

'''simple docstring'''



import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)
def 		__lowerCamelCase			(  __snake_case	:						Union[str, Any]			)			->				Any:






						"""simple docstring"""





						A__	:  Any           =VideoMAEConfig()

						set_architecture_configs(UpperCAmelCase__,						UpperCAmelCase__			)

						if "finetuned" not in model_name:
												A__	:  Any           =False

						if "finetuned" in model_name:
												A__	:  Tuple           ="""huggingface/label-files"""
												if "kinetics" in model_name:
																		A__	:  Any           =400
																		A__	:  Dict           ="""kinetics400-id2label.json"""
												elif "ssv2" in model_name:
																		A__	:  str           =174
																		A__	:  Tuple           ="""something-something-v2-id2label.json"""
												else:
																		raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."""			)
												A__	:  Any           =json.load(open(hf_hub_download(UpperCAmelCase__,						UpperCAmelCase__,						repo_type="""dataset"""			),						"""r"""			)			)
												A__	:  str           ={int(UpperCAmelCase__			): v for k, v in idalabel.items()}
												A__	:  str           =idalabel
												A__	:  int           ={v: k for k, v in idalabel.items()}

						return config
def 		__lowerCamelCase			(  __snake_case	:						int,						__snake_case	:						Any			)			->				int:






						"""simple docstring"""





						if "small" in model_name:
												A__	:  Dict           =384
												A__	:  Union[str, Any]           =1_536
												A__	:  List[str]           =12
												A__	:  int           =16
												A__	:  List[Any]           =12
												A__	:  int           =3
												A__	:  int           =192
												A__	:  Union[str, Any]           =768
						elif "large" in model_name:
												A__	:  List[Any]           =1_024
												A__	:  Optional[Any]           =4_096
												A__	:  Dict           =24
												A__	:  Optional[int]           =16
												A__	:  Union[str, Any]           =12
												A__	:  List[Any]           =8
												A__	:  List[str]           =512
												A__	:  str           =2_048
						elif "huge" in model_name:
												A__	:  List[Any]           =1_280
												A__	:  Dict           =5_120
												A__	:  Optional[Any]           =32
												A__	:  Tuple           =16
												A__	:  Dict           =12
												A__	:  Union[str, Any]           =8
												A__	:  Optional[Any]           =640
												A__	:  Any           =2_560
						elif "base" not in model_name:
												raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""			)
def 		__lowerCamelCase			(  __snake_case	:						Dict			)			->				Dict:






						"""simple docstring"""





