


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  A_ (			a_  ):







 """simple docstring"""





 _UpperCamelCase					:			Optional[Any]     	=       ['''image_processor''', '''tokenizer''']
 _UpperCamelCase					:			Union[str, Any]     	=       '''LayoutLMv2ImageProcessor'''
 _UpperCamelCase					:			Optional[Any]     	=       ('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')
 def __init__(      self       :			Any							,     _a       :			List[str]=None							,     _a       :			List[Any]=None							,     **_a       :			Any					)     ->      Tuple:
      UpperCAmelCase    :					Optional[int]			=      None
      if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."							,     _a							,     )
           UpperCAmelCase    :					Union[str, Any]			=      kwargs.pop("feature_extractor"					)

      UpperCAmelCase    :					int			=      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[Any]							,     _a       :			Tuple							,     _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] = False							,     _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       :			Tuple							,     )     ->      BatchEncoding:
      # 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
      UpperCAmelCase    :					str			=      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					):
                UpperCAmelCase    :					List[str]			=      [text]  # add batch dimension (as the image processor always adds a batch dimension)
           UpperCAmelCase    :					Union[str, Any]			=      features["words"]

      UpperCAmelCase    :					Optional[int]			=      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
      UpperCAmelCase    :					Optional[Any]			=      features.pop("pixel_values"					)
      if return_overflowing_tokens is True:
           UpperCAmelCase    :					Tuple			=      self.get_overflowing_images(_a							,     encoded_inputs["overflow_to_sample_mapping"]					)
      UpperCAmelCase    :					Optional[Any]			=      images

      return encoded_inputs
 def 	_UpperCamelCase     (      self       :			Tuple							,     _a       :			Optional[int]							,     _a       :			Union[str, Any]					)     ->      List[str]:
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      UpperCAmelCase    :					List[Any]			=      []
      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 	_UpperCamelCase     (      self       :			List[Any]							,     *_a       :			List[Any]							,     **_a       :			Tuple					)     ->      Any:
      return self.tokenizer.batch_decode(*_a							,     **_a					)
 def 	_UpperCamelCase     (      self       :			Optional[Any]							,     *_a       :			Union[str, Any]							,     **_a       :			Union[str, Any]					)     ->      List[Any]:
      return self.tokenizer.decode(*_a							,     **_a					)
 @property
 def 	_UpperCamelCase     (      self       :			Any					)     ->      Tuple:
      return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
 @property
 def 	_UpperCamelCase     (      self       :			Dict					)     ->      Union[str, 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 	_UpperCamelCase     (      self       :			Any					)     ->      Optional[int]:
      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,     _a							,     )
      return self.image_processor







import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_snake_case				=			[[1_2, 1_0], [3, 9]]
_snake_case				=			[[3, 4], [7, 4]]
_snake_case				=			[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_snake_case				=			[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_snake_case				=			[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_snake_case				=			[1]
_snake_case				=			[2]

_snake_case				=			logging.getLogger()
_snake_case				=			logging.DEBUG
_snake_case				=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_addition.__name__} returned integer'			)
               matop.add(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_addition.__name__} with same matrix dims'			)
          UpperCAmelCase    :					Tuple			=      (np.array(A__			) + np.array(A__			)).tolist()
          UpperCAmelCase    :					Optional[Any]			=      matop.add(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_addition.__name__} with different matrix dims'			)
               matop.add(A__		,		A__			)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_subtraction.__name__} returned integer'			)
               matop.subtract(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_subtraction.__name__} with same matrix dims'			)
          UpperCAmelCase    :					Tuple			=      (np.array(A__			) - np.array(A__			)).tolist()
          UpperCAmelCase    :					Optional[int]			=      matop.subtract(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_subtraction.__name__} with different matrix dims'			)
               assert matop.subtract(A__		,		A__			)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          logger.info(f'\n\t{test_multiplication.__name__} returned integer'			)
          with pytest.raises(A__			):
               matop.add(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_multiplication.__name__} meets dim requirements'			)
          UpperCAmelCase    :					List[str]			=      (np.matmul(A__		,		A__			)).tolist()
          UpperCAmelCase    :					int			=      matop.multiply(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(
                   f'\n\t{test_multiplication.__name__} does not meet dim requirements'			)
               assert matop.subtract(A__		,		A__			)







@pytest.mark.mat_ops
def 	A (		):
     UpperCAmelCase    :					int			=      (3.5 * np.array(A__			)).tolist()
     UpperCAmelCase    :					Optional[int]			=      matop.scalar_multiply(A__		,		3.5			)
     assert theo == act







@pytest.mark.mat_ops
def 	A (		):
     UpperCAmelCase    :					List[Any]			=      (np.identity(5			)).tolist()
     UpperCAmelCase    :					Optional[int]			=      matop.identity(5			)
     assert theo == act







@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"		,		[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]			)
def 	A (		A__			):
     if (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_transpose.__name__} returned integer'			)
               matop.transpose(A__			)
     else:
          UpperCAmelCase    :					Dict			=      (np.transpose(A__			)).tolist()
          UpperCAmelCase    :					List[str]			=      matop.transpose(A__		,		return_map=A__			)
          assert theo == act



