




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  :						Any							,	__A  :						Optional[Any]							,	__A  :						int=7							,	__A  :						Any=3							,	__A  :						Tuple=1_8							,	__A  :						List[Any]=3_0							,	__A  :						Union[str, Any]=4_0_0							,	__A  :						Tuple=True							,	__A  :						int=None							,	__A  :						List[Any]=True							,	):
						snake_case__				:   List[str]     =						size if size is not None else {"""height""": 1_8, """width""": 1_8}
						snake_case__				:   Any     =						parent
						snake_case__				:   Union[str, Any]     =						batch_size
						snake_case__				:   Tuple     =						num_channels
						snake_case__				:   int     =						image_size
						snake_case__				:   Optional[Any]     =						min_resolution
						snake_case__				:   int     =						max_resolution
						snake_case__				:   List[Any]     =						do_resize
						snake_case__				:   Tuple     =						size
						snake_case__				:   Union[str, Any]     =						do_normalize






					def  _lowercase  (      self  :						Union[str, Any] ):
						return {
						    # here we create 2 clusters for the sake of simplicity
						    "clusters": np.asarray(
						        [
						            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
						            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
						        ] ),
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						}



@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__		(       _UpperCAmelCase				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	ImageGPTImageProcessor if is_vision_available() else None



					def  _lowercase  (      self  :						int ):
						snake_case__				:   List[str]     =						ImageGPTImageProcessingTester(self )



					@property
					def  _lowercase  (      self  :						str ):
						return self.image_processor_tester.prepare_image_processor_dict()



					def  _lowercase  (      self  :						List[Any] ):
						snake_case__				:   Dict     =						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  _lowercase  (      self  :						int ):
						snake_case__				:   int     =						self.image_processing_class.from_dict(self.image_processor_dict )
						self.assertEqual(image_processor.size							,	{"height": 1_8, "width": 1_8} )

						snake_case__				:   str     =						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  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Optional[Any]     =						self.image_processing_class(**self.image_processor_dict )
						snake_case__				:   str     =						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  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Optional[Any]     =						self.image_processing_class(**self.image_processor_dict )

						with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__				:   Optional[Any]     =						os.path.join(lowercase_							,	"image_processor.json" )
							image_processor_first.to_json_file(lowercase_ )
							snake_case__				:   Optional[int]     =						self.image_processing_class.from_json_file(lowercase_ ).to_dict()

						snake_case__				:   List[str]     =						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  _lowercase  (      self  :						Dict ):
						snake_case__				:   List[Any]     =						self.image_processing_class(**self.image_processor_dict )

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

						snake_case__				:   Any     =						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  _lowercase  (      self  :						List[Any] ):
						pass







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   int     =						load_dataset("hf-internal-testing/fixtures_image_utils"  ,						split="test"     )

	snake_case__				:   Union[str, Any]     =						Image.open(dataset[4]["file"]     )
	snake_case__				:   Optional[Any]     =						Image.open(dataset[5]["file"]     )

	snake_case__				:   Optional[Any]     =						[imagea, imagea]

	return images





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





					"""simple docstring"""






					@slow
					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Union[str, Any]     =						ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small" )

						snake_case__				:   Tuple     =						prepare_images()

						# test non-batched
						snake_case__				:   Optional[Any]     =						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) )

						snake_case__				:   Union[str, Any]     =						[3_0_6, 1_9_1, 1_9_1]
						self.assertEqual(encoding.input_ids[0, :3].tolist()							,	lowercase_ )

						# test batched
						snake_case__				:   int     =						image_processing(lowercase_							,	return_tensors="pt" )

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

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











import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.activations import gelu_new, gelu_python, get_activation



@require_torch
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   List[str]     =						torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
						snake_case__				:   Tuple     =						get_activation("gelu" )
						self.assertTrue(torch.allclose(gelu_python(__A )							,	torch_builtin(__A ) ) )
						self.assertFalse(torch.allclose(gelu_python(__A )							,	gelu_new(__A ) ) )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   str     =						torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
						snake_case__				:   Union[str, Any]     =						get_activation("gelu" )
						snake_case__				:   int     =						get_activation("gelu_10" )

						snake_case__				:   Optional[int]     =						torch_builtin(__A )
						snake_case__				:   Dict     =						geluaa(__A )

						snake_case__				:   Optional[Any]     =						torch.where(y_gelu_aa < 1_0.0							,	1							,	0 )

						self.assertTrue(torch.max(__A ).item() == 1_0.0 )
						self.assertTrue(torch.allclose(y_gelu * clipped_mask							,	y_gelu_aa * clipped_mask ) )



					def  _lowercase  (      self  :						str ):
						get_activation("gelu" )
						get_activation("gelu_10" )
						get_activation("gelu_fast" )
						get_activation("gelu_new" )
						get_activation("gelu_python" )
						get_activation("gelu_pytorch_tanh" )
						get_activation("linear" )
						get_activation("mish" )
						get_activation("quick_gelu" )
						get_activation("relu" )
						get_activation("sigmoid" )
						get_activation("silu" )
						get_activation("swish" )
						get_activation("tanh" )
						with self.assertRaises(__A ):
							get_activation("bogus" )
						with self.assertRaises(__A ):
							get_activation(__A )






					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   List[str]     =						get_activation("gelu" )
						snake_case__				:   Any     =						1
						snake_case__				:   Union[str, Any]     =						get_activation("gelu" )
						self.assertEqual(acta.a							,	1 )
						with self.assertRaises(__A ):
							snake_case__				:   int     =						acta.a



