


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
			import PIL

if is_torch_available():
			import torch


_snake_case  :	List[Any]			      =       logging.get_logger(__name__)

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							List[str]  						=						["pixel_values"]

							def __init__(							self :						Optional[int]					,			lowerCamelCase :						bool = True					,			lowerCamelCase :						Dict[str, int] = None					,			lowerCamelCase :						PILImageResampling = PILImageResampling.BILINEAR					,			lowerCamelCase :						bool = True					,			lowerCamelCase :						Union[int, float] = 1 / 255					,			lowerCamelCase :						bool = True					,			lowerCamelCase :						Dict[str, int] = None					,			lowerCamelCase :						bool = True					,			**lowerCamelCase :						Optional[Any]					,			)			->					None:
														super().__init__(**lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     size if size is not None else {"shortest_edge": 224}
														__snake_case	:    List[str]											=     get_size_dict(lowerCamelCase					,			default_to_square=lowerCamelCase       )
														__snake_case	:    Union[str, Any]											=     crop_size if crop_size is not None else {"height": 256, "width": 256}
														__snake_case	:    Dict											=     get_size_dict(lowerCamelCase					,			param_name="crop_size"       )

														__snake_case	:    Union[str, Any]											=     do_resize
														__snake_case	:    Optional[Any]											=     size
														__snake_case	:    int											=     resample
														__snake_case	:    Tuple											=     do_rescale
														__snake_case	:    Any											=     rescale_factor
														__snake_case	:    int											=     do_center_crop
														__snake_case	:    Union[str, Any]											=     crop_size
														__snake_case	:    Optional[Any]											=     do_flip_channel_order

							def 					__snake_case      (							self :						int					,			lowerCamelCase :						np.ndarray					,			lowerCamelCase :						Dict[str, int]					,			lowerCamelCase :						PILImageResampling = PIL.Image.BILINEAR					,			lowerCamelCase :						Optional[Union[str, ChannelDimension]] = None					,			**lowerCamelCase :						Dict					,			)			->					np.ndarray:
														__snake_case	:    Union[str, Any]											=     get_size_dict(lowerCamelCase					,			default_to_square=lowerCamelCase       )
														if "shortest_edge" not in size:
																					raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'       )
														__snake_case	:    List[str]											=     get_resize_output_image_size(lowerCamelCase					,			size=size["shortest_edge"]					,			default_to_square=lowerCamelCase       )
														return resize(lowerCamelCase					,			size=lowerCamelCase					,			resample=lowerCamelCase					,			data_format=lowerCamelCase					,			**lowerCamelCase       )

							def 					__snake_case      (							self :						Any					,			lowerCamelCase :						np.ndarray					,			lowerCamelCase :						Dict[str, int]					,			lowerCamelCase :						Optional[Union[str, ChannelDimension]] = None					,			**lowerCamelCase :						Dict					,			)			->					np.ndarray:
														__snake_case	:    Optional[int]											=     get_size_dict(lowerCamelCase       )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'       )
														return center_crop(lowerCamelCase					,			size=(size["height"], size["width"])					,			data_format=lowerCamelCase					,			**lowerCamelCase       )

							def 					__snake_case      (							self :						List[Any]					,			lowerCamelCase :						np.ndarray					,			lowerCamelCase :						Union[int, float]					,			lowerCamelCase :						Optional[Union[str, ChannelDimension]] = None					,			**lowerCamelCase :						Optional[int]					,			)			->					Union[str, Any]:
														return rescale(lowerCamelCase					,			scale=lowerCamelCase					,			data_format=lowerCamelCase					,			**lowerCamelCase       )

							def 					__snake_case      (							self :						Tuple					,			lowerCamelCase :						np.ndarray					,			lowerCamelCase :						Optional[Union[str, ChannelDimension]] = None       )			->					np.ndarray:
														return flip_channel_order(lowerCamelCase					,			data_format=lowerCamelCase       )

							def 					__snake_case      (							self :						Any					,			lowerCamelCase :						ImageInput					,			lowerCamelCase :						bool = None					,			lowerCamelCase :						Dict[str, int] = None					,			lowerCamelCase :						PILImageResampling = None					,			lowerCamelCase :						bool = None					,			lowerCamelCase :						float = None					,			lowerCamelCase :						bool = None					,			lowerCamelCase :						Dict[str, int] = None					,			lowerCamelCase :						bool = None					,			lowerCamelCase :						Optional[Union[str, TensorType]] = None					,			lowerCamelCase :						ChannelDimension = ChannelDimension.FIRST					,			**lowerCamelCase :						str					,			)			->					PIL.Image.Image:
														__snake_case	:    str											=     do_resize if do_resize is not None else self.do_resize
														__snake_case	:    int											=     resample if resample is not None else self.resample
														__snake_case	:    Optional[Any]											=     do_rescale if do_rescale is not None else self.do_rescale
														__snake_case	:    Dict											=     rescale_factor if rescale_factor is not None else self.rescale_factor
														__snake_case	:    int											=     do_center_crop if do_center_crop is not None else self.do_center_crop
														__snake_case	:    Optional[int]											=     (
														    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
														)

