
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 DeiTImageProcessor



class 	__lowerCAmelCase       (		unittest.TestCase				):




 def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase=7		,			__UpperCAmelCase=3		,			__UpperCAmelCase=18		,			__UpperCAmelCase=30		,			__UpperCAmelCase=400		,			__UpperCAmelCase=True		,			__UpperCAmelCase=None		,			__UpperCAmelCase=True		,			__UpperCAmelCase=None		,			__UpperCAmelCase=True		,			__UpperCAmelCase=[0.5, 0.5, 0.5]		,			__UpperCAmelCase=[0.5, 0.5, 0.5]		,			):



    '''simple docstring'''
    __lowerCamelCase  							=      size if size is not None else {'''height''': 20, '''width''': 20}
    __lowerCamelCase  							=      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

    __lowerCamelCase  							=      parent
    __lowerCamelCase  							=      batch_size
    __lowerCamelCase  							=      num_channels
    __lowerCamelCase  							=      image_size
    __lowerCamelCase  							=      min_resolution
    __lowerCamelCase  							=      max_resolution
    __lowerCamelCase  							=      do_resize
    __lowerCamelCase  							=      size
    __lowerCamelCase  							=      do_center_crop
    __lowerCamelCase  							=      crop_size
    __lowerCamelCase  							=      do_normalize
    __lowerCamelCase  							=      image_mean
    __lowerCamelCase  							=      image_std




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
    }



@require_torch
@require_vision
class 	__lowerCAmelCase       (		lowerCAmelCase__					,		unittest.TestCase				):
 lowerCAmelCase__       =					DeiTImageProcessor if is_vision_available() else None
 lowerCAmelCase__       =					True




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      DeiTImageProcessingTester(self			)




 @property
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return self.image_processor_tester.prepare_image_processor_dict()




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.image_processing_class(**self.image_processor_dict			)
    self.assertTrue(hasattr(__UpperCAmelCase		,			'''do_resize'''			)			)
    self.assertTrue(hasattr(__UpperCAmelCase		,			'''size'''			)			)
    self.assertTrue(hasattr(__UpperCAmelCase		,			'''do_center_crop'''			)			)
    self.assertTrue(hasattr(__UpperCAmelCase		,			'''center_crop'''			)			)
    self.assertTrue(hasattr(__UpperCAmelCase		,			'''do_normalize'''			)			)
    self.assertTrue(hasattr(__UpperCAmelCase		,			'''image_mean'''			)			)
    self.assertTrue(hasattr(__UpperCAmelCase		,			'''image_std'''			)			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.image_processing_class.from_dict(self.image_processor_dict			)
    self.assertEqual(image_processor.size		,			{'''height''': 20, '''width''': 20}			)
    self.assertEqual(image_processor.crop_size		,			{'''height''': 18, '''width''': 18}			)

    __lowerCamelCase  							=      self.image_processing_class.from_dict(self.image_processor_dict		,			size=42		,			crop_size=84			)
    self.assertEqual(image_processor.size		,			{'''height''': 42, '''width''': 42}			)
    self.assertEqual(image_processor.crop_size		,			{'''height''': 84, '''width''': 84}			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    pass




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.image_processing_class(**self.image_processor_dict			)
    # create random PIL images
    __lowerCamelCase  							=      prepare_image_inputs(self.image_processor_tester		,			equal_resolution=__UpperCAmelCase			)
    for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase		,			Image.Image			)

    # Test not batched input
    __lowerCamelCase  							=      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
    __lowerCamelCase  							=      image_processing(__UpperCAmelCase		,			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     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.image_processing_class(**self.image_processor_dict			)
    # create random numpy tensors
    __lowerCamelCase  							=      prepare_image_inputs(self.image_processor_tester		,			equal_resolution=__UpperCAmelCase		,			numpify=__UpperCAmelCase			)
    for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase		,			np.ndarray			)

    # Test not batched input
    __lowerCamelCase  							=      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
    __lowerCamelCase  							=      image_processing(__UpperCAmelCase		,			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     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.image_processing_class(**self.image_processor_dict			)
    # create random PyTorch tensors
    __lowerCamelCase  							=      prepare_image_inputs(self.image_processor_tester		,			equal_resolution=__UpperCAmelCase		,			torchify=__UpperCAmelCase			)
    for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase		,			torch.Tensor			)

    # Test not batched input
    __lowerCamelCase  							=      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
    __lowerCamelCase  							=      image_processing(__UpperCAmelCase		,			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'''],
        )		,			)











import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand





def 						a__     (						_UpperCamelCase     :	Optional[Any]							):
   return EnvironmentCommand()





class 	__lowerCAmelCase       (		lowerCAmelCase__				):




 @staticmethod
 def     lowerCamelCase (  __UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      parser.add_parser('''env'''		,			help='''Print relevant system environment info.'''			)
    download_parser.set_defaults(func=__UpperCAmelCase			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      {
        '''`datasets` version''': version,
        '''Platform''': platform.platform(),
        '''Python version''': platform.python_version(),
        '''Huggingface_hub version''': huggingface_hub.__version__,
        '''PyArrow version''': pyarrow.__version__,
        '''Pandas version''': pandas.__version__,
    }

    print('''\nCopy-and-paste the text below in your GitHub issue.\n'''			)
    print(self.format_dict(__UpperCAmelCase			)			)

    return info




 @staticmethod
 def     lowerCamelCase (  __UpperCAmelCase			):



    '''simple docstring'''
    return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]			) + "\n"






