"""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 ConvNextImageProcessor






class    __UpperCAmelCase						(	unittest.TestCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase=7	, _lowerCamelCase=3	, _lowerCamelCase=18	, _lowerCamelCase=30	, _lowerCamelCase=400	, _lowerCamelCase=True	, _lowerCamelCase=None	, _lowerCamelCase=0.8_7_5	, _lowerCamelCase=True	, _lowerCamelCase=[0.5, 0.5, 0.5]	, _lowerCamelCase=[0.5, 0.5, 0.5]	, ):
										lowerCamelCase__					=size if size is not None else {"shortest_edge": 20}
										lowerCamelCase__					=parent
										lowerCamelCase__					=batch_size
										lowerCamelCase__					=num_channels
										lowerCamelCase__					=image_size
										lowerCamelCase__					=min_resolution
										lowerCamelCase__					=max_resolution
										lowerCamelCase__					=do_resize
										lowerCamelCase__					=size
										lowerCamelCase__					=crop_pct
										lowerCamelCase__					=do_normalize
										lowerCamelCase__					=image_mean
										lowerCamelCase__					=image_std






				def 		_a	(      self      ):
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_pct": self.crop_pct,
										}






@require_torch
@require_vision
class    __UpperCAmelCase						(	__lowerCAmelCase      ,      unittest.TestCase	):
				A__      :   Dict  							=       ConvNextImageProcessor if is_vision_available() else None

				def 		_a	(      self      ):
										lowerCamelCase__					=ConvNextImageProcessingTester(self      )

				@property
				def 		_a	(      self      ):
										return self.image_processor_tester.prepare_image_processor_dict()

				def 		_a	(      self      ):
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										self.assertTrue(hasattr(_lowerCamelCase	, "do_resize"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "size"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "crop_pct"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "do_normalize"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "image_mean"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "image_std"      )      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.image_processing_class.from_dict(self.image_processor_dict      )
										self.assertEqual(image_processor.size	, {"shortest_edge": 20}      )

										lowerCamelCase__					=self.image_processing_class.from_dict(self.image_processor_dict	, size=42      )
										self.assertEqual(image_processor.size	, {"shortest_edge": 42}      )

				def 		_a	(      self      ):
										pass

				def 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										lowerCamelCase__					=prepare_image_inputs(self.image_processor_tester	, equal_resolution=_lowerCamelCase      )
										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase	, Image.Image      )

										# Test not batched input
										lowerCamelCase__					=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.size["shortest_edge"],
										        self.image_processor_tester.size["shortest_edge"],
										    )	, )

										# Test batched
										lowerCamelCase__					=image_processing(_lowerCamelCase	, 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.size["shortest_edge"],
										        self.image_processor_tester.size["shortest_edge"],
										    )	, )

				def 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random numpy tensors
										lowerCamelCase__					=prepare_image_inputs(self.image_processor_tester	, equal_resolution=_lowerCamelCase	, numpify=_lowerCamelCase      )
										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase	, np.ndarray      )

										# Test not batched input
										lowerCamelCase__					=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.size["shortest_edge"],
										        self.image_processor_tester.size["shortest_edge"],
										    )	, )

										# Test batched
										lowerCamelCase__					=image_processing(_lowerCamelCase	, 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.size["shortest_edge"],
										        self.image_processor_tester.size["shortest_edge"],
										    )	, )






				def 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random PyTorch tensors
										lowerCamelCase__					=prepare_image_inputs(self.image_processor_tester	, equal_resolution=_lowerCamelCase	, torchify=_lowerCamelCase      )
										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase	, torch.Tensor      )

										# Test not batched input
										lowerCamelCase__					=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.size["shortest_edge"],
										        self.image_processor_tester.size["shortest_edge"],
										    )	, )

										# Test batched
										lowerCamelCase__					=image_processing(_lowerCamelCase	, 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.size["shortest_edge"],
										        self.image_processor_tester.size["shortest_edge"],
										    )	, )



"""simple docstring"""


import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


a							=logging.get_logger(__name__)






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Any  							=       '''hybrid-clip'''
				A__      :   Optional[Any]  							=       True

				def __init__(      self	, _lowerCamelCase=512	, **_lowerCamelCase      ):
										super().__init__(**_lowerCamelCase      )

										if "text_config" not in kwargs:
																raise ValueError("`text_config` can not be `None`."      )

										if "vision_config" not in kwargs:
																raise ValueError("`vision_config` can not be `None`."      )

										lowerCamelCase__					=kwargs.pop("text_config"      )
										lowerCamelCase__					=kwargs.pop("vision_config"      )

										lowerCamelCase__					=text_config.pop("model_type"      )
										lowerCamelCase__					=vision_config.pop("model_type"      )

										from transformers import AutoConfig

										lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      )

										if vision_model_type == "clip":
																lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      ).vision_config
										elif vision_model_type == "clip_vision_model":
																from transformers import CLIPVisionConfig

																lowerCamelCase__					=CLIPVisionConfig(**_lowerCamelCase      )
										else:
																lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      )

										lowerCamelCase__					=projection_dim
										lowerCamelCase__					=1.0

				@classmethod
				def 		_a	(      cls	, _lowerCamelCase	, _lowerCamelCase	, **_lowerCamelCase      ):
										return cls(text_config=text_config.to_dict()	, vision_config=vision_config.to_dict()	, **_lowerCamelCase      )






				def 		_a	(      self      ):
										lowerCamelCase__					=copy.deepcopy(self.__dict__      )
										lowerCamelCase__					=self.text_config.to_dict()
										lowerCamelCase__					=self.vision_config.to_dict()
										lowerCamelCase__					=self.__class__.model_type
										return output


