



import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					List[Any]              =						384
			if "tiny" in model_name:
						A							:					List[str]              =						[3, 3, 9, 3]
						A							:					List[str]              =						[96, 192, 384, 768]
			if "small" in model_name:
						A							:					Optional[Any]              =						[3, 3, 27, 3]
						A							:					str              =						[96, 192, 384, 768]
			if "base" in model_name:
						A							:					str              =						[3, 3, 27, 3]
						A							:					Tuple              =						[128, 256, 512, 1024]
						A							:					str              =						512
			if "large" in model_name:
						A							:					Optional[int]              =						[3, 3, 27, 3]
						A							:					int              =						[192, 384, 768, 1536]
						A							:					str              =						768
			if "xlarge" in model_name:
						A							:					Dict              =						[3, 3, 27, 3]
						A							:					Optional[Any]              =						[256, 512, 1024, 2048]
						A							:					Tuple              =						1024

			# set label information
			A							:					List[str]              =						150
			A							:					Dict              =						"huggingface/label-files"
			A							:					Union[str, Any]              =						"ade20k-id2label.json"
			A							:					List[Any]              =						json.load(open(hf_hub_download(_lowerCamelCase ,    _lowerCamelCase ,    repo_type="dataset"    ) ,    "r"    )    )
			A							:					List[Any]              =						{int(_lowerCamelCase    ): v for k, v in idalabel.items()}
			A							:					int              =						{v: k for k, v in idalabel.items()}

			A							:					Optional[int]              =						ConvNextConfig(
			    depths=_lowerCamelCase ,    hidden_sizes=_lowerCamelCase ,    out_features=["stage1", "stage2", "stage3", "stage4"]    )
			A							:					Optional[Any]              =						UperNetConfig(
			    backbone_config=_lowerCamelCase ,    auxiliary_in_channels=_lowerCamelCase ,    num_labels=_lowerCamelCase ,    idalabel=_lowerCamelCase ,    labelaid=_lowerCamelCase ,    )

			return config




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					str              =						[]

			# fmt: off
			# stem
			rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight")    )
			rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias")    )
			rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight")    )
			rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.bias")    )
			# stages
			for i in range(len(config.backbone_config.depths    )    ):
						for j in range(config.backbone_config.depths[i]    ):
									rename_keys.append((f"""backbone.stages.{i}.{j}.gamma""", f"""backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter""")    )
									rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.weight""")    )
									rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.bias""")    )
									rename_keys.append((f"""backbone.stages.{i}.{j}.norm.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.weight""")    )
									rename_keys.append((f"""backbone.stages.{i}.{j}.norm.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.bias""")    )
									rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight""")    )
									rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias""")    )
									rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight""")    )
									rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias""")    )
						if i > 0:
									rename_keys.append((f"""backbone.downsample_layers.{i}.0.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.weight""")    )
									rename_keys.append((f"""backbone.downsample_layers.{i}.0.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.bias""")    )
									rename_keys.append((f"""backbone.downsample_layers.{i}.1.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.weight""")    )
									rename_keys.append((f"""backbone.downsample_layers.{i}.1.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.bias""")    )

						rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")    )
						rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")    )

			# decode head
			rename_keys.extend(
			    [
			        ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
			        ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
			        ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
			        ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
			    ]    )
			# fmt: on

			return rename_keys




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Union[str, Any]              =						dct.pop(_lowerCamelCase    )
			A							:					List[str]              =						val




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Union[str, Any]              =						{
			    "upernet-convnext-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth",
			    "upernet-convnext-small": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth",
			    "upernet-convnext-base": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth",
			    "upernet-convnext-large": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth",
			    "upernet-convnext-xlarge": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth",
			}
			A							:					Optional[Any]              =						model_name_to_url[model_name]
			A							:					Dict              =						torch.hub.load_state_dict_from_url(_lowerCamelCase ,    map_location="cpu"    )["state_dict"]

			A							:					Optional[Any]              =						get_upernet_config(_lowerCamelCase    )
			A							:					Dict              =						UperNetForSemanticSegmentation(_lowerCamelCase    )
			model.eval()

