
'''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 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Dict:
	_snake_case   						=		SwinvaConfig()
	_snake_case   						=		swinva_name.split('_'	)

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

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

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

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		_snake_case   						=		21_841
		_snake_case   						=		'huggingface/label-files'
		_snake_case   						=		'imagenet-22k-id2label.json'
		_snake_case   						=		json.load(open(hf_hub_download(__A			,				__A			,				repo_type='dataset'	)			,				'r'	)	)
		_snake_case   						=		{int(__A	): v for k, v in idalabel.items()}
		_snake_case   						=		idalabel
		_snake_case   						=		{v: k for k, v in idalabel.items()}

	else:
		_snake_case   						=		1_000
		_snake_case   						=		'huggingface/label-files'
		_snake_case   						=		'imagenet-1k-id2label.json'
		_snake_case   						=		json.load(open(hf_hub_download(__A			,				__A			,				repo_type='dataset'	)			,				'r'	)	)
		_snake_case   						=		{int(__A	): v for k, v in idalabel.items()}
		_snake_case   						=		idalabel
		_snake_case   						=		{v: k for k, v in idalabel.items()}

	_snake_case   						=		img_size
	_snake_case   						=		num_classes
	_snake_case   						=		embed_dim
	_snake_case   						=		depths
	_snake_case   						=		num_heads
	_snake_case   						=		window_size

	return config







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

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

	return name







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Optional[int]:
	for key in orig_state_dict.copy().keys():
		_snake_case   						=		orig_state_dict.pop(__A	)

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

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

	return orig_state_dict







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Optional[Any]:
	_snake_case   						=		timm.create_model(__A			,				pretrained=__A	)
	timm_model.eval()

	_snake_case   						=		get_swinva_config(__A	)
	_snake_case   						=		SwinvaForImageClassification(__A	)
	model.eval()

	_snake_case   						=		convert_state_dict(timm_model.state_dict()			,				__A	)
	model.load_state_dict(__A	)

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

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

	_snake_case   						=		timm_model(inputs['pixel_values']	)
	_snake_case   						=		model(**__A	).logits

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

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

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

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


if __name__ == "__main__":
					lowercase    :							int      =					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."
					)

					lowercase    :							Dict      =					parser.parse_args()
					convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)



'''simple docstring'''







import tensorflow as tf

from ...tf_utils import shape_list


class 						__UpperCAmelCase      (					tf.keras.layers.Layer						):


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_=False    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								super().__init__(**lowerCAmelCase_  )

								_snake_case   						=		vocab_size
								_snake_case   						=		d_embed
								_snake_case   						=		d_proj

								_snake_case   						=		cutoffs + [vocab_size]
								_snake_case   						=		[0] + self.cutoffs
								_snake_case   						=		div_val

								_snake_case   						=		self.cutoffs[0]
								_snake_case   						=		len(self.cutoffs  ) - 1
								_snake_case   						=		self.shortlist_size + self.n_clusters
								_snake_case   						=		keep_order

								_snake_case   						=		[]
								_snake_case   						=		[]


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								if self.n_clusters > 0:
									_snake_case   						=		self.add_weight(
									    shape=(self.n_clusters, self.d_embed)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name='cluster_weight'  )
									_snake_case   						=		self.add_weight(
									    shape=(self.n_clusters,)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name='cluster_bias'  )

								if self.div_val == 1:
									for i in range(len(self.cutoffs  )  ):
										if self.d_proj != self.d_embed:
											_snake_case   						=		self.add_weight(
											    shape=(self.d_embed, self.d_proj)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_projs_._{i}'    ,   )
											self.out_projs.append(lowerCAmelCase_  )
										else:
											self.out_projs.append(lowerCAmelCase_  )
										_snake_case   						=		self.add_weight(
										    shape=(self.vocab_size, self.d_embed)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_layers_._{i}_._weight'    ,   )
										_snake_case   						=		self.add_weight(
										    shape=(self.vocab_size,)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_layers_._{i}_._bias'    ,   )
										self.out_layers.append((weight, bias)  )
								else:
									for i in range(len(self.cutoffs  )  ):
										_snake_case						, _snake_case   						=		self.cutoff_ends[i], self.cutoff_ends[i + 1]
										_snake_case   						=		self.d_embed // (self.div_val**i)

										_snake_case   						=		self.add_weight(
										    shape=(d_emb_i, self.d_proj)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_projs_._{i}'  )
										self.out_projs.append(lowerCAmelCase_  )
										_snake_case   						=		self.add_weight(
										    shape=(r_idx - l_idx, d_emb_i)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_layers_._{i}_._weight'    ,   )
										_snake_case   						=		self.add_weight(
										    shape=(r_idx - l_idx,)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_layers_._{i}_._bias'    ,   )
										self.out_layers.append((weight, bias)  )
								super().build(lowerCAmelCase_  )


