'''simple docstring'''


import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[int]       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[int]       =				SwinvaConfig()
     UpperCAmelCase_							:       Optional[Any]       =				swinva_name.split('_'       )

     UpperCAmelCase_							:       str       =				name_split[1]
     if "to" in name_split[3]:
          UpperCAmelCase_							:       Tuple       =				int(name_split[3][-3:]       )
     else:
          UpperCAmelCase_							:       Optional[int]       =				int(name_split[3]       )
     if "to" in name_split[2]:
          UpperCAmelCase_							:       List[Any]       =				int(name_split[2][-2:]       )
     else:
          UpperCAmelCase_							:       Union[str, Any]       =				int(name_split[2][6:]       )

     if model_size == "tiny":
          UpperCAmelCase_							:       Optional[Any]       =				96
          UpperCAmelCase_							:       List[str]       =				(2, 2, 6, 2)
          UpperCAmelCase_							:       Optional[Any]       =				(3, 6, 12, 24)
     elif model_size == "small":
          UpperCAmelCase_							:       str       =				96
          UpperCAmelCase_							:       Any       =				(2, 2, 18, 2)
          UpperCAmelCase_							:       List[Any]       =				(3, 6, 12, 24)
     elif model_size == "base":
          UpperCAmelCase_							:       str       =				128
          UpperCAmelCase_							:       Dict       =				(2, 2, 18, 2)
          UpperCAmelCase_							:       Tuple       =				(4, 8, 16, 32)
     else:
          UpperCAmelCase_							:       List[Any]       =				192
          UpperCAmelCase_							:       List[str]       =				(2, 2, 18, 2)
          UpperCAmelCase_							:       List[Any]       =				(6, 12, 24, 48)

     if "to" in swinva_name:
          UpperCAmelCase_							:       Dict       =				(12, 12, 12, 6)

     if ("22k" in swinva_name) and ("to" not in swinva_name):
          UpperCAmelCase_							:       Dict       =				21841
          UpperCAmelCase_							:       Optional[int]       =				'huggingface/label-files'
          UpperCAmelCase_							:       List[Any]       =				'imagenet-22k-id2label.json'
          UpperCAmelCase_							:       List[str]       =				json.load(open(hf_hub_download(lowerCamelCase_     ,					lowerCamelCase_     ,					repo_type='dataset'       )     ,					'r'       )       )
          UpperCAmelCase_							:       Optional[Any]       =				{int(lowerCamelCase_       ): v for k, v in idalabel.items()}
          UpperCAmelCase_							:       Optional[int]       =				idalabel
          UpperCAmelCase_							:       Optional[Any]       =				{v: k for k, v in idalabel.items()}

     else:
          UpperCAmelCase_							:       List[str]       =				1000
          UpperCAmelCase_							:       Dict       =				'huggingface/label-files'
          UpperCAmelCase_							:       Any       =				'imagenet-1k-id2label.json'
          UpperCAmelCase_							:       Any       =				json.load(open(hf_hub_download(lowerCamelCase_     ,					lowerCamelCase_     ,					repo_type='dataset'       )     ,					'r'       )       )
          UpperCAmelCase_							:       int       =				{int(lowerCamelCase_       ): v for k, v in idalabel.items()}
          UpperCAmelCase_							:       Union[str, Any]       =				idalabel
          UpperCAmelCase_							:       Any       =				{v: k for k, v in idalabel.items()}

     UpperCAmelCase_							:       Tuple       =				img_size
     UpperCAmelCase_							:       List[Any]       =				num_classes
     UpperCAmelCase_							:       Optional[int]       =				embed_dim
     UpperCAmelCase_							:       Union[str, Any]       =				depths
     UpperCAmelCase_							:       List[Any]       =				num_heads
     UpperCAmelCase_							:       Dict       =				window_size

     return config


def  _lowerCamelCase   (				lowerCamelCase_ :							Any       ):



