




'''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 		snake_case_   (UpperCamelCase	:  Any				):





							'''simple docstring'''


							_a     					=    SwinvaConfig()
							_a     					=    swinva_name.split('''_'''				)

							_a     					=    name_split[1]
							if "to" in name_split[3]:
														_a     					=    int(name_split[3][-3:]				)
							else:
														_a     					=    int(name_split[3]				)
							if "to" in name_split[2]:
														_a     					=    int(name_split[2][-2:]				)
							else:
														_a     					=    int(name_split[2][6:]				)

							if model_size == "tiny":
														_a     					=    96
														_a     					=    (2, 2, 6, 2)
														_a     					=    (3, 6, 12, 24)
							elif model_size == "small":
														_a     					=    96
														_a     					=    (2, 2, 18, 2)
														_a     					=    (3, 6, 12, 24)
							elif model_size == "base":
														_a     					=    128
														_a     					=    (2, 2, 18, 2)
														_a     					=    (4, 8, 16, 32)
							else:
														_a     					=    192
														_a     					=    (2, 2, 18, 2)
														_a     					=    (6, 12, 24, 48)

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

							if ("22k" in swinva_name) and ("to" not in swinva_name):
														_a     					=    2_1841
														_a     					=    '''huggingface/label-files'''
														_a     					=    '''imagenet-22k-id2label.json'''
														_a     					=    json.load(open(hf_hub_download(UpperCamelCase			,						UpperCamelCase			,						repo_type='''dataset'''				)			,						'''r'''				)				)
														_a     					=    {int(UpperCamelCase				): v for k, v in idalabel.items()}
														_a     					=    idalabel
														_a     					=    {v: k for k, v in idalabel.items()}

							else:
														_a     					=    1000
														_a     					=    '''huggingface/label-files'''
														_a     					=    '''imagenet-1k-id2label.json'''
														_a     					=    json.load(open(hf_hub_download(UpperCamelCase			,						UpperCamelCase			,						repo_type='''dataset'''				)			,						'''r'''				)				)
														_a     					=    {int(UpperCamelCase				): v for k, v in idalabel.items()}
														_a     					=    idalabel
														_a     					=    {v: k for k, v in idalabel.items()}

							_a     					=    img_size
							_a     					=    num_classes
							_a     					=    embed_dim
							_a     					=    depths
							_a     					=    num_heads
							_a     					=    window_size

							return config
def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


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

							if "head" in name:
														_a     					=    name.replace('''head'''			,						'''classifier'''				)
							else:
														_a     					=    '''swinv2.''' + name

							return name
def 		snake_case_   (UpperCamelCase	:  Optional[Any]			,						UpperCamelCase	:  Optional[int]				):





							'''simple docstring'''


							for key in orig_state_dict.copy().keys():
														_a     					=    orig_state_dict.pop(UpperCamelCase				)

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

																					if "weight" in key:
																												_a     					=    val[:dim, :]
																												_a     					=    val[dim : dim * 2, :]
																												_a     					=    val[-dim:, :]
																					else:
																												_a     					=    val[:dim]
																												_a     					=    val[
																												    dim : dim * 2
																												]
																												_a     					=    val[-dim:]
														else:
																					_a     					=    val

							return orig_state_dict
def 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Optional[int]				):





							'''simple docstring'''


							_a     					=    timm.create_model(UpperCamelCase			,						pretrained=UpperCamelCase				)
							timm_model.eval()

							_a     					=    get_swinva_config(UpperCamelCase				)
							_a     					=    SwinvaForImageClassification(UpperCamelCase				)
							model.eval()

							_a     					=    convert_state_dict(timm_model.state_dict()			,						UpperCamelCase				)
							model.load_state_dict(UpperCamelCase				)

							_a     					=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''

							_a     					=    AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''			,						'''-'''				)				)				)
							_a     					=    Image.open(requests.get(UpperCamelCase			,						stream=UpperCamelCase				).raw				)
							_a     					=    image_processor(images=UpperCamelCase			,						return_tensors='''pt'''				)

