


'''simple docstring'''





import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


lowerCAmelCase__						:Optional[int]         =							1_0_0_0

class 	__a   :
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=1.2    ,					_SCREAMING_SNAKE_CASE="g"    ,					_SCREAMING_SNAKE_CASE=0.5    ,					_SCREAMING_SNAKE_CASE="-"    ,					_SCREAMING_SNAKE_CASE="test_out.jpg"    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=0.7    ,					)				-> List[Any]:




									"""simple docstring"""





									if isinstance(_SCREAMING_SNAKE_CASE    ,					torch.Tensor		):
																_UpperCAmelCase					=    img.numpy().astype('np.uint8'		)
									if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    img_tensorize(_SCREAMING_SNAKE_CASE		)
									assert isinstance(_SCREAMING_SNAKE_CASE    ,					np.ndarray		)

									_UpperCAmelCase	, _UpperCAmelCase					=    img.shape[1], img.shape[0]
									_UpperCAmelCase					=    mplfigure.Figure(frameon=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    fig.get_dpi()
									_UpperCAmelCase					=    (width * scale + 1e-2) / dpi
									_UpperCAmelCase					=    (height * scale + 1e-2) / dpi
									fig.set_size_inches(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    fig.add_axes([0.0, 0.0, 1.0, 1.0]		)
									ax.axis('off'		)
									ax.set_xlim(0.0    ,					_SCREAMING_SNAKE_CASE		)
									ax.set_ylim(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    saveas
									_UpperCAmelCase					=    rgb
									_UpperCAmelCase					=    pynb
									_UpperCAmelCase					=    img
									_UpperCAmelCase					=    edgecolor
									_UpperCAmelCase					=    0.5
									_UpperCAmelCase					=    linestyle
									_UpperCAmelCase					=    int(np.sqrt(min(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)		) * scale // 3		)
									_UpperCAmelCase					=    width
									_UpperCAmelCase					=    height
									_UpperCAmelCase					=    scale
									_UpperCAmelCase					=    fig
									_UpperCAmelCase					=    ax
									_UpperCAmelCase					=    pad
									_UpperCAmelCase					=    idaobj
									_UpperCAmelCase					=    idaattr
									_UpperCAmelCase					=    FigureCanvasAgg(_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> Optional[Any]:




									"""simple docstring"""





									if color is None:
																_UpperCAmelCase					=    self.edgecolor
									((_UpperCAmelCase)	, (_UpperCAmelCase)	, (_UpperCAmelCase)	, (_UpperCAmelCase))					=    box
									_UpperCAmelCase					=    xa - xa
									_UpperCAmelCase					=    ya - ya
									self.ax.add_patch(
									    mpl.patches.Rectangle(
									        (xa, ya)    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					fill=_SCREAMING_SNAKE_CASE    ,					edgecolor=_SCREAMING_SNAKE_CASE    ,					linewidth=self.font_size // 3    ,					alpha=self.alpha    ,					linestyle=self.linestyle    ,					)		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None		)				-> Any:




									"""simple docstring"""





									if len(boxes.shape		) > 2:
																_UpperCAmelCase					=    boxes[0]
									if len(obj_ids.shape		) > 1:
																_UpperCAmelCase					=    obj_ids[0]
									if len(obj_scores.shape		) > 1:
																_UpperCAmelCase					=    obj_scores[0]
									if len(attr_ids.shape		) > 1:
																_UpperCAmelCase					=    attr_ids[0]
									if len(attr_scores.shape		) > 1:
																_UpperCAmelCase					=    attr_scores[0]
									if isinstance(_SCREAMING_SNAKE_CASE    ,					torch.Tensor		):
																_UpperCAmelCase					=    boxes.numpy()
									if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    np.array(_SCREAMING_SNAKE_CASE		)
									assert isinstance(_SCREAMING_SNAKE_CASE    ,					np.ndarray		)
									_UpperCAmelCase					=    np.prod(boxes[:, 2:] - boxes[:, :2]    ,					axis=1		)
									_UpperCAmelCase					=    np.argsort(-areas		).tolist()
									_UpperCAmelCase					=    boxes[sorted_idxs] if boxes is not None else None
									_UpperCAmelCase					=    obj_ids[sorted_idxs] if obj_ids is not None else None
									_UpperCAmelCase					=    obj_scores[sorted_idxs] if obj_scores is not None else None
									_UpperCAmelCase					=    attr_ids[sorted_idxs] if attr_ids is not None else None
									_UpperCAmelCase					=    attr_scores[sorted_idxs] if attr_scores is not None else None