			# replace "bn" => "batch_norm"
			for key in state_dict.copy().keys():
						A							:					List[str]              =						state_dict.pop(_lowerCamelCase    )
						if "bn" in key:
									A							:					Tuple              =						key.replace("bn" ,    "batch_norm"    )
						A							:					Optional[int]              =						val

			# rename keys
			A							:					str              =						create_rename_keys(_lowerCamelCase    )
			for src, dest in rename_keys:
						rename_key(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )

			model.load_state_dict(_lowerCamelCase    )

			# verify on image
			A							:					Any              =						"https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
			A							:					List[str]              =						Image.open(requests.get(_lowerCamelCase ,    stream=_lowerCamelCase    ).raw    ).convert("RGB"    )

			A							:					List[Any]              =						SegformerImageProcessor()
			A							:					Optional[int]              =						processor(_lowerCamelCase ,    return_tensors="pt"    ).pixel_values

			with torch.no_grad():
						A							:					int              =						model(_lowerCamelCase    )

			if model_name == "upernet-convnext-tiny":
						A							:					Union[str, Any]              =						torch.tensor(
						    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]    )
			elif model_name == "upernet-convnext-small":
						A							:					Optional[int]              =						torch.tensor(
						    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]    )
			elif model_name == "upernet-convnext-base":
						A							:					str              =						torch.tensor(
						    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]    )
			elif model_name == "upernet-convnext-large":
						A							:					int              =						torch.tensor(
						    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]    )
			elif model_name == "upernet-convnext-xlarge":
						A							:					Any              =						torch.tensor(
						    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]    )
			print("Logits:" ,    outputs.logits[0, 0, :3, :3]    )
			assert torch.allclose(outputs.logits[0, 0, :3, :3] ,    _lowerCamelCase ,    atol=1e-4    )
			print("Looks ok!"    )

			if pytorch_dump_folder_path is not None:
						print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""    )
						model.save_pretrained(_lowerCamelCase    )
						print(f"""Saving processor to {pytorch_dump_folder_path}"""    )
						processor.save_pretrained(_lowerCamelCase    )

			if push_to_hub:
						print(f"""Pushing model and processor for {model_name} to hub"""    )
						model.push_to_hub(f"""openmmlab/{model_name}"""    )
						processor.push_to_hub(f"""openmmlab/{model_name}"""    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--model_name""",
							    default="""upernet-convnext-tiny""",
							    type=str,
							    choices=[F"""upernet-convnext-{size}""" for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
							    help="""Name of the ConvNext UperNet model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
							)
							parser.add_argument(
							    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
							)

							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			List[Any]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			List[Any]=3	,	__lowerCamelCase      :			Union[str, Any]=32	,	__lowerCamelCase      :			List[Any]=3	,	__lowerCamelCase      :			Optional[int]=10	,	__lowerCamelCase      :			Optional[Any]=[10, 20, 30, 40]	,	__lowerCamelCase      :			Tuple=[1, 1, 2, 1]	,	__lowerCamelCase      :			Optional[Any]=True	,	__lowerCamelCase      :			Dict=True	,	__lowerCamelCase      :			List[str]="relu"	,	__lowerCamelCase      :			Tuple=3	,	__lowerCamelCase      :			Union[str, Any]=None	,	)      ->				str:
									A							:					Optional[Any]              =						parent
									A							:					Optional[int]              =						batch_size
									A							:					List[str]              =						image_size
									A							:					List[str]              =						num_channels
									A							:					Tuple              =						embeddings_size
									A							:					Optional[int]              =						hidden_sizes
									A							:					Dict              =						depths
									A							:					Optional[int]              =						is_training
									A							:					List[str]              =						use_labels
									A							:					List[Any]              =						hidden_act
									A							:					Optional[int]              =						num_labels
									A							:					int              =						scope
									A							:					List[Any]              =						len(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				List[Any]:
									A							:					str              =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

									A							:					Optional[Any]              =						None
									if self.use_labels:
												A							:					Any              =						ids_tensor([self.batch_size]	,	self.num_labels )

									A							:					List[Any]              =						self.get_config()
									return config, pixel_values, labels







