

import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=7 ,				__UpperCamelCase=3 ,				__UpperCamelCase=18 ,				__UpperCamelCase=30 ,				__UpperCamelCase=4_00 ,				__UpperCamelCase=True ,				__UpperCamelCase=None ,				__UpperCamelCase=True ,				):

				"""simple docstring"""


				snake_case_      		= size if size is not None else {'height': 18, 'width': 18}
				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= num_channels
				snake_case_      		= image_size
				snake_case_      		= min_resolution
				snake_case_      		= max_resolution
				snake_case_      		= do_resize
				snake_case_      		= size
				snake_case_      		= do_normalize







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return {
				    # here we create 2 clusters for the sake of simplicity
				    "clusters": np.asarray(
				        [
				            [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
				            [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
				        ]							),
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				}







@require_torch
@require_vision
class SCREAMING_SNAKE_CASE  (		__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  ImageGPTImageProcessor if is_vision_available() else None


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= ImageGPTImageProcessingTester(self							)


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return self.image_processor_tester.prepare_image_processor_dict()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(__UpperCamelCase ,				'clusters'							)							)
				self.assertTrue(hasattr(__UpperCamelCase ,				'do_resize'							)							)
				self.assertTrue(hasattr(__UpperCamelCase ,				'size'							)							)
				self.assertTrue(hasattr(__UpperCamelCase ,				'do_normalize'							)							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.image_processing_class.from_dict(self.image_processor_dict							)
				self.assertEqual(image_processor.size ,				{'height': 18, 'width': 18}							)

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.image_processing_class(**self.image_processor_dict							)
				snake_case_      		= json.loads(image_processor.to_json_string()							)
				for key, value in self.image_processor_dict.items():
					if key == "clusters":
						self.assertTrue(np.array_equal(__UpperCamelCase ,				obj[key]							)							)
					else:
						self.assertEqual(obj[key] ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

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

				snake_case_      		= image_processor_first.to_dict()
				for key, value in image_processor_first.items():
					if key == "clusters":
						self.assertTrue(np.array_equal(__UpperCamelCase ,				image_processor_second[key]							)							)
					else:
						self.assertEqual(image_processor_first[key] ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

				with tempfile.TemporaryDirectory() as tmpdirname:
					image_processor_first.save_pretrained(__UpperCamelCase							)
					snake_case_      		= self.image_processing_class.from_pretrained(__UpperCamelCase							).to_dict()

				snake_case_      		= image_processor_first.to_dict()
				for key, value in image_processor_first.items():
					if key == "clusters":
						self.assertTrue(np.array_equal(__UpperCamelCase ,				image_processor_second[key]							)							)
					else:
						self.assertEqual(image_processor_first[key] ,				__UpperCamelCase							)







			@unittest.skip('ImageGPT requires clusters at initialization'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass







def a():





	'''simple docstring'''
	snake_case_      		= load_dataset('hf-internal-testing/fixtures_image_utils'			, split='test'	)

	snake_case_      		= Image.open(dataset[4]['file']	)
	snake_case_      		= Image.open(dataset[5]['file']	)

	snake_case_      		= [imagea, imagea]

	return images






@require_vision
@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'							)

				snake_case_      		= prepare_images()

				# test non-batched
				snake_case_      		= image_processing(images[0] ,				return_tensors='pt'							)

				self.assertIsInstance(encoding.input_ids ,				torch.LongTensor							)
				self.assertEqual(encoding.input_ids.shape ,				(1, 10_24)							)

				snake_case_      		= [3_06, 1_91, 1_91]
				self.assertEqual(encoding.input_ids[0, :3].tolist() ,				__UpperCamelCase							)

				# test batched
				snake_case_      		= image_processing(__UpperCamelCase ,				return_tensors='pt'							)

				self.assertIsInstance(encoding.input_ids ,				torch.LongTensor							)
				self.assertEqual(encoding.input_ids.shape ,				(2, 10_24)							)

				snake_case_      		= [3_03, 13, 13]
				self.assertEqual(encoding.input_ids[1, -3:].tolist() ,				__UpperCamelCase							)



def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	if not isinstance(lowercase__			, lowercase__	):
		raise ValueError('iterations must be defined as integers'	)
	if not isinstance(lowercase__			, lowercase__	) or not number >= 1:
		raise ValueError(
		    'starting number must be\n                         and integer and be more than 0'	)
	if not iterations >= 1:
		raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'	)

	snake_case_      		= ''
	while number <= iterations:
		if number % 3 == 0:
			out += "Fizz"
		if number % 5 == 0:
			out += "Buzz"
		if 0 not in (number % 3, number % 5):
			out += str(lowercase__	)

		# print(out)
		number += 1
		out += " "
	return out


if __name__ == "__main__":
		import doctest

		doctest.testmod()