									_UpperCAmelCase					=    [self._random_color(maximum=1		) for _ in range(len(_SCREAMING_SNAKE_CASE		)		)]
									_UpperCAmelCase					=    [assigned_colors[idx] for idx in sorted_idxs]
									if obj_ids is not None:
																_UpperCAmelCase					=    self._create_text_labels_attr(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																for i in range(len(_SCREAMING_SNAKE_CASE		)		):
																							_UpperCAmelCase					=    assigned_colors[i]
																							self.add_box(boxes[i]    ,					_SCREAMING_SNAKE_CASE		)
																							self.draw_labels(labels[i]    ,					boxes[i]    ,					_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase	, _UpperCAmelCase	, _UpperCAmelCase	, _UpperCAmelCase					=    box
									_UpperCAmelCase					=    (xa, ya)
									_UpperCAmelCase					=    (ya - ya) * (xa - xa)
									_UpperCAmelCase					=    _SMALL_OBJ * self.scale
									if instance_area < small or ya - ya < 40 * self.scale:
																if ya >= self.height - 5:
																							_UpperCAmelCase					=    (xa, ya)
																else:
																							_UpperCAmelCase					=    (xa, ya)

									_UpperCAmelCase					=    (ya - ya) / np.sqrt(self.height * self.width		)
									_UpperCAmelCase					=    self._change_color_brightness(_SCREAMING_SNAKE_CASE    ,					brightness_factor=0.7		)
									_UpperCAmelCase					=    np.clip((height_ratio - 0.02) / 0.08 + 1    ,					1.2    ,					2		)
									font_size *= 0.75 * self.font_size

									self.draw_text(
									    text=_SCREAMING_SNAKE_CASE    ,					position=_SCREAMING_SNAKE_CASE    ,					color=_SCREAMING_SNAKE_CASE    ,					)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE="g"    ,					_SCREAMING_SNAKE_CASE="left"    ,					)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    0
									_UpperCAmelCase					=    self.font_size
									_UpperCAmelCase					=    np.maximum(list(mplc.to_rgb(_SCREAMING_SNAKE_CASE		)		)    ,					0.2		)
									_UpperCAmelCase					=    max(0.8    ,					np.max(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    {
									    'facecolor': 'black',
									    'alpha': self.alpha,
									    'pad': self.pad,
									    'edgecolor': 'none',
									}
									_UpperCAmelCase	, _UpperCAmelCase					=    position
									self.ax.text(
									    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					size=font_size * self.scale    ,					family='sans-serif'    ,					bbox=_SCREAMING_SNAKE_CASE    ,					verticalalignment='top'    ,					horizontalalignment=_SCREAMING_SNAKE_CASE    ,					color=_SCREAMING_SNAKE_CASE    ,					zorder=10    ,					rotation=_SCREAMING_SNAKE_CASE    ,					)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=None		)				-> List[Any]:




									"""simple docstring"""





									if saveas is None:
																_UpperCAmelCase					=    self.saveas
									if saveas.lower().endswith('.jpg'		) or saveas.lower().endswith('.png'		):
																cva.imwrite(
																    _SCREAMING_SNAKE_CASE    ,					self._get_buffer()[:, :, ::-1]    ,					)
									else:
																self.fig.savefig(_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> int:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.idaobj[i] for i in classes]
									_UpperCAmelCase					=    [self.idaattr[i] for i in attr_classes]
									_UpperCAmelCase					=    [
									    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
									    for label, score, attr, attr_score in zip(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									]
									return labels
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Dict:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.idaobj[i] for i in classes]
									if scores is not None:
																if labels is None:
																							_UpperCAmelCase					=    ['{:.0f}%'.format(s * 100		) for s in scores]
																else:
																							_UpperCAmelCase					=    ['{} {:.0f}%'.format(_SCREAMING_SNAKE_CASE    ,					s * 100		) for li, s in zip(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)]
									return labels
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=255		)				-> str:




									"""simple docstring"""





									_UpperCAmelCase					=    np.random.randint(0    ,					len(_COLORS		)		)
									_UpperCAmelCase					=    _COLORS[idx] * maximum
									if not self.rgb:
																_UpperCAmelCase					=    ret[::-1]
									return ret
		def 			UpperCAmelCase__       (    self		)				-> Optional[Any]:




									"""simple docstring"""





									if not self.pynb:
																_UpperCAmelCase	, _UpperCAmelCase					=    self.canvas.print_to_buffer()
																if (width, height) != (self.width, self.height):
																							_UpperCAmelCase					=    cva.resize(self.img    ,					(width, height)		)
																else:
																							_UpperCAmelCase					=    self.img
									else:
																_UpperCAmelCase					=    io.BytesIO()  # works for cairo backend
																self.canvas.print_rgba(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase	, _UpperCAmelCase					=    self.width, self.height
																_UpperCAmelCase					=    buf.getvalue()
																_UpperCAmelCase					=    self.img

									_UpperCAmelCase					=    np.frombuffer(_SCREAMING_SNAKE_CASE    ,					dtype='uint8'		)
									_UpperCAmelCase					=    buffer.reshape(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					4		)
									_UpperCAmelCase	, _UpperCAmelCase					=    np.split(_SCREAMING_SNAKE_CASE    ,					[3]    ,					axis=2		)

									try:
																import numexpr as ne  # fuse them with numexpr

																_UpperCAmelCase					=    ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'		)
									except ImportError:
																_UpperCAmelCase					=    alpha.astype('float32'		) / 255.0
																_UpperCAmelCase					=    img * (1 - alpha) + rgb * alpha

									return visualized_image.astype('uint8'		)






		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Dict:




									"""simple docstring"""





									assert brightness_factor >= -1.0 and brightness_factor <= 1.0
									_UpperCAmelCase					=    mplc.to_rgb(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    colorsys.rgb_to_hls(*mplc.to_rgb(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    polygon_color[1] + (brightness_factor * polygon_color[1])
									_UpperCAmelCase					=    0.0 if modified_lightness < 0.0 else modified_lightness
									_UpperCAmelCase					=    1.0 if modified_lightness > 1.0 else modified_lightness
									_UpperCAmelCase					=    colorsys.hls_to_rgb(polygon_color[0]    ,					_SCREAMING_SNAKE_CASE    ,					polygon_color[2]		)
									return modified_color



lowerCAmelCase__						:List[Any]         =							(
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)




import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax
		import jax.numpy as jnp

		from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor

class 	__a   (       unittest.TestCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=32    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=10    ,					_SCREAMING_SNAKE_CASE=[10, 20, 30, 40]    ,					_SCREAMING_SNAKE_CASE=[1, 1, 2, 1]    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE="relu"    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=None    ,					)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    parent
									_UpperCAmelCase					=    batch_size
									_UpperCAmelCase					=    image_size
									_UpperCAmelCase					=    num_channels
									_UpperCAmelCase					=    embeddings_size
									_UpperCAmelCase					=    hidden_sizes
									_UpperCAmelCase					=    depths
									_UpperCAmelCase					=    is_training
									_UpperCAmelCase					=    use_labels
									_UpperCAmelCase					=    hidden_act
									_UpperCAmelCase					=    num_labels
									_UpperCAmelCase					=    scope
									_UpperCAmelCase					=    len(_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_UpperCAmelCase					=    self.get_config()

									return config, pixel_values
		def 			UpperCAmelCase__       (    self		)				-> Union[str, Any]:




