"""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					(		__lowerCAmelCase ):
    _snake_case  :	int  				=				["""image_processor""", """tokenizer"""]
    _snake_case  :	str  				=				"""LayoutLMv3ImageProcessor"""
    _snake_case  :	Dict  				=				("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")


    def __init__(       self						:	List[Any] ,					__lowerCAmelCase						:	List[str]=None ,					__lowerCAmelCase						:	Optional[Any]=None ,					**__lowerCAmelCase						:	Optional[int]       ):
      _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.""" ,					_SCREAMING_SNAKE_CASE ,					)
        _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__(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE       )


    def __call__(       self						:	List[Any] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Union[str, Any] = None ,					__lowerCAmelCase						:	str = None ,					__lowerCAmelCase						:	str = None ,					__lowerCAmelCase						:	Dict = None ,					__lowerCAmelCase						:	List[Any] = True ,					__lowerCAmelCase						:	List[str] = False ,					__lowerCAmelCase						:	List[Any] = None ,					__lowerCAmelCase						:	int = None ,					__lowerCAmelCase						:	Union[str, Any] = 0 ,					__lowerCAmelCase						:	int = None ,					__lowerCAmelCase						:	Optional[int] = None ,					__lowerCAmelCase						:	Union[str, Any] = None ,					__lowerCAmelCase						:	Any = False ,					__lowerCAmelCase						:	List[str] = False ,					__lowerCAmelCase						:	Union[str, Any] = False ,					__lowerCAmelCase						:	List[str] = False ,					__lowerCAmelCase						:	Dict = True ,					__lowerCAmelCase						:	List[Any] = None ,					**__lowerCAmelCase						:	str ,					):
      # 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."""       )

      # first, apply the image processor
      _UpperCAmelCase    			=   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    			=   [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=_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    			=   features.pop("""pixel_values"""       )
      if return_overflowing_tokens is True:
        _UpperCAmelCase    			=   self.get_overflowing_images(_SCREAMING_SNAKE_CASE ,					encoded_inputs["""overflow_to_sample_mapping"""]       )
      _UpperCAmelCase    			=   images

      return encoded_inputs


    def   lowerCAmelCase_	(       self						:	Any ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	str       ):
      # 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(_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   lowerCAmelCase_	(       self						:	List[Any] ,					*__lowerCAmelCase						:	Union[str, Any] ,					**__lowerCAmelCase						:	Tuple       ):
      return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE       )


    def   lowerCAmelCase_	(       self						:	Any ,					*__lowerCAmelCase						:	Any ,					**__lowerCAmelCase						:	Tuple       ):
      return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE       )


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


    @property
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      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   lowerCAmelCase_	(       self						:	str       ):
      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'''





from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image


UpperCamelCase_		:     int          =		['''text''', '''image''', '''audio''']





def       __a      (  _UpperCamelCase:       List[str]       )		->					Dict:

     """simple docstring"""




     _snake_case				= []

     for input_type in input_types:
          if input_type == "text":
               inputs.append("Text input"       )
          elif input_type == "image":
               inputs.append(
                   Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"       )       ) / "000000039769.png"       ).resize((512, 512)       )       )
          elif input_type == "audio":
               inputs.append(torch.ones(3_000       )       )
          elif isinstance(_UpperCamelCase   ,		_UpperCamelCase       ):
               inputs.append(create_inputs(_UpperCamelCase       )       )
          else:
               raise ValueError(F"""Invalid type requested: {input_type}"""       )

     return inputs





def       __a      (  _UpperCamelCase:       List       )		->					Dict:

     """simple docstring"""




     _snake_case				= []

     for output in outputs:
          if isinstance(_UpperCamelCase   ,		(str, AgentText)       ):
               output_types.append("text"       )
          elif isinstance(_UpperCamelCase   ,		(Image.Image, AgentImage)       ):
               output_types.append("image"       )
          elif isinstance(_UpperCamelCase   ,		(torch.Tensor, AgentAudio)       ):
               output_types.append("audio"       )
          else:
               raise ValueError(F"""Invalid output: {output}"""       )

     return output_types





@is_tool_test
class   _a							:






     def 							_lowercase      (					self       )				->     Any:
          self.assertTrue(hasattr(self.tool   ,"inputs"       )       )
          self.assertTrue(hasattr(self.tool   ,"outputs"       )       )

          _snake_case				= self.tool.inputs
          for _input in inputs:
               if isinstance(_input   ,_SCREAMING_SNAKE_CASE       ):
                    for __input in _input:
                         self.assertTrue(__input in authorized_types       )
               else:
                    self.assertTrue(_input in authorized_types       )

          _snake_case				= self.tool.outputs
          for _output in outputs:
               self.assertTrue(_output in authorized_types       )






     def 							_lowercase      (					self       )				->     Any:
          _snake_case				= create_inputs(self.tool.inputs       )
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          # There is a single output
          if len(self.tool.outputs       ) == 1:
               _snake_case				= [outputs]

          self.assertListEqual(output_types(_SCREAMING_SNAKE_CASE       )   ,self.tool.outputs       )






     def 							_lowercase      (					self       )				->     str:
          self.assertTrue(hasattr(self.tool   ,"description"       )       )
          self.assertTrue(hasattr(self.tool   ,"default_checkpoint"       )       )
          self.assertTrue(self.tool.description.startswith("This is a tool that"       )       )






     def 							_lowercase      (					self       )				->     Tuple:
          _snake_case				= create_inputs(self.tool.inputs       )
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          if not isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= [outputs]

          self.assertEqual(len(_SCREAMING_SNAKE_CASE       )   ,len(self.tool.outputs       )       )

          for output, output_type in zip(_SCREAMING_SNAKE_CASE   ,self.tool.outputs       ):
               _snake_case				= AGENT_TYPE_MAPPING[output_type]
               self.assertTrue(isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )       )






     def 							_lowercase      (					self       )				->     Optional[Any]:
          _snake_case				= create_inputs(self.tool.inputs       )

          _snake_case				= []

          for _input, input_type in zip(_SCREAMING_SNAKE_CASE   ,self.tool.inputs       ):
               if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                    _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input       ) for _input_type in input_type]       )
               else:
                    _inputs.append(AGENT_TYPE_MAPPING[input_type](_input       )       )

        # Should not raise an error
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          if not isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= [outputs]

          self.assertEqual(len(_SCREAMING_SNAKE_CASE       )   ,len(self.tool.outputs       )       )