						if "encoder." in name:
												A__	:  List[str]           =name.replace("""encoder.""",						""""""			)
						if "cls_token" in name:
												A__	:  List[Any]           =name.replace("""cls_token""",						"""videomae.embeddings.cls_token"""			)
						if "decoder_pos_embed" in name:
												A__	:  List[Any]           =name.replace("""decoder_pos_embed""",						"""decoder.decoder_pos_embed"""			)
						if "pos_embed" in name and "decoder" not in name:
												A__	:  Union[str, Any]           =name.replace("""pos_embed""",						"""videomae.embeddings.position_embeddings"""			)
						if "patch_embed.proj" in name:
												A__	:  str           =name.replace("""patch_embed.proj""",						"""videomae.embeddings.patch_embeddings.projection"""			)
						if "patch_embed.norm" in name:
												A__	:  Optional[Any]           =name.replace("""patch_embed.norm""",						"""videomae.embeddings.norm"""			)
						if "decoder.blocks" in name:
												A__	:  Optional[int]           =name.replace("""decoder.blocks""",						"""decoder.decoder_layers"""			)
						if "blocks" in name:
												A__	:  List[str]           =name.replace("""blocks""",						"""videomae.encoder.layer"""			)
						if "attn.proj" in name:
												A__	:  List[Any]           =name.replace("""attn.proj""",						"""attention.output.dense"""			)
						if "attn" in name and "bias" not in name:
												A__	:  List[str]           =name.replace("""attn""",						"""attention.self"""			)
						if "attn" in name:
												A__	:  Tuple           =name.replace("""attn""",						"""attention.attention"""			)
						if "norm1" in name:
												A__	:  List[Any]           =name.replace("""norm1""",						"""layernorm_before"""			)
						if "norm2" in name:
												A__	:  Dict           =name.replace("""norm2""",						"""layernorm_after"""			)
						if "mlp.fc1" in name:
												A__	:  Optional[Any]           =name.replace("""mlp.fc1""",						"""intermediate.dense"""			)
						if "mlp.fc2" in name:
												A__	:  List[str]           =name.replace("""mlp.fc2""",						"""output.dense"""			)
						if "decoder_embed" in name:
												A__	:  Any           =name.replace("""decoder_embed""",						"""decoder.decoder_embed"""			)
						if "decoder_norm" in name:
												A__	:  str           =name.replace("""decoder_norm""",						"""decoder.decoder_norm"""			)
						if "decoder_pred" in name:
												A__	:  int           =name.replace("""decoder_pred""",						"""decoder.decoder_pred"""			)
						if "norm.weight" in name and "decoder" not in name and "fc" not in name:
												A__	:  Optional[Any]           =name.replace("""norm.weight""",						"""videomae.layernorm.weight"""			)
						if "norm.bias" in name and "decoder" not in name and "fc" not in name:
												A__	:  Any           =name.replace("""norm.bias""",						"""videomae.layernorm.bias"""			)
						if "head" in name and "decoder" not in name:
												A__	:  Dict           =name.replace("""head""",						"""classifier"""			)

						return name
def 		__lowerCamelCase			(  __snake_case	:						Optional[int],						__snake_case	:						List[Any]			)			->				Union[str, Any]:






						"""simple docstring"""





						for key in orig_state_dict.copy().keys():
												A__	:  int           =orig_state_dict.pop(UpperCAmelCase__			)

												if key.startswith("""encoder."""			):
																		A__	:  int           =key.replace("""encoder.""",						""""""			)

												if "qkv" in key:
																		A__	:  Optional[Any]           =key.split("""."""			)
																		if key.startswith("""decoder.blocks"""			):
																								A__	:  Union[str, Any]           =config.decoder_hidden_size
																								A__	:  List[str]           =int(key_split[2]			)
																								A__	:  Dict           ="""decoder.decoder_layers."""
																								if "weight" in key:
																														A__	:  Any           =val[:dim, :]
																														A__	:  Dict           =val[dim : dim * 2, :]
																														A__	:  Optional[Any]           =val[-dim:, :]
																		else:
																								A__	:  List[Any]           =config.hidden_size
																								A__	:  List[Any]           =int(key_split[1]			)
																								A__	:  List[str]           ="""videomae.encoder.layer."""
																								if "weight" in key:
																														A__	:  Tuple           =val[:dim, :]
																														A__	:  Dict           =val[dim : dim * 2, :]
																														A__	:  str           =val[-dim:, :]
												else:
																		A__	:  Dict           =val

						return orig_state_dict
def 		__lowerCamelCase			(  )			->				int:






						"""simple docstring"""





						A__	:  List[Any]           =hf_hub_download(
						    repo_id="""hf-internal-testing/spaghetti-video""",						filename="""eating_spaghetti.npy""",						repo_type="""dataset"""			)
						A__	:  List[Any]           =np.load(UpperCAmelCase__			)
						return list(UpperCAmelCase__			)
def 		__lowerCamelCase			(  __snake_case	:						Union[str, Any],						__snake_case	:						int,						__snake_case	:						int,						__snake_case	:						Any			)			->				Union[str, Any]:






						"""simple docstring"""





						A__	:  str           =get_videomae_config(UpperCAmelCase__			)

						if "finetuned" in model_name:
												A__	:  Dict           =VideoMAEForVideoClassification(UpperCAmelCase__			)
						else:
												A__	:  Any           =VideoMAEForPreTraining(UpperCAmelCase__			)

