


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
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.models.swin.modeling_tf_swin import (
				    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSwinForImageClassification,
				    TFSwinForMaskedImageModeling,
				    TFSwinModel,
				)


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor







class      __lowercase  :





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__=1_3       ,		lowerCAmelCase__=3_2       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=3       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=[1, 2, 1]       ,		lowerCAmelCase__=[2, 2, 4]       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=2.0       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=0.0       ,		lowerCAmelCase__=0.0       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__="gelu"       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=0.02       ,		lowerCAmelCase__=1E-5       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=1_0       ,		lowerCAmelCase__=8       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= parent
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= batch_size
										SCREAMING_SNAKE_CASE_						:			Tuple       						= image_size
										SCREAMING_SNAKE_CASE_						:			Tuple       						= patch_size
										SCREAMING_SNAKE_CASE_						:			List[str]       						= num_channels
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= embed_dim
										SCREAMING_SNAKE_CASE_						:			Any       						= depths
										SCREAMING_SNAKE_CASE_						:			Dict       						= num_heads
										SCREAMING_SNAKE_CASE_						:			int       						= window_size
										SCREAMING_SNAKE_CASE_						:			Dict       						= mlp_ratio
										SCREAMING_SNAKE_CASE_						:			str       						= qkv_bias
										SCREAMING_SNAKE_CASE_						:			List[str]       						= hidden_dropout_prob
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_						:			str       						= drop_path_rate
										SCREAMING_SNAKE_CASE_						:			str       						= hidden_act
										SCREAMING_SNAKE_CASE_						:			Dict       						= use_absolute_embeddings
										SCREAMING_SNAKE_CASE_						:			Dict       						= patch_norm
										SCREAMING_SNAKE_CASE_						:			Any       						= layer_norm_eps
										SCREAMING_SNAKE_CASE_						:			Any       						= initializer_range
										SCREAMING_SNAKE_CASE_						:			Dict       						= is_training
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= scope
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= use_labels
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= type_sequence_label_size
										SCREAMING_SNAKE_CASE_						:			int       						= encoder_stride






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= None
										if self.use_labels:
													SCREAMING_SNAKE_CASE_						:			List[str]       						= ids_tensor([self.batch_size]       ,		self.type_sequence_label_size    )

										SCREAMING_SNAKE_CASE_						:			int       						= self.get_config()

										return config, pixel_values, labels






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return SwinConfig(
										    image_size=self.image_size       ,		patch_size=self.patch_size       ,		num_channels=self.num_channels       ,		embed_dim=self.embed_dim       ,		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       ,		)






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= TFSwinModel(config=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= model(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 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  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= TFSwinForMaskedImageModeling(config=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Any       						= model(lowerCAmelCase__    )
										self.parent.assertEqual(
										    result.logits.shape       ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

										# test greyscale images
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 1
										SCREAMING_SNAKE_CASE_						:			str       						= TFSwinForMaskedImageModeling(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
										SCREAMING_SNAKE_CASE_						:			int       						= model(lowerCAmelCase__    )
										self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, 1, self.image_size, self.image_size)    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= self.type_sequence_label_size
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= TFSwinForImageClassification(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model(lowerCAmelCase__       ,		labels=lowerCAmelCase__    )
										self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.type_sequence_label_size)    )

										# test greyscale images
										SCREAMING_SNAKE_CASE_						:			int       						= 1
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= TFSwinForImageClassification(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= model(lowerCAmelCase__    )
										self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.type_sequence_label_size)    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			str       						= config_and_inputs
										SCREAMING_SNAKE_CASE_						:			Any       						= {'pixel_values': pixel_values}
										return config, inputs_dict







@require_tf
class      __lowercase  (__SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase   ):





							"""simple docstring"""







							_UpperCAmelCase     =		(
							    (
							        TFSwinModel,
							        TFSwinForImageClassification,
							        TFSwinForMaskedImageModeling,
							    )
							    if is_tf_available()
							    else ()
							)
							_UpperCAmelCase     =		(
							    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
							    if is_tf_available()
							    else {}
							)

							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		False






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= TFSwinModelTester(self    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= ConfigTester(self       ,		config_class=lowerCAmelCase__       ,		embed_dim=3_7    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__    )






							@unittest.skip(reason='Swin does not use inputs_embeds'    )
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= model_class(lowerCAmelCase__    )
													self.assertIsInstance(model.get_input_embeddings()       ,		tf.keras.layers.Layer    )
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCAmelCase__       ,		tf.keras.layers.Dense    )    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[str]       						= self.model_tester.prepare_config_and_inputs_for_common()

