"""simple docstring"""




import unittest

import numpy as np
import requests

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

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    __lowercase										= False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor


class      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase=7			,			__lowercase=3			,			__lowercase=18			,			__lowercase=30			,			__lowercase=400			,			__lowercase=None			,			__lowercase=True			,			__lowercase=True			,			__lowercase=None			,			)	->     Optional[int]:
        __UpperCamelCase					:Optional[Any]        =		size if size is not None else {'''height''': 20, '''width''': 20}
        __UpperCamelCase					:List[Any]        =		parent
        __UpperCamelCase					:Dict        =		batch_size
        __UpperCamelCase					:Any        =		num_channels
        __UpperCamelCase					:Dict        =		image_size
        __UpperCamelCase					:Union[str, Any]        =		min_resolution
        __UpperCamelCase					:Tuple        =		max_resolution
        __UpperCamelCase					:Any        =		size
        __UpperCamelCase					:Optional[int]        =		do_normalize
        __UpperCamelCase					:Tuple        =		do_convert_rgb
        __UpperCamelCase					:List[str]        =		[512, 1_024, 2_048, 4_096]
        __UpperCamelCase					:str        =		patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}







    def       UpperCamelCase__       (     self)	->     Tuple:
        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:Union[str, Any]        =		'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
        __UpperCamelCase					:Optional[Any]        =		Image.open(requests.get(__lowercase			,			stream=__lowercase).raw).convert('''RGB''')
        return raw_image


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11						,     reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""						,     )
@require_torch
@require_vision
class      lowerCamelCase_					(       UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						Optional[Any]      =				PixaStructImageProcessor if is_vision_available() else None







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:Union[str, Any]        =		PixaStructImageProcessingTester(self)







    @property
    def       UpperCamelCase__       (     self)	->     str:
        return self.image_processor_tester.prepare_image_processor_dict()







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Tuple        =		self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(__lowercase			,			'''do_normalize'''))
        self.assertTrue(hasattr(__lowercase			,			'''do_convert_rgb'''))







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:Union[str, Any]        =		self.image_processor_tester.prepare_dummy_image()

        __UpperCamelCase					:Any        =		self.image_processing_class(**self.image_processor_dict)
        __UpperCamelCase					:int        =		2_048

        __UpperCamelCase					:Tuple        =		image_processor(__lowercase			,			return_tensors='''pt'''			,			max_patches=__lowercase)
        self.assertTrue(torch.allclose(inputs.flattened_patches.mean()			,			torch.tensor(0.06_06)			,			atol=1E-3			,			rtol=1E-3))







    def       UpperCamelCase__       (     self)	->     List[Any]:
        # Initialize image_processor
        __UpperCamelCase					:List[Any]        =		self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        __UpperCamelCase					:Tuple        =		prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase			,			Image.Image)

        # Test not batched input
        __UpperCamelCase					:Optional[int]        =		(
            (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            __UpperCamelCase					:Union[str, Any]        =		image_processor(
                image_inputs[0]			,			return_tensors='''pt'''			,			max_patches=__lowercase).flattened_patches
            self.assertEqual(
                encoded_images.shape			,			(1, max_patch, expected_hidden_dim)			,			)

            # Test batched
            __UpperCamelCase					:Any        =		image_processor(
                __lowercase			,			return_tensors='''pt'''			,			max_patches=__lowercase).flattened_patches
            self.assertEqual(
                encoded_images.shape			,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,			)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        # Initialize image_processor
        __UpperCamelCase					:Any        =		self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        __UpperCamelCase					:str        =		prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase			,			Image.Image)

        # Test not batched input
        __UpperCamelCase					:Any        =		(
            (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
            * self.image_processor_tester.num_channels
        ) + 2

        __UpperCamelCase					:str        =		True

        for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(__lowercase):
                __UpperCamelCase					:Tuple        =		image_processor(
                    image_inputs[0]			,			return_tensors='''pt'''			,			max_patches=__lowercase).flattened_patches

            __UpperCamelCase					:int        =		'''Hello'''

            __UpperCamelCase					:str        =		image_processor(
                image_inputs[0]			,			return_tensors='''pt'''			,			max_patches=__lowercase			,			header_text=__lowercase).flattened_patches
            self.assertEqual(
                encoded_images.shape			,			(1, max_patch, expected_hidden_dim)			,			)

            # Test batched
            __UpperCamelCase					:List[Any]        =		image_processor(
                __lowercase			,			return_tensors='''pt'''			,			max_patches=__lowercase			,			header_text=__lowercase).flattened_patches
            self.assertEqual(
                encoded_images.shape			,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,			)







    def       UpperCamelCase__       (     self)	->     List[str]:
        # Initialize image_processor
        __UpperCamelCase					:Optional[int]        =		self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        __UpperCamelCase					:str        =		prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__lowercase			,			numpify=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase			,			np.ndarray)

