



from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


A__  :  Optional[int]								=					logging.get_logger(__name__)


def 			UpperCamelCase(  __UpperCamelCase							:		int     ,__UpperCamelCase							:		Optional[Any] ):
		try:
				with open(__UpperCamelCase     ,'''rb''' ) as flax_state_f:
						lowerCAmelCase_			:     List[Any]									= from_bytes(__UpperCamelCase     ,flax_state_f.read() )
		except UnpicklingError as e:
				try:
						with open(__UpperCamelCase ) as f:
								if f.read().startswith('''version''' ):
										raise OSError(
										    '''You seem to have cloned a repository without having git-lfs installed. Please'''
										    ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
										    ''' folder you cloned.''' )
								else:
										raise ValueError from e
				except (UnicodeDecodeError, ValueError):
						raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """ )

		return load_flax_weights_in_pytorch_model(__UpperCamelCase     ,__UpperCamelCase )





def 			UpperCamelCase(  __UpperCamelCase							:		int     ,__UpperCamelCase							:		Optional[int] ):

		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
				    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
				    ''' instructions.''' )
				raise

		# check if we have bf16 weights
		lowerCAmelCase_			:     Dict									= flatten_dict(jax.tree_util.tree_map(lambda __UpperCamelCase : x.dtype == jnp.bfloataa     ,__UpperCamelCase ) ).values()
		if any(__UpperCamelCase ):
				# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

				# and bf16 is not fully supported in PT yet.
				logger.warning(
				    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
				    '''before loading those in PyTorch model.''' )
				lowerCAmelCase_			:     Optional[Any]									= jax.tree_util.tree_map(
				    lambda __UpperCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params     ,__UpperCamelCase )

		lowerCAmelCase_			:     Optional[int]									= ''''''

		lowerCAmelCase_			:     Optional[int]									= flatten_dict(__UpperCamelCase     ,sep='''.''' )
		lowerCAmelCase_			:     List[Any]									= pt_model.state_dict()

		# keep track of unexpected & missing keys
		lowerCAmelCase_			:     Union[str, Any]									= []
		lowerCAmelCase_			:     int									= set(pt_model_dict.keys() )

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				lowerCAmelCase_			:     Any									= flax_key_tuple.split('''.''' )

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						lowerCAmelCase_			:     int									= flax_key_tuple_array[:-1] + ['''weight''']
						lowerCAmelCase_			:     Tuple									= jnp.transpose(__UpperCamelCase     ,(3, 2, 0, 1) )
				elif flax_key_tuple_array[-1] == "kernel":
						lowerCAmelCase_			:     str									= flax_key_tuple_array[:-1] + ['''weight''']
						lowerCAmelCase_			:     Optional[Any]									= flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						lowerCAmelCase_			:     Optional[int]									= flax_key_tuple_array[:-1] + ['''weight''']

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(__UpperCamelCase ):
								lowerCAmelCase_			:     str									= (
								    flax_key_tuple_string.replace('''_0'''     ,'''.0''' )
								    .replace('''_1'''     ,'''.1''' )
								    .replace('''_2'''     ,'''.2''' )
								    .replace('''_3'''     ,'''.3''' )
								    .replace('''_4'''     ,'''.4''' )
								    .replace('''_5'''     ,'''.5''' )
								    .replace('''_6'''     ,'''.6''' )
								    .replace('''_7'''     ,'''.7''' )
								    .replace('''_8'''     ,'''.8''' )
								    .replace('''_9'''     ,'''.9''' )
								)

				lowerCAmelCase_			:     Tuple									= '''.'''.join(__UpperCamelCase )

				if flax_key in pt_model_dict:
						if flax_tensor.shape != pt_model_dict[flax_key].shape:
								raise ValueError(
								    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
								    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
						else:
								# add weight to pytorch dict
								lowerCAmelCase_			:     List[Any]									= np.asarray(__UpperCamelCase ) if not isinstance(__UpperCamelCase     ,np.ndarray ) else flax_tensor
								lowerCAmelCase_			:     Any									= torch.from_numpy(__UpperCamelCase )
								# remove from missing keys
								missing_keys.remove(__UpperCamelCase )
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(__UpperCamelCase )

		pt_model.load_state_dict(__UpperCamelCase )

		# re-transform missing_keys to list
		lowerCAmelCase_			:     Dict									= list(__UpperCamelCase )

		if len(__UpperCamelCase ) > 0:
				logger.warning(
				    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
				    f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
				    f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
				    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
				    f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
				    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
				    ''' FlaxBertForSequenceClassification model).''' )
		if len(__UpperCamelCase ) > 0:
				logger.warning(
				    f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
				    f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
				    ''' use it for predictions and inference.''' )

		return pt_model










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 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     List[str]									= SwinvaConfig()
		lowerCAmelCase_			:     List[str]									= swinva_name.split('''_''' )

		lowerCAmelCase_			:     str									= name_split[1]
		if "to" in name_split[3]:
				lowerCAmelCase_			:     List[Any]									= int(name_split[3][-3:] )
		else:
				lowerCAmelCase_			:     List[Any]									= int(name_split[3] )
		if "to" in name_split[2]:
				lowerCAmelCase_			:     List[str]									= int(name_split[2][-2:] )
		else:
				lowerCAmelCase_			:     int									= int(name_split[2][6:] )

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

		if "to" in swinva_name:
				lowerCAmelCase_			:     Union[str, Any]									= (12, 12, 12, 6)

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

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

		lowerCAmelCase_			:     Optional[int]									= img_size
		lowerCAmelCase_			:     Dict									= num_classes
		lowerCAmelCase_			:     Dict									= embed_dim
		lowerCAmelCase_			:     Optional[Any]									= depths
		lowerCAmelCase_			:     Optional[int]									= num_heads
		lowerCAmelCase_			:     Dict									= window_size

		return config


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

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

		return name


def 			UpperCamelCase(  __UpperCamelCase							:		Any     ,__UpperCamelCase							:		Optional[int] ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_			:     Optional[int]									= orig_state_dict.pop(__UpperCamelCase )

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

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

		return orig_state_dict


def 			UpperCamelCase(  __UpperCamelCase							:		int     ,__UpperCamelCase							:		Dict ):
		lowerCAmelCase_			:     Optional[Any]									= timm.create_model(__UpperCamelCase     ,pretrained=__UpperCamelCase )
		timm_model.eval()

		lowerCAmelCase_			:     List[str]									= get_swinva_config(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= SwinvaForImageClassification(__UpperCamelCase )
		model.eval()

		lowerCAmelCase_			:     str									= convert_state_dict(timm_model.state_dict()     ,__UpperCamelCase )
		model.load_state_dict(__UpperCamelCase )

		lowerCAmelCase_			:     List[Any]									= '''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

		lowerCAmelCase_			:     List[str]									= timm_model(inputs['''pixel_values'''] )
		lowerCAmelCase_			:     Union[str, Any]									= 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__":
						A__  :  Union[str, 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.'''
						)

						A__  :  Optional[Any]								=					parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





