






"""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


lowercase           =							1000

class     A_				:







				def __init__(				self				:    List[str]    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    List[Any]=1.2    ,     __lowerCamelCase				:    Optional[int]="g"    ,     __lowerCamelCase				:    int=0.5    ,     __lowerCamelCase				:    List[str]="-"    ,     __lowerCamelCase				:    Optional[int]="test_out.jpg"    ,     __lowerCamelCase				:    int=True    ,     __lowerCamelCase				:    List[Any]=False    ,     __lowerCamelCase				:    Tuple=None    ,     __lowerCamelCase				:    Union[str, Any]=None    ,     __lowerCamelCase				:    Optional[int]=0.7    ,     )					->       int:
										if isinstance(__lowerCamelCase    ,     torch.Tensor						):
																__magic_name__      =  img.numpy().astype("np.uint8"						)
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  img_tensorize(__lowerCamelCase						)
										assert isinstance(__lowerCamelCase    ,     np.ndarray						)

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

										__magic_name__      =  saveas
										__magic_name__      =  rgb
										__magic_name__      =  pynb
										__magic_name__      =  img
										__magic_name__      =  edgecolor
										__magic_name__      =  0.5
										__magic_name__      =  linestyle
										__magic_name__      =  int(np.sqrt(min(__lowerCamelCase    ,     __lowerCamelCase						)						) * scale // 3						)
										__magic_name__      =  width
										__magic_name__      =  height
										__magic_name__      =  scale
										__magic_name__      =  fig
										__magic_name__      =  ax
										__magic_name__      =  pad
										__magic_name__      =  idaobj
										__magic_name__      =  idaattr
										__magic_name__      =  FigureCanvasAgg(__lowerCamelCase						)







				def      _snake_case       (				self				:    Optional[Any]    ,     __lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    Tuple=None						)					->       Optional[int]:
										if color is None:
																__magic_name__      =  self.edgecolor
										((__magic_name__)   ,     (__magic_name__)   ,     (__magic_name__)   ,     (__magic_name__))      =  box
										__magic_name__      =  xa - xa
										__magic_name__      =  ya - ya
										self.ax.add_patch(
										    mpl.patches.Rectangle(
										        (xa, ya)    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     fill=__lowerCamelCase    ,     edgecolor=__lowerCamelCase    ,     linewidth=self.font_size // 3    ,     alpha=self.alpha    ,     linestyle=self.linestyle    ,     )						)







				def      _snake_case       (				self				:    str    ,     __lowerCamelCase				:    Optional[int]    ,     __lowerCamelCase				:    List[str]=None    ,     __lowerCamelCase				:    List[str]=None    ,     __lowerCamelCase				:    Optional[int]=None    ,     __lowerCamelCase				:    Tuple=None						)					->       Tuple:
										if len(boxes.shape						) > 2:
																__magic_name__      =  boxes[0]
										if len(obj_ids.shape						) > 1:
																__magic_name__      =  obj_ids[0]
										if len(obj_scores.shape						) > 1:
																__magic_name__      =  obj_scores[0]
										if len(attr_ids.shape						) > 1:
																__magic_name__      =  attr_ids[0]
										if len(attr_scores.shape						) > 1:
																__magic_name__      =  attr_scores[0]
										if isinstance(__lowerCamelCase    ,     torch.Tensor						):
																__magic_name__      =  boxes.numpy()
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  np.array(__lowerCamelCase						)
										assert isinstance(__lowerCamelCase    ,     np.ndarray						)
										__magic_name__      =  np.prod(boxes[:, 2:] - boxes[:, :2]    ,     axis=1						)
										__magic_name__      =  np.argsort(-areas						).tolist()
										__magic_name__      =  boxes[sorted_idxs] if boxes is not None else None
										__magic_name__      =  obj_ids[sorted_idxs] if obj_ids is not None else None
										__magic_name__      =  obj_scores[sorted_idxs] if obj_scores is not None else None
										__magic_name__      =  attr_ids[sorted_idxs] if attr_ids is not None else None
										__magic_name__      =  attr_scores[sorted_idxs] if attr_scores is not None else None

										__magic_name__      =  [self._random_color(maximum=1						) for _ in range(len(__lowerCamelCase						)						)]
										__magic_name__      =  [assigned_colors[idx] for idx in sorted_idxs]
										if obj_ids is not None:
																__magic_name__      =  self._create_text_labels_attr(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)
																for i in range(len(__lowerCamelCase						)						):
																						__magic_name__      =  assigned_colors[i]
																						self.add_box(boxes[i]    ,     __lowerCamelCase						)
																						self.draw_labels(labels[i]    ,     boxes[i]    ,     __lowerCamelCase						)







				def      _snake_case       (				self				:    int    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    Optional[Any]    ,     __lowerCamelCase				:    List[Any]						)					->       Union[str, Any]:
										__magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__      =  box
										__magic_name__      =  (xa, ya)
										__magic_name__      =  (ya - ya) * (xa - xa)
										__magic_name__      =  _SMALL_OBJ * self.scale
										if instance_area < small or ya - ya < 4_0 * self.scale:
																if ya >= self.height - 5:
																						__magic_name__      =  (xa, ya)
																else:
																						__magic_name__      =  (xa, ya)

