






"""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__				):
				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(lowerCAmelCase__       ,lowerCAmelCase__       ,repo_type='dataset'				)       ,'r'				)				)
								A__							 =       {int(lowerCAmelCase__				): 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(lowerCAmelCase__       ,lowerCAmelCase__       ,repo_type='dataset'				)       ,'r'				)				)
								A__							 =       {int(lowerCAmelCase__				): 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  __lowerCamelCase					(					lowerCAmelCase__				):
				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  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				for key in orig_state_dict.copy().keys():
								A__							 =       orig_state_dict.pop(lowerCAmelCase__				)

								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  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       timm.create_model(lowerCAmelCase__       ,pretrained=lowerCAmelCase__				)
				timm_model.eval()

				A__							 =       get_swinva_config(lowerCAmelCase__				)
				A__							 =       SwinvaForImageClassification(lowerCAmelCase__				)
				model.eval()

				A__							 =       convert_state_dict(timm_model.state_dict()       ,lowerCAmelCase__				)
				model.load_state_dict(lowerCAmelCase__				)

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

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

				A__							 =       timm_model(inputs['pixel_values']				)
				A__							 =       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__":
					SCREAMING_SNAKE_CASE						:						Dict					   =       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.'''
					)

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










"""simple docstring"""






import os

import numpy
import onnx



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       a.name
				A__							 =       b.name

				A__							 =       ''
				A__							 =       ''

				A__							 =       a == b

				A__							 =       name_a
				A__							 =       name_b

				return res



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				for i, input_name in enumerate(node_proto.input				):
								if input_name == name:
												node_proto.input.insert(lowerCAmelCase__       ,lowerCAmelCase__				)
												node_proto.input.pop(i + 1				)

				if node_proto.op_type == "If":
								_graph_replace_input_with(node_proto.attribute[0].g       ,lowerCAmelCase__       ,lowerCAmelCase__				)
								_graph_replace_input_with(node_proto.attribute[1].g       ,lowerCAmelCase__       ,lowerCAmelCase__				)
				if node_proto.op_type == "Loop":
								_graph_replace_input_with(node_proto.attribute[0].g       ,lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(model.graph.initializer				)
				A__							 =       list(model_without_ext.graph.initializer				)
				for i, ref_i in ind_to_replace:
								assert inits_with_data[i].name == inits[i].name
								assert inits_with_data[ref_i].name == inits[ref_i].name
								assert i > ref_i

								A__							 =       inits[i].name
								A__							 =       inits[ref_i].name

								model_without_ext.graph.initializer.remove(inits[i]				)

								# for n in model.graph.node:
								_graph_replace_input_with(model_without_ext.graph       ,lowerCAmelCase__       ,lowerCAmelCase__				)







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       os.path.dirname(lowerCAmelCase__				)
				A__							 =       os.path.basename(lowerCAmelCase__				)

				A__							 =       onnx.load(os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)				)

				A__							 =       list(model.graph.initializer				)

				A__							 =       set()
				A__							 =       {}
				A__							 =       []

				A__							 =       0

				for i in range(len(lowerCAmelCase__				)				):
								if i in dup_set:
												continue

								for j in range(i + 1       ,len(lowerCAmelCase__				)				):
												if j in dup_set:
																continue
												if _is_equal_tensor_proto(inits[i]       ,inits[j]				):
																dup_set.add(lowerCAmelCase__				)
																dup_set.add(lowerCAmelCase__				)

																A__							 =       inits[j].data_type
																A__							 =       numpy.prod(inits[j].dims				)
																if dtype == 1:
																				mem_size *= 4
																elif dtype == 6:
																				mem_size *= 4
																elif dtype == 7 or dtype == 11:
																				mem_size *= 8
																else:
																				print('unexpected data type: '       ,lowerCAmelCase__				)
																total_reduced_size += mem_size

																A__							 =       inits[i].name
																A__							 =       inits[j].name

																if name_i in dup_map:
																				dup_map[name_i].append(lowerCAmelCase__				)
																else:
																				A__							 =       [name_j]
																ind_to_replace.append((j, i)				)

				print('total reduced size: '       ,total_reduced_size / 1024 / 1024 / 1024       ,'GB'				)

				A__							 =       sorted(lowerCAmelCase__				)
				_remove_dup_initializers_from_model(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)

				A__							 =       'optimized_' + model_file_name
				A__							 =       os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)
				onnx.save(lowerCAmelCase__       ,lowerCAmelCase__				)

				return new_model


