
"""simple docstring"""


import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:







		class 				lowerCAmelCase_				:



								'''simple docstring'''


								@staticmethod
								def 						_SCREAMING_SNAKE_CASE      ( *A_     :      str  ,**A_     :      Tuple      )     ->   str:
									pass







@is_pipeline_test
@require_vision
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@require_torch
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Optional[int]:
							A						   =   pipeline(
							    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'  ,)
							A						   =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
							A						   =   image_classifier(A_  ,candidate_labels=['a', 'b', 'c']      )

							# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
							# python and torch versions.
							self.assertIn(
							    nested_simplify(A_      )  ,[
							        [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'b'}, {'score': 0.3_33, 'label': 'c'}],
							        [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'c'}, {'score': 0.3_33, 'label': 'b'}],
							    ]  ,)

							A						   =   image_classifier([image] * 5  ,candidate_labels=['A', 'B', 'C']  ,batch_size=2      )
							self.assertEqual(
							    nested_simplify(A_      )  ,[
							        [
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							        ],
							    ]  ,)


						@require_tf
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   str:
							A						   =   pipeline(
							    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'  ,framework='tf'      )
							A						   =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
							A						   =   image_classifier(A_  ,candidate_labels=['a', 'b', 'c']      )

							self.assertEqual(
							    nested_simplify(A_      )  ,[{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'b'}, {'score': 0.3_33, 'label': 'c'}]  ,)

							A						   =   image_classifier([image] * 5  ,candidate_labels=['A', 'B', 'C']  ,batch_size=2      )
							self.assertEqual(
							    nested_simplify(A_      )  ,[
							        [
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							            {'score': 0.3_33, 'label': ANY(A_      )},
							        ],
							    ]  ,)


						@slow
						@require_torch
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   pipeline(
							    task='zero-shot-image-classification'  ,model='openai/clip-vit-base-patch32'  ,)
							# This is an image of 2 cats with remotes and no planes
							A						   =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
							A						   =   image_classifier(A_  ,candidate_labels=['cat', 'plane', 'remote']      )

							self.assertEqual(
							    nested_simplify(A_      )  ,[
							        {'score': 0.5_11, 'label': 'remote'},
							        {'score': 0.4_85, 'label': 'cat'},
							        {'score': 0.0_04, 'label': 'plane'},
							    ]  ,)

							A						   =   image_classifier([image] * 5  ,candidate_labels=['cat', 'plane', 'remote']  ,batch_size=2      )
							self.assertEqual(
							    nested_simplify(A_      )  ,[
							        [
							            {'score': 0.5_11, 'label': 'remote'},
							            {'score': 0.4_85, 'label': 'cat'},
							            {'score': 0.0_04, 'label': 'plane'},
							        ],
							    ]
							    * 5  ,)




						@slow
						@require_tf
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Tuple:
							A						   =   pipeline(
							    task='zero-shot-image-classification'  ,model='openai/clip-vit-base-patch32'  ,framework='tf'      )
							# This is an image of 2 cats with remotes and no planes
							A						   =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
							A						   =   image_classifier(A_  ,candidate_labels=['cat', 'plane', 'remote']      )
							self.assertEqual(
							    nested_simplify(A_      )  ,[
							        {'score': 0.5_11, 'label': 'remote'},
							        {'score': 0.4_85, 'label': 'cat'},
							        {'score': 0.0_04, 'label': 'plane'},
							    ]  ,)

							A						   =   image_classifier([image] * 5  ,candidate_labels=['cat', 'plane', 'remote']  ,batch_size=2      )
							self.assertEqual(
							    nested_simplify(A_      )  ,[
							        [
							            {'score': 0.5_11, 'label': 'remote'},
							            {'score': 0.4_85, 'label': 'cat'},
							            {'score': 0.0_04, 'label': 'plane'},
							        ],
							    ]
							    * 5  ,)

"""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    ):



						'''simple docstring'''


						def __init__( self     :      int  ,A_     :      Tuple  ,A_     :      str=7  ,A_     :      Tuple=3  ,A_     :      List[Any]=18  ,A_     :      List[str]=30  ,A_     :      Optional[Any]=400  ,A_     :      Any=True  ,A_     :      Optional[Any]=None  ,A_     :      List[str]=True  ,)     ->   str:
							A						   =   size if size is not None else {'height': 18, 'width': 18}
							A						   =   parent
							A						   =   batch_size
							A						   =   num_channels
							A						   =   image_size
							A						   =   min_resolution
							A						   =   max_resolution
							A						   =   do_resize
							A						   =   size
							A						   =   do_normalize




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
							            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
							        ]      ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}







@require_torch
@require_vision
class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    ImageGPTImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							A						   =   ImageGPTImageProcessingTester(self      )


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   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 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Tuple:
							A						   =   self.image_processing_class.from_dict(self.image_processor_dict      )
							self.assertEqual(image_processor.size  ,{'height': 18, 'width': 18}      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   str:
							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():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,obj[key]      )      )
								else:
									self.assertEqual(obj[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

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

							A						   =   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 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

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

							A						   =   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 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Union[str, Any]:
							pass






def       _snake_case				(		):
	A						   =   load_dataset('hf-internal-testing/fixtures_image_utils'	,      split='test'     )

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

	A						   =   [imagea, imagea]

	return images







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



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							A						   =   ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

							A						   =   prepare_images()

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

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

							A						   =   [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,A_      )

							# test batched
							A						   =   image_processing(A_  ,return_tensors='pt'      )

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

							A						   =   [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,A_      )