							_a     					=    timm_model(inputs['''pixel_values''']				)
							_a     					=    model(**UpperCamelCase				).logits

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

							print(f'Saving model {swinva_name} to {pytorch_dump_folder_path}'				)
							model.save_pretrained(UpperCamelCase				)

							print(f'Saving image processor to {pytorch_dump_folder_path}'				)
							image_processor.save_pretrained(UpperCamelCase				)

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


if __name__ == "__main__":
				_snake_case				:				Any       							=		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				:				Dict       							=		parser.parse_args()
				convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)







'''simple docstring'''


import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
				import os

				from flax.core.frozen_dict import unfreeze
				from flax.traverse_util import flatten_dict

				from transformers import FlaxBertModel

				_snake_case				:				Optional[Any]       							=		'0.12'  # assumed parallelism: 8

@require_flax
@is_staging_test
class       A   (		unittest.TestCase   ):







							@classmethod
							def        __lowerCAmelCase      (  cls	:  Tuple )      ->							int:


														"""simple docstring"""


														_a     					=    TOKEN
														HfFolder.save_token(lowerCAmelCase_ )







							@classmethod
							def        __lowerCAmelCase      (  cls	:  Tuple )      ->							Optional[int]:


														"""simple docstring"""


														try:
																					delete_repo(token=cls._token							,      repo_id='''test-model-flax''' )
														except HTTPError:
																					pass

														try:
																					delete_repo(token=cls._token							,      repo_id='''valid_org/test-model-flax-org''' )
														except HTTPError:
																					pass







							def        __lowerCAmelCase      (  self	:  Any )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    BertConfig(
														    vocab_size=99							,      hidden_size=32							,      num_hidden_layers=5							,      num_attention_heads=4							,      intermediate_size=37 )
														_a     					=    FlaxBertModel(lowerCAmelCase_ )
														model.push_to_hub('''test-model-flax'''							,      use_auth_token=self._token )

														_a     					=    FlaxBertModel.from_pretrained(F'{USER}/test-model-flax' )

														_a     					=    flatten_dict(unfreeze(model.params ) )
														_a     					=    flatten_dict(unfreeze(new_model.params ) )

														for key in base_params.keys():
																					_a     					=    (base_params[key] - new_params[key]).sum().item()
																					self.assertLessEqual(lowerCAmelCase_							,      1e-3							,      msg=F'{key} not identical' )

														# Reset repo
														delete_repo(token=self._token							,      repo_id='''test-model-flax''' )

														# Push to hub via save_pretrained
														with tempfile.TemporaryDirectory() as tmp_dir:
																					model.save_pretrained(lowerCAmelCase_							,      repo_id='''test-model-flax'''							,      push_to_hub=lowerCAmelCase_							,      use_auth_token=self._token )

														_a     					=    FlaxBertModel.from_pretrained(F'{USER}/test-model-flax' )

														_a     					=    flatten_dict(unfreeze(model.params ) )
														_a     					=    flatten_dict(unfreeze(new_model.params ) )

														for key in base_params.keys():
																					_a     					=    (base_params[key] - new_params[key]).sum().item()
																					self.assertLessEqual(lowerCAmelCase_							,      1e-3							,      msg=F'{key} not identical' )







							def        __lowerCAmelCase      (  self	:  int )      ->							Tuple:


														"""simple docstring"""


														_a     					=    BertConfig(
														    vocab_size=99							,      hidden_size=32							,      num_hidden_layers=5							,      num_attention_heads=4							,      intermediate_size=37 )
														_a     					=    FlaxBertModel(lowerCAmelCase_ )
														model.push_to_hub('''valid_org/test-model-flax-org'''							,      use_auth_token=self._token )

														_a     					=    FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )

														_a     					=    flatten_dict(unfreeze(model.params ) )
														_a     					=    flatten_dict(unfreeze(new_model.params ) )

														for key in base_params.keys():
																					_a     					=    (base_params[key] - new_params[key]).sum().item()
																					self.assertLessEqual(lowerCAmelCase_							,      1e-3							,      msg=F'{key} not identical' )