														__snake_case	:    str											=     size if size is not None else self.size
														__snake_case	:    List[str]											=     get_size_dict(lowerCamelCase					,			default_to_square=lowerCamelCase       )
														__snake_case	:    Union[str, Any]											=     crop_size if crop_size is not None else self.crop_size
														__snake_case	:    Optional[Any]											=     get_size_dict(lowerCamelCase					,			param_name="crop_size"       )

														__snake_case	:    Optional[Any]											=     make_list_of_images(lowerCamelCase       )

														if not valid_images(lowerCamelCase       ):
																					raise ValueError(
																					    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																					    "torch.Tensor, tf.Tensor or jax.ndarray."       )

														if do_resize and size is None:
																					raise ValueError("Size must be specified if do_resize is True."       )

														if do_rescale and rescale_factor is None:
																					raise ValueError("Rescale factor must be specified if do_rescale is True."       )

														if do_center_crop and crop_size is None:
																					raise ValueError("Crop size must be specified if do_center_crop is True."       )

														# All transformations expect numpy arrays.
														__snake_case	:    List[str]											=     [to_numpy_array(lowerCamelCase       ) for image in images]

														if do_resize:
																					__snake_case	:    Optional[Any]											=     [self.resize(image=lowerCamelCase					,			size=lowerCamelCase					,			resample=lowerCamelCase       ) for image in images]

														if do_center_crop:
																					__snake_case	:    Tuple											=     [self.center_crop(image=lowerCamelCase					,			size=lowerCamelCase       ) for image in images]

														if do_rescale:
																					__snake_case	:    List[Any]											=     [self.rescale(image=lowerCamelCase					,			scale=lowerCamelCase       ) for image in images]

														# the pretrained checkpoints assume images are BGR, not RGB
														if do_flip_channel_order:
																					__snake_case	:    Optional[Any]											=     [self.flip_channel_order(image=lowerCamelCase       ) for image in images]

														__snake_case	:    List[str]											=     [to_channel_dimension_format(lowerCamelCase					,			lowerCamelCase       ) for image in images]

														__snake_case	:    List[Any]											=     {"pixel_values": images}
														return BatchFeature(data=lowerCamelCase					,			tensor_type=lowerCamelCase       )






							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						List[str]					,			lowerCamelCase :						List[Tuple] = None       )			->					Optional[Any]:
														__snake_case	:    Tuple											=     outputs.logits

														# Resize logits and compute semantic segmentation maps
														if target_sizes is not None:
																					if len(lowerCamelCase       ) != len(lowerCamelCase       ):
																												raise ValueError(
																												    "Make sure that you pass in as many target sizes as the batch dimension of the logits"       )

																					if is_torch_tensor(lowerCamelCase       ):
																												__snake_case	:    List[str]											=     target_sizes.numpy()

																					__snake_case	:    Optional[Any]											=     []

																					for idx in range(len(lowerCamelCase       )       ):
																												__snake_case	:    Tuple											=     torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0       )					,			size=target_sizes[idx]					,			mode="bilinear"					,			align_corners=lowerCamelCase       )
																												__snake_case	:    List[str]											=     resized_logits[0].argmax(dim=0       )
																												semantic_segmentation.append(lowerCamelCase       )
														else:
																					__snake_case	:    Optional[int]											=     logits.argmax(dim=1       )
																					__snake_case	:    List[Any]											=     [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

														return semantic_segmentation





import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import CvtForImageClassification, CvtModel
			from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						str       )			->					Dict:
														__snake_case	:    Any											=     self.config_class(**self.inputs_dict       )
														self.parent.assertTrue(hasattr(lowerCamelCase					,			"embed_dim"       )       )
														self.parent.assertTrue(hasattr(lowerCamelCase					,			"num_heads"       )       )

class        a	:




