
"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__           =	logging.get_logger(__name__)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		List[Any]		    =   []

	# fmt: off
	# stem:
	rename_keys.append(('cls_token', 'vit.embeddings.cls_token')	)
	rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')	)

	rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')	)
	rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')	)

	# backbone
	rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')	)
	rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')	)
	rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')	)

	for stage_idx in range(len(config.backbone_config.depths	)	):
		for layer_idx in range(config.backbone_config.depths[stage_idx]	):
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")	)
			rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")	)

		rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")	)
		rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")	)
		rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")	)

	# transformer encoder
	for i in range(config.num_hidden_layers	):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F"blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")	)
		rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")	)
		rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")	)
		rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")	)
		rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")	)
		rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")	)
		rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")	)
		rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")	)
		rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")	)
		rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")	)

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ('norm.weight', 'layernorm.weight'),
		        ('norm.bias', 'layernorm.bias'),
		        ('pre_logits.fc.weight', 'pooler.dense.weight'),
		        ('pre_logits.fc.bias', 'pooler.dense.bias'),
		    ]	)

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		__lowerCAmelCase    :		Optional[Any]		    =   [(pair[0], pair[1][4:]) if pair[1].startswith('vit'	) else pair for pair in rename_keys]
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ('norm.weight', 'vit.layernorm.weight'),
		        ('norm.bias', 'vit.layernorm.bias'),
		        ('head.weight', 'classifier.weight'),
		        ('head.bias', 'classifier.bias'),
		    ]	)
	# fmt: on

	return rename_keys
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False	):
	for i in range(config.num_hidden_layers	):
		if base_model:
			__lowerCAmelCase    :		Tuple		    =   ''
		else:
			__lowerCAmelCase    :		int		    =   'vit.'
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		__lowerCAmelCase    :		int		    =   state_dict.pop(F"blocks.{i}.attn.qkv.weight"	)
		__lowerCAmelCase    :		Dict		    =   state_dict.pop(F"blocks.{i}.attn.qkv.bias"	)
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase    :		Tuple		    =   in_proj_weight[
		    : config.hidden_size, :
		]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias[: config.hidden_size]
		__lowerCAmelCase    :		List[Any]		    =   in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		__lowerCAmelCase    :		str		    =   in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_weight[
		    -config.hidden_size :, :
		]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias[-config.hidden_size :]
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   ['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(_UpperCamelCase ,						_UpperCamelCase	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		int		    =   dct.pop(_UpperCamelCase	)
	__lowerCAmelCase    :		List[Any]		    =   val
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Optional[int]		    =   'http://images.cocodataset.org/val2017/000000039769.jpg'
	__lowerCAmelCase    :		Optional[Any]		    =   Image.open(requests.get(_UpperCamelCase ,						stream=_UpperCamelCase	).raw	)
	return im
@torch.no_grad()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		List[str]		    =   BitConfig(
	    global_padding='same' ,						layer_type='bottleneck' ,						depths=(3, 4, 9) ,						out_features=['stage3'] ,						embedding_dynamic_padding=_UpperCamelCase ,						)
	__lowerCAmelCase    :		Tuple		    =   ViTHybridConfig(backbone_config=_UpperCamelCase ,						image_size=384 ,						num_labels=1000	)
	__lowerCAmelCase    :		Optional[Any]		    =   False

	# load original model from timm
	__lowerCAmelCase    :		Tuple		    =   timm.create_model(_UpperCamelCase ,						pretrained=_UpperCamelCase	)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__lowerCAmelCase    :		str		    =   timm_model.state_dict()
	if base_model:
		remove_classification_head_(_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   create_rename_keys(_UpperCamelCase ,						_UpperCamelCase	)
	for src, dest in rename_keys:
		rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
	read_in_q_k_v(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)

	__lowerCAmelCase    :		int		    =   'huggingface/label-files'
	__lowerCAmelCase    :		str		    =   'imagenet-1k-id2label.json'
	__lowerCAmelCase    :		Any		    =   json.load(open(hf_hub_download(_UpperCamelCase ,						_UpperCamelCase ,						repo_type='dataset'	) ,						'r'	)	)
	__lowerCAmelCase    :		int		    =   {int(_UpperCamelCase	): v for k, v in idalabel.items()}
	__lowerCAmelCase    :		List[str]		    =   idalabel
	__lowerCAmelCase    :		Optional[Any]		    =   {v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__lowerCAmelCase    :		str		    =   ViTHybridModel(_UpperCamelCase	).eval()
	else:
		__lowerCAmelCase    :		Optional[Any]		    =   ViTHybridForImageClassification(_UpperCamelCase	).eval()
	model.load_state_dict(_UpperCamelCase	)