														# Reset repo
														delete_repo(token=self._token							,      repo_id='''valid_org/test-model-flax-org''' )

														# Push to hub via save_pretrained
														with tempfile.TemporaryDirectory() as tmp_dir:
																					model.save_pretrained(
																					    lowerCAmelCase_							,      repo_id='''valid_org/test-model-flax-org'''							,      push_to_hub=lowerCAmelCase_							,      use_auth_token=self._token )

														_a     					=    FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )

														_a     					=    flatten_dict(unfreeze(model.params ) )
														_a     					=    flatten_dict(unfreeze(new_model.params ) )

														for key in base_params.keys():
																					_a     					=    (base_params[key] - new_params[key]).sum().item()
																					self.assertLessEqual(lowerCAmelCase_							,      1e-3							,      msg=F'{key} not identical' )



def 		snake_case_   (UpperCamelCase	:  List[str]			,						UpperCamelCase	:  Optional[Any]				):





							'''simple docstring'''


							_a     					=    True
							_a     					=    flatten_dict(modela.params				)
							_a     					=    flatten_dict(modela.params				)
							for key in flat_params_a.keys():
														if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]				)				) > 1e-4:
																					_a     					=    False

							return models_are_equal

@require_flax
class       A   (		unittest.TestCase   ):







							def        __lowerCAmelCase      (  self	:  Dict )      ->							int:


														"""simple docstring"""


														_a     					=    BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
														_a     					=    FlaxBertModel(lowerCAmelCase_ )

														_a     					=    '''bert'''
														with tempfile.TemporaryDirectory() as tmp_dir:
																					model.save_pretrained(os.path.join(lowerCAmelCase_							,      lowerCAmelCase_ ) )

																					with self.assertRaises(lowerCAmelCase_ ):
																												_a     					=    FlaxBertModel.from_pretrained(lowerCAmelCase_ )

																					_a     					=    FlaxBertModel.from_pretrained(lowerCAmelCase_							,      subfolder=lowerCAmelCase_ )

														self.assertTrue(check_models_equal(lowerCAmelCase_							,      lowerCAmelCase_ ) )







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
														_a     					=    FlaxBertModel(lowerCAmelCase_ )

														_a     					=    '''bert'''
														with tempfile.TemporaryDirectory() as tmp_dir:
																					model.save_pretrained(os.path.join(lowerCAmelCase_							,      lowerCAmelCase_ )							,      max_shard_size='''10KB''' )

																					with self.assertRaises(lowerCAmelCase_ ):
																												_a     					=    FlaxBertModel.from_pretrained(lowerCAmelCase_ )

																					_a     					=    FlaxBertModel.from_pretrained(lowerCAmelCase_							,      subfolder=lowerCAmelCase_ )

														self.assertTrue(check_models_equal(lowerCAmelCase_							,      lowerCAmelCase_ ) )







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							List[str]:


														"""simple docstring"""


														_a     					=    '''bert'''
														_a     					=    '''hf-internal-testing/tiny-random-bert-subfolder'''

														with self.assertRaises(lowerCAmelCase_ ):
																					_a     					=    FlaxBertModel.from_pretrained(lowerCAmelCase_ )

														_a     					=    FlaxBertModel.from_pretrained(lowerCAmelCase_							,      subfolder=lowerCAmelCase_ )

														self.assertIsNotNone(lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Tuple:


														"""simple docstring"""


														_a     					=    '''bert'''
														_a     					=    '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
														with self.assertRaises(lowerCAmelCase_ ):
																					_a     					=    FlaxBertModel.from_pretrained(lowerCAmelCase_ )

														_a     					=    FlaxBertModel.from_pretrained(lowerCAmelCase_							,      subfolder=lowerCAmelCase_ )

														self.assertIsNotNone(lowerCAmelCase_ )