						# download original checkpoint, hosted on Google Drive
						A__	:  str           ="""pytorch_model.bin"""
						gdown.cached_download(UpperCAmelCase__,						UpperCAmelCase__,						quiet=UpperCAmelCase__			)
						A__	:  Optional[Any]           =torch.load(UpperCAmelCase__,						map_location="""cpu"""			)
						if "model" in files:
												A__	:  Tuple           =files["""model"""]
						else:
												A__	:  Optional[Any]           =files["""module"""]
						A__	:  Tuple           =convert_state_dict(UpperCAmelCase__,						UpperCAmelCase__			)

						model.load_state_dict(UpperCAmelCase__			)
						model.eval()

						# verify model on basic input
						A__	:  Dict           =VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5],						image_std=[0.5, 0.5, 0.5]			)
						A__	:  List[str]           =prepare_video()
						A__	:  Any           =image_processor(UpperCAmelCase__,						return_tensors="""pt"""			)

						if "finetuned" not in model_name:
												A__	:  Optional[int]           =hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""",						filename="""bool_masked_pos.pt"""			)
												A__	:  Union[str, Any]           =torch.load(UpperCAmelCase__			)

						A__	:  Optional[Any]           =model(**UpperCAmelCase__			)
						A__	:  int           =outputs.logits

						A__	:  Dict           =[
						    """videomae-small-finetuned-kinetics""",
						    """videomae-small-finetuned-ssv2""",
						    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
						    """videomae-base-short""",
						    """videomae-base-short-finetuned-kinetics""",
						    """videomae-base""",
						    """videomae-base-finetuned-kinetics""",
						    """videomae-large""",
						    """videomae-large-finetuned-kinetics""",
						    """videomae-huge-finetuned-kinetics""",
						    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
						    """videomae-base-short-ssv2""",
						    """videomae-base-short-finetuned-ssv2""",
						    """videomae-base-ssv2""",
						    """videomae-base-finetuned-ssv2""",
						]

						# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
						if model_name == "videomae-small-finetuned-kinetics":
												A__	:  Tuple           =torch.Size([1, 400]			)
												A__	:  Dict           =torch.tensor([-0.92_91, -0.40_61, -0.93_07]			)
						elif model_name == "videomae-small-finetuned-ssv2":
												A__	:  Optional[Any]           =torch.Size([1, 174]			)
												A__	:  Union[str, Any]           =torch.tensor([0.26_71, -0.46_89, -0.82_35]			)
						elif model_name == "videomae-base":
												A__	:  List[str]           =torch.Size([1, 1_408, 1_536]			)
												A__	:  Any           =torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]]			)
						elif model_name == "videomae-base-short":
												A__	:  int           =torch.Size([1, 1_408, 1_536]			)
												A__	:  List[str]           =torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]]			)
												# we verified the loss both for normalized and unnormalized targets for this one
												A__	:  Any           =torch.tensor([0.51_42]			) if config.norm_pix_loss else torch.tensor([0.64_69]			)
						elif model_name == "videomae-large":
												A__	:  List[str]           =torch.Size([1, 1_408, 1_536]			)
												A__	:  str           =torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]]			)
						elif model_name == "videomae-large-finetuned-kinetics":
												A__	:  List[str]           =torch.Size([1, 400]			)
												A__	:  int           =torch.tensor([0.07_71, 0.00_11, -0.36_25]			)
						elif model_name == "videomae-huge-finetuned-kinetics":
												A__	:  Optional[int]           =torch.Size([1, 400]			)
												A__	:  Dict           =torch.tensor([0.24_33, 0.16_32, -0.48_94]			)
						elif model_name == "videomae-base-short-finetuned-kinetics":
												A__	:  Union[str, Any]           =torch.Size([1, 400]			)
												A__	:  Dict           =torch.tensor([0.65_88, 0.09_90, -0.24_93]			)
						elif model_name == "videomae-base-finetuned-kinetics":
												A__	:  Any           =torch.Size([1, 400]			)
												A__	:  Union[str, Any]           =torch.tensor([0.36_69, -0.06_88, -0.24_21]			)
						elif model_name == "videomae-base-short-ssv2":
												A__	:  List[str]           =torch.Size([1, 1_408, 1_536]			)
												A__	:  Union[str, Any]           =torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]]			)
						elif model_name == "videomae-base-short-finetuned-ssv2":
												A__	:  List[Any]           =torch.Size([1, 174]			)
												A__	:  List[Any]           =torch.tensor([-0.05_37, -0.15_39, -0.32_66]			)
						elif model_name == "videomae-base-ssv2":
												A__	:  int           =torch.Size([1, 1_408, 1_536]			)
												A__	:  Any           =torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]]			)
						elif model_name == "videomae-base-finetuned-ssv2":
												A__	:  Optional[int]           =torch.Size([1, 174]			)
												A__	:  Tuple           =torch.tensor([0.19_61, -0.83_37, -0.63_89]			)
						else:
												raise ValueError(f"Model name not supported. Should be one of {model_names}"			)