	# create image processor
	__lowerCAmelCase    :		Union[str, Any]		    =   create_transform(**resolve_data_config({} ,						model=_UpperCamelCase	)	)
	__lowerCAmelCase    :		Optional[int]		    =   transform.transforms

	__lowerCAmelCase    :		List[str]		    =   {
	    'bilinear': PILImageResampling.BILINEAR,
	    'bicubic': PILImageResampling.BICUBIC,
	    'nearest': PILImageResampling.NEAREST,
	}

	__lowerCAmelCase    :		List[str]		    =   ViTHybridImageProcessor(
	    do_resize=_UpperCamelCase ,						size={'shortest_edge': timm_transforms[0].size} ,						resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,						do_center_crop=_UpperCamelCase ,						crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]} ,						do_normalize=_UpperCamelCase ,						image_mean=timm_transforms[-1].mean.tolist() ,						image_std=timm_transforms[-1].std.tolist() ,						)

	__lowerCAmelCase    :		Optional[Any]		    =   prepare_img()
	__lowerCAmelCase    :		Union[str, Any]		    =   transform(_UpperCamelCase	).unsqueeze(0	)
	__lowerCAmelCase    :		Union[str, Any]		    =   processor(_UpperCamelCase ,						return_tensors='pt'	).pixel_values

	# verify pixel values
	assert torch.allclose(_UpperCamelCase ,						_UpperCamelCase	)

	# verify logits
	with torch.no_grad():
		__lowerCAmelCase    :		Any		    =   model(_UpperCamelCase	)
		__lowerCAmelCase    :		List[Any]		    =   outputs.logits

	print('Predicted class:' ,						logits.argmax(-1	).item()	)
	if base_model:
		__lowerCAmelCase    :		Optional[int]		    =   timm_model.forward_features(_UpperCamelCase	)
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(_UpperCamelCase ,						outputs.pooler_output ,						atol=1e-3	)
	else:
		__lowerCAmelCase    :		List[str]		    =   timm_model(_UpperCamelCase	)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(_UpperCamelCase ,						outputs.logits ,						atol=1e-3	)
	print('Looks ok!'	)

	if pytorch_dump_folder_path is not None:
		Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
		print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"	)
		model.save_pretrained(_UpperCamelCase	)
		print(F"Saving processor to {pytorch_dump_folder_path}"	)
		processor.save_pretrained(_UpperCamelCase	)

	if push_to_hub:
		print(F"Pushing model and processor to the hub {vit_name}"	)
		model.push_to_hub(F"ybelkada/{vit_name}"	)
		processor.push_to_hub(F"ybelkada/{vit_name}"	)


if __name__ == "__main__":
							lowerCamelCase__           =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--vit_name""",
							    default="""vit_base_r50_s16_384""",
							    type=str,
							    help="""Name of the hybrid ViT timm 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 to upload the model to the HuggingFace hub."""
							)

							lowerCamelCase__           =	parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""







import inspect
import unittest

from transformers import RegNetConfig
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 torch import nn

							from transformers import RegNetForImageClassification, RegNetModel
							from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class 				A__ :

		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 ,	):
			__lowerCAmelCase    :		Optional[Any]		    =   parent
			__lowerCAmelCase    :		int		    =   batch_size
			__lowerCAmelCase    :		Union[str, Any]		    =   image_size
			__lowerCAmelCase    :		Optional[Any]		    =   num_channels
			__lowerCAmelCase    :		List[Any]		    =   embeddings_size
			__lowerCAmelCase    :		Dict		    =   hidden_sizes
			__lowerCAmelCase    :		Optional[int]		    =   depths
			__lowerCAmelCase    :		Optional[Any]		    =   is_training
			__lowerCAmelCase    :		int		    =   use_labels
			__lowerCAmelCase    :		List[Any]		    =   hidden_act
			__lowerCAmelCase    :		Optional[Any]		    =   num_labels
			__lowerCAmelCase    :		List[str]		    =   scope
			__lowerCAmelCase    :		Optional[Any]		    =   len(_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

