
import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


class 							lowercase   (						TensorFormatter[Mapping, np.ndarray, Mapping]  ):



		def __init__(					self							, A_=None							, **A_			)    ->   Optional[Any]:
				"""simple docstring"""
				super().__init__(features=lowerCAmelCase__			)
				UpperCamelCase            =	np_array_kwargs



		def __UpperCamelCase					(					self							, A_			)    ->   Union[str, Any]:
				"""simple docstring"""
				if isinstance(lowerCAmelCase__							, lowerCAmelCase__			):
						if column and all(
						    isinstance(lowerCAmelCase__							, np.ndarray			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
								return np.stack(lowerCAmelCase__			)
						else:
								# don't use np.array(column, dtype=object)
								# since it fails in certain cases
								# see https://stackoverflow.com/q/51005699
								UpperCamelCase            =	np.empty(len(lowerCAmelCase__			)							, dtype=lowerCAmelCase__			)
								UpperCamelCase            =	column
								return out
				return column



		def __UpperCamelCase					(					self							, A_			)    ->   List[Any]:
				"""simple docstring"""
				if isinstance(lowerCAmelCase__							, (str, bytes, type(lowerCAmelCase__			))			):
						return value
				elif isinstance(lowerCAmelCase__							, (np.character, np.ndarray)			) and np.issubdtype(value.dtype							, np.character			):
						return value
				elif isinstance(lowerCAmelCase__							, np.number			):
						return value

				UpperCamelCase            =	{}

				if isinstance(lowerCAmelCase__							, np.ndarray			) and np.issubdtype(value.dtype							, np.integer			):
						UpperCamelCase            =	{'dtype': np.intaa}
				elif isinstance(lowerCAmelCase__							, np.ndarray			) and np.issubdtype(value.dtype							, np.floating			):
						UpperCamelCase            =	{'dtype': np.floataa}
				elif config.PIL_AVAILABLE and "PIL" in sys.modules:
						import PIL.Image

						if isinstance(lowerCAmelCase__							, PIL.Image.Image			):
								return np.asarray(lowerCAmelCase__							, **self.np_array_kwargs			)

				return np.asarray(lowerCAmelCase__							, **{**default_dtype, **self.np_array_kwargs}			)



		def __UpperCamelCase					(					self							, A_			)    ->   Optional[int]:
				"""simple docstring"""
				# support for torch, tf, jax etc.
				if config.TORCH_AVAILABLE and "torch" in sys.modules:
						import torch

						if isinstance(lowerCAmelCase__							, torch.Tensor			):
								return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
				if hasattr(lowerCAmelCase__							, '__array__'			) and not isinstance(lowerCAmelCase__							, (np.ndarray, np.character, np.number)			):
						UpperCamelCase            =	data_struct.__array__()
				# support for nested types like struct of list of struct
				if isinstance(lowerCAmelCase__							, np.ndarray			):
						if data_struct.dtype == object:
								return self._consolidate([self.recursive_tensorize(lowerCAmelCase__			) for substruct in data_struct]			)
				if isinstance(lowerCAmelCase__							, (list, tuple)			):
						return self._consolidate([self.recursive_tensorize(lowerCAmelCase__			) for substruct in data_struct]			)
				return self._tensorize(lowerCAmelCase__			)



		def __UpperCamelCase					(					self							, A_			)    ->   Union[str, Any]:
				"""simple docstring"""
				return map_nested(self._recursive_tensorize							, lowerCAmelCase__							, map_list=lowerCAmelCase__			)



		def __UpperCamelCase					(					self							, A_			)    ->   Mapping:
				"""simple docstring"""
				UpperCamelCase            =	self.numpy_arrow_extractor().extract_row(lowerCAmelCase__			)
				UpperCamelCase            =	self.python_features_decoder.decode_row(lowerCAmelCase__			)
				return self.recursive_tensorize(lowerCAmelCase__			)



		def __UpperCamelCase					(					self							, A_			)    ->   np.ndarray:
				"""simple docstring"""
				UpperCamelCase            =	self.numpy_arrow_extractor().extract_column(lowerCAmelCase__			)
				UpperCamelCase            =	self.python_features_decoder.decode_column(lowerCAmelCase__							, pa_table.column_names[0]			)
				UpperCamelCase            =	self.recursive_tensorize(lowerCAmelCase__			)
				UpperCamelCase            =	self._consolidate(lowerCAmelCase__			)
				return column



		def __UpperCamelCase					(					self							, A_			)    ->   Mapping:
				"""simple docstring"""
				UpperCamelCase            =	self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__			)
				UpperCamelCase            =	self.python_features_decoder.decode_batch(lowerCAmelCase__			)
				UpperCamelCase            =	self.recursive_tensorize(lowerCAmelCase__			)
				for column_name in batch:
						UpperCamelCase            =	self._consolidate(batch[column_name]			)
				return batch



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     __magic_name__     (							_snake_case     ):
 UpperCAmelCase				     =    ["""image_processor""", """tokenizer"""]
 UpperCAmelCase				     =    """LayoutLMv2ImageProcessor"""
 UpperCAmelCase				     =    ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")


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

   UpperCAmelCase          =	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__(lowerCAmelCase__      ,    lowerCAmelCase__     )


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

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

   UpperCAmelCase          =	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=lowerCAmelCase__      ,    add_special_tokens=lowerCAmelCase__      ,    padding=lowerCAmelCase__      ,    truncation=lowerCAmelCase__      ,    max_length=lowerCAmelCase__      ,    stride=lowerCAmelCase__      ,    pad_to_multiple_of=lowerCAmelCase__      ,    return_token_type_ids=lowerCAmelCase__      ,    return_attention_mask=lowerCAmelCase__      ,    return_overflowing_tokens=lowerCAmelCase__      ,    return_special_tokens_mask=lowerCAmelCase__      ,    return_offsets_mapping=lowerCAmelCase__      ,    return_length=lowerCAmelCase__      ,    verbose=lowerCAmelCase__      ,    return_tensors=lowerCAmelCase__      ,    **lowerCAmelCase__      ,    )

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

   return encoded_inputs


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   int:
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   UpperCAmelCase          =	[]
   for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]     )

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

   return images_with_overflow


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    *lowerCAmelCase__		:			int      ,    **lowerCAmelCase__		:			Union[str, Any]     )     ->   List[str]:
   return self.tokenizer.batch_decode(*lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    *lowerCAmelCase__		:			Tuple      ,    **lowerCAmelCase__		:			Union[str, Any]     )     ->   List[Any]:
   return self.tokenizer.decode(*lowerCAmelCase__      ,    **lowerCAmelCase__     )


 @property
 def _UpperCamelCase						(    self		:			int     )     ->   List[str]:
   return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


 @property
 def _UpperCamelCase						(    self		:			Dict     )     ->   List[str]:
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,    lowerCAmelCase__      ,    )
   return self.image_processor_class


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



