'''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 		__A       (__magic_name__   ):
       snake_case       :str				    =					["image_processor", "tokenizer"]
       snake_case       :Union[str, Any]				    =					"LayoutLMv3ImageProcessor"
       snake_case       :List[str]				    =					("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






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

              __UpperCAmelCase  :				str       				=		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__(UpperCamelCase_				,     UpperCamelCase_      )






       def __call__(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = True				,     UpperCamelCase_ = False				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = 0				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = False				,     UpperCamelCase_ = False				,     UpperCamelCase_ = False				,     UpperCamelCase_ = False				,     UpperCamelCase_ = True				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):




              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."      )

              # first, apply the image processor
              __UpperCAmelCase  :				str       				=		self.image_processor(images=UpperCamelCase_				,     return_tensors=UpperCamelCase_      )

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

              __UpperCAmelCase  :				Union[str, Any]       				=		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=UpperCamelCase_				,     add_special_tokens=UpperCamelCase_				,     padding=UpperCamelCase_				,     truncation=UpperCamelCase_				,     max_length=UpperCamelCase_				,     stride=UpperCamelCase_				,     pad_to_multiple_of=UpperCamelCase_				,     return_token_type_ids=UpperCamelCase_				,     return_attention_mask=UpperCamelCase_				,     return_overflowing_tokens=UpperCamelCase_				,     return_special_tokens_mask=UpperCamelCase_				,     return_offsets_mapping=UpperCamelCase_				,     return_length=UpperCamelCase_				,     verbose=UpperCamelCase_				,     return_tensors=UpperCamelCase_				,     **UpperCamelCase_				,     )

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

              return encoded_inputs






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		[]
              for sample_idx in overflow_to_sample_mapping:
                     images_with_overflow.append(images[sample_idx]      )

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

              return images_with_overflow






       def 			_snake_case						(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              return self.tokenizer.batch_decode(*UpperCamelCase_				,     **UpperCamelCase_      )






       def 			_snake_case						(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              return self.tokenizer.decode(*UpperCamelCase_				,     **UpperCamelCase_      )






       @property
       def 			_snake_case						(      self      ):
              return ["input_ids", "bbox", "attention_mask", "pixel_values"]






       @property
       def 			_snake_case						(      self      ):
              warnings.warn(
                  "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,     UpperCamelCase_				,     )
              return self.image_processor_class







       @property
       def 			_snake_case						(      self      ):
              warnings.warn(
                  "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,     UpperCamelCase_				,     )
              return self.image_processor







'''simple docstring'''




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers.generation import (
       MaxLengthCriteria,
       MaxNewTokensCriteria,
       MaxTimeCriteria,
       StoppingCriteriaList,
       validate_stopping_criteria,
   )






@require_torch
class 		__A       (unittest.TestCase   ):






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		3
              __UpperCAmelCase  :				Tuple       				=		2_50

              __UpperCAmelCase  :				str       				=		ids_tensor((batch_size, length)				,     UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		torch.ones((batch_size, length)				,     device=UpperCamelCase_				,     dtype=torch.float      ) / length
              return input_ids, scores






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._get_tensors(5      )

              __UpperCAmelCase  :				Tuple       				=		StoppingCriteriaList(
                  [
                      MaxLengthCriteria(max_length=10      ),
                      MaxTimeCriteria(max_time=0.1      ),
                  ]      )

              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				int       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		MaxLengthCriteria(max_length=10      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._get_tensors(5      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		MaxNewTokensCriteria(start_length=5				,     max_new_tokens=5      )

              __UpperCAmelCase				, __UpperCAmelCase  :				List[str]       				=		self._get_tensors(5      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StoppingCriteriaList([criteria]      )
              self.assertEqual(criteria_list.max_length				,     10      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self._get_tensors(5      )

              __UpperCAmelCase  :				str       				=		MaxTimeCriteria(max_time=0.1      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase  :				str       				=		MaxTimeCriteria(max_time=0.1				,     initial_timestamp=time.time() - 0.2      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )







       def 			_snake_case						(      self      ):
              validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )				,     10      )

              with self.assertWarns(UpperCamelCase_      ):
                     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )				,     11      )

              __UpperCAmelCase  :				Optional[int]       				=		validate_stopping_criteria(StoppingCriteriaList()				,     11      )

              self.assertEqual(len(UpperCamelCase_      )				,     1      )






