
'''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			(    _a	):
		a_          =					["image_processor", "tokenizer"]
		a_          =					"LayoutLMv2ImageProcessor"
		a_          =					("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")




		def __init__(  self					:List[str]			,    _SCREAMING_SNAKE_CASE					:List[str]=None			,    _SCREAMING_SNAKE_CASE					:str=None			,    **_SCREAMING_SNAKE_CASE					:Union[str, Any])					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	Union[str, Any]					     =     None
								if "feature_extractor" in kwargs:
														warnings.warn(
														    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
														    """ instead."""			,    _SCREAMING_SNAKE_CASE			,    )
														UpperCamelCase_     :	Tuple					     =     kwargs.pop("""feature_extractor""")

								UpperCamelCase_     :	Optional[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__(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)




		def __call__(  self					:Dict			,    _SCREAMING_SNAKE_CASE					:Dict			,    _SCREAMING_SNAKE_CASE					:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,    _SCREAMING_SNAKE_CASE					:Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,    _SCREAMING_SNAKE_CASE					:Union[List[List[int]], List[List[List[int]]]] = None			,    _SCREAMING_SNAKE_CASE					:Optional[Union[List[int], List[List[int]]]] = None			,    _SCREAMING_SNAKE_CASE					:bool = True			,    _SCREAMING_SNAKE_CASE					:Union[bool, str, PaddingStrategy] = False			,    _SCREAMING_SNAKE_CASE					:Union[bool, str, TruncationStrategy] = False			,    _SCREAMING_SNAKE_CASE					:Optional[int] = None			,    _SCREAMING_SNAKE_CASE					:int = 0			,    _SCREAMING_SNAKE_CASE					:Optional[int] = None			,    _SCREAMING_SNAKE_CASE					:Optional[bool] = None			,    _SCREAMING_SNAKE_CASE					:Optional[bool] = None			,    _SCREAMING_SNAKE_CASE					:bool = False			,    _SCREAMING_SNAKE_CASE					:bool = False			,    _SCREAMING_SNAKE_CASE					:bool = False			,    _SCREAMING_SNAKE_CASE					:bool = False			,    _SCREAMING_SNAKE_CASE					:bool = True			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, TensorType]] = None			,    **_SCREAMING_SNAKE_CASE					:List[Any]			,    )					->			BatchEncoding:






								'''simple docstring'''






								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_     :	List[str]					     =     self.image_processor(images=_SCREAMING_SNAKE_CASE			,    return_tensors=_SCREAMING_SNAKE_CASE)

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

								UpperCamelCase_     :	Dict					     =     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=_SCREAMING_SNAKE_CASE			,    add_special_tokens=_SCREAMING_SNAKE_CASE			,    padding=_SCREAMING_SNAKE_CASE			,    truncation=_SCREAMING_SNAKE_CASE			,    max_length=_SCREAMING_SNAKE_CASE			,    stride=_SCREAMING_SNAKE_CASE			,    pad_to_multiple_of=_SCREAMING_SNAKE_CASE			,    return_token_type_ids=_SCREAMING_SNAKE_CASE			,    return_attention_mask=_SCREAMING_SNAKE_CASE			,    return_overflowing_tokens=_SCREAMING_SNAKE_CASE			,    return_special_tokens_mask=_SCREAMING_SNAKE_CASE			,    return_offsets_mapping=_SCREAMING_SNAKE_CASE			,    return_length=_SCREAMING_SNAKE_CASE			,    verbose=_SCREAMING_SNAKE_CASE			,    return_tensors=_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE			,    )

								# add pixel values
								UpperCamelCase_     :	str					     =     features.pop("""pixel_values""")
								if return_overflowing_tokens is True:
														UpperCamelCase_     :	int					     =     self.get_overflowing_images(_SCREAMING_SNAKE_CASE			,    encoded_inputs["""overflow_to_sample_mapping"""])
								UpperCamelCase_     :	List[str]					     =     images

								return encoded_inputs




		def 	_snake_case	(  self					:str			,    _SCREAMING_SNAKE_CASE					:Tuple			,    _SCREAMING_SNAKE_CASE					:Any)					->			Union[str, Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     []
								for sample_idx in overflow_to_sample_mapping:
														images_with_overflow.append(images[sample_idx])

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

								return images_with_overflow




		def 	_snake_case	(  self					:List[str]			,    *_SCREAMING_SNAKE_CASE					:Union[str, Any]			,    **_SCREAMING_SNAKE_CASE					:Optional[int])					->			Any:






								'''simple docstring'''






								return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:List[str]			,    *_SCREAMING_SNAKE_CASE					:str			,    **_SCREAMING_SNAKE_CASE					:Optional[int])					->			str:






								'''simple docstring'''






								return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)




		@property
		def 	_snake_case	(  self					:List[Any])					->			Tuple:






								'''simple docstring'''






								return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]




		@property
		def 	_snake_case	(  self					:Optional[Any])					->			Tuple:






								'''simple docstring'''






								warnings.warn(
								    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,    _SCREAMING_SNAKE_CASE			,    )
								return self.image_processor_class





		@property
		def 	_snake_case	(  self					:List[Any])					->			List[Any]:






								'''simple docstring'''






								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,    _SCREAMING_SNAKE_CASE			,    )
								return self.image_processor






'''simple docstring'''


import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers.activations_tf import get_tf_activation




@require_tf
class     __snake_case			(    unittest.TestCase	):




		def 	_snake_case	(  self					:Tuple)					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	List[str]					     =     tf.constant([-1_0_0, -1.0, -0.1, 0, 0.1, 1.0, 100.0])
								UpperCamelCase_     :	Any					     =     get_tf_activation("""gelu""")
								UpperCamelCase_     :	List[Any]					     =     get_tf_activation("""gelu_10""")

								UpperCamelCase_     :	Optional[int]					     =     gelu(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Dict					     =     geluaa(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	List[Any]					     =     tf.where(y_gelu_aa < 10.0			,    1.0			,    0.0)

								self.assertEqual(tf.math.reduce_max(_SCREAMING_SNAKE_CASE).numpy().item()			,    10.0)
								self.assertTrue(np.allclose(y_gelu * clipped_mask			,    y_gelu_aa * clipped_mask))





		def 	_snake_case	(  self					:Any)					->			Dict:






								'''simple docstring'''






								get_tf_activation("""gelu""")
								get_tf_activation("""gelu_10""")
								get_tf_activation("""gelu_fast""")
								get_tf_activation("""gelu_new""")
								get_tf_activation("""glu""")
								get_tf_activation("""mish""")
								get_tf_activation("""quick_gelu""")
								get_tf_activation("""relu""")
								get_tf_activation("""sigmoid""")
								get_tf_activation("""silu""")
								get_tf_activation("""swish""")
								get_tf_activation("""tanh""")
								with self.assertRaises(_SCREAMING_SNAKE_CASE):
														get_tf_activation("""bogus""")
								with self.assertRaises(_SCREAMING_SNAKE_CASE):
														get_tf_activation(_SCREAMING_SNAKE_CASE)