     """simple docstring"""





     if "patch_embed.proj" in name:
          UpperCAmelCase_							:       Optional[Any]       =				name.replace('patch_embed.proj'     ,					'embeddings.patch_embeddings.projection'       )
     if "patch_embed.norm" in name:
          UpperCAmelCase_							:       List[str]       =				name.replace('patch_embed.norm'     ,					'embeddings.norm'       )
     if "layers" in name:
          UpperCAmelCase_							:       List[str]       =				'encoder.' + name
     if "attn.proj" in name:
          UpperCAmelCase_							:       Dict       =				name.replace('attn.proj'     ,					'attention.output.dense'       )
     if "attn" in name:
          UpperCAmelCase_							:       str       =				name.replace('attn'     ,					'attention.self'       )
     if "norm1" in name:
          UpperCAmelCase_							:       Dict       =				name.replace('norm1'     ,					'layernorm_before'       )
     if "norm2" in name:
          UpperCAmelCase_							:       Any       =				name.replace('norm2'     ,					'layernorm_after'       )
     if "mlp.fc1" in name:
          UpperCAmelCase_							:       Union[str, Any]       =				name.replace('mlp.fc1'     ,					'intermediate.dense'       )
     if "mlp.fc2" in name:
          UpperCAmelCase_							:       Dict       =				name.replace('mlp.fc2'     ,					'output.dense'       )
     if "q_bias" in name:
          UpperCAmelCase_							:       Any       =				name.replace('q_bias'     ,					'query.bias'       )
     if "k_bias" in name:
          UpperCAmelCase_							:       str       =				name.replace('k_bias'     ,					'key.bias'       )
     if "v_bias" in name:
          UpperCAmelCase_							:       Any       =				name.replace('v_bias'     ,					'value.bias'       )
     if "cpb_mlp" in name:
          UpperCAmelCase_							:       Dict       =				name.replace('cpb_mlp'     ,					'continuous_position_bias_mlp'       )
     if name == "norm.weight":
          UpperCAmelCase_							:       Optional[int]       =				'layernorm.weight'
     if name == "norm.bias":
          UpperCAmelCase_							:       Union[str, Any]       =				'layernorm.bias'

     if "head" in name:
          UpperCAmelCase_							:       Union[str, Any]       =				name.replace('head'     ,					'classifier'       )
     else:
          UpperCAmelCase_							:       int       =				'swinv2.' + name

     return name


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[int]     ,					lowerCamelCase_ :							Union[str, Any]       ):



     """simple docstring"""





     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_							:       Union[str, Any]       =				orig_state_dict.pop(lowerCamelCase_       )

          if "mask" in key:
               continue
          elif "qkv" in key:
               UpperCAmelCase_							:       Optional[int]       =				key.split('.'       )
               UpperCAmelCase_							:       int       =				int(key_split[1]       )
               UpperCAmelCase_							:       int       =				int(key_split[3]       )
               UpperCAmelCase_							:       Dict       =				model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    UpperCAmelCase_							:       Optional[Any]       =				val[:dim, :]
                    UpperCAmelCase_							:       Any       =				val[dim : dim * 2, :]
                    UpperCAmelCase_							:       Any       =				val[-dim:, :]
               else:
                    UpperCAmelCase_							:       Any       =				val[:dim]
                    UpperCAmelCase_							:       Any       =				val[
                        dim : dim * 2
                    ]
                    UpperCAmelCase_							:       List[str]       =				val[-dim:]
          else:
               UpperCAmelCase_							:       Tuple       =				val

     return orig_state_dict


def  _lowerCamelCase   (				lowerCamelCase_ :							Any     ,					lowerCamelCase_ :							Union[str, Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       List[Any]       =				timm.create_model(lowerCamelCase_     ,					pretrained=lowerCamelCase_       )
     timm_model.eval()

     UpperCAmelCase_							:       Optional[int]       =				get_swinva_config(lowerCamelCase_       )
     UpperCAmelCase_							:       Optional[Any]       =				SwinvaForImageClassification(lowerCamelCase_       )
     model.eval()

     UpperCAmelCase_							:       int       =				convert_state_dict(timm_model.state_dict()     ,					lowerCamelCase_       )
     model.load_state_dict(lowerCamelCase_       )

     UpperCAmelCase_							:       List[str]       =				'http://images.cocodataset.org/val2017/000000039769.jpg'

     UpperCAmelCase_							:       List[Any]       =				AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_'     ,					'-'       )       )       )
     UpperCAmelCase_							:       List[str]       =				Image.open(requests.get(lowerCamelCase_     ,					stream=lowerCamelCase_       ).raw       )
     UpperCAmelCase_							:       str       =				image_processor(images=lowerCamelCase_     ,					return_tensors='pt'       )