						# verify logits
						assert logits.shape == expected_shape
						if "finetuned" in model_name:
												assert torch.allclose(logits[0, :3],						UpperCAmelCase__,						atol=1E-4			)
						else:
												print("""Logits:""",						logits[0, :3, :3]			)
												assert torch.allclose(logits[0, :3, :3],						UpperCAmelCase__,						atol=1E-4			)
						print("""Logits ok!"""			)

						# verify loss, if applicable
						if model_name == "videomae-base-short":
												A__	:  Optional[int]           =outputs.loss
												assert torch.allclose(UpperCAmelCase__,						UpperCAmelCase__,						atol=1E-4			)
												print("""Loss ok!"""			)

						if pytorch_dump_folder_path is not None:
												print(f"Saving model and image processor to {pytorch_dump_folder_path}"			)
												image_processor.save_pretrained(UpperCAmelCase__			)
												model.save_pretrained(UpperCAmelCase__			)

						if push_to_hub:
												print("""Pushing to the hub..."""			)
												model.push_to_hub(UpperCAmelCase__,						organization="""nielsr"""			)


if __name__ == "__main__":
		__snake_case					:      Tuple 		=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--checkpoint_url',
		    default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
		    type=str,
		    help=(
		        'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
		        ' download link.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default='/Users/nielsrogge/Documents/VideoMAE/Test',
		    type=str,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
		)

		__snake_case					:      str 		=     parser.parse_args()
		convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)



import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
		import torch
		from torch import nn

		from transformers import (
		    FocalNetBackbone,
		    FocalNetForImageClassification,
		    FocalNetForMaskedImageModeling,
		    FocalNetModel,
		)
		from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor





class    __A:

					def __init__(						self       :  Tuple ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  Optional[Any]=1_3 ,       __UpperCamelCase       :  str=3_2 ,       __UpperCamelCase       :  List[Any]=2 ,       __UpperCamelCase       :  Tuple=3 ,       __UpperCamelCase       :  List[Any]=1_6 ,       __UpperCamelCase       :  int=[3_2, 6_4, 1_2_8] ,       __UpperCamelCase       :  Tuple=[1, 2, 1] ,       __UpperCamelCase       :  List[Any]=[2, 2, 4] ,       __UpperCamelCase       :  int=2 ,       __UpperCamelCase       :  Tuple=2.0 ,       __UpperCamelCase       :  List[str]=True ,       __UpperCamelCase       :  List[Any]=0.0 ,       __UpperCamelCase       :  List[Any]=0.0 ,       __UpperCamelCase       :  Tuple=0.1 ,       __UpperCamelCase       :  Any="gelu" ,       __UpperCamelCase       :  int=False ,       __UpperCamelCase       :  List[Any]=True ,       __UpperCamelCase       :  Tuple=0.02 ,       __UpperCamelCase       :  Union[str, Any]=1E-5 ,       __UpperCamelCase       :  Dict=True ,       __UpperCamelCase       :  List[Any]=None ,       __UpperCamelCase       :  Optional[Any]=True ,       __UpperCamelCase       :  Optional[int]=1_0 ,       __UpperCamelCase       :  Union[str, Any]=8 ,       __UpperCamelCase       :  Optional[Any]=["stage1", "stage2"] ,       __UpperCamelCase       :  Tuple=[1, 2] ,       ):
												lowerCamelCase_					=       parent
												lowerCamelCase_					=       batch_size
												lowerCamelCase_					=       image_size
												lowerCamelCase_					=       patch_size
												lowerCamelCase_					=       num_channels
												lowerCamelCase_					=       embed_dim
												lowerCamelCase_					=       hidden_sizes
												lowerCamelCase_					=       depths
												lowerCamelCase_					=       num_heads
												lowerCamelCase_					=       window_size
												lowerCamelCase_					=       mlp_ratio
												lowerCamelCase_					=       qkv_bias
												lowerCamelCase_					=       hidden_dropout_prob
												lowerCamelCase_					=       attention_probs_dropout_prob
												lowerCamelCase_					=       drop_path_rate
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       use_absolute_embeddings
												lowerCamelCase_					=       patch_norm
												lowerCamelCase_					=       layer_norm_eps
												lowerCamelCase_					=       initializer_range
												lowerCamelCase_					=       is_training
												lowerCamelCase_					=       scope
												lowerCamelCase_					=       use_labels
												lowerCamelCase_					=       type_sequence_label_size
												lowerCamelCase_					=       encoder_stride
												lowerCamelCase_					=       out_features
												lowerCamelCase_					=       out_indices

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

												lowerCamelCase_					=       self.get_config()

												return config, pixel_values, labels

					def  lowercase__   (						self       :  Dict ):
												return FocalNetConfig(
												    image_size=self.image_size ,       patch_size=self.patch_size ,       num_channels=self.num_channels ,       embed_dim=self.embed_dim ,       hidden_sizes=self.hidden_sizes ,       depths=self.depths ,       num_heads=self.num_heads ,       window_size=self.window_size ,       mlp_ratio=self.mlp_ratio ,       qkv_bias=self.qkv_bias ,       hidden_dropout_prob=self.hidden_dropout_prob ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob ,       drop_path_rate=self.drop_path_rate ,       hidden_act=self.hidden_act ,       use_absolute_embeddings=self.use_absolute_embeddings ,       path_norm=self.patch_norm ,       layer_norm_eps=self.layer_norm_eps ,       initializer_range=self.initializer_range ,       encoder_stride=self.encoder_stride ,       out_features=self.out_features ,       out_indices=self.out_indices ,       )

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Union[str, Any] ):
												lowerCamelCase_					=       FocalNetModel(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase )

												lowerCamelCase_					=       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
												lowerCamelCase_					=       int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

												self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, expected_seq_len, expected_dim) )

					def  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  Union[str, Any] ):
												lowerCamelCase_					=       FocalNetBackbone(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase )

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps ) ,       len(config.out_features ) )
												self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,       [self.batch_size, self.image_size, 8, 8] )

												# verify channels
												self.parent.assertEqual(len(model.channels ) ,       len(config.out_features ) )
												self.parent.assertListEqual(model.channels ,       config.hidden_sizes[:-1] )

												# verify backbone works with out_features=None
												lowerCamelCase_					=       None
												lowerCamelCase_					=       FocalNetBackbone(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase )

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps ) ,       1 )
												self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,       [self.batch_size, self.image_size * 2, 4, 4] )

												# verify channels
												self.parent.assertEqual(len(model.channels ) ,       1 )
												self.parent.assertListEqual(model.channels ,       [config.hidden_sizes[-1]] )

					def  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Any ):
												lowerCamelCase_					=       FocalNetForMaskedImageModeling(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase )
												self.parent.assertEqual(
												    result.reconstruction.shape ,       (self.batch_size, self.num_channels, self.image_size, self.image_size) )