							"""simple docstring"""


							def __init__(							self :						str					,			lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						Any=13					,			lowerCamelCase :						Any=64					,			lowerCamelCase :						int=3					,			lowerCamelCase :						Tuple=[16, 48, 96]					,			lowerCamelCase :						Optional[int]=[1, 3, 6]					,			lowerCamelCase :						List[str]=[1, 2, 10]					,			lowerCamelCase :						Any=[7, 3, 3]					,			lowerCamelCase :						Any=[4, 2, 2]					,			lowerCamelCase :						Optional[Any]=[2, 1, 1]					,			lowerCamelCase :						str=[2, 2, 2]					,			lowerCamelCase :						Dict=[False, False, True]					,			lowerCamelCase :						Dict=[0.0, 0.0, 0.0]					,			lowerCamelCase :						Optional[Any]=0.02					,			lowerCamelCase :						Tuple=1E-12					,			lowerCamelCase :						Union[str, Any]=True					,			lowerCamelCase :						Union[str, Any]=True					,			lowerCamelCase :						List[str]=2					,			)			->					Optional[int]:
														__snake_case	:    Tuple											=     parent
														__snake_case	:    List[str]											=     batch_size
														__snake_case	:    Optional[int]											=     image_size
														__snake_case	:    Optional[int]											=     patch_sizes
														__snake_case	:    Union[str, Any]											=     patch_stride
														__snake_case	:    int											=     patch_padding
														__snake_case	:    Optional[Any]											=     is_training
														__snake_case	:    Optional[Any]											=     use_labels
														__snake_case	:    Tuple											=     num_labels
														__snake_case	:    Union[str, Any]											=     num_channels
														__snake_case	:    Tuple											=     embed_dim
														__snake_case	:    List[str]											=     num_heads
														__snake_case	:    Dict											=     stride_kv
														__snake_case	:    Optional[int]											=     depth
														__snake_case	:    List[Any]											=     cls_token
														__snake_case	:    List[Any]											=     attention_drop_rate
														__snake_case	:    Any											=     initializer_range
														__snake_case	:    str											=     layer_norm_eps

							def 					__snake_case      (							self :						List[str]       )			->					str:
														__snake_case	:    str											=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

														__snake_case	:    Tuple											=     None
														if self.use_labels:
																					__snake_case	:    Tuple											=     ids_tensor([self.batch_size]					,			self.num_labels       )

														__snake_case	:    Optional[Any]											=     self.get_config()
														return config, pixel_values, labels

							def 					__snake_case      (							self :						Tuple       )			->					Optional[Any]:
														return CvtConfig(
														    image_size=self.image_size					,			num_labels=self.num_labels					,			num_channels=self.num_channels					,			embed_dim=self.embed_dim					,			num_heads=self.num_heads					,			patch_sizes=self.patch_sizes					,			patch_padding=self.patch_padding					,			patch_stride=self.patch_stride					,			stride_kv=self.stride_kv					,			depth=self.depth					,			cls_token=self.cls_token					,			attention_drop_rate=self.attention_drop_rate					,			initializer_range=self.initializer_range					,			)

							def 					__snake_case      (							self :						Optional[int]					,			lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						str					,			lowerCamelCase :						str       )			->					Tuple:
														__snake_case	:    int											=     CvtModel(config=lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    List[str]											=     model(lowerCamelCase       )
														__snake_case	:    List[Any]											=     (self.image_size, self.image_size)
														__snake_case				,    __snake_case	:    List[str]											=     image_size[0], image_size[1]
														for i in range(len(self.depth       )       ):
																					__snake_case	:    int											=     floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
																					__snake_case	:    Union[str, Any]											=     floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
														self.parent.assertEqual(result.last_hidden_state.shape					,			(self.batch_size, self.embed_dim[-1], height, width)       )

							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						int       )			->					Optional[Any]:
														__snake_case	:    str											=     self.num_labels
														__snake_case	:    Any											=     CvtForImageClassification(lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    List[Any]											=     model(lowerCamelCase					,			labels=lowerCamelCase       )
														self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.num_labels)       )






							def 					__snake_case      (							self :						Union[str, Any]       )			->					List[Any]:
														__snake_case	:    List[Any]											=     self.prepare_config_and_inputs()
														__snake_case				,    __snake_case				,    __snake_case	:    str											=     config_and_inputs
														__snake_case	:    List[str]											=     {"pixel_values": pixel_values}
														return config, inputs_dict

