

"""simple docstring"""






import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase				      =   logging.get_logger(__name__)
def 		A_	(   __UpperCamelCase     :				Tuple				):
		lowercase										=							tf.image.resize(UpperCamelCase_       ,	(3_46, 3_46)				)
		lowercase										=							tf.image.crop_to_bounding_box(UpperCamelCase_       ,	(3_46 - 2_89) // 2       ,	(3_46 - 2_89) // 2       ,	2_89       ,	2_89				)
		return image
def 		A_	(   ):
		lowercase										=							EfficientNetConfig.from_pretrained('''google/efficientnet-b7'''				)
		lowercase										=							2_89
		lowercase										=							6_40
		lowercase										=							{'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
		lowercase										=							{'''LABEL_0''': 0, '''LABEL_1''': 1}
		lowercase										=							[]

		lowercase										=							BertConfig()
		lowercase										=							AlignConfig.from_text_vision_configs(
		    text_config=UpperCamelCase_       ,	vision_config=UpperCamelCase_       ,	projection_dim=6_40				)
		return config
def 		A_	(   ):
		lowercase										=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase										=							Image.open(requests.get(UpperCamelCase_       ,	stream=UpperCamelCase_				).raw				)
		return im
def 		A_	(   ):
		lowercase										=							EfficientNetImageProcessor(
		    do_center_crop=UpperCamelCase_       ,	rescale_factor=1 / 1_27.5       ,	rescale_offset=UpperCamelCase_       ,	do_normalize=UpperCamelCase_       ,	include_top=UpperCamelCase_       ,	resample=Image.BILINEAR       ,	)
		lowercase										=							BertTokenizer.from_pretrained('''bert-base-uncased'''				)
		lowercase										=							64
		lowercase										=							AlignProcessor(image_processor=UpperCamelCase_       ,	tokenizer=UpperCamelCase_				)
		return processor
def 		A_	(   __UpperCamelCase     :				List[str]				):
		lowercase										=							[v.split('''_'''				)[0].split('''block'''				)[1] for v in original_param_names if v.startswith('''block'''				)]
		lowercase										=							list(set(UpperCamelCase_				)				)
		lowercase										=							sorted(UpperCamelCase_				)
		lowercase										=							len(UpperCamelCase_				)
		lowercase										=							{b: str(UpperCamelCase_				) for b, i in zip(UpperCamelCase_       ,	range(UpperCamelCase_				)				)}

		lowercase										=							[]
		rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''')				)
		rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''')				)
		rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''')				)
		rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''')				)
		rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''')				)

		for b in block_names:
				lowercase										=							block_name_mapping[b]
				rename_keys.append((f"""block{b}_expand_conv/kernel:0""", f"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")				)
				rename_keys.append((f"""block{b}_expand_bn/gamma:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")				)
				rename_keys.append((f"""block{b}_expand_bn/beta:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")				)
				rename_keys.append(
				    (f"""block{b}_expand_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")				)
				rename_keys.append(
				    (f"""block{b}_expand_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")				)
				rename_keys.append(
				    (f"""block{b}_dwconv/depthwise_kernel:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")				)
				rename_keys.append((f"""block{b}_bn/gamma:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")				)
				rename_keys.append((f"""block{b}_bn/beta:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")				)
				rename_keys.append(
				    (f"""block{b}_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")				)
				rename_keys.append(
				    (f"""block{b}_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")				)

				rename_keys.append((f"""block{b}_se_reduce/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")				)
				rename_keys.append((f"""block{b}_se_reduce/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")				)
				rename_keys.append((f"""block{b}_se_expand/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")				)
				rename_keys.append((f"""block{b}_se_expand/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")				)
				rename_keys.append(
				    (f"""block{b}_project_conv/kernel:0""", f"""encoder.blocks.{hf_b}.projection.project_conv.weight""")				)
				rename_keys.append((f"""block{b}_project_bn/gamma:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.weight""")				)
				rename_keys.append((f"""block{b}_project_bn/beta:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.bias""")				)
				rename_keys.append(
				    (f"""block{b}_project_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")				)
				rename_keys.append(
				    (f"""block{b}_project_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")				)

		lowercase										=							{}
		for item in rename_keys:
				if item[0] in original_param_names:
						lowercase										=							'''vision_model.''' + item[1]