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				int:
									return RegNetConfig(
									    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	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Tuple:
									A							:					List[str]              =						TFRegNetModel(config=__lowerCamelCase )
									A							:					str              =						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 // 32, self.image_size // 32)	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Tuple )      ->				List[str]:
									A							:					List[Any]              =						self.num_labels
									A							:					int              =						TFRegNetForImageClassification(__lowerCamelCase )
									A							:					str              =						model(__lowerCamelCase	,	labels=__lowerCamelCase	,	training=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_labels) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Dict:
									A							:					Any              =						self.prepare_config_and_inputs()
									A       , A       , A							:					str              =						config_and_inputs
									A							:					Any              =						{"pixel_values": pixel_values}
									return config, inputs_dict







@require_tf
class 	lowerCamelCase_    ( _A						,_A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
						a__   =  (
						    {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						a__   =  False
						a__   =  False
						a__   =  False
						a__   =  False
						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									A							:					Optional[Any]              =						TFRegNetModelTester(self )
									A							:					int              =						ConfigTester(self	,	config_class=__lowerCamelCase	,	has_text_modality=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Tuple:
									return







						@unittest.skip(reason="RegNet does not use inputs_embeds" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Optional[Any]:
									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 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Any:
									super().test_keras_fit()







						@unittest.skip(reason="RegNet does not support input and output embeddings" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Union[str, Any]:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Tuple:
									A       , A							:					List[str]              =						self.model_tester.prepare_config_and_inputs_for_common()

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

												A							:					Optional[int]              =						["pixel_values"]
												self.assertListEqual(arg_names[:1]	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Tuple:
									A							:					Tuple              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									def check_hidden_states_output(__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			str ):
												A							:					int              =						model_class(__lowerCamelCase )
												A							:					int              =						model(**self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )	,	training=__lowerCamelCase )

												A							:					Any              =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												A							:					Dict              =						self.model_tester.num_stages
												self.assertEqual(len(__lowerCamelCase )	,	expected_num_stages + 1 )

												# RegNet'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 // 2, self.model_tester.image_size // 2]	,	)

									A       , A							:					int              =						self.model_tester.prepare_config_and_inputs_for_common()
									A							:					str              =						["basic", "bottleneck"]
									for model_class in self.all_model_classes:
												for layer_type in layers_type:
															A							:					List[str]              =						layer_type
															A							:					List[Any]              =						True
															check_hidden_states_output(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															A							:					Union[str, Any]              =						True

															check_hidden_states_output(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
									A       , A							:					Tuple              =						self.model_tester.prepare_config_and_inputs_for_common()

									def check_equivalence(__lowerCamelCase      :			int	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Optional[Any]={} ):
												A							:					Optional[int]              =						model(__lowerCamelCase	,	return_dict=__lowerCamelCase	,	**__lowerCamelCase )
												A							:					int              =						model(__lowerCamelCase	,	return_dict=__lowerCamelCase	,	**__lowerCamelCase ).to_tuple()

												def recursive_check(__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			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:
												A							:					Tuple              =						model_class(__lowerCamelCase )

												A							:					Optional[int]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Tuple              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )
												check_equivalence(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )

												A							:					Optional[int]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												A							:					List[Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												check_equivalence(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )

												A							:					Optional[Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )
												A							:					str              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )
												check_equivalence(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	{"output_hidden_states": True} )

												A							:					Optional[Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												A							:					Tuple              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												check_equivalence(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	{"output_hidden_states": True} )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Optional[Any]:
									A							:					List[str]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Union[str, Any]:
									for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A							:					Union[str, Any]              =						TFRegNetModel.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )







def 	UpperCAmelCase   (						):
			A							:					Dict              =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
			return image






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






						'''simple docstring'''







						@cached_property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Optional[Any]:
									return (
									    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
									    if is_vision_available()
									    else None
									)







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[Any]:
									A							:					List[Any]              =						TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

									A							:					Optional[int]              =						self.default_image_processor
									A							:					List[Any]              =						prepare_img()
									A							:					str              =						image_processor(images=__lowerCamelCase	,	return_tensors="tf" )

									# forward pass
									A							:					List[Any]              =						model(**__lowerCamelCase	,	training=__lowerCamelCase )

									# verify the logits
									A							:					Dict              =						tf.TensorShape((1, 10_00) )
									self.assertEqual(outputs.logits.shape	,	__lowerCamelCase )

									A							:					Optional[Any]              =						tf.constant([-0.4180, -1.5051, -3.4836] )

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