@require_torch
class        a	(_lowerCAmelCase  ,       _lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							List[str]  						=						(CvtModel, CvtForImageClassification) if is_torch_available() else ()
							__UpperCAmelCase					:							List[str]  						=						(
							    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase					:							List[str]  						=						False
							__UpperCAmelCase					:							Tuple  						=						False
							__UpperCAmelCase					:							List[Any]  						=						False
							__UpperCAmelCase					:							Optional[int]  						=						False
							__UpperCAmelCase					:							List[Any]  						=						False

							def 					__snake_case      (							self :						str       )			->					List[str]:
														__snake_case	:    Tuple											=     CvtModelTester(self       )
														__snake_case	:    Tuple											=     ConfigTester(self					,			config_class=lowerCamelCase					,			has_text_modality=lowerCamelCase					,			hidden_size=37       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					Union[str, Any]:
														self.create_and_test_config_common_properties()
														self.config_tester.create_and_test_config_to_json_string()
														self.config_tester.create_and_test_config_to_json_file()
														self.config_tester.create_and_test_config_from_and_save_pretrained()
														self.config_tester.create_and_test_config_with_num_labels()
														self.config_tester.check_config_can_be_init_without_params()
														self.config_tester.check_config_arguments_init()

							def 					__snake_case      (							self :						Tuple       )			->					Union[str, Any]:
														return

							@unittest.skip(reason="Cvt does not output attentions"       )
							def 					__snake_case      (							self :						Optional[int]       )			->					List[str]:
														pass

							@unittest.skip(reason="Cvt does not use inputs_embeds"       )
							def 					__snake_case      (							self :						List[str]       )			->					Any:
														pass

							@unittest.skip(reason="Cvt does not support input and output embeddings"       )
							def 					__snake_case      (							self :						int       )			->					List[Any]:
														pass

							def 					__snake_case      (							self :						int       )			->					int:
														__snake_case				,    __snake_case	:    Optional[int]											=     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case	:    str											=     model_class(lowerCamelCase       )
																					__snake_case	:    List[Any]											=     inspect.signature(model.forward       )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					__snake_case	:    List[str]											=     [*signature.parameters.keys()]

																					__snake_case	:    Dict											=     ["pixel_values"]
																					self.assertListEqual(arg_names[:1]					,			lowerCamelCase       )

							def 					__snake_case      (							self :						str       )			->					Dict:
														__snake_case	:    List[str]											=     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase       )

							def 					__snake_case      (							self :						int       )			->					Optional[Any]:
														def check_hidden_states_output(lowerCamelCase :						Dict					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Optional[Any]       ):
																					__snake_case	:    Any											=     model_class(lowerCamelCase       )
																					model.to(lowerCamelCase       )
																					model.eval()

																					with torch.no_grad():
																												__snake_case	:    List[str]											=     model(**self._prepare_for_class(lowerCamelCase					,			lowerCamelCase       )       )

																					__snake_case	:    List[Any]											=     outputs.hidden_states

																					__snake_case	:    Optional[int]											=     len(self.model_tester.depth       )
																					self.assertEqual(len(lowerCamelCase       )					,			lowerCamelCase       )

																					# verify the first hidden states (first block)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-3:]       )					,			[
																					        self.model_tester.embed_dim[0],
																					        self.model_tester.image_size // 4,
																					        self.model_tester.image_size // 4,
																					    ]					,			)

														__snake_case				,    __snake_case	:    List[str]											=     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case	:    Union[str, Any]											=     True
																					check_hidden_states_output(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					__snake_case	:    List[str]											=     True

																					check_hidden_states_output(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

							def 					__snake_case      (							self :						Optional[int]       )			->					Optional[Any]:
														__snake_case	:    Any											=     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase       )

							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
							def 					__snake_case      (							self :						List[str]       )			->					Optional[int]:
														pass






							@slow
							def 					__snake_case      (							self :						Tuple       )			->					List[str]:
														for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case	:    int											=     CvtModel.from_pretrained(lowerCamelCase       )
																					self.assertIsNotNone(lowerCamelCase       )







def        lowerCAmelCase_  (      ):
							__snake_case	:    Any											=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
							return image

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




							"""simple docstring"""


							@cached_property
							def 					__snake_case      (							self :						Optional[Any]       )			->					Union[str, Any]:
														return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )






							@slow
							def 					__snake_case      (							self :						int       )			->					Tuple:
														__snake_case	:    Optional[Any]											=     CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(lowerCamelCase       )

														__snake_case	:    Optional[int]											=     self.default_image_processor
														__snake_case	:    Union[str, Any]											=     prepare_img()
														__snake_case	:    Dict											=     image_processor(images=lowerCamelCase					,			return_tensors="pt"       ).to(lowerCamelCase       )

														# forward pass
														with torch.no_grad():
																					__snake_case	:    Any											=     model(**lowerCamelCase       )

														# verify the logits
														__snake_case	:    Any											=     torch.Size((1, 1000)       )
														self.assertEqual(outputs.logits.shape					,			lowerCamelCase       )

														__snake_case	:    Optional[Any]											=     torch.tensor([0.92_85, 0.90_15, -0.31_50]       ).to(lowerCamelCase       )

														self.assertTrue(torch.allclose(outputs.logits[0, :3]					,			lowerCamelCase					,			atol=1E-4       )       )