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

													SCREAMING_SNAKE_CASE_						:			Dict       						= ['pixel_values']
													self.assertListEqual(arg_names[:1]       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[str]       						= self.model_tester.prepare_config_and_inputs_for_common()
										SCREAMING_SNAKE_CASE_						:			Dict       						= True

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= True
													SCREAMING_SNAKE_CASE_						:			str       						= False
													SCREAMING_SNAKE_CASE_						:			Dict       						= True
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model_class(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model(**self._prepare_for_class(lowerCAmelCase__       ,		lowerCAmelCase__    )    )
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= outputs.attentions
													SCREAMING_SNAKE_CASE_						:			int       						= len(self.model_tester.depths    )
													self.assertEqual(len(lowerCAmelCase__    )       ,		lowerCAmelCase__    )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= True
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= config.window_size**2
													SCREAMING_SNAKE_CASE_						:			List[str]       						= model_class(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			str       						= model(**self._prepare_for_class(lowerCAmelCase__       ,		lowerCAmelCase__    )    )
													SCREAMING_SNAKE_CASE_						:			Dict       						= outputs.attentions
													self.assertEqual(len(lowerCAmelCase__    )       ,		lowerCAmelCase__    )

													self.assertListEqual(
													    list(attentions[0].shape[-3:]    )       ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,		)
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= len(lowerCAmelCase__    )

													# Check attention is always last and order is fine
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= True
													SCREAMING_SNAKE_CASE_						:			Dict       						= True
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= model_class(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(**self._prepare_for_class(lowerCAmelCase__       ,		lowerCAmelCase__    )    )

													if hasattr(self.model_tester       ,		'num_hidden_states_types'    ):
																SCREAMING_SNAKE_CASE_						:			int       						= self.model_tester.num_hidden_states_types
													else:
																# also another +1 for reshaped_hidden_states
																SCREAMING_SNAKE_CASE_						:			int       						= 2
													self.assertEqual(out_len + added_hidden_states       ,		len(lowerCAmelCase__    )    )

													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= outputs.attentions

													self.assertEqual(len(lowerCAmelCase__    )       ,		lowerCAmelCase__    )

													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]    )       ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,		)






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Tuple       						= model_class(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= model(**self._prepare_for_class(lowerCAmelCase__       ,		lowerCAmelCase__    )    )
										SCREAMING_SNAKE_CASE_						:			str       						= outputs.hidden_states

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= getattr(
										    self.model_tester       ,		'expected_num_hidden_layers'       ,		len(self.model_tester.depths    ) + 1    )
										self.assertEqual(len(lowerCAmelCase__    )       ,		lowerCAmelCase__    )

										# Swin has a different seq_length
										SCREAMING_SNAKE_CASE_						:			str       						= to_atuple(config.patch_size    )

										SCREAMING_SNAKE_CASE_						:			Tuple       						= (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]       ,		)

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= outputs.reshaped_hidden_states
										self.assertEqual(len(lowerCAmelCase__    )       ,		lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			str       						= reshaped_hidden_states[0].shape

										SCREAMING_SNAKE_CASE_						:			int       						= tf.reshape(reshaped_hidden_states[0]       ,		(batch_size, num_channels, height * width)    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= tf.transpose(lowerCAmelCase__       ,		(0, 2, 1)    )

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






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Tuple       						= self.model_tester.prepare_config_and_inputs_for_common()

										SCREAMING_SNAKE_CASE_						:			List[str]       						= to_atuple(self.model_tester.image_size    )

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= True
													self.check_hidden_states_output(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    )

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

													self.check_hidden_states_output(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()
										SCREAMING_SNAKE_CASE_						:			int       						= 3

										SCREAMING_SNAKE_CASE_						:			Any       						= to_atuple(self.model_tester.image_size    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= to_atuple(config.patch_size    )

										SCREAMING_SNAKE_CASE_						:			str       						= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= True
													self.check_hidden_states_output(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		(padded_height, padded_width)    )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													SCREAMING_SNAKE_CASE_						:			List[str]       						= True
													self.check_hidden_states_output(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		(padded_height, padded_width)    )






							@slow
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE_						:			Any       						= TFSwinModel.from_pretrained(lowerCAmelCase__    )
													self.assertIsNotNone(lowerCAmelCase__    )







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





							"""simple docstring"""







							@cached_property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return (
										    AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'    )
										    if is_vision_available()
										    else None
										)