							@staticmethod
							def  lowerCamelCase				(       lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=None  ):





								"""simple docstring"""






								_snake_case   						=		x
								if proj is not None:
									_snake_case   						=		tf.einsum('ibd,ed->ibe'    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )
								return tf.einsum('ibd,nd->ibn'    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ) + b


							@staticmethod
							def  lowerCamelCase				(       lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		shape_list(lowerCAmelCase_  )
								_snake_case   						=		tf.range(lp_size[0]    ,   dtype=target.dtype  )
								_snake_case   						=		tf.stack([r, target]    ,   1  )
								return tf.gather_nd(lowerCAmelCase_    ,   lowerCAmelCase_  )




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=False  ):





								"""simple docstring"""






								_snake_case   						=		0
								if self.n_clusters == 0:
									_snake_case   						=		self._logit(lowerCAmelCase_    ,   self.out_layers[0][0]    ,   self.out_layers[0][1]    ,   self.out_projs[0]  )
									if target is not None:
										_snake_case   						=		tf.nn.sparse_softmax_cross_entropy_with_logits(labels=lowerCAmelCase_    ,   logits=lowerCAmelCase_  )
									_snake_case   						=		tf.nn.log_softmax(lowerCAmelCase_    ,   axis=-1  )
								else:
									_snake_case   						=		shape_list(lowerCAmelCase_  )
									_snake_case   						=		[]
									_snake_case   						=		tf.zeros(hidden_sizes[:2]  )
									for i in range(len(self.cutoffs  )  ):
										_snake_case						, _snake_case   						=		self.cutoff_ends[i], self.cutoff_ends[i + 1]
										if target is not None:
											_snake_case   						=		(target >= l_idx) & (target < r_idx)
											_snake_case   						=		tf.where(lowerCAmelCase_  )
											_snake_case   						=		tf.boolean_mask(lowerCAmelCase_    ,   lowerCAmelCase_  ) - l_idx

										if self.div_val == 1:
											_snake_case   						=		self.out_layers[0][0][l_idx:r_idx]
											_snake_case   						=		self.out_layers[0][1][l_idx:r_idx]
										else:
											_snake_case   						=		self.out_layers[i][0]
											_snake_case   						=		self.out_layers[i][1]

										if i == 0:
											_snake_case   						=		tf.concat([cur_W, self.cluster_weight]    ,   0  )
											_snake_case   						=		tf.concat([cur_b, self.cluster_bias]    ,   0  )

											_snake_case   						=		self._logit(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   self.out_projs[0]  )
											_snake_case   						=		tf.nn.log_softmax(lowerCAmelCase_  )
											out.append(head_logprob[..., : self.cutoffs[0]]  )
											if target is not None:
												_snake_case   						=		tf.boolean_mask(lowerCAmelCase_    ,   lowerCAmelCase_  )
												_snake_case   						=		self._gather_logprob(lowerCAmelCase_    ,   lowerCAmelCase_  )
										else:
											_snake_case   						=		self._logit(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   self.out_projs[i]  )
											_snake_case   						=		tf.nn.log_softmax(lowerCAmelCase_  )
											_snake_case   						=		self.cutoffs[0] + i - 1  # No probability for the head cluster
											_snake_case   						=		head_logprob[..., cluster_prob_idx, None] + tail_logprob
											out.append(lowerCAmelCase_  )
											if target is not None:
												_snake_case   						=		tf.boolean_mask(lowerCAmelCase_    ,   lowerCAmelCase_  )
												_snake_case   						=		tf.boolean_mask(lowerCAmelCase_    ,   lowerCAmelCase_  )
												_snake_case   						=		self._gather_logprob(lowerCAmelCase_    ,   lowerCAmelCase_  )
												cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
										if target is not None:
											loss += tf.scatter_nd(lowerCAmelCase_    ,   -cur_logprob    ,   shape_list(lowerCAmelCase_  )  )
									_snake_case   						=		tf.concat(lowerCAmelCase_    ,   axis=-1  )

								if target is not None:
									if return_mean:
										_snake_case   						=		tf.reduce_mean(lowerCAmelCase_  )
									# Add the training-time loss value to the layer using `self.add_loss()`.
									self.add_loss(lowerCAmelCase_  )

									# Log the loss as a metric (we could log arbitrary metrics,
									# including different metrics for training and inference.
									self.add_metric(lowerCAmelCase_    ,   name=self.name    ,   aggregation='mean' if return_mean else ''  )

								return out

