
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 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				["image_processor", "tokenizer"]
   _SCREAMING_SNAKE_CASE				:							Any     	=				"LayoutLMv2ImageProcessor"
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
   def __init__(self			: Tuple ,    snake_case_			: List[str]=None ,    snake_case_			: Tuple=None ,    **snake_case_			: Tuple       ):
    if "feature_extractor" in kwargs:
     warnings.warn(
         '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
         ''' instead.''' ,    snake_case_ ,    )
     __a			:  int		    =  kwargs.pop('''feature_extractor'''       )

    __a			:  Dict		    =  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__(snake_case_ ,    snake_case_       )
   def __call__(self			: Optional[Any] ,    snake_case_			: Tuple ,    snake_case_			: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,    snake_case_			: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None ,    snake_case_			: Union[List[List[int]], List[List[List[int]]]] = None ,    snake_case_			: Optional[Union[List[int], List[List[int]]]] = None ,    snake_case_			: bool = True ,    snake_case_			: Union[bool, str, PaddingStrategy] = False ,    snake_case_			: Union[bool, str, TruncationStrategy] = None ,    snake_case_			: Optional[int] = None ,    snake_case_			: int = 0 ,    snake_case_			: Optional[int] = None ,    snake_case_			: Optional[bool] = None ,    snake_case_			: Optional[bool] = None ,    snake_case_			: bool = False ,    snake_case_			: bool = False ,    snake_case_			: bool = False ,    snake_case_			: bool = False ,    snake_case_			: bool = True ,    snake_case_			: Optional[Union[str, TensorType]] = None ,    **snake_case_			: Dict ,    ):
    # 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
    __a			:  Any		    =  self.image_processor(images=snake_case_ ,    return_tensors=snake_case_       )

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
     if isinstance(snake_case_ ,    snake_case_       ):
      __a			:  Optional[int]		    =  [text]  # add batch dimension (as the image processor always adds a batch dimension)
     __a			:  List[Any]		    =  features['''words''']

    __a			:  str		    =  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=snake_case_ ,    add_special_tokens=snake_case_ ,    padding=snake_case_ ,    truncation=snake_case_ ,    max_length=snake_case_ ,    stride=snake_case_ ,    pad_to_multiple_of=snake_case_ ,    return_token_type_ids=snake_case_ ,    return_attention_mask=snake_case_ ,    return_overflowing_tokens=snake_case_ ,    return_special_tokens_mask=snake_case_ ,    return_offsets_mapping=snake_case_ ,    return_length=snake_case_ ,    verbose=snake_case_ ,    return_tensors=snake_case_ ,    **snake_case_ ,    )

    # add pixel values
    __a			:  List[str]		    =  features.pop('''pixel_values'''       )
    if return_overflowing_tokens is True:
     __a			:  Optional[Any]		    =  self.get_overflowing_images(snake_case_ ,    encoded_inputs['''overflow_to_sample_mapping''']       )
    __a			:  List[str]		    =  images

    return encoded_inputs
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: str ,    snake_case_			: Union[str, Any]       ):
    # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
    __a			:  Union[str, Any]		    =  []
    for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]       )

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

    return images_with_overflow
   def 				lowerCAmelCase      (self			: Optional[Any] ,    *snake_case_			: Tuple ,    **snake_case_			: List[str]       ):
    return self.tokenizer.batch_decode(*snake_case_ ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: Optional[int] ,    *snake_case_			: Union[str, Any] ,    **snake_case_			: str       ):
    return self.tokenizer.decode(*snake_case_ ,    **snake_case_       )
   @property
   def 				lowerCAmelCase      (self			: Any       ):
    return ["input_ids", "bbox", "attention_mask", "image"]
   @property
   def 				lowerCAmelCase      (self			: int       ):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,    snake_case_ ,    )
    return self.image_processor_class







   @property
   def 				lowerCAmelCase      (self			: Tuple       ):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,    snake_case_ ,    )
    return self.image_processor


import os
import sys
import unittest


lowercase__          =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowercase__          =os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
lowercase__          =os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')



class 		UpperCamelCase__  (	unittest.TestCase   ):
   def 				lowerCAmelCase      (self			: List[Any]       ):
    __a			:  str		    =  get_test_to_tester_mapping(snake_case_       )
    __a			:  Tuple		    =  get_test_to_tester_mapping(snake_case_       )

    __a			:  Union[str, Any]		    =  {'''BertModelTest''': '''BertModelTester'''}

    __a			:  Tuple		    =  {
        '''BlipModelTest''': '''BlipModelTester''',
        '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
        '''BlipTextModelTest''': '''BlipTextModelTester''',
        '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
        '''BlipVQAModelTest''': '''BlipVQAModelTester''',
        '''BlipVisionModelTest''': '''BlipVisionModelTester''',
    }

    self.assertEqual(get_test_info.to_json(snake_case_       ) ,    snake_case_       )
    self.assertEqual(get_test_info.to_json(snake_case_       ) ,    snake_case_       )
   def 				lowerCAmelCase      (self			: str       ):
    __a			:  Optional[int]		    =  get_model_to_test_mapping(snake_case_       )
    __a			:  Any		    =  get_model_to_test_mapping(snake_case_       )

    __a			:  List[Any]		    =  {
        '''BertForMaskedLM''': ['''BertModelTest'''],
        '''BertForMultipleChoice''': ['''BertModelTest'''],
        '''BertForNextSentencePrediction''': ['''BertModelTest'''],
        '''BertForPreTraining''': ['''BertModelTest'''],
        '''BertForQuestionAnswering''': ['''BertModelTest'''],
        '''BertForSequenceClassification''': ['''BertModelTest'''],
        '''BertForTokenClassification''': ['''BertModelTest'''],
        '''BertLMHeadModel''': ['''BertModelTest'''],
        '''BertModel''': ['''BertModelTest'''],
    }

    __a			:  Dict		    =  {
        '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
        '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
        '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
        '''BlipModel''': ['''BlipModelTest'''],
        '''BlipTextModel''': ['''BlipTextModelTest'''],
        '''BlipVisionModel''': ['''BlipVisionModelTest'''],
    }

    self.assertEqual(get_test_info.to_json(snake_case_       ) ,    snake_case_       )
    self.assertEqual(get_test_info.to_json(snake_case_       ) ,    snake_case_       )







   def 				lowerCAmelCase      (self			: int       ):
    __a			:  Any		    =  get_model_to_tester_mapping(snake_case_       )
    __a			:  List[str]		    =  get_model_to_tester_mapping(snake_case_       )

    __a			:  Any		    =  {
        '''BertForMaskedLM''': ['''BertModelTester'''],
        '''BertForMultipleChoice''': ['''BertModelTester'''],
        '''BertForNextSentencePrediction''': ['''BertModelTester'''],
        '''BertForPreTraining''': ['''BertModelTester'''],
        '''BertForQuestionAnswering''': ['''BertModelTester'''],
        '''BertForSequenceClassification''': ['''BertModelTester'''],
        '''BertForTokenClassification''': ['''BertModelTester'''],
        '''BertLMHeadModel''': ['''BertModelTester'''],
        '''BertModel''': ['''BertModelTester'''],
    }

    __a			:  int		    =  {
        '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
        '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
        '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
        '''BlipModel''': ['''BlipModelTester'''],
        '''BlipTextModel''': ['''BlipTextModelTester'''],
        '''BlipVisionModel''': ['''BlipVisionModelTester'''],
    }

    self.assertEqual(get_test_info.to_json(snake_case_       ) ,    snake_case_       )
    self.assertEqual(get_test_info.to_json(snake_case_       ) ,    snake_case_       )