							@slow
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.default_image_processor

										SCREAMING_SNAKE_CASE_						:			int       						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
										SCREAMING_SNAKE_CASE_						:			str       						= image_processor(images=lowerCAmelCase__       ,		return_tensors='tf'    )

										# forward pass
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model(lowerCAmelCase__    )

										# verify the logits
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= tf.TensorShape((1, 1_0_0_0)    )
										self.assertEqual(outputs.logits.shape       ,		lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= tf.constant([-0.0_948, -0.6_454, -0.0_921]    )
										self.assertTrue(np.allclose(outputs.logits[0, :3]       ,		lowerCAmelCase__       ,		atol=1E-4    )    )










"""simple docstring"""
import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE     :  Tuple										=					logging.get_logger(__name__)


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__      :				nn.Module
					lowercase__      :				List[nn.Module]        =       field(default_factory=A__			)
					lowercase__      :				list        =       field(default_factory=A__			)

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = len(list(m.modules()      )      ) == 1 or isinstance(lowerCamelCase__ ,				nn.Convad      ) or isinstance(lowerCamelCase__ ,				nn.BatchNormad      )
									if has_not_submodules:
													if not isinstance(lowerCamelCase__ ,				lowerCamelCase__      ):
																	self.traced.append(lowerCamelCase__      )

					def __call__(			self ,				lowerCamelCase__      ):
									for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook      )      )
									self.module(lowerCamelCase__      )
									[x.remove() for x in self.handles]
									return self





					@property
					def 	snake_case__					(			self      ):
									# check the len of the state_dict keys to see if we have learnable params
									return list(filter(lambda lowerCamelCase__      : len(list(x.state_dict().keys()      )      ) > 0 ,				self.traced      )      )


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__      :				nn.Module
					lowercase__      :				nn.Module
					lowercase__      :				int        =       0
					lowercase__      :				List        =       field(default_factory=A__			)
					lowercase__      :				List        =       field(default_factory=A__			)

					def __call__(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = Tracker(self.dest      )(lowerCamelCase__      ).parametrized
									_lowerCamelCase					     = Tracker(self.src      )(lowerCamelCase__      ).parametrized

									_lowerCamelCase					     = list(filter(lambda lowerCamelCase__      : type(lowerCamelCase__      ) not in self.src_skip ,				lowerCamelCase__      )      )
									_lowerCamelCase					     = list(filter(lambda lowerCamelCase__      : type(lowerCamelCase__      ) not in self.dest_skip ,				lowerCamelCase__      )      )

									if len(lowerCamelCase__      ) != len(lowerCamelCase__      ):
													raise Exception(
													    F"""Numbers of operations are different. Source module has {len(lowerCamelCase__      )} operations while"""
													    F""" destination module has {len(lowerCamelCase__      )}."""      )

									for dest_m, src_m in zip(lowerCamelCase__ ,				lowerCamelCase__      ):
													dest_m.load_state_dict(src_m.state_dict()      )
													if self.verbose == 1:
																	print(F"""Transfered from={src_m} to={dest_m}"""      )



def 				lowerCAmelCase_(      lowercase_   :							nn.Module  ,      lowercase_   :							nn.Module							)		->				nn.Module:
				# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
				_lowerCamelCase					     = from_model.state_dict()
				_lowerCamelCase					     = our_model.state_dict()
				_lowerCamelCase					     = our_model.config
				_lowerCamelCase					     = []
				for stage_idx in range(len(config.hidden_sizes							)							):
								for block_id in range(config.depths[stage_idx]							):
												_lowerCamelCase					     = F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
												_lowerCamelCase					     = F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

												all_keys.append((from_key, to_key)							)
												_lowerCamelCase					     = F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
												_lowerCamelCase					     = F"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

												all_keys.append((from_key, to_key)							)

				for from_key, to_key in all_keys:
								_lowerCamelCase					     = from_state_dict.pop(lowercase_							)

				our_model.load_state_dict(lowercase_							)
				return our_model



def 				lowerCAmelCase_(      lowercase_   :							str  ,      lowercase_   :							VanConfig  ,      lowercase_   :							str  ,      lowercase_   :							nn.Module  ,      lowercase_   :							Path  ,      lowercase_   :							bool = True  ,      )		->				Union[str, Any]:
				print(F"""Downloading weights for {name}..."""							)
				_lowerCamelCase					     = cached_download(lowercase_							)
				print(F"""Converting {name}..."""							)
				_lowerCamelCase					     = torch.load(lowercase_							)['''state_dict''']
				from_model.load_state_dict(lowercase_							)
				from_model.eval()
				with torch.no_grad():
								_lowerCamelCase					     = VanForImageClassification(lowercase_							).eval()
								_lowerCamelCase					     = ModuleTransfer(src=lowercase_  ,      dest=lowercase_							)
								_lowerCamelCase					     = torch.randn((1, 3, 2_24, 2_24)							)
								module_transfer(lowercase_							)
								_lowerCamelCase					     = copy_parameters(lowercase_  ,      lowercase_							)

