






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 						lowerCAmelCase   (       unittest.TestCase	):

					'''simple docstring'''

					def __init__(     self :		Tuple						,   __a :		str						,   __a :		Union[str, Any]=7						,   __a :		Union[str, Any]=3						,   __a :		int=18						,   __a :		List[str]=30						,   __a :		int=400						,   __a :		Optional[Any]=True						,   __a :		List[Any]=None						,   __a :		str=True						,   )	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							str			=   size if size is not None else {"""height""": 18, """width""": 18}
												__lowercase					:							Dict			=   parent
												__lowercase					:							Union[str, Any]			=   batch_size
												__lowercase					:							str			=   num_channels
												__lowercase					:							str			=   image_size
												__lowercase					:							Dict			=   min_resolution
												__lowercase					:							str			=   max_resolution
												__lowercase					:							Dict			=   do_resize
												__lowercase					:							int			=   size
												__lowercase					:							int			=   do_normalize



					def 					lowerCAmelCase (     self :		Tuple							)	->     List[str]:


												"""simple docstring"""

												return {
												    # here we create 2 clusters for the sake of simplicity
												    "clusters": np.asarray(
												        [
												            [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
												            [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
												        ]							),
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_normalize": self.do_normalize,
												}







@require_torch
@require_vision
class 						lowerCAmelCase   (       __a					,				unittest.TestCase	):

					'''simple docstring'''

					_A  :	Any     =					ImageGPTImageProcessor if is_vision_available() else None



					def 					lowerCAmelCase (     self :		Tuple							)	->     int:


												"""simple docstring"""

												__lowercase					:							Any			=   ImageGPTImageProcessingTester(self							)



					@property
					def 					lowerCAmelCase (     self :		List[str]							)	->     Optional[int]:


												"""simple docstring"""

												return self.image_processor_tester.prepare_image_processor_dict()



					def 					lowerCAmelCase (     self :		str							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							Any			=   self.image_processing_class(**self.image_processor_dict							)
												self.assertTrue(hasattr(__a						,   """clusters"""							)							)
												self.assertTrue(hasattr(__a						,   """do_resize"""							)							)
												self.assertTrue(hasattr(__a						,   """size"""							)							)
												self.assertTrue(hasattr(__a						,   """do_normalize"""							)							)



					def 					lowerCAmelCase (     self :		List[str]							)	->     int:


												"""simple docstring"""

												__lowercase					:							int			=   self.image_processing_class.from_dict(self.image_processor_dict							)
												self.assertEqual(image_processor.size						,   {"""height""": 18, """width""": 18}							)

												__lowercase					:							str			=   self.image_processing_class.from_dict(self.image_processor_dict						,   size=42							)
												self.assertEqual(image_processor.size						,   {"""height""": 42, """width""": 42}							)



					def 					lowerCAmelCase (     self :		int							)	->     str:


												"""simple docstring"""

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



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Any:


												"""simple docstring"""

												__lowercase					:							List[str]			=   self.image_processing_class(**self.image_processor_dict							)

												with tempfile.TemporaryDirectory() as tmpdirname:
																			__lowercase					:							List[Any]			=   os.path.join(__a						,   """image_processor.json"""							)
																			image_processor_first.to_json_file(__a							)
																			__lowercase					:							Tuple			=   self.image_processing_class.from_json_file(__a							).to_dict()

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



					def 					lowerCAmelCase (     self :		List[str]							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							str			=   self.image_processing_class(**self.image_processor_dict							)

												with tempfile.TemporaryDirectory() as tmpdirname:
																			image_processor_first.save_pretrained(__a							)
																			__lowercase					:							Union[str, Any]			=   self.image_processing_class.from_pretrained(__a							).to_dict()

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



					@unittest.skip("""ImageGPT requires clusters at initialization"""							)
					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     List[str]:


												"""simple docstring"""

												pass







def 					snake_case_						(   ):
							__lowercase					:							Any			=   load_dataset("""hf-internal-testing/fixtures_image_utils"""				,				split="""test"""					)

							__lowercase					:							str			=   Image.open(dataset[4]["""file"""]					)
							__lowercase					:							int			=   Image.open(dataset[5]["""file"""]					)

							__lowercase					:							List[Any]			=   [imagea, imagea]

							return images



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

					'''simple docstring'''

					@slow
					def 					lowerCAmelCase (     self :		Optional[Any]							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Tuple			=   ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""							)

												__lowercase					:							int			=   prepare_images()

												# test non-batched
												__lowercase					:							List[Any]			=   image_processing(images[0]						,   return_tensors="""pt"""							)

												self.assertIsInstance(encoding.input_ids						,   torch.LongTensor							)
												self.assertEqual(encoding.input_ids.shape						,   (1, 1024)							)

												__lowercase					:							Tuple			=   [306, 191, 191]
												self.assertEqual(encoding.input_ids[0, :3].tolist()						,   __a							)

												# test batched
												__lowercase					:							List[str]			=   image_processing(__a						,   return_tensors="""pt"""							)

												self.assertIsInstance(encoding.input_ids						,   torch.LongTensor							)
												self.assertEqual(encoding.input_ids.shape						,   (2, 1024)							)

												__lowercase					:							Tuple			=   [303, 13, 13]
												self.assertEqual(encoding.input_ids[1, -3:].tolist()						,   __a							)







from __future__ import annotations
def 					snake_case_						(   lowerCAmelCase_				:  int				,				lowerCAmelCase_				:  int					):
							if partitions <= 0:
														raise ValueError("""partitions must be a positive number!"""					)
							if partitions > number_of_bytes:
														raise ValueError("""partitions can not > number_of_bytes!"""					)
							__lowercase					:							Dict			=   number_of_bytes // partitions
							__lowercase					:							Union[str, Any]			=   []
							for i in range(lowerCAmelCase_					):
														__lowercase					:							str			=   i * bytes_per_partition + 1
														__lowercase					:							List[Any]			=   (
														    number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
														)
														allocation_list.append(F"{start_bytes}-{end_bytes}"					)
							return allocation_list


if __name__ == "__main__":
	import doctest

	doctest.testmod()