


import re
from pathlib import Path
from unittest import TestCase

import pytest







@pytest.mark.integration
class 		_snake_case  (      _snake_case							):




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):

       with open(_lowerCamelCase					,  encoding='''utf-8'''    ) as input_file:
             a						:List[str] 							=       re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''    )
             a						:Dict 							=       input_file.read()
             a						:Optional[int] 							=       regexp.search(_lowerCamelCase    )

       return match




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):

       with open(_lowerCamelCase					,  encoding='''utf-8'''    ) as input_file:
             a						:Dict 							=       re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'''					,  re.DOTALL    )
             a						:Optional[int] 							=       input_file.read()
             # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
             a						:Optional[Any] 							=       regexp.finditer(_lowerCamelCase    )

       a						:List[Any] 							=       [match for match in matches if match is not None and match.group(1    ) is not None]
       return matches[0] if matches else None




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[Any] 							=       Path('''./datasets'''    )
       a						:Optional[Any] 							=       list(dataset_paths.absolute().glob('''**/*.py'''    )    )

       for dataset in dataset_files:
             if self._no_encoding_on_file_open(str(_lowerCamelCase    )    ):
                   raise AssertionError(F'''open(...) must use utf-8 encoding in {dataset}'''    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       Path('''./datasets'''    )
       a						:int 							=       list(dataset_paths.absolute().glob('''**/*.py'''    )    )

       for dataset in dataset_files:
             if self._no_print_statements(str(_lowerCamelCase    )    ):
                   raise AssertionError(F'''print statement found in {dataset}. Use datasets.logger/logging instead.'''    )












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 MobileNetVaImageProcessor







class 				__lowerCAmelCase       (    unittest.TestCase):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=7		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8		,      lowerCAmelCase__=3_0		,      lowerCAmelCase__=4_0_0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      )   ->						Optional[int]:



        '''simple docstring'''




        a__	:					str										=size if size is not None else {"shortest_edge": 2_0}
        a__	:					Union[str, Any]										=crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
        a__	:					Tuple										=parent
        a__	:					Optional[int]										=batch_size
        a__	:					Any										=num_channels
        a__	:					List[str]										=image_size
        a__	:					Dict										=min_resolution
        a__	:					List[Any]										=max_resolution
        a__	:					Dict										=do_resize
        a__	:					Union[str, Any]										=size
        a__	:					str										=do_center_crop
        a__	:					List[str]										=crop_size







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class 				__lowerCAmelCase       (    UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					Optional[Any]						=			MobileNetVaImageProcessor if is_vision_available() else None







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										=MobileNetVaImageProcessingTester(self							)







      @property
      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					List[str]										=self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_resize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "size"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_center_crop"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "crop_size"							)							)







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Any										=self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 2_0}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 1_8, "width": 1_8}							)

        a__	:					Dict										=self.image_processing_class.from_dict(self.image_processor_dict		,      size=4_2		,      crop_size=8_4							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 4_2}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 8_4, "width": 8_4}							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        pass







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



        '''simple docstring'''




        a__	:					Dict										=self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        a__	:					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
        a__	:					List[Any]										=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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					Dict										=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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					str										=self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        a__	:					str										=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
        a__	:					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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					Union[str, 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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )







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



        '''simple docstring'''




        a__	:					Any										=self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        a__	:					int										=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
        a__	:					Optional[Any]										=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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )