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

										self.draw_text(
										    text=__lowerCamelCase    ,     position=__lowerCamelCase    ,     color=__lowerCamelCase    ,     )







				def      _snake_case       (				self				:    Dict    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    Dict="g"    ,     __lowerCamelCase				:    Optional[Any]="left"    ,     )					->       Optional[Any]:
										__magic_name__      =  0
										__magic_name__      =  self.font_size
										__magic_name__      =  np.maximum(list(mplc.to_rgb(__lowerCamelCase						)						)    ,     0.2						)
										__magic_name__      =  max(0.8    ,     np.max(__lowerCamelCase						)						)
										__magic_name__      =  {
										    "facecolor": "black",
										    "alpha": self.alpha,
										    "pad": self.pad,
										    "edgecolor": "none",
										}
										__magic_name__   ,     __magic_name__      =  position
										self.ax.text(
										    __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     size=font_size * self.scale    ,     family="sans-serif"    ,     bbox=__lowerCamelCase    ,     verticalalignment="top"    ,     horizontalalignment=__lowerCamelCase    ,     color=__lowerCamelCase    ,     zorder=1_0    ,     rotation=__lowerCamelCase    ,     )







				def      _snake_case       (				self				:    List[str]    ,     __lowerCamelCase				:    Union[str, Any]=None						)					->       Optional[Any]:
										if saveas is None:
																__magic_name__      =  self.saveas
										if saveas.lower().endswith(".jpg"						) or saveas.lower().endswith(".png"						):
																cva.imwrite(
																    __lowerCamelCase    ,     self._get_buffer()[:, :, ::-1]    ,     )
										else:
																self.fig.savefig(__lowerCamelCase						)







				def      _snake_case       (				self				:    int    ,     __lowerCamelCase				:    int    ,     __lowerCamelCase				:    List[Any]    ,     __lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    Any						)					->       List[Any]:
										__magic_name__      =  [self.idaobj[i] for i in classes]
										__magic_name__      =  [self.idaattr[i] for i in attr_classes]
										__magic_name__      =  [
										    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
										    for label, score, attr, attr_score in zip(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)
										]
										return labels







				def      _snake_case       (				self				:    Optional[int]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    Tuple						)					->       Dict:
										__magic_name__      =  [self.idaobj[i] for i in classes]
										if scores is not None:
																if labels is None:
																						__magic_name__      =  ["{:.0f}%".format(s * 1_0_0						) for s in scores]
																else:
																						__magic_name__      =  ["{} {:.0f}%".format(__lowerCamelCase    ,     s * 1_0_0						) for li, s in zip(__lowerCamelCase    ,     __lowerCamelCase						)]
										return labels







				def      _snake_case       (				self				:    int    ,     __lowerCamelCase				:    Optional[int]=2_5_5						)					->       Any:
										__magic_name__      =  np.random.randint(0    ,     len(_COLORS						)						)
										__magic_name__      =  _COLORS[idx] * maximum
										if not self.rgb:
																__magic_name__      =  ret[::-1]
										return ret







				def      _snake_case       (				self				:    Optional[Any]						)					->       str:
										if not self.pynb:
																__magic_name__   ,     __magic_name__      =  self.canvas.print_to_buffer()
																if (width, height) != (self.width, self.height):
																						__magic_name__      =  cva.resize(self.img    ,     (width, height)						)
																else:
																						__magic_name__      =  self.img
										else:
																__magic_name__      =  io.BytesIO()  # works for cairo backend
																self.canvas.print_rgba(__lowerCamelCase						)
																__magic_name__   ,     __magic_name__      =  self.width, self.height
																__magic_name__      =  buf.getvalue()
																__magic_name__      =  self.img

										__magic_name__      =  np.frombuffer(__lowerCamelCase    ,     dtype="uint8"						)
										__magic_name__      =  buffer.reshape(__lowerCamelCase    ,     __lowerCamelCase    ,     4						)
										__magic_name__   ,     __magic_name__      =  np.split(__lowerCamelCase    ,     [3]    ,     axis=2						)

										try:
																import numexpr as ne  # fuse them with numexpr

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

										return visualized_image.astype("uint8"						)







				def      _snake_case       (				self				:    str    ,     __lowerCamelCase				:    List[str]    ,     __lowerCamelCase				:    Any						)					->       int:
										assert brightness_factor >= -1.0 and brightness_factor <= 1.0
										__magic_name__      =  mplc.to_rgb(__lowerCamelCase						)
										__magic_name__      =  colorsys.rgb_to_hls(*mplc.to_rgb(__lowerCamelCase						)						)
										__magic_name__      =  polygon_color[1] + (brightness_factor * polygon_color[1])
										__magic_name__      =  0.0 if modified_lightness < 0.0 else modified_lightness
										__magic_name__      =  1.0 if modified_lightness > 1.0 else modified_lightness
										__magic_name__      =  colorsys.hls_to_rgb(polygon_color[0]    ,     __lowerCamelCase    ,     polygon_color[2]						)
										return modified_color





lowercase           =							(
    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)
)











"""simple docstring"""

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
			from PIL import Image

			from transformers import ConvNextImageProcessor

class     A_				:







				def __init__(				self				:    Optional[int]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    int=1_3    ,     __lowerCamelCase				:    Any=3_2    ,     __lowerCamelCase				:    Dict=3    ,     __lowerCamelCase				:    List[Any]=4    ,     __lowerCamelCase				:    Union[str, Any]=[1_0, 2_0, 3_0, 4_0]    ,     __lowerCamelCase				:    int=[2, 2, 3, 2]    ,     __lowerCamelCase				:    str=True    ,     __lowerCamelCase				:    List[str]=True    ,     __lowerCamelCase				:    List[Any]=3_7    ,     __lowerCamelCase				:    Optional[Any]="gelu"    ,     __lowerCamelCase				:    str=1_0    ,     __lowerCamelCase				:    int=0.02    ,     __lowerCamelCase				:    Optional[int]=3    ,     __lowerCamelCase				:    Any=None    ,     )					->       List[Any]:
										__magic_name__      =  parent
										__magic_name__      =  batch_size
										__magic_name__      =  image_size
										__magic_name__      =  num_channels
										__magic_name__      =  num_stages
										__magic_name__      =  hidden_sizes
										__magic_name__      =  depths
										__magic_name__      =  is_training
										__magic_name__      =  use_labels
										__magic_name__      =  intermediate_size
										__magic_name__      =  hidden_act
										__magic_name__      =  type_sequence_label_size
										__magic_name__      =  initializer_range
										__magic_name__      =  scope







				def      _snake_case       (				self				:    Optional[Any]						)					->       int:
										__magic_name__      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

										__magic_name__      =  None
										if self.use_labels:
																__magic_name__      =  ids_tensor([self.batch_size]    ,     self.type_sequence_label_size						)

										__magic_name__      =  self.get_config()

										return config, pixel_values, labels







				def      _snake_case       (				self				:    Optional[int]						)					->       Union[str, Any]:
										return ConvNextConfig(
										    num_channels=self.num_channels    ,     hidden_sizes=self.hidden_sizes    ,     depths=self.depths    ,     num_stages=self.num_stages    ,     hidden_act=self.hidden_act    ,     is_decoder=__lowerCamelCase    ,     initializer_range=self.initializer_range    ,     )







				def      _snake_case       (				self				:    List[Any]    ,     __lowerCamelCase				:    List[str]    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Optional[Any]						)					->       List[str]:
										__magic_name__      =  TFConvNextModel(config=__lowerCamelCase						)
										__magic_name__      =  model(__lowerCamelCase    ,     training=__lowerCamelCase						)
										# expected last hidden states: B, C, H // 32, W // 32
										self.parent.assertEqual(
										    result.last_hidden_state.shape    ,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)    ,     )







				def      _snake_case       (				self				:    List[Any]    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    List[str]						)					->       int:
										__magic_name__      =  self.type_sequence_label_size
										__magic_name__      =  TFConvNextForImageClassification(__lowerCamelCase						)
										__magic_name__      =  model(__lowerCamelCase    ,     labels=__lowerCamelCase    ,     training=__lowerCamelCase						)
										self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.type_sequence_label_size)						)







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





@require_tf
class     A_				(   snake_case_       ,				snake_case_       ,				unittest.TestCase	):
				UpperCAmelCase__										=					(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
				UpperCAmelCase__										=					(
				    {'''feature-extraction''': TFConvNextModel, '''image-classification''': TFConvNextForImageClassification}
				    if is_tf_available()
				    else {}
				)

				UpperCAmelCase__										=					False
				UpperCAmelCase__										=					False
				UpperCAmelCase__										=					False
				UpperCAmelCase__										=					False
				UpperCAmelCase__										=					False







				def      _snake_case       (				self				:    str						)					->       str:
										__magic_name__      =  TFConvNextModelTester(self						)
										__magic_name__      =  ConfigTester(
										    self    ,     config_class=__lowerCamelCase    ,     has_text_modality=__lowerCamelCase    ,     hidden_size=3_7    ,     )







				@unittest.skip(reason="ConvNext does not use inputs_embeds"						)
				def      _snake_case       (				self				:    Dict						)					->       Dict:
										pass







				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0    ,     reason="TF does not support backprop for grouped convolutions on CPU."    ,     )
				@slow
				def      _snake_case       (				self				:    List[str]						)					->       Tuple:
										super().test_keras_fit()







				@unittest.skip(reason="ConvNext does not support input and output embeddings"						)
				def      _snake_case       (				self				:    str						)					->       Tuple:
										pass







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Optional[int]:
										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs_for_common()

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

																__magic_name__      =  ["pixel_values"]
																self.assertListEqual(arg_names[:1]    ,     __lowerCamelCase						)







				def      _snake_case       (				self				:    Dict						)					->       Optional[int]:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowerCamelCase						)







				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0    ,     reason="TF does not support backprop for grouped convolutions on CPU."    ,     )
				def      _snake_case       (				self				:    Optional[int]						)					->       Tuple:
										super().test_dataset_conversion()







				def      _snake_case       (				self				:    int						)					->       List[str]:
										def check_hidden_states_output(__lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    List[str]						):
																__magic_name__      =  model_class(__lowerCamelCase						)

																__magic_name__      =  model(**self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)						)
																__magic_name__      =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																__magic_name__      =  self.model_tester.num_stages
																self.assertEqual(len(__lowerCamelCase						)    ,     expected_num_stages + 1						)

																# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
																self.assertListEqual(
																    list(hidden_states[0].shape[-2:]						)    ,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,     )

										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																__magic_name__      =  True
																check_hidden_states_output(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																__magic_name__      =  True

																check_hidden_states_output(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)







				def      _snake_case       (				self				:    List[str]						)					->       Dict:
										__magic_name__   ,     __magic_name__      =  self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(__lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Any={}						):
																__magic_name__      =  model(__lowerCamelCase    ,     return_dict=__lowerCamelCase    ,     **__lowerCamelCase						)
																__magic_name__      =  model(__lowerCamelCase    ,     return_dict=__lowerCamelCase    ,     **__lowerCamelCase						).to_tuple()

																def recursive_check(__lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Union[str, Any]						):
																						if isinstance(__lowerCamelCase    ,     (List, Tuple)						):
																												for tuple_iterable_value, dict_iterable_value in zip(__lowerCamelCase    ,     __lowerCamelCase						):
																																		recursive_check(__lowerCamelCase    ,     __lowerCamelCase						)
																						elif tuple_object is None:
																												return
																						else:
																												self.assertTrue(
																												    all(tf.equal(__lowerCamelCase    ,     __lowerCamelCase						)						)    ,     msg=(
																												        "Tuple and dict output are not equal. Difference:"
																												        f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}'''
																												    )    ,     )

																						recursive_check(__lowerCamelCase    ,     __lowerCamelCase						)

										for model_class in self.all_model_classes:
																__magic_name__      =  model_class(__lowerCamelCase						)

																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)
																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)
																check_equivalence(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)

																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase    ,     return_labels=__lowerCamelCase						)
																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase    ,     return_labels=__lowerCamelCase						)
																check_equivalence(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)

																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)
																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase						)
																check_equivalence(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     {"output_hidden_states": True}						)

																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase    ,     return_labels=__lowerCamelCase						)
																__magic_name__      =  self._prepare_for_class(__lowerCamelCase    ,     __lowerCamelCase    ,     return_labels=__lowerCamelCase						)
																check_equivalence(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     {"output_hidden_states": True}						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Dict:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase						)







				@slow
				def      _snake_case       (				self				:    List[str]						)					->       Tuple:
										__magic_name__      =  TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"						)
										self.assertIsNotNone(__lowerCamelCase						)






def _lowerCAmelCase						(	):

						'''simple docstring'''






						__magic_name__      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						return image


@require_tf
@require_vision
class     A_				(   unittest.TestCase	):







				@cached_property
				def      _snake_case       (				self				:    Tuple						)					->       Any:
										return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"						) if is_vision_available() else None







				@slow
				def      _snake_case       (				self				:    Any						)					->       str:
										__magic_name__      =  TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"						)

										__magic_name__      =  self.default_image_processor
										__magic_name__      =  prepare_img()
										__magic_name__      =  image_processor(images=__lowerCamelCase    ,     return_tensors="tf"						)

										# forward pass
										__magic_name__      =  model(**__lowerCamelCase						)

										# verify the logits
										__magic_name__      =  tf.TensorShape((1, 1_0_0_0)						)
										self.assertEqual(outputs.logits.shape    ,     __lowerCamelCase						)

										__magic_name__      =  tf.constant([-0.0260, -0.4739, 0.1911]						)

										tf.debugging.assert_near(outputs.logits[0, :3]    ,     __lowerCamelCase    ,     atol=1e-4						)



