



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ViltImageProcessor

class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     def __init__(							self			:				Tuple  ,	UpperCamelCase			:				List[Any]  ,	UpperCamelCase			:				Optional[Any]=7  ,	UpperCamelCase			:				Dict=3  ,	UpperCamelCase			:				Optional[int]=1_8  ,	UpperCamelCase			:				Dict=3_0  ,	UpperCamelCase			:				Tuple=4_0_0  ,	UpperCamelCase			:				List[str]=True  ,	UpperCamelCase			:				int=None  ,	UpperCamelCase			:				str=2  ,	UpperCamelCase			:				Union[str, Any]=True  ,	UpperCamelCase			:				int=[0.5, 0.5, 0.5]  ,	UpperCamelCase			:				List[str]=[0.5, 0.5, 0.5]  ,	)->int:
      __SCREAMING_SNAKE_CASE							:     Dict							=						size if size is not None else {'shortest_edge': 3_0}
      __SCREAMING_SNAKE_CASE							:     Dict							=						parent
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						batch_size
      __SCREAMING_SNAKE_CASE							:     Dict							=						num_channels
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_size
      __SCREAMING_SNAKE_CASE							:     int							=						min_resolution
      __SCREAMING_SNAKE_CASE							:     Dict							=						max_resolution
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						do_resize
      __SCREAMING_SNAKE_CASE							:     List[str]							=						size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						size_divisor
      __SCREAMING_SNAKE_CASE							:     Dict							=						do_normalize
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image_mean
      __SCREAMING_SNAKE_CASE							:     Any							=						image_std





     def 						__snake_case     (							self			:				List[str]					)->Dict:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }






     def 						__snake_case     (							self			:				Optional[Any]  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				Optional[int]=False					)->Optional[Any]:
      if not batched:
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.size['shortest_edge']
       __SCREAMING_SNAKE_CASE							:     str							=						image_inputs[0]
       if isinstance(_snake_case  ,	Image.Image					):
        __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image.size
       else:
        __SCREAMING_SNAKE_CASE							:     int							=						image.shape[1], image.shape[2]
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						size / min(_snake_case  ,	_snake_case					)
       if h < w:
        __SCREAMING_SNAKE_CASE							:     Any							=						size, scale * w
       else:
        __SCREAMING_SNAKE_CASE							:     Dict							=						scale * h, size

       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						int((1_3_3_3 / 8_0_0) * size					)
       if max(_snake_case  ,	_snake_case					) > max_size:
        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						max_size / max(_snake_case  ,	_snake_case					)
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						newh * scale
        __SCREAMING_SNAKE_CASE							:     Dict							=						neww * scale

       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						int(newh + 0.5					), int(neww + 0.5					)
       __SCREAMING_SNAKE_CASE							:     int							=						(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

      else:
       __SCREAMING_SNAKE_CASE							:     List[str]							=						[]
       for image in image_inputs:
        __SCREAMING_SNAKE_CASE							:     int							=						self.get_expected_values([image]					)
        expected_values.append((expected_height, expected_width)					)
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						max(_snake_case  ,	key=lambda UpperCamelCase					: item[0]					)[0]
       __SCREAMING_SNAKE_CASE							:     Dict							=						max(_snake_case  ,	key=lambda UpperCamelCase					: item[1]					)[1]

      return expected_height, expected_width






@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(a_      ,     unittest.TestCase			):
     lowerCAmelCase       =      ViltImageProcessor if is_vision_available() else None





     def 						__snake_case     (							self			:				Any					)->Any:
      __SCREAMING_SNAKE_CASE							:     Tuple							=						ViltImageProcessingTester(self					)





     @property
     def 						__snake_case     (							self			:				Union[str, Any]					)->List[str]:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 						__snake_case     (							self			:				Optional[Any]					)->int:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(_snake_case  ,	"image_mean"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"image_std"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"do_normalize"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"do_resize"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"size"					)					)
      self.assertTrue(hasattr(_snake_case  ,	"size_divisor"					)					)





     def 						__snake_case     (							self			:				Union[str, Any]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size  ,	{"shortest_edge": 3_0}					)

      __SCREAMING_SNAKE_CASE							:     int							=						self.image_processing_class.from_dict(self.image_processor_dict  ,	size=4_2					)
      self.assertEqual(image_processor.size  ,	{"shortest_edge": 4_2}					)





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





     def 						__snake_case     (							self			:				Optional[int]					)->Any:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						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=_snake_case					)
      for image in image_inputs:
       self.assertIsInstance(_snake_case  ,	Image.Image					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_processing(image_inputs[0]  ,	return_tensors="pt"					).pixel_values

      __SCREAMING_SNAKE_CASE							:     str							=						self.image_processor_tester.get_expected_values(_snake_case					)
      self.assertEqual(
          encoded_images.shape  ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processing(_snake_case  ,	return_tensors="pt"					).pixel_values

      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.image_processor_tester.get_expected_values(_snake_case  ,	batched=_snake_case					)
      self.assertEqual(
          encoded_images.shape  ,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )  ,	)





     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE							:     List[str]							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=_snake_case  ,	numpify=_snake_case					)
      for image in image_inputs:
       self.assertIsInstance(_snake_case  ,	np.ndarray					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     Dict							=						image_processing(image_inputs[0]  ,	return_tensors="pt"					).pixel_values

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processor_tester.get_expected_values(_snake_case					)
      self.assertEqual(
          encoded_images.shape  ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     Any							=						image_processing(_snake_case  ,	return_tensors="pt"					).pixel_values

      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.image_processor_tester.get_expected_values(_snake_case  ,	batched=_snake_case					)
      self.assertEqual(
          encoded_images.shape  ,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )  ,	)






     def 						__snake_case     (							self			:				Optional[Any]					)->Optional[int]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     str							=						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=_snake_case  ,	torchify=_snake_case					)
      for image in image_inputs:
       self.assertIsInstance(_snake_case  ,	torch.Tensor					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     List[str]							=						image_processing(image_inputs[0]  ,	return_tensors="pt"					).pixel_values

      __SCREAMING_SNAKE_CASE							:     Dict							=						self.image_processor_tester.get_expected_values(_snake_case					)
      self.assertEqual(
          encoded_images.shape  ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     Any							=						image_processing(_snake_case  ,	return_tensors="pt"					).pixel_values

      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.image_processor_tester.get_expected_values(_snake_case  ,	batched=_snake_case					)
      self.assertEqual(
          encoded_images.shape  ,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )  ,	)









'''simple docstring'''


import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
						import torch



class        lowercase    (	unittest.TestCase,       a_			):


							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   int													=       load_tool('speech-to-text')
								self.tool.setup()


							def     _snake_case				(      self)				->					Any:
								UpperCAmelCase_		:   str													=       self.tool(torch.ones(3000))
								self.assertEqual(_snake_case     ,      ' you')





							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_		:   List[Any]													=       self.tool(audio=torch.ones(3000))
								self.assertEqual(_snake_case     ,      ' you')






