

"""simple docstring"""




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			):

			def __init__(					self				:						List[Any]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Any=7    ,				__UpperCAmelCase				:						str=3    ,				__UpperCAmelCase				:						Optional[int]=1_8    ,				__UpperCAmelCase				:						Optional[Any]=3_0    ,				__UpperCAmelCase				:						int=4_0_0    ,				__UpperCAmelCase				:						Any=True    ,				__UpperCAmelCase				:						List[Any]=None    ,				__UpperCAmelCase				:						Union[str, Any]=True    ,				)   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      size if size is not None else {"height": 1_8, "width": 1_8}
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      min_resolution
						SCREAMING_SNAKE_CASE__								=      max_resolution
						SCREAMING_SNAKE_CASE__								=      do_resize
						SCREAMING_SNAKE_CASE__								=      size
						SCREAMING_SNAKE_CASE__								=      do_normalize





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Tuple:
						return {
						    # here we create 2 clusters for the sake of simplicity
						    "clusters": np.asarray(
						        [
						            [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
						            [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
						        ]       ),
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						}





@require_torch
@require_vision
class    lowerCamelCase						(snake_case__			,unittest.TestCase			):
			lowerCamelCase__   :		str							=							ImageGPTImageProcessor if is_vision_available() else None

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      ImageGPTImageProcessingTester(self       )

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    str:
						return self.image_processor_tester.prepare_image_processor_dict()

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      self.image_processing_class(**self.image_processor_dict       )
						self.assertTrue(hasattr(lowercase_    ,				"""clusters"""       )       )
						self.assertTrue(hasattr(lowercase_    ,				"""do_resize"""       )       )
						self.assertTrue(hasattr(lowercase_    ,				"""size"""       )       )
						self.assertTrue(hasattr(lowercase_    ,				"""do_normalize"""       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.image_processing_class.from_dict(self.image_processor_dict       )
						self.assertEqual(image_processor.size    ,				{"""height""": 1_8, """width""": 1_8}       )

						SCREAMING_SNAKE_CASE__								=      self.image_processing_class.from_dict(self.image_processor_dict    ,				size=4_2       )
						self.assertEqual(image_processor.size    ,				{"""height""": 4_2, """width""": 4_2}       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.image_processing_class(**self.image_processor_dict       )
						SCREAMING_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(lowercase_    ,				obj[key]       )       )
									else:
												self.assertEqual(obj[key]    ,				lowercase_       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.image_processing_class(**self.image_processor_dict       )

						with tempfile.TemporaryDirectory() as tmpdirname:
									SCREAMING_SNAKE_CASE__								=      os.path.join(lowercase_    ,				"""image_processor.json"""       )
									image_processor_first.to_json_file(lowercase_       )
									SCREAMING_SNAKE_CASE__								=      self.image_processing_class.from_json_file(lowercase_       ).to_dict()

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      self.image_processing_class(**self.image_processor_dict       )

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

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





			@unittest.skip("""ImageGPT requires clusters at initialization"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Optional[int]:
						pass

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      load_dataset("""hf-internal-testing/fixtures_image_utils"""		,				split="""test"""    )

			SCREAMING_SNAKE_CASE__								=      Image.open(dataset[4]["""file"""]    )
			SCREAMING_SNAKE_CASE__								=      Image.open(dataset[5]["""file"""]    )

			SCREAMING_SNAKE_CASE__								=      [imagea, imagea]

			return images





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

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""       )

						SCREAMING_SNAKE_CASE__								=      prepare_images()

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

						self.assertIsInstance(encoding.input_ids    ,				torch.LongTensor       )
						self.assertEqual(encoding.input_ids.shape    ,				(1, 1_0_2_4)       )

						SCREAMING_SNAKE_CASE__								=      [3_0_6, 1_9_1, 1_9_1]
						self.assertEqual(encoding.input_ids[0, :3].tolist()    ,				lowercase_       )

						# test batched
						SCREAMING_SNAKE_CASE__								=      image_processing(lowercase_    ,				return_tensors="""pt"""       )

						self.assertIsInstance(encoding.input_ids    ,				torch.LongTensor       )
						self.assertEqual(encoding.input_ids.shape    ,				(2, 1_0_2_4)       )

						SCREAMING_SNAKE_CASE__								=      [3_0_3, 1_3, 1_3]
						self.assertEqual(encoding.input_ids[1, -3:].tolist()    ,				lowercase_       )







"""simple docstring"""




import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT    ):
						with pytest.raises(snake_case__    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )
						with pytest.raises(requests.exceptions.ConnectTimeout    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""		,				timeout=1.0    )

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_FAILS    ):
						with pytest.raises(requests.exceptions.ConnectionError    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )




def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1    ):
						with pytest.raises(snake_case__    ):
									http_head("""https://huggingface.co"""    )


