelCase_)["last_hidden_state"]
   self.parent.assertFalse(torch.isnan(UpperCAmelCase_).any().item())



@require_torch
class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				unittest.TestCase       ):







 '''simple docstring'''
 lowercase_						=     (
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 lowercase_						=     (UMTaForConditionalGeneration,) if is_torch_available() else ()
 lowercase_						=     (
     {
         "conversational": UMTaForConditionalGeneration,
         "feature-extraction": UMTaModel,
         "summarization": UMTaForConditionalGeneration,
         "text2text-generation": UMTaForConditionalGeneration,
         "translation": UMTaForConditionalGeneration,
         "question-answering": UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 lowercase_						=     True
 lowercase_						=     False
 lowercase_						=     False
 lowercase_						=     True
 lowercase_						=     True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 lowercase_						=     [0.8, 0.9]

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =UMTaModelTester(self)

 @unittest.skip("Test has a segmentation fault on torch 1.8.0")
 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: Dict         =self.model_tester.prepare_config_and_inputs()
   lowerCamelCase__: int         =UMTaModel(config_and_inputs[0]).to(UpperCAmelCase_)
   with tempfile.TemporaryDirectory() as tmpdirname:
     torch.onnx.export(
         UpperCAmelCase_			,					(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])			,					F"""{tmpdirname}/t5_test.onnx"""			,					export_params=UpperCAmelCase_			,					opset_version=9			,					input_names=["input_ids", "decoder_input_ids"]			,					)

 @unittest.skipIf(torch_device == "cpu"			,					"Cant do half precision")
 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any])			->Any:



   '''simple docstring'''



   lowerCamelCase__: Dict         =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->int:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =["encoder_attentions", "decoder_attentions", "cross_attentions"]
   lowerCamelCase__: Any         =self.model_tester.prepare_config_and_inputs()
   lowerCamelCase__: List[Any]         =config_and_inputs[0]
   lowerCamelCase__: Tuple         =UMTaForConditionalGeneration(UpperCAmelCase_).eval()
   model.to(UpperCAmelCase_)

   lowerCamelCase__: int         ={
       "head_mask": torch.zeros(config.num_layers			,					config.num_heads			,					device=UpperCAmelCase_),
       "decoder_head_mask": torch.zeros(config.num_decoder_layers			,					config.num_heads			,					device=UpperCAmelCase_),
       "cross_attn_head_mask": torch.zeros(config.num_decoder_layers			,					config.num_heads			,					device=UpperCAmelCase_),
   }

   for attn_name, (name, mask) in zip(UpperCAmelCase_			,					head_masking.items()):
     lowerCamelCase__: Any         ={name: mask}
     # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
     if name == "head_mask":
       lowerCamelCase__: Union[str, Any]         =torch.ones(
           config.num_decoder_layers			,					config.num_heads			,					device=UpperCAmelCase_)

     lowerCamelCase__: str         =model.generate(
         config_and_inputs[1]["input_ids"]			,					num_beams=1			,					max_length=3			,					output_attentions=UpperCAmelCase_			,					return_dict_in_generate=UpperCAmelCase_			,					**UpperCAmelCase_			,					)
     # We check the state of decoder_attentions and cross_attentions just from the last step
     lowerCamelCase__: Union[str, Any]         =out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
     self.assertEqual(sum([w.sum().item() for w in attn_weights])			,					0.0)







 @unittest.skip("Does not work on the tiny model as we keep hitting edge cases.")
 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->str:



   '''simple docstring'''



   pass



@require_torch
@require_sentencepiece
@require_tokenizers
class   _SCREAMING_SNAKE_CASE		(      unittest.TestCase       ):







 '''simple docstring'''

 @slow
 @unittest.skip(
     "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged")
 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->List[str]:



   '''simple docstring'''



   lowerCamelCase__: str         =UMTaForConditionalGeneration.from_pretrained("google/umt5-small"			,					return_dict=UpperCAmelCase_).to(UpperCAmelCase_)
   lowerCamelCase__: Optional[int]         =AutoTokenizer.from_pretrained("google/umt5-small"			,					use_fast=UpperCAmelCase_			,					legacy=UpperCAmelCase_)
   lowerCamelCase__: List[str]         =[
       "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
       "No se como puedo <extra_id_0>.",
       "This is the reason why we <extra_id_0> them.",
       "The <extra_id_0> walks in <extra_id_1>, seats",
       "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
   ]
   lowerCamelCase__: Optional[int]         =tokenizer(UpperCAmelCase_			,					return_tensors="pt"			,					padding=UpperCAmelCase_).input_ids
   # fmt: off
   lowerCamelCase__: Optional[Any]         =torch.tensor(
       [
           [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
           [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
           [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
           [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
           [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
       ])
   # fmt: on
   torch.testing.assert_allclose(UpperCAmelCase_			,					UpperCAmelCase_)

   lowerCamelCase__: Union[str, Any]         =model.generate(input_ids.to(UpperCAmelCase_))
   lowerCamelCase__: Optional[Any]         =[
       "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
       "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
       "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
       "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
       "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
   ]
   lowerCamelCase__: int         =tokenizer.batch_decode(UpperCAmelCase_)
   self.assertEqual(UpperCAmelCase_			,					UpperCAmelCase_)





