






import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase  							= logging.get_logger(__name__)





def 						__UpperCAmelCase      (					a_):
							snake_case_           =							MobileNetVaConfig(layer_norm_eps=0.0_01)

							if "quant" in model_name:
														raise ValueError('Quantized models are not supported.')

							snake_case_           =							re.match(R'^.*mobilenet_v2_([^_]*)_([^_]*)$'							,					a_)
							if matches:
														snake_case_           =							float(matches[1])
														snake_case_           =							int(matches[2])

							if model_name.startswith('deeplabv3_'):
														snake_case_           =							8
														snake_case_           =							21
														snake_case_           =							'pascal-voc-id2label.json'
							else:
														# The TensorFlow version of MobileNetV2 predicts 1001 classes instead
														# of the usual 1000. The first class (index 0) is "background".
														snake_case_           =							10_01
														snake_case_           =							'imagenet-1k-id2label.json'

							snake_case_           =							'huggingface/label-files'
							snake_case_           =							json.load(open(hf_hub_download(a_							,					a_							,					repo_type='dataset')							,					'r'))

							if config.num_labels == 10_01:
														snake_case_           =							{int(a_) + 1: v for k, v in idalabel.items()}
														snake_case_           =							'background'
							else:
														snake_case_           =							{int(a_): v for k, v in idalabel.items()}

							snake_case_           =							idalabel
							snake_case_           =							{v: k for k, v in idalabel.items()}

							return config





def 						__UpperCAmelCase      (					):
							snake_case_           =							'http://images.cocodataset.org/val2017/000000039769.jpg'
							snake_case_           =							Image.open(requests.get(a_							,					stream=a_).raw)
							return im





@torch.no_grad()
def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_=False):
							snake_case_           =							get_mobilenet_va_config(a_)

							# Load 🤗 model
							if model_name.startswith('deeplabv3_'):
														snake_case_           =							MobileNetVaForSemanticSegmentation(a_).eval()
							else:
														snake_case_           =							MobileNetVaForImageClassification(a_).eval()

							# Load weights from TensorFlow checkpoint
							load_tf_weights_in_mobilenet_va(a_							,					a_							,					a_)

							# Check outputs on an image, prepared by MobileNetV2ImageProcessor
							snake_case_           =							MobileNetVaImageProcessor(
							    crop_size={'width': config.image_size, 'height': config.image_size}							,					size={'shortest_edge': config.image_size + 32}							,					)
							snake_case_           =							image_processor(images=prepare_img()							,					return_tensors='pt')
							snake_case_           =							model(**a_)
							snake_case_           =							outputs.logits

							if model_name.startswith('deeplabv3_'):
														assert logits.shape == (1, 21, 65, 65)

														if model_name == "deeplabv3_mobilenet_v2_1.0_513":
																					snake_case_           =							torch.tensor(
																					    [
																					        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
																					        [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
																					        [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
																					    ])

														else:
																					raise ValueError(f'''Unknown model name: {model_name}''')

														assert torch.allclose(logits[0, :3, :3, :3]							,					a_							,					atol=1E-4)
							else:
														assert logits.shape == (1, 10_01)

														if model_name == "mobilenet_v2_1.4_224":
																					snake_case_           =							torch.tensor([0.01_81, -1.00_15, 0.46_88])
														elif model_name == "mobilenet_v2_1.0_224":
																					snake_case_           =							torch.tensor([0.24_45, -1.19_93, 0.19_05])
														elif model_name == "mobilenet_v2_0.75_160":
																					snake_case_           =							torch.tensor([0.24_82, 0.41_36, 0.66_69])
														elif model_name == "mobilenet_v2_0.35_96":
																					snake_case_           =							torch.tensor([0.14_51, -0.46_24, 0.71_92])
														else:
																					snake_case_           =							None

														if expected_logits is not None:
																					assert torch.allclose(logits[0, :3]							,					a_							,					atol=1E-4)