												# test greyscale images
												lowerCamelCase_					=       1
												lowerCamelCase_					=       FocalNetForMaskedImageModeling(__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()

												lowerCamelCase_					=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
												lowerCamelCase_					=       model(__UpperCamelCase )
												self.parent.assertEqual(result.reconstruction.shape ,       (self.batch_size, 1, self.image_size, self.image_size) )

					def  lowercase__   (						self       :  str ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Union[str, Any] ):
												lowerCamelCase_					=       self.type_sequence_label_size
												lowerCamelCase_					=       FocalNetForImageClassification(__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase ,       labels=__UpperCamelCase )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size) )

												# test greyscale images
												lowerCamelCase_					=       1
												lowerCamelCase_					=       FocalNetForImageClassification(__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()

												lowerCamelCase_					=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
												lowerCamelCase_					=       model(__UpperCamelCase )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size) )



					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       self.prepare_config_and_inputs()

												lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       config_and_inputs
												lowerCamelCase_					=       {"""pixel_values""": pixel_values}
												return config, inputs_dict





@require_torch
class    __A(					UpperCAmelCase  ,		UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     (
					    (
					        FocalNetModel,
					        FocalNetForImageClassification,
					        FocalNetForMaskedImageModeling,
					        FocalNetBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE           =     (
					    {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
					    if is_torch_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE           =     False

					SCREAMING_SNAKE_CASE           =     False
					SCREAMING_SNAKE_CASE           =     False
					SCREAMING_SNAKE_CASE           =     False
					SCREAMING_SNAKE_CASE           =     False

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       FocalNetModelTester(self )
												lowerCamelCase_					=       ConfigTester(self ,       config_class=__UpperCamelCase ,       embed_dim=3_7 ,       has_text_modality=__UpperCamelCase )

					def  lowercase__   (						self       :  Optional[Any] ):
												self.create_and_test_config_common_properties()
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()

					def  lowercase__   (						self       :  Tuple ):
												return

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase )

					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*__UpperCamelCase )

					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase )

					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )

					@unittest.skip(reason="""FocalNet does not use inputs_embeds""" )
					def  lowercase__   (						self       :  str ):
												pass

					@unittest.skip(reason="""FocalNet does not use feedforward chunking""" )
					def  lowercase__   (						self       :  List[str] ):
												pass

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes[:-1]:
																			lowerCamelCase_					=       model_class(__UpperCamelCase )
																			self.assertIsInstance(model.get_input_embeddings() ,       (nn.Module) )
																			lowerCamelCase_					=       model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__UpperCamelCase ,       nn.Linear ) )

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes[:-1]:
																			lowerCamelCase_					=       model_class(__UpperCamelCase )
																			lowerCamelCase_					=       inspect.signature(model.forward )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			lowerCamelCase_					=       [*signature.parameters.keys()]

																			lowerCamelCase_					=       ["""pixel_values"""]
																			self.assertListEqual(arg_names[:1] ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[Any] ):
												lowerCamelCase_					=       model_class(__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()

												with torch.no_grad():
																			lowerCamelCase_					=       model(**self._prepare_for_class(__UpperCamelCase ,       __UpperCamelCase ) )

												lowerCamelCase_					=       outputs.hidden_states

												lowerCamelCase_					=       getattr(
												    self.model_tester ,       """expected_num_hidden_layers""" ,       len(self.model_tester.depths ) + 1 )
												self.assertEqual(len(__UpperCamelCase ) ,       __UpperCamelCase )

