





"""simple docstring"""


import collections
import inspect
import unittest

from transformers import SwinConfig
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
   from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor






class __a   :



  def __init__(self  :		str				,			lowercase_  :		Union[str, Any]				,			lowercase_  :		Tuple=13				,			lowercase_  :		Any=32				,			lowercase_  :		str=2				,			lowercase_  :		int=3				,			lowercase_  :		Optional[int]=16				,			lowercase_  :		Any=[1, 2, 1]				,			lowercase_  :		Tuple=[2, 2, 4]				,			lowercase_  :		Union[str, Any]=2				,			lowercase_  :		str=2.0				,			lowercase_  :		Dict=True				,			lowercase_  :		List[Any]=0.0				,			lowercase_  :		List[str]=0.0				,			lowercase_  :		List[Any]=0.1				,			lowercase_  :		str="gelu"				,			lowercase_  :		Dict=False				,			lowercase_  :		Dict=True				,			lowercase_  :		int=0.02				,			lowercase_  :		List[Any]=1E-5				,			lowercase_  :		List[Any]=True				,			lowercase_  :		Dict=None				,			lowercase_  :		List[Any]=True				,			lowercase_  :		Union[str, Any]=10				,			lowercase_  :		Dict=8				,			lowercase_  :		Dict=["stage1", "stage2"]				,			lowercase_  :		List[str]=[1, 2]				,			)		->							Any:
   __A	:       Tuple       =	parent
   __A	:       List[Any]       =	batch_size
   __A	:       List[Any]       =	image_size
   __A	:       List[Any]       =	patch_size
   __A	:       Dict       =	num_channels
   __A	:       List[Any]       =	embed_dim
   __A	:       Dict       =	depths
   __A	:       Any       =	num_heads
   __A	:       Optional[Any]       =	window_size
   __A	:       int       =	mlp_ratio
   __A	:       List[str]       =	qkv_bias
   __A	:       List[str]       =	hidden_dropout_prob
   __A	:       Dict       =	attention_probs_dropout_prob
   __A	:       Tuple       =	drop_path_rate
   __A	:       Union[str, Any]       =	hidden_act
   __A	:       str       =	use_absolute_embeddings
   __A	:       Any       =	patch_norm
   __A	:       Optional[int]       =	layer_norm_eps
   __A	:       Tuple       =	initializer_range
   __A	:       Union[str, Any]       =	is_training
   __A	:       Optional[Any]       =	scope
   __A	:       str       =	use_labels
   __A	:       int       =	type_sequence_label_size
   __A	:       Union[str, Any]       =	encoder_stride
   __A	:       Any       =	out_features
   __A	:       List[str]       =	out_indices



  def 						A_				(self  :		Union[str, Any]   )		->							str:
   __A	:       Tuple       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

   __A	:       List[Any]       =	self.get_config()

   return config, pixel_values, labels



  def 						A_				(self  :		Tuple   )		->							Dict:
   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				,			out_features=self.out_features				,			out_indices=self.out_indices				,			)



  def 						A_				(self  :		Dict				,			lowercase_  :		Any				,			lowercase_  :		Any				,			lowercase_  :		int   )		->							Any:
   __A	:       Any       =	SwinModel(config=__a   )
   model.to(__a   )
   model.eval()
   __A	:       Any       =	model(__a   )

   __A	:       Optional[Any]       =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
   __A	:       Dict       =	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 						A_				(self  :		Any				,			lowercase_  :		Optional[int]				,			lowercase_  :		List[str]				,			lowercase_  :		Tuple   )		->							List[str]:
   __A	:       Dict       =	SwinBackbone(config=__a   )
   model.to(__a   )
   model.eval()
   __A	:       Optional[Any]       =	model(__a   )

   # verify hidden states
   self.parent.assertEqual(len(result.feature_maps   )				,			len(config.out_features   )   )
   self.parent.assertListEqual(list(result.feature_maps[0].shape   )				,			[self.batch_size, model.channels[0], 16, 16]   )

   # verify channels
   self.parent.assertEqual(len(model.channels   )				,			len(config.out_features   )   )

   # verify backbone works with out_features=None
   __A	:       Tuple       =	None
   __A	:       List[Any]       =	SwinBackbone(config=__a   )
   model.to(__a   )
   model.eval()
   __A	:       List[Any]       =	model(__a   )