    # BERT text encoder
		lowercase										=							[]
		lowercase										=							'''tf_bert_model/bert'''
		lowercase										=							'''text_model'''
		for i in range(12				):
				rename_keys.append(
				    (
				        f"""{old}/encoder/layer_._{i}/attention/self/query/kernel:0""",
				        f"""{new}.encoder.layer.{i}.attention.self.query.weight""",
				    )				)
				rename_keys.append(
				    (
				        f"""{old}/encoder/layer_._{i}/attention/self/query/bias:0""",
				        f"""{new}.encoder.layer.{i}.attention.self.query.bias""",
				    )				)
				rename_keys.append(
				    (
				        f"""{old}/encoder/layer_._{i}/attention/self/key/kernel:0""",
				        f"""{new}.encoder.layer.{i}.attention.self.key.weight""",
				    )				)
				rename_keys.append(
				    (
				        f"""{old}/encoder/layer_._{i}/attention/self/key/bias:0""",
				        f"""{new}.encoder.layer.{i}.attention.self.key.bias""",
				    )				)
				rename_keys.append(
				    (
				        f"""{old}/encoder/layer_._{i}/attention/self/value/kernel:0""",
				        f"""{new}.encoder.layer.{i}.attention.self.value.weight""",
				    )				)
				rename_keys.append(
				    (
				        f"""{old}/encoder/layer_._{i}/attention/self/value/bias:0""",
				        f"""{new}.encoder.layer.{i}.attention.self.value.bias""",
				    )				)
				rename_keys.append(
				    (
				        f"""{old}/encoder/layer_._{i}/attention/output/dense/kernel:0""",
				        f"""{new}.encoder.layer.{i}.attention.output.dense.weight""",
				    )				)
				rename_keys.append(
				    (
				        f"""{old}/encoder/layer_._{i}/attention/output/dense/bias:0""",
				        f"""{new}.encoder.layer.{i}.attention.output.dense.bias""",
				    )				)
				rename_keys.append(
				    (
				        f"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0""",
				        f"""{new}.encoder.layer.{i}.attention.output.LayerNorm.weight""",
				    )				)
				rename_keys.append(
				    (
				        f"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0""",
				        f"""{new}.encoder.layer.{i}.attention.output.LayerNorm.bias""",
				    )				)
				rename_keys.append(
				    (
				        f"""{old}/encoder/layer_._{i}/intermediate/dense/kernel:0""",
				        f"""{new}.encoder.layer.{i}.intermediate.dense.weight""",
				    )				)
				rename_keys.append(
				    (
				        f"""{old}/encoder/layer_._{i}/intermediate/dense/bias:0""",
				        f"""{new}.encoder.layer.{i}.intermediate.dense.bias""",
				    )				)
				rename_keys.append(
				    (f"""{old}/encoder/layer_._{i}/output/dense/kernel:0""", f"""{new}.encoder.layer.{i}.output.dense.weight""")				)
				rename_keys.append(
				    (f"""{old}/encoder/layer_._{i}/output/dense/bias:0""", f"""{new}.encoder.layer.{i}.output.dense.bias""")				)
				rename_keys.append(
				    (f"""{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0""", f"""{new}.encoder.layer.{i}.output.LayerNorm.weight""")				)
				rename_keys.append(
				    (f"""{old}/encoder/layer_._{i}/output/LayerNorm/beta:0""", f"""{new}.encoder.layer.{i}.output.LayerNorm.bias""")				)

		rename_keys.append((f"""{old}/embeddings/word_embeddings/weight:0""", f"""{new}.embeddings.word_embeddings.weight""")				)
		rename_keys.append(
		    (f"""{old}/embeddings/position_embeddings/embeddings:0""", f"""{new}.embeddings.position_embeddings.weight""")				)
		rename_keys.append(
		    (f"""{old}/embeddings/token_type_embeddings/embeddings:0""", f"""{new}.embeddings.token_type_embeddings.weight""")				)
		rename_keys.append((f"""{old}/embeddings/LayerNorm/gamma:0""", f"""{new}.embeddings.LayerNorm.weight""")				)
		rename_keys.append((f"""{old}/embeddings/LayerNorm/beta:0""", f"""{new}.embeddings.LayerNorm.bias""")				)

		rename_keys.append((f"""{old}/pooler/dense/kernel:0""", f"""{new}.pooler.dense.weight""")				)
		rename_keys.append((f"""{old}/pooler/dense/bias:0""", f"""{new}.pooler.dense.bias""")				)
		rename_keys.append(('''dense/kernel:0''', '''text_projection.weight''')				)
		rename_keys.append(('''dense/bias:0''', '''text_projection.bias''')				)
		rename_keys.append(('''dense/bias:0''', '''text_projection.bias''')				)
		rename_keys.append(('''temperature:0''', '''temperature''')				)

