
import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger


def  A			(   lowercase       ,			lowercase					)  ->    Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase            =	copy.deepcopy(__A					)
		UpperCamelCase            =	sorted(__A					)
		UpperCamelCase            =	[bisect.bisect_right(__A       ,			__A					) for y in x]
		return quantized


def  A			(   lowercase       ,			lowercase=0					)  ->    List[str]:
		'''simple docstring'''

		UpperCamelCase            =	np.arange(start=3       ,			stop=__A       ,			step=4					).tolist() if k > 0 else [10]
		UpperCamelCase            =	_quantize(__A       ,			__A					)
		# count number of elements per group
		UpperCamelCase            =	np.unique(__A       ,			return_counts=__A					)[1]
		UpperCamelCase            =	[0] + bins + [np.inf]
		logger.info('Using {} as bins for aspect lengths quantization'.format(__A					)					)
		logger.info('Count of instances per bin: {}'.format(__A					)					)
		return groups


class 							lowercase   (						_snake_case  ):



		def __init__(					self							, A_							, A_							, A_			)    ->   Optional[int]:
				"""simple docstring"""
				if not isinstance(lowerCAmelCase__							, lowerCAmelCase__			):
						raise ValueError(
						    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowerCAmelCase__			)			)
				UpperCamelCase            =	sampler
				UpperCamelCase            =	group_ids
				UpperCamelCase            =	batch_size



		def __iter__(					self			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	defaultdict(lowerCAmelCase__			)
				UpperCamelCase            =	defaultdict(lowerCAmelCase__			)

				UpperCamelCase            =	0
				for idx in self.sampler:
						UpperCamelCase            =	self.group_ids[idx]
						buffer_per_group[group_id].append(lowerCAmelCase__			)
						samples_per_group[group_id].append(lowerCAmelCase__			)
						if len(buffer_per_group[group_id]			) == self.batch_size:
								yield buffer_per_group[group_id]  # TODO
								num_batches += 1
								del buffer_per_group[group_id]
						assert len(buffer_per_group[group_id]			) < self.batch_size

				# now we have run out of elements that satisfy
				# the group criteria, let's return the remaining
				# elements so that the size of the sampler is
				# deterministic
				UpperCamelCase            =	len(self			)
				UpperCamelCase            =	expected_num_batches - num_batches
				if num_remaining > 0:
						# for the remaining batches, group the batches by similar lengths
						UpperCamelCase            =	[]
						for group_id, idxs in sorted(buffer_per_group.items()							, key=lambda A_			: x[0]			):
								batch_idx.extend(lowerCAmelCase__			)
								if len(lowerCAmelCase__			) >= self.batch_size:
										yield batch_idx[: self.batch_size]
										UpperCamelCase            =	batch_idx[self.batch_size :]
										num_remaining -= 1
						if len(lowerCAmelCase__			) > 0:
								yield batch_idx
								num_remaining -= 1
				assert num_remaining == 0



		def __len__(					self			)    ->   Tuple:
				"""simple docstring"""
				return (len(self.sampler			) + self.batch_size - 1) // self.batch_size



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



