"""simple docstring"""







import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import MobileViTImageProcessor

class 		UpperCamelCase      (   unittest.TestCase						):



				def __init__(self    :				Union[str, Any]     ,  _A    :				Optional[Any]     ,  _A    :				Union[str, Any]=7     ,  _A    :				List[Any]=3     ,  _A    :				Tuple=18     ,  _A    :				List[Any]=30     ,  _A    :				Tuple=4_00     ,  _A    :				Union[str, Any]=True     ,  _A    :				Union[str, Any]=None     ,  _A    :				Optional[int]=True     ,  _A    :				int=None     ,  _A    :				Tuple=True     ,  )   ->     List[Any]:
					__snake_case      :							List[str]						=			size if size is not None else {'shortest_edge': 20}
					__snake_case      :							int						=			crop_size if crop_size is not None else {'height': 18, 'width': 18}
					__snake_case      :							Dict						=			parent
					__snake_case      :							List[str]						=			batch_size
					__snake_case      :							Dict						=			num_channels
					__snake_case      :							List[str]						=			image_size
					__snake_case      :							Any						=			min_resolution
					__snake_case      :							Optional[Any]						=			max_resolution
					__snake_case      :							Union[str, Any]						=			do_resize
					__snake_case      :							List[Any]						=			size
					__snake_case      :							Dict						=			do_center_crop
					__snake_case      :							Tuple						=			crop_size
					__snake_case      :							Union[str, Any]						=			do_flip_channel_order






				def  _lowercase					(self    :				Optional[int])   ->     int:
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_flip_channel_order": self.do_flip_channel_order,
					}







@require_torch
@require_vision
class 		UpperCamelCase      (   lowercase				,     unittest.TestCase						):
				UpperCAmelCase    :				int   =		MobileViTImageProcessor if is_vision_available() else None



				def  _lowercase					(self    :				str)   ->     Any:
					__snake_case      :							str						=			MobileViTImageProcessingTester(self)



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



				def  _lowercase					(self    :				str)   ->     Tuple:
					__snake_case      :							int						=			self.image_processing_class(**self.image_processor_dict)
					self.assertTrue(hasattr(_A     ,  'do_resize'))
					self.assertTrue(hasattr(_A     ,  'size'))
					self.assertTrue(hasattr(_A     ,  'do_center_crop'))
					self.assertTrue(hasattr(_A     ,  'center_crop'))
					self.assertTrue(hasattr(_A     ,  'do_flip_channel_order'))



				def  _lowercase					(self    :				Optional[int])   ->     List[Any]:
					__snake_case      :							Optional[int]						=			self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size     ,  {'shortest_edge': 20})
					self.assertEqual(image_processor.crop_size     ,  {'height': 18, 'width': 18})

					__snake_case      :							Tuple						=			self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42     ,  crop_size=84)
					self.assertEqual(image_processor.size     ,  {'shortest_edge': 42})
					self.assertEqual(image_processor.crop_size     ,  {'height': 84, 'width': 84})



				def  _lowercase					(self    :				Dict)   ->     Union[str, Any]:
					pass



				def  _lowercase					(self    :				Optional[Any])   ->     List[Any]:
					# Initialize image_processing
					__snake_case      :							Any						=			self.image_processing_class(**self.image_processor_dict)
					# create random PIL images
					__snake_case      :							Optional[int]						=			prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=_A)
					for image in image_inputs:
						self.assertIsInstance(_A     ,  Image.Image)

					# Test not batched input
					__snake_case      :							List[str]						=			image_processing(image_inputs[0]     ,  return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape     ,  (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,  )

					# Test batched
					__snake_case      :							Tuple						=			image_processing(_A     ,  return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape     ,  (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,  )



				def  _lowercase					(self    :				List[str])   ->     List[Any]:
					# Initialize image_processing
					__snake_case      :							str						=			self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					__snake_case      :							int						=			prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=_A     ,  numpify=_A)
					for image in image_inputs:
						self.assertIsInstance(_A     ,  np.ndarray)

					# Test not batched input
					__snake_case      :							Dict						=			image_processing(image_inputs[0]     ,  return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape     ,  (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,  )

					# Test batched
					__snake_case      :							Tuple						=			image_processing(_A     ,  return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape     ,  (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,  )






				def  _lowercase					(self    :				int)   ->     List[Any]:
					# Initialize image_processing
					__snake_case      :							Tuple						=			self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					__snake_case      :							Optional[Any]						=			prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=_A     ,  torchify=_A)
					for image in image_inputs:
						self.assertIsInstance(_A     ,  torch.Tensor)