		for item in rename_keys:
				if item[0] in original_param_names:
						lowercase										=							item[1]
		return key_mapping
def 		A_	(   __UpperCamelCase     :				Union[str, Any]       ,	__UpperCamelCase     :				Dict       ,	__UpperCamelCase     :				Optional[int]				):
		list(hf_params.keys()				)

		for key, value in tf_params.items():
				if key not in key_mapping:
						continue

				lowercase										=							key_mapping[key]
				if "_conv" in key and "kernel" in key:
						lowercase										=							torch.from_numpy(UpperCamelCase_				).permute(3       ,	2       ,	0       ,	1				)
				elif "embeddings" in key:
						lowercase										=							torch.from_numpy(UpperCamelCase_				)
				elif "depthwise_kernel" in key:
						lowercase										=							torch.from_numpy(UpperCamelCase_				).permute(2       ,	3       ,	0       ,	1				)
				elif "kernel" in key:
						lowercase										=							torch.from_numpy(np.transpose(UpperCamelCase_				)				)
				elif "temperature" in key:
						lowercase										=							value
				elif "bn/gamma" or "bn/beta" in key:
						lowercase										=							torch.from_numpy(np.transpose(UpperCamelCase_				)				).squeeze()
				else:
						lowercase										=							torch.from_numpy(UpperCamelCase_				)

				# Replace HF parameters with original TF model parameters
				hf_params[hf_key].copy_(UpperCamelCase_				)
@torch.no_grad()
def 		A_	(   __UpperCamelCase     :				Union[str, Any]       ,	__UpperCamelCase     :				Any       ,	__UpperCamelCase     :				Any       ,	__UpperCamelCase     :				Union[str, Any]				):
		lowercase										=							64
		lowercase										=							Tokenizer(UpperCamelCase_				)
		lowercase										=							align.Align('''efficientnet-b7'''       ,	'''bert-base'''       ,	6_40       ,	UpperCamelCase_       ,	tok.get_vocab_size()				)
		original_model.compile()
		original_model.load_weights(UpperCamelCase_				)

		lowercase										=							original_model.trainable_variables
		lowercase										=							original_model.non_trainable_variables
		lowercase										=							{param.name: param.numpy() for param in tf_params}
		for param in tf_non_train_params:
				lowercase										=							param.numpy()
		lowercase										=							list(tf_params.keys()				)

		# Load HuggingFace model
		lowercase										=							get_align_config()
		lowercase										=							AlignModel(UpperCamelCase_				).eval()
		lowercase										=							hf_model.state_dict()

		# Create src-to-dst parameter name mapping dictionary
		print('''Converting parameters...'''				)
		lowercase										=							rename_keys(UpperCamelCase_				)
		replace_params(UpperCamelCase_       ,	UpperCamelCase_       ,	UpperCamelCase_				)

		# Initialize processor
		lowercase										=							get_processor()
		lowercase										=							processor(
		    images=prepare_img()       ,	text='''A picture of a cat'''       ,	padding='''max_length'''       ,	max_length=64       ,	return_tensors='''pt'''				)

		# HF model inference
		hf_model.eval()
		with torch.no_grad():
				lowercase										=							hf_model(**UpperCamelCase_				)

		lowercase										=							outputs.image_embeds.detach().numpy()
		lowercase										=							outputs.text_embeds.detach().numpy()

		# Original model inference
		lowercase										=							False
		lowercase										=							EfficientNetImageProcessor(
		    do_center_crop=UpperCamelCase_       ,	do_rescale=UpperCamelCase_       ,	do_normalize=UpperCamelCase_       ,	include_top=UpperCamelCase_       ,	resample=Image.BILINEAR       ,	)
		lowercase										=							tf_image_processor(images=prepare_img()       ,	return_tensors='''tf'''       ,	data_format='''channels_last'''				)['''pixel_values''']
		lowercase										=							tok(tf.constant(['''A picture of a cat''']				)				)

		lowercase										=							original_model.image_encoder(UpperCamelCase_       ,	training=UpperCamelCase_				)
		lowercase										=							original_model.text_encoder(UpperCamelCase_       ,	training=UpperCamelCase_				)

		lowercase										=							tf.nn.la_normalize(UpperCamelCase_       ,	axis=-1				)
		lowercase										=							tf.nn.la_normalize(UpperCamelCase_       ,	axis=-1				)

