





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(						__UpperCAmelCase ):
     __A	  =       ["image_processor", "tokenizer"]
     __A	  =       "LayoutLMv3ImageProcessor"
     __A	  =       ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")

     def __init__(      self,       A=None,       A=None,       **A      ):





            """simple docstring"""

            _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.''',       A,       )
                   _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__(A,       A      )

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





            """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.'''      )

            # first, apply the image processor
            _UpperCamelCase              =						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              =						[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=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              =						features.pop('''pixel_values'''      )
            if return_overflowing_tokens is True:
                   _UpperCamelCase              =						self.get_overflowing_images(A,       encoded_inputs['''overflow_to_sample_mapping''']      )
            _UpperCamelCase              =						images

            return encoded_inputs

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						[]
            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,       *A,       **A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       *A,       **A      ):





            """simple docstring"""

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

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return ["input_ids", "bbox", "attention_mask", "pixel_values"]

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            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      ):





            """simple docstring"""

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













from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch

@require_torch
@require_tf
class  __A(						__UpperCAmelCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						SMALL_MODEL_IDENTIFIER
            _UpperCamelCase              =						'''pt'''
            _UpperCamelCase              =						'''tf'''

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						AutoModel.from_pretrained(self.test_model      )
            model_pt.save_pretrained(A      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						TFAutoModel.from_pretrained(self.test_model,       from_pt=A      )
            model_tf.save_pretrained(A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						'''mock_framework'''

            # Framework provided - return whatever the user provides
            _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model,       A      )
            self.assertEqual(A,       A      )

            # Local checkpoint and framework provided - return provided framework
            # PyTorch checkpoint
            with TemporaryDirectory() as local_pt_ckpt:
                   self._setup_pt_ckpt(A      )
                   _UpperCamelCase              =						FeaturesManager.determine_framework(A,       A      )
                   self.assertEqual(A,       A      )

            # TensorFlow checkpoint
            with TemporaryDirectory() as local_tf_ckpt:
                   self._setup_tf_ckpt(A      )
                   _UpperCamelCase              =						FeaturesManager.determine_framework(A,       A      )
                   self.assertEqual(A,       A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            with TemporaryDirectory() as local_pt_ckpt:
                   self._setup_pt_ckpt(A      )
                   _UpperCamelCase              =						FeaturesManager.determine_framework(A      )
                   self.assertEqual(A,       self.framework_pt      )

            # TensorFlow checkpoint
            with TemporaryDirectory() as local_tf_ckpt:
                   self._setup_tf_ckpt(A      )
                   _UpperCamelCase              =						FeaturesManager.determine_framework(A      )
                   self.assertEqual(A,       self.framework_tf      )

            # Invalid local checkpoint
            with TemporaryDirectory() as local_invalid_ckpt:
                   with self.assertRaises(A      ):
                          _UpperCamelCase              =						FeaturesManager.determine_framework(A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MagicMock(return_value=A      )
            with patch('''transformers.onnx.features.is_tf_available''',       A      ):
                   _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model      )
                   self.assertEqual(A,       self.framework_pt      )

            # PyTorch not in environment -> use TensorFlow
            _UpperCamelCase              =						MagicMock(return_value=A      )
            with patch('''transformers.onnx.features.is_torch_available''',       A      ):
                   _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model      )
                   self.assertEqual(A,       self.framework_tf      )

            # Both in environment -> use PyTorch
            _UpperCamelCase              =						MagicMock(return_value=A      )
            _UpperCamelCase              =						MagicMock(return_value=A      )
            with patch('''transformers.onnx.features.is_tf_available''',       A      ), patch(
                '''transformers.onnx.features.is_torch_available''',       A      ):
                   _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model      )
                   self.assertEqual(A,       self.framework_pt      )

            # Both not in environment -> raise error
            _UpperCamelCase              =						MagicMock(return_value=A      )
            _UpperCamelCase              =						MagicMock(return_value=A      )
            with patch('''transformers.onnx.features.is_tf_available''',       A      ), patch(
                '''transformers.onnx.features.is_torch_available''',       A      ):
                   with self.assertRaises(A      ):
                          _UpperCamelCase              =						FeaturesManager.determine_framework(self.test_model      )