					# Test not batched input
					__snake_case      :							Optional[int]						=			image_processing(image_inputs[0]     ,  return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape     ,  (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,  )

					# Test batched
					__snake_case      :							Optional[Any]						=			image_processing(_A     ,  return_tensors='pt').pixel_values
					self.assertEqual(
					    encoded_images.shape     ,  (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,  )






"""simple docstring"""







import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			0



				def  _lowercase					(self    :				Tuple)   ->     int:
					__snake_case      :							Optional[Any]						=			AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
					self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				str)   ->     List[str]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							List[str]						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							Optional[Any]						=			Path(_A) / 'config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
						json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					# Ensure we can load the image processor from the feature extractor config
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							Any						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							List[Any]						=			Path(_A) / 'config.json'
						json.dump(
						    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
						json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				List[Any])   ->     Optional[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							str						=			CLIPConfig()

						# Create a dummy config file with image_proceesor_type
						__snake_case      :							List[Any]						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							Optional[Any]						=			Path(_A) / 'config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
						json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

						# remove image_processor_type to make sure config.json alone is enough to load image processor locally
						__snake_case      :							List[str]						=			AutoImageProcessor.from_pretrained(_A).to_dict()

						config_dict.pop('image_processor_type')
						__snake_case      :							Optional[int]						=			CLIPImageProcessor(**_A)

						# save in new folder
						model_config.save_pretrained(_A)
						config.save_pretrained(_A)

						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A)

						# make sure private variable is not incorrectly saved
						__snake_case      :							int						=			json.loads(config.to_json_string())
						self.assertTrue('_processor_class' not in dict_as_saved)

					self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							int						=			Path(_A) / 'preprocessor_config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )

						__snake_case      :							List[str]						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					with self.assertRaisesRegex(
					    _A     ,  'clip-base is not a local folder and is not a valid model identifier'):
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained('clip-base')



				def  _lowercase					(self    :				str)   ->     int:
					with self.assertRaisesRegex(
					    _A     ,  r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
						__snake_case      :							str						=			AutoImageProcessor.from_pretrained(_A     ,  revision='aaaaaa')



				def  _lowercase					(self    :				List[Any])   ->     str:
					with self.assertRaisesRegex(
					    _A     ,  'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'     ,  ):
						__snake_case      :							List[Any]						=			AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model')



				def  _lowercase					(self    :				Optional[int])   ->     List[str]:
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(_A):
						__snake_case      :							Any						=			AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(_A):
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)

					__snake_case      :							Union[str, Any]						=			AutoImageProcessor.from_pretrained(
					    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)
					self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')

					# Test image processor can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
						image_processor.save_pretrained(_A)
						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A     ,  trust_remote_code=_A)
					self.assertEqual(reloaded_image_processor.__class__.__name__     ,  'NewImageProcessor')



				def  _lowercase					(self    :				int)   ->     Optional[int]:
					try:
						AutoConfig.register('custom'     ,  _A)
						AutoImageProcessor.register(_A     ,  _A)
						# Trying to register something existing in the Transformers library will raise an error
						with self.assertRaises(_A):
							AutoImageProcessor.register(_A     ,  _A)

						with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case      :							Tuple						=			Path(_A) / 'preprocessor_config.json'
							__snake_case      :							Dict						=			Path(_A) / 'config.json'
							json.dump(
							    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
							json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

							__snake_case      :							Tuple						=			CustomImageProcessor.from_pretrained(_A)

						# Now that the config is registered, it can be used as any other config with the auto-API
						with tempfile.TemporaryDirectory() as tmp_dir:
							image_processor.save_pretrained(_A)
							__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained(_A)
							self.assertIsInstance(_A     ,  _A)

					finally:
						if "custom" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
							del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]






				def  _lowercase					(self    :				List[Any])   ->     Tuple:

					class 		UpperCamelCase      (   lowercase						):
									UpperCAmelCase    :				str   =		True

					try:
						AutoConfig.register('custom'     ,  _A)
						AutoImageProcessor.register(_A     ,  _A)
						# If remote code is not set, the default is to use local
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
						self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')
						self.assertTrue(image_processor.is_local)

						# If remote code is disabled, we load the local one.
						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)
						self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')
						self.assertTrue(image_processor.is_local)

						# If remote is enabled, we load from the Hub
						__snake_case      :							List[Any]						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)
						self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')
						self.assertTrue(not hasattr(_A     ,  'is_local'))

					finally:
						if "custom" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
							del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