   # verify feature maps
   self.parent.assertEqual(len(result.feature_maps   )				,			1   )
   self.parent.assertListEqual(list(result.feature_maps[0].shape   )				,			[self.batch_size, model.channels[-1], 4, 4]   )

   # verify channels
   self.parent.assertEqual(len(model.channels   )				,			1   )



  def 						A_				(self  :		Union[str, Any]				,			lowercase_  :		Optional[int]				,			lowercase_  :		int				,			lowercase_  :		List[Any]   )		->							str:
   __A	:       int       =	SwinForMaskedImageModeling(config=__a   )
   model.to(__a   )
   model.eval()
   __A	:       Dict       =	model(__a   )
   self.parent.assertEqual(
       result.logits.shape				,			(self.batch_size, self.num_channels, self.image_size, self.image_size)   )

   # test greyscale images
   __A	:       Optional[int]       =	1
   __A	:       Optional[Any]       =	SwinForMaskedImageModeling(__a   )
   model.to(__a   )
   model.eval()

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



  def 						A_				(self  :		Dict				,			lowercase_  :		Optional[int]				,			lowercase_  :		List[str]				,			lowercase_  :		List[Any]   )		->							Any:
   __A	:       List[Any]       =	self.type_sequence_label_size
   __A	:       int       =	SwinForImageClassification(__a   )
   model.to(__a   )
   model.eval()
   __A	:       List[str]       =	model(__a				,			labels=__a   )
   self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.type_sequence_label_size)   )

   # test greyscale images
   __A	:       List[Any]       =	1
   __A	:       Optional[Any]       =	SwinForImageClassification(__a   )
   model.to(__a   )
   model.eval()

   __A	:       Any       =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
   __A	:       str       =	model(__a   )
   self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.type_sequence_label_size)   )



  def 						A_				(self  :		Optional[Any]   )		->							List[Any]:
   __A	:       Optional[Any]       =	self.prepare_config_and_inputs()
   (
       __A       
   )	:       Any       =	config_and_inputs
   __A	:       Union[str, Any]       =	{'pixel_values': pixel_values}
   return config, inputs_dict






@require_torch
class __a   (   lowerCAmelCase_ ,lowerCAmelCase_ ,unittest.TestCase				):
  __lowercase    :    Any						=					(
      (
          SwinModel,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  __lowercase    :    Tuple						=					(
      {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
      if is_torch_available()
      else {}
  )
  __lowercase    :    Union[str, Any]						=					True

  __lowercase    :    Any						=					False
  __lowercase    :    Tuple						=					False
  __lowercase    :    Dict						=					False



  def 						A_				(self  :		Optional[int]   )		->							Dict:
   __A	:       List[str]       =	SwinModelTester(self   )
   __A	:       Dict       =	ConfigTester(self				,			config_class=__a				,			embed_dim=37   )



  def 						A_				(self  :		Tuple   )		->							Tuple:
   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 						A_				(self  :		Optional[int]   )		->							Union[str, Any]:
   return



  def 						A_				(self  :		str   )		->							Any:
   __A	:       Dict       =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__a   )



  @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'   )
  def 						A_				(self  :		Tuple   )		->							Optional[Any]:
   pass



  def 						A_				(self  :		Optional[int]   )		->							Union[str, Any]:
   super().test_training_gradient_checkpointing()



  def 						A_				(self  :		List[Any]   )		->							Optional[Any]:
   __A	:       int       =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_backbone(*__a   )



  def 						A_				(self  :		Dict   )		->							Optional[int]:
   __A	:       str       =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*__a   )



  def 						A_				(self  :		Optional[Any]   )		->							Union[str, Any]:
   __A	:       Optional[Any]       =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*__a   )



  @unittest.skip(reason='Swin does not use inputs_embeds'   )
  def 						A_				(self  :		Tuple   )		->							Optional[Any]:
   pass



  @unittest.skip(reason='Swin Transformer does not use feedforward chunking'   )
  def 						A_				(self  :		Any   )		->							List[str]:
   pass



  def 						A_				(self  :		List[Any]   )		->							Union[str, Any]:
   __A	:       Optional[int]       =	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __A	:       Optional[int]       =	model_class(__a   )
    self.assertIsInstance(model.get_input_embeddings()				,			(nn.Module)   )
    __A	:       Dict       =	model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(__a				,			nn.Linear   )   )



  def 						A_				(self  :		List[str]   )		->							str:
   __A	:       Optional[int]       =	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __A	:       Optional[Any]       =	model_class(__a   )
    __A	:       List[str]       =	inspect.signature(model.forward   )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __A	:       Tuple       =	[*signature.parameters.keys()]

