



"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_pytesseract_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor
class 					a	(  unittest.TestCase       ):



  def __init__(						self :					int       ,		lowerCAmelCase :					Tuple       ,		lowerCAmelCase :					Optional[int]=7       ,		lowerCAmelCase :					int=3       ,		lowerCAmelCase :					List[str]=18       ,		lowerCAmelCase :					Dict=30       ,		lowerCAmelCase :					int=400       ,		lowerCAmelCase :					int=True       ,		lowerCAmelCase :					str=None       ,		lowerCAmelCase :					Optional[Any]=True       ,		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=size if size is not None else {"""height""": 18, """width""": 18}
     SCREAMING_SNAKE_CASE_: Optional[int]     			=parent
     SCREAMING_SNAKE_CASE_: List[Any]     			=batch_size
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=num_channels
     SCREAMING_SNAKE_CASE_: Tuple     			=image_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=min_resolution
     SCREAMING_SNAKE_CASE_: int     			=max_resolution
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=do_resize
     SCREAMING_SNAKE_CASE_: str     			=size
     SCREAMING_SNAKE_CASE_: Any     			=apply_ocr





  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					Any:


     '''simple docstring'''




     return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class 					a	(  UpperCAmelCase__	,      unittest.TestCase       ):
  UpperCamelCase	:				str     						=					LayoutLMvaImageProcessor if is_pytesseract_available() else None



  def 	lowerCamelCase__       (						self :					str		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=LayoutLMvaImageProcessingTester(self		)



  @property
  def 	lowerCamelCase__       (						self :					Tuple		)     ->					Union[str, Any]:


     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()



  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.image_processing_class(**self.image_processor_dict		)
     self.assertTrue(hasattr(lowerCAmelCase       ,		"""do_resize"""		)		)
     self.assertTrue(hasattr(lowerCAmelCase       ,		"""size"""		)		)
     self.assertTrue(hasattr(lowerCAmelCase       ,		"""apply_ocr"""		)		)



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=self.image_processing_class.from_dict(self.image_processor_dict		)
     self.assertEqual(image_processor.size       ,		{"""height""": 18, """width""": 18}		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.image_processing_class.from_dict(self.image_processor_dict       ,		size=42		)
     self.assertEqual(image_processor.size       ,		{"""height""": 42, """width""": 42}		)



  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					str:


     '''simple docstring'''




     pass



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=self.image_processing_class(**self.image_processor_dict		)
     # create random PIL images
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=lowerCAmelCase		)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase       ,		Image.Image		)

     # Test not batched input
     SCREAMING_SNAKE_CASE_: List[str]     			=image_processing(image_inputs[0]       ,		return_tensors="""pt"""		)
     self.assertEqual(
         encoding.pixel_values.shape       ,		(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )       ,		)

     self.assertIsInstance(encoding.words       ,		lowerCAmelCase		)
     self.assertIsInstance(encoding.boxes       ,		lowerCAmelCase		)

     # Test batched
     SCREAMING_SNAKE_CASE_: int     			=image_processing(lowerCAmelCase       ,		return_tensors="""pt"""		).pixel_values
     self.assertEqual(
         encoded_images.shape       ,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )       ,		)



  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=self.image_processing_class(**self.image_processor_dict		)
     # create random numpy tensors
     SCREAMING_SNAKE_CASE_: int     			=prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=lowerCAmelCase       ,		numpify=lowerCAmelCase		)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase       ,		np.ndarray		)

     # Test not batched input
     SCREAMING_SNAKE_CASE_: Optional[int]     			=image_processing(image_inputs[0]       ,		return_tensors="""pt"""		).pixel_values
     self.assertEqual(
         encoded_images.shape       ,		(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )       ,		)

     # Test batched
     SCREAMING_SNAKE_CASE_: List[str]     			=image_processing(lowerCAmelCase       ,		return_tensors="""pt"""		).pixel_values
     self.assertEqual(
         encoded_images.shape       ,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )       ,		)