     UpperCAmelCase_							:       int       =				timm_model(inputs['pixel_values']       )
     UpperCAmelCase_							:       int       =				model(**lowerCamelCase_       ).logits

     assert torch.allclose(lowerCamelCase_     ,					lowerCamelCase_     ,					atol=1e-3       )

     print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''       )
     model.save_pretrained(lowerCamelCase_       )

     print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
     image_processor.save_pretrained(lowerCamelCase_       )

     model.push_to_hub(
         repo_path_or_name=Path(lowerCamelCase_     ,					lowerCamelCase_       )     ,					organization='nandwalritik'     ,					commit_message='Add model'     ,					)


if __name__ == "__main__":
 snake_case__					:      Tuple				      =  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--swinv2_name''',
     default='''swinv2_tiny_patch4_window8_256''',
     type=str,
     help='''Name of the Swinv2 timm model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )

 snake_case__					:      List[str]				      =  parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)







'''simple docstring'''


import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)






class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				['a', 'b', 'c']

       # Defaults to last layer if both are None
       UpperCAmelCase_       ,      UpperCAmelCase_							:       List[str]       =				get_aligned_output_features_output_indices(snake_case_				,		snake_case_				,		snake_case_						)
       self.assertEqual(snake_case_				,		['c']						)
       self.assertEqual(snake_case_				,		[2]						)

       # Out indices set to match out features
       UpperCAmelCase_       ,      UpperCAmelCase_							:       int       =				get_aligned_output_features_output_indices(['a', 'c']				,		snake_case_				,		snake_case_						)
       self.assertEqual(snake_case_				,		['a', 'c']						)
       self.assertEqual(snake_case_				,		[0, 2]						)

       # Out features set to match out indices
       UpperCAmelCase_       ,      UpperCAmelCase_							:       List[Any]       =				get_aligned_output_features_output_indices(snake_case_				,		[0, 2]				,		snake_case_						)
       self.assertEqual(snake_case_				,		['a', 'c']						)
       self.assertEqual(snake_case_				,		[0, 2]						)

       # Out features selected from negative indices
       UpperCAmelCase_       ,      UpperCAmelCase_							:       int       =				get_aligned_output_features_output_indices(snake_case_				,		[-3, -1]				,		snake_case_						)
       self.assertEqual(snake_case_				,		['a', 'c']						)
       self.assertEqual(snake_case_				,		[-3, -1]						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(['a', 'b']				,		(0, 1)				,		snake_case_						)

       # Out features must be a list
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(('a', 'b')				,		(0, 1)				,		['a', 'b']						)

       # Out features must be a subset of stage names
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(['a', 'b']				,		(0, 1)				,		['a']						)

       # Out indices must be a list or tuple
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(snake_case_				,		0				,		['a', 'b']						)

       # Out indices must be a subset of stage names
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(snake_case_				,		(0, 1)				,		['a']						)

       # Out features and out indices must be the same length
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(['a', 'b']				,		(0,)				,		['a', 'b', 'c']						)

       # Out features should match out indices
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(['a', 'b']				,		(0, 2)				,		['a', 'b', 'c']						)

       # Out features and out indices should be in order
       with self.assertRaises(snake_case_						):
            verify_out_features_out_indices(['b', 'a']				,		(0, 1)				,		['a', 'b']						)

       # Check passes with valid inputs
       verify_out_features_out_indices(['a', 'b', 'd']				,		(0, 1, -1)				,		['a', 'b', 'c', 'd']						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				BackboneMixin()

       UpperCAmelCase_							:       Any       =				['a', 'b', 'c']
       UpperCAmelCase_							:       str       =				['a', 'c']
       UpperCAmelCase_							:       str       =				[0, 2]

       # Check that the output features and indices are set correctly
       self.assertEqual(backbone.out_features				,		['a', 'c']						)
       self.assertEqual(backbone.out_indices				,		[0, 2]						)

       # Check out features and indices are updated correctly
       UpperCAmelCase_							:       str       =				['a', 'b']
       self.assertEqual(backbone.out_features				,		['a', 'b']						)
       self.assertEqual(backbone.out_indices				,		[0, 1]						)

       UpperCAmelCase_							:       Optional[int]       =				[-3, -1]
       self.assertEqual(backbone.out_features				,		['a', 'c']						)
       self.assertEqual(backbone.out_indices				,		[-3, -1]						)