							Path(a_).mkdir(exist_ok=a_)
							print(f'''Saving model {model_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_)

							if push_to_hub:
														print('Pushing to the hub...')
														snake_case_           =							'google/' + model_name
														image_processor.push_to_hub(a_)
														model.push_to_hub(a_)


if __name__ == "__main__":
	lowercase  							= argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="mobilenet_v2_1.0_224",
	    type=str,
	    help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.",
	)
	parser.add_argument(
	    "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
	)

	lowercase  							= parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)













import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 						__UpperCAmelCase      (					a_							,					a_=1):
							if n_shave_prefix_segments >= 0:
														return ".".join(path.split('.')[n_shave_prefix_segments:])
							else:
														return ".".join(path.split('.')[:n_shave_prefix_segments])





def 						__UpperCAmelCase      (					a_							,					a_=0):
							snake_case_           =							[]
							for old_item in old_list:
														snake_case_           =							old_item
														snake_case_           =							new_item.replace('block.'							,					'resnets.')
														snake_case_           =							new_item.replace('conv_shorcut'							,					'conv1')
														snake_case_           =							new_item.replace('in_shortcut'							,					'conv_shortcut')
														snake_case_           =							new_item.replace('temb_proj'							,					'time_emb_proj')

														snake_case_           =							shave_segments(a_							,					n_shave_prefix_segments=a_)

														mapping.append({'old': old_item, 'new': new_item})

							return mapping





def 						__UpperCAmelCase      (					a_							,					a_=0							,					a_=False):
							snake_case_           =							[]
							for old_item in old_list:
														snake_case_           =							old_item

														# In `model.mid`, the layer is called `attn`.
														if not in_mid:
																					snake_case_           =							new_item.replace('attn'							,					'attentions')
														snake_case_           =							new_item.replace('.k.'							,					'.key.')
														snake_case_           =							new_item.replace('.v.'							,					'.value.')
														snake_case_           =							new_item.replace('.q.'							,					'.query.')

														snake_case_           =							new_item.replace('proj_out'							,					'proj_attn')
														snake_case_           =							new_item.replace('norm'							,					'group_norm')

														snake_case_           =							shave_segments(a_							,					n_shave_prefix_segments=a_)
														mapping.append({'old': old_item, 'new': new_item})

							return mapping





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_=None							,					a_=None							,					a_=None):
							assert isinstance(a_							,					a_), "Paths should be a list of dicts containing 'old' and 'new' keys."

							if attention_paths_to_split is not None:
														if config is None:
																					raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.')

														for path, path_map in attention_paths_to_split.items():
																					snake_case_           =							old_checkpoint[path]
																					snake_case_           =							old_tensor.shape[0] // 3

																					snake_case_           =							(-1, channels) if len(old_tensor.shape) == 3 else (-1)

																					snake_case_           =							old_tensor.shape[0] // config.get('num_head_channels'							,					1) // 3

																					snake_case_           =							old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
																					snake_case_      ,		snake_case_      ,		snake_case_           =							old_tensor.split(channels // num_heads							,					dim=1)

																					snake_case_           =							query.reshape(a_).squeeze()
																					snake_case_           =							key.reshape(a_).squeeze()
																					snake_case_           =							value.reshape(a_).squeeze()

							for path in paths:
														snake_case_           =							path['new']

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														snake_case_           =							new_path.replace('down.'							,					'down_blocks.')
														snake_case_           =							new_path.replace('up.'							,					'up_blocks.')

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												snake_case_           =							new_path.replace(replacement['old']							,					replacement['new'])

														if "attentions" in new_path:
																					snake_case_           =							old_checkpoint[path['old']].squeeze()
														else:
																					snake_case_           =							old_checkpoint[path['old']]





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							{}

							snake_case_           =							checkpoint['temb.dense.0.weight']
							snake_case_           =							checkpoint['temb.dense.0.bias']
							snake_case_           =							checkpoint['temb.dense.1.weight']
							snake_case_           =							checkpoint['temb.dense.1.bias']

							snake_case_           =							checkpoint['norm_out.weight']
							snake_case_           =							checkpoint['norm_out.bias']

							snake_case_           =							checkpoint['conv_in.weight']
							snake_case_           =							checkpoint['conv_in.bias']
							snake_case_           =							checkpoint['conv_out.weight']
							snake_case_           =							checkpoint['conv_out.bias']

							snake_case_           =							len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'down' in layer})
							snake_case_           =							{
							    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(a_)
							}

							snake_case_           =							len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'up' in layer})
							snake_case_           =							{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(a_)}

							for i in range(a_):
														snake_case_           =							(i - 1) // (config['layers_per_block'] + 1)

														if any('downsample' in layer for layer in down_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''down.{i}.downsample.op.weight'''
																					]
																					snake_case_           =							checkpoint[f'''down.{i}.downsample.op.bias''']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

														if any('block' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in down_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_)

														if any('attn' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in down_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					config=a_)

							snake_case_           =							[key for key in checkpoint if 'mid.block_1' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.block_2' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.attn_1' in key]