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=self.image_processing_class(**self.image_processor_dict		)
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE_: Any     			=prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=lowerCAmelCase       ,		torchify=lowerCAmelCase		)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase       ,		torch.Tensor		)

     # Test not batched input
     SCREAMING_SNAKE_CASE_: List[str]     			=image_processing(image_inputs[0]       ,		return_tensors="""pt"""		).pixel_values
     self.assertEqual(
         encoded_images.shape       ,		(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )       ,		)

     # Test batched
     SCREAMING_SNAKE_CASE_: Any     			=image_processing(lowerCAmelCase       ,		return_tensors="""pt"""		).pixel_values
     self.assertEqual(
         encoded_images.shape       ,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )       ,		)





  def 	lowerCamelCase__       (						self :					int		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=LayoutLMvaImageProcessor()

     from datasets import load_dataset

     SCREAMING_SNAKE_CASE_: Any     			=load_dataset("""hf-internal-testing/fixtures_docvqa"""       ,		split="""test"""		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=Image.open(ds[0]["""file"""]		).convert("""RGB"""		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=image_processing(lowerCAmelCase       ,		return_tensors="""pt"""		)

     self.assertEqual(encoding.pixel_values.shape       ,		(1, 3, 224, 224)		)
     self.assertEqual(len(encoding.words		)       ,		len(encoding.boxes		)		)

     # fmt: off
     # the words and boxes were obtained with Tesseract 4.1.1
     SCREAMING_SNAKE_CASE_: List[str]     			=[["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
     SCREAMING_SNAKE_CASE_: Dict     			=[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
     # fmt: on

     self.assertListEqual(encoding.words       ,		lowerCAmelCase		)
     self.assertListEqual(encoding.boxes       ,		lowerCAmelCase		)

     # with apply_OCR = False
     SCREAMING_SNAKE_CASE_: Tuple     			=LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Tuple     			=image_processing(lowerCAmelCase       ,		return_tensors="""pt"""		)

     self.assertEqual(encoding.pixel_values.shape       ,		(1, 3, 224, 224)		)








"""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     =			["""text""", """image""", """audio"""]





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=[]

   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(3000 ) )
      elif isinstance(lowercase							,			lowercase ):
         inputs.append(create_inputs(lowercase ) )
      else:
         raise ValueError(f'''Invalid type requested: {input_type}''' )

   return inputs





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=[]

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

   return output_types
@is_tool_test
class 					a	:



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					List[Any]:


     '''simple docstring'''




     self.assertTrue(hasattr(self.tool       ,		"""inputs"""		)		)
     self.assertTrue(hasattr(self.tool       ,		"""outputs"""		)		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.tool.inputs
     for _input in inputs:
        if isinstance(_input       ,		lowerCAmelCase		):
           for __input in _input:
              self.assertTrue(__input in authorized_types		)
        else:
           self.assertTrue(_input in authorized_types		)

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



  def 	lowerCamelCase__       (						self :					str		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=create_inputs(self.tool.inputs		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.tool(*lowerCAmelCase		)

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

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



  def 	lowerCamelCase__       (						self :					str		)     ->					Optional[Any]:


     '''simple docstring'''




     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 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=create_inputs(self.tool.inputs		)
     SCREAMING_SNAKE_CASE_: Tuple     			=self.tool(*lowerCAmelCase		)

     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: int     			=[outputs]

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

     for output, output_type in zip(lowerCAmelCase       ,		self.tool.outputs		):
        SCREAMING_SNAKE_CASE_: int     			=AGENT_TYPE_MAPPING[output_type]
        self.assertTrue(isinstance(lowerCAmelCase       ,		lowerCAmelCase		)		)





  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=create_inputs(self.tool.inputs		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[]

     for _input, input_type in zip(lowerCAmelCase       ,		self.tool.inputs		):
        if isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
           _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
     SCREAMING_SNAKE_CASE_: Dict     			=self.tool(*lowerCAmelCase		)

     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: List[str]     			=[outputs]

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