    __A	:       List[Any]       =	['pixel_values']
    self.assertListEqual(arg_names[:1]				,			__a   )



  def 						A_				(self  :		List[Any]   )		->							Dict:
   __A	:       Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()
   __A	:       Dict       =	True

   for model_class in self.all_model_classes:
    __A	:       int       =	True
    __A	:       List[Any]       =	False
    __A	:       int       =	True
    __A	:       List[str]       =	model_class(__a   )
    model.to(__a   )
    model.eval()
    with torch.no_grad():
     __A	:       List[str]       =	model(**self._prepare_for_class(__a				,			__a   )   )
    __A	:       List[Any]       =	outputs.attentions
    __A	:       Optional[int]       =	len(self.model_tester.depths   )
    self.assertEqual(len(__a   )				,			__a   )

    # check that output_attentions also work using config
    del inputs_dict["output_attentions"]
    __A	:       List[Any]       =	True
    __A	:       str       =	config.window_size**2
    __A	:       Optional[Any]       =	model_class(__a   )
    model.to(__a   )
    model.eval()
    with torch.no_grad():
     __A	:       Optional[int]       =	model(**self._prepare_for_class(__a				,			__a   )   )
    __A	:       str       =	outputs.attentions
    self.assertEqual(len(__a   )				,			__a   )

    self.assertListEqual(
        list(attentions[0].shape[-3:]   )				,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,			)
    __A	:       List[str]       =	len(__a   )

    # Check attention is always last and order is fine
    __A	:       int       =	True
    __A	:       int       =	True
    __A	:       Optional[int]       =	model_class(__a   )
    model.to(__a   )
    model.eval()
    with torch.no_grad():
     __A	:       Union[str, Any]       =	model(**self._prepare_for_class(__a				,			__a   )   )

    # also another +1 for reshaped_hidden_states
    __A	:       List[Any]       =	1 if model_class.__name__ == 'SwinBackbone' else 2
    self.assertEqual(out_len + added_hidden_states				,			len(__a   )   )

    __A	:       Tuple       =	outputs.attentions

    self.assertEqual(len(__a   )				,			__a   )

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



  def 						A_				(self  :		Optional[int]				,			lowercase_  :		str				,			lowercase_  :		Union[str, Any]				,			lowercase_  :		Optional[int]				,			lowercase_  :		Union[str, Any]   )		->							str:
   __A	:       str       =	model_class(__a   )
   model.to(__a   )
   model.eval()

   with torch.no_grad():
    __A	:       int       =	model(**self._prepare_for_class(__a				,			__a   )   )

   __A	:       Any       =	outputs.hidden_states

   __A	:       Any       =	getattr(
       self.model_tester				,			'expected_num_hidden_layers'				,			len(self.model_tester.depths   ) + 1   )
   self.assertEqual(len(__a   )				,			__a   )

   # Swin has a different seq_length
   __A	:       List[Any]       =	(
       config.patch_size
       if isinstance(config.patch_size				,			collections.abc.Iterable   )
       else (config.patch_size, config.patch_size)
   )

   __A	:       Optional[Any]       =	(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]				,			)

   if not model_class.__name__ == "SwinBackbone":
    __A	:       List[str]       =	outputs.reshaped_hidden_states
    self.assertEqual(len(__a   )				,			__a   )

    __A	:       List[str]       =	reshaped_hidden_states[0].shape
    __A	:       int       =	(
        reshaped_hidden_states[0].view(__a				,			__a				,			height * width   ).permute(0				,			2				,			1   )
    )
    self.assertListEqual(
        list(reshaped_hidden_states.shape[-2:]   )				,			[num_patches, self.model_tester.embed_dim]				,			)



  def 						A_				(self  :		Any   )		->							str:
   __A	:       Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

   __A	:       List[Any]       =	(
       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:
    __A	:       Union[str, Any]       =	True
    self.check_hidden_states_output(__a				,			__a				,			__a				,			__a   )

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

    self.check_hidden_states_output(__a				,			__a				,			__a				,			__a   )



  def 						A_				(self  :		Optional[Any]   )		->							Optional[int]:
   __A	:       Optional[int]       =	self.model_tester.prepare_config_and_inputs_for_common()
   __A	:       Dict       =	3

   __A	:       str       =	(
       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)
   )
   __A	:       List[Any]       =	(
       config.patch_size
       if isinstance(config.patch_size				,			collections.abc.Iterable   )
       else (config.patch_size, config.patch_size)
   )