									"""simple docstring"""





									return ResNetConfig(
									    num_channels=self.num_channels    ,					embeddings_size=self.embeddings_size    ,					hidden_sizes=self.hidden_sizes    ,					depths=self.depths    ,					hidden_act=self.hidden_act    ,					num_labels=self.num_labels    ,					image_size=self.image_size    ,					)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    FlaxResNetModel(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE		)

									# Output shape (b, c, h, w)
									self.parent.assertEqual(
									    result.last_hidden_state.shape    ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,					)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    self.num_labels
									_UpperCAmelCase					=    FlaxResNetForImageClassification(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)		)






		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    self.prepare_config_and_inputs()
									_UpperCAmelCase	, _UpperCAmelCase					=    config_and_inputs
									_UpperCAmelCase					=    {'pixel_values': pixel_values}
									return config, inputs_dict



@require_flax
class 	__a   (       UpperCAmelCase					,   unittest.TestCase    ):
		_a			:						Any    			=			(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

		_a			:						Dict    			=			False
		_a			:						Optional[Any]    			=			False
		_a			:						Optional[Any]    			=			False
		def 			UpperCAmelCase__       (    self		)				-> None:




									"""simple docstring"""





									_UpperCAmelCase					=    FlaxResNetModelTester(self		)
									_UpperCAmelCase					=    ConfigTester(self    ,					config_class=_SCREAMING_SNAKE_CASE    ,					has_text_modality=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									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 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									return
		def 			UpperCAmelCase__       (    self		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)
		@unittest.skip(reason='ResNet does not use inputs_embeds'		)
		def 			UpperCAmelCase__       (    self		)				-> Optional[Any]:




									"""simple docstring"""





									pass
		@unittest.skip(reason='ResNet does not support input and output embeddings'		)
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									pass
		def 			UpperCAmelCase__       (    self		)				-> Dict:




									"""simple docstring"""





									_UpperCAmelCase	, _UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase					=    model_class(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    inspect.signature(model.__call__		)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_UpperCAmelCase					=    [*signature.parameters.keys()]

																_UpperCAmelCase					=    ['pixel_values']
																self.assertListEqual(arg_names[:1]    ,					_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									def check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    model_class(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)		)

																_UpperCAmelCase					=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																_UpperCAmelCase					=    self.model_tester.num_stages
																self.assertEqual(len(_SCREAMING_SNAKE_CASE		)    ,					expected_num_stages + 1		)
		@unittest.skip(reason='ResNet does not use feedforward chunking'		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									pass






		def 			UpperCAmelCase__       (    self		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase	, _UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__		):
																							_UpperCAmelCase					=    self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    model_class(_SCREAMING_SNAKE_CASE		)

																							@jax.jit
																							def model_jitted(_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		):
																														return model(pixel_values=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

																							with self.subTest('JIT Enabled'		):
																														_UpperCAmelCase					=    model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

																							with self.subTest('JIT Disabled'		):
																														with jax.disable_jit():
																																					_UpperCAmelCase					=    model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

																							self.assertEqual(len(_SCREAMING_SNAKE_CASE		)    ,					len(_SCREAMING_SNAKE_CASE		)		)
																							for jitted_output, output in zip(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																														self.assertEqual(jitted_output.shape    ,					output.shape		)



def  lowerCAmelCase__				(    )						->	Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
							return image







@require_flax
class 	__a   (       unittest.TestCase    ):
		@cached_property
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									return AutoImageProcessor.from_pretrained('microsoft/resnet-50'		) if is_vision_available() else None






		@slow
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'		)

									_UpperCAmelCase					=    self.default_image_processor
									_UpperCAmelCase					=    prepare_img()
									_UpperCAmelCase					=    image_processor(images=_SCREAMING_SNAKE_CASE    ,					return_tensors='np'		)

									_UpperCAmelCase					=    model(**_SCREAMING_SNAKE_CASE		)

									# verify the logits
									_UpperCAmelCase					=    (1, 1000)
									self.assertEqual(outputs.logits.shape    ,					_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    jnp.array([-11.1069, -9.7877, -8.3777]		)

									self.assertTrue(jnp.allclose(outputs.logits[0, :3]    ,					_SCREAMING_SNAKE_CASE    ,					atol=1e-4		)		)