        __UpperCamelCase					:Optional[Any]        =		(
            (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            __UpperCamelCase					:Dict        =		image_processor(
                image_inputs[0]			,			return_tensors='''pt'''			,			max_patches=__lowercase).flattened_patches
            self.assertEqual(
                encoded_images.shape			,			(1, max_patch, expected_hidden_dim)			,			)

            # Test batched
            __UpperCamelCase					:str        =		image_processor(
                __lowercase			,			return_tensors='''pt'''			,			max_patches=__lowercase).flattened_patches
            self.assertEqual(
                encoded_images.shape			,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,			)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        # Initialize image_processor
        __UpperCamelCase					:Any        =		self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        __UpperCamelCase					:Any        =		prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__lowercase			,			torchify=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase			,			torch.Tensor)

        # Test not batched input
        __UpperCamelCase					:Union[str, Any]        =		(
            (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            __UpperCamelCase					:Tuple        =		image_processor(
                image_inputs[0]			,			return_tensors='''pt'''			,			max_patches=__lowercase).flattened_patches
            self.assertEqual(
                encoded_images.shape			,			(1, max_patch, expected_hidden_dim)			,			)

            # Test batched
            __UpperCamelCase					:Optional[Any]        =		image_processor(
                __lowercase			,			return_tensors='''pt'''			,			max_patches=__lowercase).flattened_patches
            self.assertEqual(
                encoded_images.shape			,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,			)


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11						,     reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""						,     )
@require_torch
@require_vision
class      lowerCamelCase_					(       UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						Tuple      =				PixaStructImageProcessor if is_vision_available() else None







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:List[Any]        =		PixaStructImageProcessingTester(self			,			num_channels=4)
        __UpperCamelCase					:int        =		3







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







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:Optional[int]        =		self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(__lowercase			,			'''do_normalize'''))
        self.assertTrue(hasattr(__lowercase			,			'''do_convert_rgb'''))







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        # Initialize image_processor
        __UpperCamelCase					:List[str]        =		self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        __UpperCamelCase					:str        =		prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase			,			Image.Image)

        # Test not batched input
        __UpperCamelCase					:Union[str, Any]        =		(
            (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
            * (self.image_processor_tester.num_channels - 1)
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            __UpperCamelCase					:int        =		image_processor(
                image_inputs[0]			,			return_tensors='''pt'''			,			max_patches=__lowercase).flattened_patches
            self.assertEqual(
                encoded_images.shape			,			(1, max_patch, expected_hidden_dim)			,			)

            # Test batched
            __UpperCamelCase					:Any        =		image_processor(
                __lowercase			,			return_tensors='''pt'''			,			max_patches=__lowercase).flattened_patches
            self.assertEqual(
                encoded_images.shape			,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,			)






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      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:str        =		0







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''')
        self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:int        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Dict        =		Path(__lowercase) / '''config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

            __UpperCamelCase					:Union[str, Any]        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







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

            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:int        =		CLIPConfig()

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

            # remove image_processor_type to make sure config.json alone is enough to load image processor locally
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(__lowercase).to_dict()

            config_dict.pop('''image_processor_type''')
            __UpperCamelCase					:List[str]        =		CLIPImageProcessor(**__lowercase)

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

            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained(__lowercase)

            # make sure private variable is not incorrectly saved
            __UpperCamelCase					:Union[str, Any]        =		json.loads(config.to_json_string())
            self.assertTrue('''_processor_class''' not in dict_as_saved)

        self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     List[str]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:Tuple        =		Path(__lowercase) / '''preprocessor_config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)

            __UpperCamelCase					:List[str]        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with self.assertRaisesRegex(
            __lowercase			,			'''clip-base is not a local folder and is not a valid model identifier'''):
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained('''clip-base''')







    def       UpperCamelCase__       (     self)	->     List[Any]:
        with self.assertRaisesRegex(
            __lowercase			,			r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
            __UpperCamelCase					:str        =		AutoImageProcessor.from_pretrained(__lowercase			,			revision='''aaaaaa''')







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







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

        __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(
            '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)
        self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')

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







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

            with tempfile.TemporaryDirectory() as tmpdirname:
                __UpperCamelCase					:int        =		Path(__lowercase) / '''preprocessor_config.json'''
                __UpperCamelCase					:List[str]        =		Path(__lowercase) / '''config.json'''
                json.dump(
                    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
                json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

                __UpperCamelCase					:int        =		CustomImageProcessor.from_pretrained(__lowercase)

            # 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(__lowercase)
                __UpperCamelCase					:int        =		AutoImageProcessor.from_pretrained(__lowercase)
                self.assertIsInstance(__lowercase			,			__lowercase)

        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       UpperCamelCase__       (     self)	->     List[Any]:


        class      lowerCamelCase_					(       UpperCAmelCase_			):






            '''simple docstring'''






            a__     :						List[str]      =				True

        try:
            AutoConfig.register('''custom'''			,			__lowercase)
            AutoImageProcessor.register(__lowercase			,			__lowercase)
            # If remote code is not set, the default is to use local
            __UpperCamelCase					:str        =		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.
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(
                '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)
            self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')
            self.assertTrue(image_processor.is_local)

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