   __A	:       Dict       =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
   __A	:       List[str]       =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

   for model_class in self.all_model_classes:
    __A	:       str       =	True
    self.check_hidden_states_output(__a				,			__a				,			__a				,			(padded_height, padded_width)   )

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



  @slow
  def 						A_				(self  :		List[Any]   )		->							List[str]:





   for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __A	:       List[str]       =	SwinModel.from_pretrained(__a   )
    self.assertIsNotNone(__a   )



  def 						A_				(self  :		str   )		->							str:
   __A	:       Union[str, Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

   __A	:       Any       =	_config_zero_init(__a   )
   for model_class in self.all_model_classes:
    __A	:       Optional[Any]       =	model_class(config=__a   )
    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 						A_				(self  :		Dict   )		->							Any:
   return (
       AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'   )
       if is_vision_available()
       else None
   )



  @slow
  def 						A_				(self  :		int   )		->							int:
   __A	:       Union[str, Any]       =	SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'   ).to(__a   )
   __A	:       List[str]       =	self.default_image_processor

   __A	:       Optional[Any]       =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
   __A	:       List[str]       =	image_processor(images=__a				,			return_tensors='pt'   ).to(__a   )

   # forward pass
   with torch.no_grad():
    __A	:       List[Any]       =	model(**__a   )

   # verify the logits
   __A	:       Union[str, Any]       =	torch.Size((1, 1_000)   )
   self.assertEqual(outputs.logits.shape				,			__a   )
   __A	:       Any       =	torch.tensor([-0.0948, -0.6454, -0.0921]   ).to(__a   )
   self.assertTrue(torch.allclose(outputs.logits[0, :3]				,			__a				,			atol=1E-4   )   )






@require_torch
class __a   (   unittest.TestCase ,lowerCAmelCase_				):
  __lowercase    :    List[str]						=					(SwinBackbone,) if is_torch_available() else ()
  __lowercase    :    Dict						=					SwinConfig



  def 						A_				(self  :		Optional[Any]   )		->							Optional[int]:
   __A	:       Optional[int]       =	SwinModelTester(self   )













"""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()
_UpperCamelCase   :						Union[str, Any]     						= logging.get_logger(__name__)






@dataclass
class __a   :
  __lowercase    :    nn.Module
  __lowercase    :    List[nn.Module]						=					field(default_factory=snake_case_				)
  __lowercase    :    list						=					field(default_factory=snake_case_				)



  def 						A_				(self  :		List[Any]				,			lowercase_  :		List[str]				,			lowercase_  :		Tensor				,			lowercase_  :		Tensor   )		->							Any:
   __A	:       Optional[int]       =	len(list(m.modules()   )   ) == 1 or isinstance(lowercase_				,			nn.Convad   ) or isinstance(lowercase_				,			nn.BatchNormad   )
   if has_not_submodules:
    if not isinstance(lowercase_				,			lowercase_   ):
     self.traced.append(lowercase_   )



  def __call__(self  :		Tuple				,			lowercase_  :		Tensor   )		->							List[Any]:
   for m in self.module.modules():
    self.handles.append(m.register_forward_hook(self._forward_hook   )   )
   self.module(lowercase_   )
   [x.remove() for x in self.handles]
   return self



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






@dataclass
class __a   :
  __lowercase    :    nn.Module
  __lowercase    :    nn.Module
  __lowercase    :    int						=					0
  __lowercase    :    List						=					field(default_factory=snake_case_				)
  __lowercase    :    List						=					field(default_factory=snake_case_				)



  def __call__(self  :		List[str]				,			lowercase_  :		Tensor   )		->							Optional[Any]:
   __A	:       List[Any]       =	Tracker(self.dest   )(lowercase_   ).parametrized
   __A	:       Dict       =	Tracker(self.src   )(lowercase_   ).parametrized

   __A	:       Dict       =	list(filter(lambda lowercase_   : type(lowercase_   ) not in self.src_skip				,			lowercase_   )   )
   __A	:       Any       =	list(filter(lambda lowercase_   : type(lowercase_   ) not in self.dest_skip				,			lowercase_   )   )

