



import json
import os
import tempfile

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


if is_torch_available():
 import numpy as np
 import torch

if is_vision_available():
 from PIL import Image





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase=False  ,					_UpperCAmelCase=False  ,					_UpperCAmelCase=False							):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    __a					=							[]
    for i in range(image_processor_tester.batch_size							):
        if equal_resolution:
            __a					=							__a					=							image_processor_tester.max_resolution
        else:
            # To avoid getting image width/height 0
            __a					=							image_processor_tester.min_resolution
            if getattr(_UpperCAmelCase  ,					'''size_divisor'''  ,					_UpperCAmelCase							):
                # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                __a					=							max(image_processor_tester.size_divisor  ,					_UpperCAmelCase							)
            __a			,  __a					=							np.random.choice(np.arange(_UpperCAmelCase  ,					image_processor_tester.max_resolution							)  ,					2							)
        image_inputs.append(
            np.random.randint(255  ,					size=(image_processor_tester.num_channels, width, height)  ,					dtype=np.uinta							)							)

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __a					=							[Image.fromarray(np.moveaxis(_UpperCAmelCase  ,					0  ,					-1							)							) for image in image_inputs]

    if torchify:
        __a					=							[torch.from_numpy(_UpperCAmelCase							) for image in image_inputs]

    return image_inputs





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase=10  ,					_UpperCAmelCase=10  ,					_UpperCAmelCase=False  ,					_UpperCAmelCase=False							):
    __a					=							[]
    for i in range(image_processor_tester.num_frames							):
        video.append(np.random.randint(255  ,					size=(image_processor_tester.num_channels, width, height)  ,					dtype=np.uinta							)							)

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __a					=							[Image.fromarray(np.moveaxis(_UpperCAmelCase  ,					0  ,					-1							)							) for frame in video]

    if torchify:
        __a					=							[torch.from_numpy(_UpperCAmelCase							) for frame in video]

    return video





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase=False  ,					_UpperCAmelCase=False  ,					_UpperCAmelCase=False							):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    __a					=							[]
    for i in range(image_processor_tester.batch_size							):
        if equal_resolution:
            __a					=							__a					=							image_processor_tester.max_resolution
        else:
            __a			,  __a					=							np.random.choice(
                np.arange(image_processor_tester.min_resolution  ,					image_processor_tester.max_resolution							)  ,					2							)
            __a					=							prepare_video(
                image_processor_tester=_UpperCAmelCase  ,					width=_UpperCAmelCase  ,					height=_UpperCAmelCase  ,					numpify=_UpperCAmelCase  ,					torchify=_UpperCAmelCase  ,					)
        video_inputs.append(_UpperCAmelCase							)

    return video_inputs

class        _A			:
       UpperCamelCase__    :       Union[str, Any]		   =		None



       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           __a					=							self.image_processing_class(**self.image_processor_dict)
           __a					=							json.loads(image_processor.to_json_string())
           for key, value in self.image_processor_dict.items():
               self.assertEqual(obj[key]	,						__SCREAMING_SNAKE_CASE)



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           __a					=							self.image_processing_class(**self.image_processor_dict)

           with tempfile.TemporaryDirectory() as tmpdirname:
               __a					=							os.path.join(__SCREAMING_SNAKE_CASE	,						'''image_processor.json''')
               image_processor_first.to_json_file(__SCREAMING_SNAKE_CASE)
               __a					=							self.image_processing_class.from_json_file(__SCREAMING_SNAKE_CASE)

           self.assertEqual(image_processor_second.to_dict()	,						image_processor_first.to_dict())



       def 						_lowerCamelCase     (						self				:	Optional[int]):


           '''simple docstring'''

           __a					=							self.image_processing_class(**self.image_processor_dict)

           with tempfile.TemporaryDirectory() as tmpdirname:
               __a					=							image_processor_first.save_pretrained(__SCREAMING_SNAKE_CASE)[0]
               check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE)
               __a					=							self.image_processing_class.from_pretrained(__SCREAMING_SNAKE_CASE)

           self.assertEqual(image_processor_second.to_dict()	,						image_processor_first.to_dict())



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           __a					=							self.image_processing_class()
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





       @require_torch
       @require_vision
       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           if self.test_cast_dtype is not None:
               # Initialize image_processor
               __a					=							self.image_processing_class(**self.image_processor_dict)

               # create random PyTorch tensors
               __a					=							prepare_image_inputs(self.image_processor_tester	,						equal_resolution=__SCREAMING_SNAKE_CASE	,						torchify=__SCREAMING_SNAKE_CASE)

               __a					=							image_processor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''')
               # for layoutLM compatiblity
               self.assertEqual(encoding.pixel_values.device	,						torch.device('''cpu'''))
               self.assertEqual(encoding.pixel_values.dtype	,						torch.floataa)

               __a					=							image_processor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').to(torch.floataa)
               self.assertEqual(encoding.pixel_values.device	,						torch.device('''cpu'''))
               self.assertEqual(encoding.pixel_values.dtype	,						torch.floataa)

               __a					=							image_processor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').to('''cpu'''	,						torch.bfloataa)
               self.assertEqual(encoding.pixel_values.device	,						torch.device('''cpu'''))
               self.assertEqual(encoding.pixel_values.dtype	,						torch.bfloataa)

               with self.assertRaises(__SCREAMING_SNAKE_CASE):
                   __a					=							image_processor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').to(torch.bfloataa	,						'''cpu''')

               # Try with text + image feature
               __a					=							image_processor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''')
               encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
               __a					=							encoding.to(torch.floataa)

               self.assertEqual(encoding.pixel_values.device	,						torch.device('''cpu'''))
               self.assertEqual(encoding.pixel_values.dtype	,						torch.floataa)
               self.assertEqual(encoding.input_ids.dtype	,						torch.long)










def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    __a					=							[1 / first_term]
    __a					=							1 / first_term
    for _ in range(number_of_terms - 1							):
        first_term += common_difference
        arithmetic_progression.append(_UpperCAmelCase							)
    __a					=							[1 / step for step in arithmetic_progression]
    return sum(_UpperCAmelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(sum_of_harmonic_progression(1 / 2, 2, 2))