			__lowerCAmelCase    :		int		    =   self.get_config()

			return config, pixel_values, labels

		def 							__lowerCamelCase							(   self				):
			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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Any		    =   RegNetModel(config=_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		Any		    =   model(_SCREAMING_SNAKE_CASE				)
			# 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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.num_labels
			__lowerCAmelCase    :		str		    =   RegNetForImageClassification(_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		Any		    =   model(_SCREAMING_SNAKE_CASE ,	labels=_SCREAMING_SNAKE_CASE				)
			self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.num_labels)				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   config_and_inputs
			__lowerCAmelCase    :		List[str]		    =   {'pixel_values': pixel_values}
			return config, inputs_dict




@require_torch
class 				A__ (			_lowerCamelCase ,  _lowerCamelCase ,  unittest.TestCase):
		A_		: List[str]            =			(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
		A_		: List[Any]            =			(
		    {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
		    if is_torch_available()
		    else {}
		)

		A_		: Dict            =			False
		A_		: str            =			False
		A_		: int            =			False
		A_		: Union[str, Any]            =			False

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   RegNetModelTester(self				)
			__lowerCAmelCase    :		int		    =   ConfigTester(self ,	config_class=_SCREAMING_SNAKE_CASE ,	has_text_modality=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			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 							__lowerCamelCase							(   self				):
			return

		@unittest.skip(reason='RegNet does not use inputs_embeds'				)
		def 							__lowerCamelCase							(   self				):
			pass

		@unittest.skip(reason='RegNet does not support input and output embeddings'				)
		def 							__lowerCamelCase							(   self				):
			pass

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase    :		Optional[int]		    =   model_class(_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Dict		    =   inspect.signature(model.forward				)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				__lowerCAmelCase    :		Tuple		    =   [*signature.parameters.keys()]

				__lowerCAmelCase    :		Optional[int]		    =   ['pixel_values']
				self.assertListEqual(arg_names[:1] ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[str]		    =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase    :		Union[str, Any]		    =   model_class(config=_SCREAMING_SNAKE_CASE				)
				for name, module in model.named_modules():
					if isinstance(_SCREAMING_SNAKE_CASE ,	(nn.BatchNormad, nn.GroupNorm)				):
						self.assertTrue(
						    torch.all(module.weight == 1				) ,	msg=f"Parameter {name} of model {model_class} seems not properly initialized" ,	)
						self.assertTrue(
						    torch.all(module.bias == 0				) ,	msg=f"Parameter {name} of model {model_class} seems not properly initialized" ,	)

		def 							__lowerCamelCase							(   self				):
			def check_hidden_states_output(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Any		    =   model_class(_SCREAMING_SNAKE_CASE				)
				model.to(_SCREAMING_SNAKE_CASE				)
				model.eval()

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

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

				__lowerCAmelCase    :		List[str]		    =   self.model_tester.num_stages
				self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	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] ,	)

			__lowerCAmelCase    ,     __lowerCAmelCase    :		Dict		    =   self.model_tester.prepare_config_and_inputs_for_common()
			__lowerCAmelCase    :		List[Any]		    =   ['basic', 'bottleneck']
			for model_class in self.all_model_classes:
				for layer_type in layers_type:
					__lowerCAmelCase    :		List[Any]		    =   layer_type
					__lowerCAmelCase    :		Tuple		    =   True
					check_hidden_states_output(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					__lowerCAmelCase    :		Dict		    =   True

					check_hidden_states_output(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)







		@slow
		def 							__lowerCamelCase							(   self				):
			for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__lowerCAmelCase    :		Dict		    =   RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)



def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		str		    =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image




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

		@cached_property
		def 							__lowerCamelCase							(   self				):
			return (
			    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
			    if is_vision_available()
			    else None
			)







		@slow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   self.default_image_processor
			__lowerCAmelCase    :		Any		    =   prepare_img()
			__lowerCAmelCase    :		Any		    =   image_processor(images=_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				).to(_SCREAMING_SNAKE_CASE				)

			# forward pass
			with torch.no_grad():
				__lowerCAmelCase    :		List[str]		    =   model(**_SCREAMING_SNAKE_CASE				)

			# verify the logits
			__lowerCAmelCase    :		Union[str, Any]		    =   torch.Size((1, 10_00)				)
			self.assertEqual(outputs.logits.shape ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   torch.tensor([-0.4180, -1.5051, -3.4836]				).to(_SCREAMING_SNAKE_CASE				)

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