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

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






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

   all_keys.append((from_key, to_key)		)
   __A	:       str       =	F"block{stage_idx + 1}.{block_id}.layer_scale_2"
   __A	:       List[str]       =	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:
  __A	:       int       =	from_state_dict.pop(UpperCamelCase_		)

 our_model.load_state_dict(UpperCamelCase_		)
 return our_model




def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				VanConfig     ,	UpperCamelCase_							:				str     ,	UpperCamelCase_							:				nn.Module     ,	UpperCamelCase_							:				Path     ,	UpperCamelCase_							:				bool = True     ,	)  ->		int:
 print(F"Downloading weights for {name}..."		)
 __A	:       Dict       =	cached_download(UpperCamelCase_		)
 print(F"Converting {name}..."		)
 __A	:       Union[str, Any]       =	torch.load(UpperCamelCase_		)['state_dict']
 from_model.load_state_dict(UpperCamelCase_		)
 from_model.eval()
 with torch.no_grad():
  __A	:       Any       =	VanForImageClassification(UpperCamelCase_		).eval()
  __A	:       List[str]       =	ModuleTransfer(src=UpperCamelCase_     ,	dest=UpperCamelCase_		)
  __A	:       List[str]       =	torch.randn((1, 3, 2_2_4, 2_2_4)		)
  module_transfer(UpperCamelCase_		)
  __A	:       Union[str, Any]       =	copy_parameters(UpperCamelCase_     ,	UpperCamelCase_		)

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

 __A	:       str       =	name
 print(UpperCamelCase_		)

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

  # we can use the convnext one
  __A	:       str       =	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=UpperCamelCase_     ,	)

  print(F"Pushed {checkpoint_name}"		)




def 				a (     UpperCamelCase_							:				Path     ,	UpperCamelCase_							:				str = None     ,	UpperCamelCase_							:				bool = True		)  ->		Optional[int]:
 __A	:       Union[str, Any]       =	'imagenet-1k-id2label.json'
 __A	:       List[str]       =	1_0_0_0

 __A	:       List[Any]       =	'huggingface/label-files'
 __A	:       Tuple       =	num_labels
 __A	:       str       =	json.load(open(hf_hub_download(UpperCamelCase_     ,	UpperCamelCase_     ,	repo_type='dataset'		)     ,	'r'		)		)
 __A	:       int       =	{int(UpperCamelCase_		): v for k, v in idalabel.items()}

 __A	:       Tuple       =	idalabel
 __A	:       Union[str, Any]       =	{v: k for k, v in idalabel.items()}

 __A	:       List[str]       =	partial(UpperCamelCase_     ,	num_labels=UpperCamelCase_     ,	idalabel=UpperCamelCase_     ,	labelaid=UpperCamelCase_		)

 __A	:       Dict       =	{
     'van-tiny': ImageNetPreTrainedConfig(
         hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]     ,	depths=[3, 3, 5, 2]     ,	mlp_ratios=[8, 8, 4, 4]     ,	),
     'van-small': ImageNetPreTrainedConfig(
         hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]     ,	depths=[2, 2, 4, 2]     ,	mlp_ratios=[8, 8, 4, 4]     ,	),
     'van-base': ImageNetPreTrainedConfig(
         hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]     ,	depths=[3, 3, 1_2, 3]     ,	mlp_ratios=[8, 8, 4, 4]     ,	),
     'van-large': ImageNetPreTrainedConfig(
         hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]     ,	depths=[3, 5, 2_7, 3]     ,	mlp_ratios=[8, 8, 4, 4]     ,	),
 }

 __A	:       List[str]       =	{
     'van-tiny': van_tiny,
     'van-small': van_small,
     'van-base': van_base,
     'van-large': van_large,
 }

 __A	:       List[Any]       =	{
     '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(
      UpperCamelCase_     ,	names_to_config[model_name]     ,	checkpoint=names_to_original_checkpoints[model_name]     ,	from_model=names_to_original_models[model_name]()     ,	save_directory=UpperCamelCase_     ,	push_to_hub=UpperCamelCase_     ,	)
 else:
  for model_name, config in names_to_config.items():
   convert_weight_and_push(
       UpperCamelCase_     ,	UpperCamelCase_     ,	checkpoint=names_to_original_checkpoints[model_name]     ,	from_model=names_to_original_models[model_name]()     ,	save_directory=UpperCamelCase_     ,	push_to_hub=UpperCamelCase_     ,	)


if __name__ == "__main__":
   _UpperCamelCase   :						List[str]     						= 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.""",
   )

   _UpperCamelCase   :						List[Any]     						= parser.parse_args()
   _UpperCamelCase   :						Path     						= args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   _UpperCamelCase   :						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)