												# FocalNet has a different seq_length
												lowerCamelCase_					=       (
												    config.patch_size
												    if isinstance(config.patch_size ,       collections.abc.Iterable )
												    else (config.patch_size, config.patch_size)
												)

												lowerCamelCase_					=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

												self.assertListEqual(
												    list(hidden_states[0].shape[-2:] ) ,       [num_patches, self.model_tester.embed_dim] ,       )

												lowerCamelCase_					=       outputs.reshaped_hidden_states
												self.assertEqual(len(__UpperCamelCase ) ,       __UpperCamelCase )

												lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       reshaped_hidden_states[0].shape
												lowerCamelCase_					=       (
												    reshaped_hidden_states[0].view(__UpperCamelCase ,       __UpperCamelCase ,       height * width ).permute(0 ,       2 ,       1 )
												)
												self.assertListEqual(
												    list(reshaped_hidden_states.shape[-2:] ) ,       [num_patches, self.model_tester.embed_dim] ,       )

					def  lowercase__   (						self       :  int ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

												lowerCamelCase_					=       (
												    self.model_tester.image_size
												    if isinstance(self.model_tester.image_size ,       collections.abc.Iterable )
												    else (self.model_tester.image_size, self.model_tester.image_size)
												)

												for model_class in self.all_model_classes[:-1]:
																			lowerCamelCase_					=       True
																			self.check_hidden_states_output(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

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

																			self.check_hidden_states_output(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()
												lowerCamelCase_					=       3

												lowerCamelCase_					=       (
												    self.model_tester.image_size
												    if isinstance(self.model_tester.image_size ,       collections.abc.Iterable )
												    else (self.model_tester.image_size, self.model_tester.image_size)
												)
												lowerCamelCase_					=       (
												    config.patch_size
												    if isinstance(config.patch_size ,       collections.abc.Iterable )
												    else (config.patch_size, config.patch_size)
												)

												lowerCamelCase_					=       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
												lowerCamelCase_					=       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

												for model_class in self.all_model_classes[:-1]:
																			lowerCamelCase_					=       True
																			self.check_hidden_states_output(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       (padded_height, padded_width) )

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			lowerCamelCase_					=       True
																			self.check_hidden_states_output(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       (padded_height, padded_width) )

					@slow
					def  lowercase__   (						self       :  Dict ):
												for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_					=       FocalNetModel.from_pretrained(__UpperCamelCase )
																			self.assertIsNotNone(__UpperCamelCase )



					def  lowercase__   (						self       :  int ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

												lowerCamelCase_					=       _config_zero_init(__UpperCamelCase )
												for model_class in self.all_model_classes:
																			lowerCamelCase_					=       model_class(config=__UpperCamelCase )
																			for name, param in model.named_parameters():
																										if "embeddings" not in name and param.requires_grad:
																																	self.assertIn(
																																	    ((param.data.mean() * 1E9).round() / 1E9).item() ,       [0.0, 1.0] ,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,       )





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

					@cached_property
					def  lowercase__   (						self       :  int ):
												# TODO update organization
												return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny""" ) if is_vision_available() else None



					@slow
					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny""" ).to(__UpperCamelCase )
												lowerCamelCase_					=       self.default_image_processor

												lowerCamelCase_					=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
												lowerCamelCase_					=       image_processor(images=__UpperCamelCase ,       return_tensors="""pt""" ).to(__UpperCamelCase )

												# forward pass
												with torch.no_grad():
																			lowerCamelCase_					=       model(**__UpperCamelCase )

												# verify the logits
												lowerCamelCase_					=       torch.Size((1, 1_0_0_0) )
												self.assertEqual(outputs.logits.shape ,       __UpperCamelCase )
												lowerCamelCase_					=       torch.tensor([0.2166, -0.4368, 0.2191] ).to(__UpperCamelCase )
												self.assertTrue(torch.allclose(outputs.logits[0, :3] ,       __UpperCamelCase ,       atol=1E-4 ) )
												self.assertTrue(outputs.logits.argmax(dim=-1 ).item() ,       2_8_1 )





@require_torch
class    __A(					UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     (FocalNetBackbone,) if is_torch_available() else ()
					SCREAMING_SNAKE_CASE           =     FocalNetConfig

					SCREAMING_SNAKE_CASE           =     False

					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_					=       FocalNetModelTester(self )