							# Mid new 2
							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,					)

							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,					)

							snake_case_           =							renew_attention_paths(a_							,					in_mid=a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,					)

							for i in range(a_):
														snake_case_           =							num_up_blocks - 1 - i

														if any('upsample' in layer for layer in up_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''up.{i}.upsample.conv.weight'''
																					]
																					snake_case_           =							checkpoint[f'''up.{i}.upsample.conv.bias''']

														if any('block' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in up_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

														if any('attn' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in up_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

							snake_case_           =							{k.replace('mid_new_2'							,					'mid_block'): v for k, v in new_checkpoint.items()}
							return new_checkpoint





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							{}

							snake_case_           =							checkpoint['encoder.norm_out.weight']
							snake_case_           =							checkpoint['encoder.norm_out.bias']

							snake_case_           =							checkpoint['encoder.conv_in.weight']
							snake_case_           =							checkpoint['encoder.conv_in.bias']
							snake_case_           =							checkpoint['encoder.conv_out.weight']
							snake_case_           =							checkpoint['encoder.conv_out.bias']

							snake_case_           =							checkpoint['decoder.norm_out.weight']
							snake_case_           =							checkpoint['decoder.norm_out.bias']

							snake_case_           =							checkpoint['decoder.conv_in.weight']
							snake_case_           =							checkpoint['decoder.conv_in.bias']
							snake_case_           =							checkpoint['decoder.conv_out.weight']
							snake_case_           =							checkpoint['decoder.conv_out.bias']

							snake_case_           =							len({'.'.join(layer.split('.')[:3]) for layer in checkpoint if 'down' in layer})
							snake_case_           =							{
							    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(a_)
							}

							snake_case_           =							len({'.'.join(layer.split('.')[:3]) for layer in checkpoint if 'up' in layer})
							snake_case_           =							{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(a_)}

							for i in range(a_):
														snake_case_           =							(i - 1) // (config['layers_per_block'] + 1)

														if any('downsample' in layer for layer in down_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''encoder.down.{i}.downsample.conv.weight'''
																					]
																					snake_case_           =							checkpoint[
																					    f'''encoder.down.{i}.downsample.conv.bias'''
																					]

														if any('block' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in down_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_)

														if any('attn' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in down_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					config=a_)

							snake_case_           =							[key for key in checkpoint if 'mid.block_1' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.block_2' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.attn_1' in key]

							# Mid new 2
							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,					)

							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,					)

							snake_case_           =							renew_attention_paths(a_							,					in_mid=a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,					)

							for i in range(a_):
														snake_case_           =							num_up_blocks - 1 - i

														if any('upsample' in layer for layer in up_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''decoder.up.{i}.upsample.conv.weight'''
																					]
																					snake_case_           =							checkpoint[
																					    f'''decoder.up.{i}.upsample.conv.bias'''
																					]

														if any('block' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in up_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

														if any('attn' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in up_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

							snake_case_           =							{k.replace('mid_new_2'							,					'mid_block'): v for k, v in new_checkpoint.items()}
							snake_case_           =							checkpoint['quant_conv.weight']
							snake_case_           =							checkpoint['quant_conv.bias']
							if "quantize.embedding.weight" in checkpoint:
														snake_case_           =							checkpoint['quantize.embedding.weight']
							snake_case_           =							checkpoint['post_quant_conv.weight']
							snake_case_           =							checkpoint['post_quant_conv.bias']

							return new_checkpoint


if __name__ == "__main__":
	lowercase  							= argparse.ArgumentParser()

	parser.add_argument(
	    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
	)

	parser.add_argument(
	    "--config_file",
	    default=None,
	    type=str,
	    required=True,
	    help="The config json file corresponding to the architecture.",
	)

	parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

	lowercase  							= parser.parse_args()
	lowercase  							= torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		lowercase  							= json.loads(f.read())

	# unet case
	lowercase  							= {key.split(".")[0] for key in checkpoint.keys()}
	if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
		lowercase  							= convert_vq_autoenc_checkpoint(checkpoint, config)
	else:
		lowercase  							= convert_ddpm_checkpoint(checkpoint, config)

	if "ddpm" in config:
		del config["ddpm"]

	if config["_class_name"] == "VQModel":
		lowercase  							= VQModel(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	elif config["_class_name"] == "AutoencoderKL":
		lowercase  							= AutoencoderKL(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	else:
		lowercase  							= UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		lowercase  							= DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

		lowercase  							= DDPMPipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)