"""simple docstring"""




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       ['''image_processor''', '''tokenizer''']
							__a					 =       '''LayoutLMv2ImageProcessor'''
							__a					 =       ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')




							def __init__(     self:    Any     ,							A_:    Optional[Any]=None     ,							A_:    Dict=None     ,							**A_:    Any   ):
										if "feature_extractor" in kwargs:
													warnings.warn(
													    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
													    """ instead."""     ,							A_     ,							)
													__lowerCamelCase          =      kwargs.pop("""feature_extractor"""   )

										__lowerCamelCase          =      image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
													raise ValueError("""You need to specify an `image_processor`."""   )
										if tokenizer is None:
													raise ValueError("""You need to specify a `tokenizer`."""   )

										super().__init__(A_     ,							A_   )




							def __call__(     self:    List[str]     ,							A_:    Any     ,							A_:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,							A_:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None     ,							A_:    Union[List[List[int]], List[List[List[int]]]] = None     ,							A_:    Optional[Union[List[int], List[List[int]]]] = None     ,							A_:    bool = True     ,							A_:    Union[bool, str, PaddingStrategy] = False     ,							A_:    Union[bool, str, TruncationStrategy] = None     ,							A_:    Optional[int] = None     ,							A_:    int = 0     ,							A_:    Optional[int] = None     ,							A_:    Optional[bool] = None     ,							A_:    Optional[bool] = None     ,							A_:    bool = False     ,							A_:    bool = False     ,							A_:    bool = False     ,							A_:    bool = False     ,							A_:    bool = True     ,							A_:    Optional[Union[str, TensorType]] = None     ,							**A_:    Any     ,							):
										# verify input
										if self.image_processor.apply_ocr and (boxes is not None):
													raise ValueError(
													    """You cannot provide bounding boxes """
													    """if you initialized the image processor with apply_ocr set to True."""   )

										if self.image_processor.apply_ocr and (word_labels is not None):
													raise ValueError(
													    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""   )

										if return_overflowing_tokens is True and return_offsets_mapping is False:
													raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""   )

										# first, apply the image processor
										__lowerCamelCase          =      self.image_processor(images=A_     ,							return_tensors=A_   )

										# second, apply the tokenizer
										if text is not None and self.image_processor.apply_ocr and text_pair is None:
													if isinstance(A_     ,							A_   ):
																__lowerCamelCase          =      [text]  # add batch dimension (as the image processor always adds a batch dimension)
													__lowerCamelCase          =      features["""words"""]

										__lowerCamelCase          =      self.tokenizer(
										    text=text if text is not None else features["""words"""]     ,							text_pair=text_pair if text_pair is not None else None     ,							boxes=boxes if boxes is not None else features["""boxes"""]     ,							word_labels=A_     ,							add_special_tokens=A_     ,							padding=A_     ,							truncation=A_     ,							max_length=A_     ,							stride=A_     ,							pad_to_multiple_of=A_     ,							return_token_type_ids=A_     ,							return_attention_mask=A_     ,							return_overflowing_tokens=A_     ,							return_special_tokens_mask=A_     ,							return_offsets_mapping=A_     ,							return_length=A_     ,							verbose=A_     ,							return_tensors=A_     ,							**A_     ,							)

										# add pixel values
										__lowerCamelCase          =      features.pop("""pixel_values"""   )
										if return_overflowing_tokens is True:
													__lowerCamelCase          =      self.get_overflowing_images(A_     ,							encoded_inputs["""overflow_to_sample_mapping"""]   )
										__lowerCamelCase          =      images

										return encoded_inputs




							def   __a   (     self:    Optional[Any]     ,							A_:    Any     ,							A_:    Tuple   ):
										# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
										__lowerCamelCase          =      []
										for sample_idx in overflow_to_sample_mapping:
													images_with_overflow.append(images[sample_idx]   )

										if len(A_   ) != len(A_   ):
													raise ValueError(
													    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
													    f' {len(A_   )} and {len(A_   )}'   )

										return images_with_overflow




							def   __a   (     self:    Optional[Any]     ,							*A_:    str     ,							**A_:    Dict   ):
										return self.tokenizer.batch_decode(*A_     ,							**A_   )




							def   __a   (     self:    Union[str, Any]     ,							*A_:    Optional[Any]     ,							**A_:    Tuple   ):
										return self.tokenizer.decode(*A_     ,							**A_   )




							@property
							def   __a   (     self:    Union[str, Any]   ):
										return ["input_ids", "bbox", "attention_mask", "image"]




							@property
							def   __a   (     self:    Optional[Any]   ):
										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,							A_     ,							)
										return self.image_processor_class




							@property
							def   __a   (     self:    List[Any]   ):
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,							A_     ,							)
										return self.image_processor




