
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   __UpperCamelCase				(    unittest.TestCase   ):






     """simple docstring"""







     def __init__(							self    :    Dict      ,		_A    :    int      ,		_A    :    Optional[int]=7      ,		_A    :    Any=3      ,		_A    :    Optional[Any]=18      ,		_A    :    Any=30      ,		_A    :    Optional[int]=400      ,		_A    :    str=True      ,		_A    :    Union[str, Any]=None      ,		_A    :    Any=2      ,		_A    :    Any=True      ,		_A    :    int=[0.5, 0.5, 0.5]      ,		_A    :    List[str]=[0.5, 0.5, 0.5]      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						size if size is not None else {'''shortest_edge''': 30}
       __SCREAMING_SNAKE_CASE     :					str			=						parent
       __SCREAMING_SNAKE_CASE     :					Tuple			=						batch_size
       __SCREAMING_SNAKE_CASE     :					Any			=						num_channels
       __SCREAMING_SNAKE_CASE     :					Any			=						image_size
       __SCREAMING_SNAKE_CASE     :					Dict			=						min_resolution
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						max_resolution
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						do_resize
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						size
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						size_divisor
       __SCREAMING_SNAKE_CASE     :					Any			=						do_normalize
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						image_mean
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						image_std



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       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 				UpperCAmelCase__    (							self    :    int      ,		_A    :    Tuple      ,		_A    :    Tuple=False				):







       """simple docstring"""




       if not batched:
         __SCREAMING_SNAKE_CASE     :					Dict			=						self.size['''shortest_edge''']
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						image_inputs[0]
         if isinstance(_A      ,		Image.Image				):
           __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					str			=						image.size
         else:
           __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[Any]			=						image.shape[1], image.shape[2]
         __SCREAMING_SNAKE_CASE     :					List[str]			=						size / min(_A      ,		_A				)
         if h < w:
           __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					str			=						size, scale * w
         else:
           __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[str]			=						scale * h, size

         __SCREAMING_SNAKE_CASE     :					str			=						int((1333 / 800) * size				)
         if max(_A      ,		_A				) > max_size:
           __SCREAMING_SNAKE_CASE     :					Dict			=						max_size / max(_A      ,		_A				)
           __SCREAMING_SNAKE_CASE     :					List[Any]			=						newh * scale
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						neww * scale

         __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					int			=						int(newh + 0.5				), int(neww + 0.5				)
         __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Dict			=						(
             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,						__SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.get_expected_values([image]				)
           expected_values.append((expected_height, expected_width)				)
         __SCREAMING_SNAKE_CASE     :					Dict			=						max(_A      ,		key=lambda _A				: item[0]				)[0]
         __SCREAMING_SNAKE_CASE     :					Tuple			=						max(_A      ,		key=lambda _A				: item[1]				)[1]

       return expected_height, expected_width






@require_torch
@require_vision
class   __UpperCamelCase				(    lowerCAmelCase__				,		unittest.TestCase   ):






     """simple docstring"""







     lowerCAmelCase_     =	ViltImageProcessor if is_vision_available() else None



     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						ViltImageProcessingTester(self				)



     @property
     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       return self.image_processor_tester.prepare_image_processor_dict()



     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(_A      ,		'''image_mean'''				)				)
       self.assertTrue(hasattr(_A      ,		'''image_std'''				)				)
       self.assertTrue(hasattr(_A      ,		'''do_normalize'''				)				)
       self.assertTrue(hasattr(_A      ,		'''do_resize'''				)				)
       self.assertTrue(hasattr(_A      ,		'''size'''				)				)
       self.assertTrue(hasattr(_A      ,		'''size_divisor'''				)				)



     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.image_processing_class.from_dict(self.image_processor_dict				)
       self.assertEqual(image_processor.size      ,		{'''shortest_edge''': 30}				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.image_processing_class.from_dict(self.image_processor_dict      ,		size=42				)
       self.assertEqual(image_processor.size      ,		{'''shortest_edge''': 42}				)



     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       pass



     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						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=_A				)
       for image in image_inputs:
         self.assertIsInstance(_A      ,		Image.Image				)

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

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[str]			=						self.image_processor_tester.get_expected_values(_A				)
       self.assertEqual(
           encoded_images.shape      ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,		)

       # Test batched
       __SCREAMING_SNAKE_CASE     :					Any			=						image_processing(_A      ,		return_tensors='''pt'''				).pixel_values

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



     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




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

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

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

       # Test batched
       __SCREAMING_SNAKE_CASE     :					List[str]			=						image_processing(_A      ,		return_tensors='''pt'''				).pixel_values

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




     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A				)
       for image in image_inputs:
         self.assertIsInstance(_A      ,		torch.Tensor				)

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

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

       # Test batched
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						image_processing(_A      ,		return_tensors='''pt'''				).pixel_values

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


from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


lowercase_											=	[
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

lowercase_											=	get_logger(__name__)


lowercase_											=	{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					str			=						task_template_dict.get('''task'''    )
  if task_name is None:
    logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(snake_case    )}'''    )
    return None
  __SCREAMING_SNAKE_CASE     :					Any			=						NAMEaTEMPLATE.get(snake_case    )
  return template.from_dict(snake_case    )