		# Check whether original and HF model outputs match  -> np.allclose
		if not np.allclose(UpperCamelCase_       ,	UpperCamelCase_       ,	atol=1e-3				):
				raise ValueError('''The predicted image features are not the same.'''				)
		if not np.allclose(UpperCamelCase_       ,	UpperCamelCase_       ,	atol=1e-3				):
				raise ValueError('''The predicted text features are not the same.'''				)
		print('''Model outputs match!'''				)

		if save_model:
				# Create folder to save model
				if not os.path.isdir(UpperCamelCase_				):
						os.mkdir(UpperCamelCase_				)
				# Save converted model and image processor
				hf_model.save_pretrained(UpperCamelCase_				)
				processor.save_pretrained(UpperCamelCase_				)

		if push_to_hub:
				# Push model and image processor to hub
				print('''Pushing converted ALIGN to the hub...'''				)
				processor.push_to_hub('''align-base'''				)
				hf_model.push_to_hub('''align-base'''				)


if __name__ == "__main__":
							__lowerCAmelCase				      =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--checkpoint_path''',
							    default='''./weights/model-weights''',
							    type=str,
							    help='''Path to the pretrained TF ALIGN checkpoint.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default='''hf_model''',
							    type=str,
							    help='''Path to the output PyTorch model directory.''',
							)
							parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
							parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

							__lowerCAmelCase				      =   parser.parse_args()
							convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)







import argparse
import json

import torch

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







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       Optional[Any]=1		)	-> Optional[int]:

						"""simple docstring"""



						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 (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       int=0		)	-> Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []
						for old_item in old_list:
												lowerCAmelCase__         =    old_item
												lowerCAmelCase__         =    new_item.replace('block.'     ,							'resnets.'		)
												lowerCAmelCase__         =    new_item.replace('conv_shorcut'     ,							'conv1'		)
												lowerCAmelCase__         =    new_item.replace('in_shortcut'     ,							'conv_shortcut'		)
												lowerCAmelCase__         =    new_item.replace('temb_proj'     ,							'time_emb_proj'		)

												lowerCAmelCase__         =    shave_segments(UpperCamelCase_     ,							n_shave_prefix_segments=UpperCamelCase_		)

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

						return mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Union[str, Any]=0     ,							UpperCamelCase_							:       Dict=False		)	-> List[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []
						for old_item in old_list:
												lowerCAmelCase__         =    old_item

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

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

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

						return mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       Any=None     ,							UpperCamelCase_							:       str=None     ,							UpperCamelCase_							:       str=None		)	-> Dict:

						"""simple docstring"""



						assert isinstance(UpperCamelCase_     ,							UpperCamelCase_		), "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():
																		lowerCAmelCase__         =    old_checkpoint[path]
																		lowerCAmelCase__         =    old_tensor.shape[0] // 3

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

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

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

																		lowerCAmelCase__         =    query.reshape(UpperCamelCase_		).squeeze()
																		lowerCAmelCase__         =    key.reshape(UpperCamelCase_		).squeeze()
																		lowerCAmelCase__         =    value.reshape(UpperCamelCase_		).squeeze()

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

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

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

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

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







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Dict		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    {}

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

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

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

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'down' in layer}		)
						lowerCAmelCase__         =    {
						    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
						}

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'up' in layer}		)
						lowerCAmelCase__         =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)}

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

												if any('downsample' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"down.{i}.downsample.op.weight"
																		]
																		lowerCAmelCase__         =    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]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

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

												if any('attn' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

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

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

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

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

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

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    num_up_blocks - 1 - i

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

												if any('block' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

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

												if any('attn' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

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

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







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       int		)	-> Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__         =    {}

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

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

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

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

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'down' in layer}		)
						lowerCAmelCase__         =    {
						    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
						}

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'up' in layer}		)
						lowerCAmelCase__         =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)}

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

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

												if any('block' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

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

												if any('attn' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

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

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

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

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

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

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    num_up_blocks - 1 - i

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

												if any('block' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

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

												if any('attn' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

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

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

						return new_checkpoint


if __name__ == "__main__":
					__snake_case     :     Tuple       =  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.""")

					__snake_case     :     str       =  parser.parse_args()
					__snake_case     :     Any       =  torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										__snake_case     :     Optional[int]       =  json.loads(f.read())

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

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

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

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

										__snake_case     :     Tuple       =  DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)