				if not torch.allclose(from_model(lowercase_							)  ,      our_model(lowercase_							).logits							):
								raise ValueError('''The model logits don\'t match the original one.'''							)

				_lowerCamelCase					     = name
				print(lowercase_							)

				if push_to_hub:
								our_model.push_to_hub(
								    repo_path_or_name=save_directory / checkpoint_name  ,      commit_message='''Add model'''  ,      use_temp_dir=lowercase_  ,      )

								# we can use the convnext one
								_lowerCamelCase					     = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''							)
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / checkpoint_name  ,      commit_message='''Add image processor'''  ,      use_temp_dir=lowercase_  ,      )

								print(F"""Pushed {checkpoint_name}"""							)



def 				lowerCAmelCase_(      lowercase_   :							Path  ,      lowercase_   :							str = None  ,      lowercase_   :							bool = True							)		->				List[str]:
				_lowerCamelCase					     = '''imagenet-1k-id2label.json'''
				_lowerCamelCase					     = 10_00

				_lowerCamelCase					     = '''huggingface/label-files'''
				_lowerCamelCase					     = num_labels
				_lowerCamelCase					     = json.load(open(hf_hub_download(lowercase_  ,      lowercase_  ,      repo_type='''dataset'''							)  ,      '''r'''							)							)
				_lowerCamelCase					     = {int(lowercase_							): v for k, v in idalabel.items()}

				_lowerCamelCase					     = idalabel
				_lowerCamelCase					     = {v: k for k, v in idalabel.items()}

				_lowerCamelCase					     = partial(lowercase_  ,      num_labels=lowercase_  ,      idalabel=lowercase_  ,      labelaid=lowercase_							)

				_lowerCamelCase					     = {
				    '''van-tiny''': ImageNetPreTrainedConfig(
				        hidden_sizes=[32, 64, 1_60, 2_56]  ,      depths=[3, 3, 5, 2]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-small''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[2, 2, 4, 2]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-base''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[3, 3, 12, 3]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-large''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[3, 5, 27, 3]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				}

				_lowerCamelCase					     = {
				    '''van-tiny''': van_tiny,
				    '''van-small''': van_small,
				    '''van-base''': van_base,
				    '''van-large''': van_large,
				}

				_lowerCamelCase					     = {
				    '''van-tiny''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
				    ),
				    '''van-small''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
				    ),
				    '''van-base''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
				    ),
				    '''van-large''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
				    ),
				}

				if model_name:
								convert_weight_and_push(
								    lowercase_  ,      names_to_config[model_name]  ,      checkpoint=names_to_original_checkpoints[model_name]  ,      from_model=names_to_original_models[model_name]()  ,      save_directory=lowercase_  ,      push_to_hub=lowercase_  ,      )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    lowercase_  ,      lowercase_  ,      checkpoint=names_to_original_checkpoints[model_name]  ,      from_model=names_to_original_models[model_name]()  ,      save_directory=lowercase_  ,      push_to_hub=lowercase_  ,      )


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE     :  Any										=					argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model-name''',
			    default=None,
			    type=str,
			    help=(
			        '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
			        ''' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=Path,
			    required=True,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument(
			    '''--van_dir''',
			    required=True,
			    type=Path,
			    help=(
			        '''A path to VAN\'s original implementation directory. You can download from here:'''
			        ''' https://github.com/Visual-Attention-Network/VAN-Classification'''
			    ),
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    default=True,
			    type=bool,
			    required=False,
			    help='''If True, push model and image processor to the hub.''',
			)

			__SCREAMING_SNAKE_CASE     :  int										=					parser.parse_args()
			__SCREAMING_SNAKE_CASE     :  Path										=					args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			__SCREAMING_SNAKE_CASE     :  List[str]										=					args.van_dir
			# append the path to the parents to maskformer dir
			sys.path.append(str(van_dir.parent))
			from van.models.van import van_base, van_large, van_small, van_tiny

			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
