


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Dict=1		):





	"""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 lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Dict=0		):





	"""simple docstring"""







	__magic_name__	:   Tuple					     =					[]
	for old_item in old_list:
		__magic_name__	:   Optional[Any]					     =					old_item.replace('in_layers.0'		,		'norm1'		)
		__magic_name__	:   str					     =					new_item.replace('in_layers.2'		,		'conv1'		)

		__magic_name__	:   Dict					     =					new_item.replace('out_layers.0'		,		'norm2'		)
		__magic_name__	:   Optional[Any]					     =					new_item.replace('out_layers.3'		,		'conv2'		)

		__magic_name__	:   List[Any]					     =					new_item.replace('emb_layers.1'		,		'time_emb_proj'		)
		__magic_name__	:   Tuple					     =					new_item.replace('skip_connection'		,		'conv_shortcut'		)

		__magic_name__	:   Tuple					     =					shave_segments(lowerCAmelCase		,		n_shave_prefix_segments=lowerCAmelCase		)

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

	return mapping





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Tuple=0		):





	"""simple docstring"""







	__magic_name__	:   Tuple					     =					[]
	for old_item in old_list:
		__magic_name__	:   Union[str, Any]					     =					old_item

		__magic_name__	:   Optional[Any]					     =					new_item.replace('norm.weight'		,		'group_norm.weight'		)
		__magic_name__	:   Union[str, Any]					     =					new_item.replace('norm.bias'		,		'group_norm.bias'		)

		__magic_name__	:   int					     =					new_item.replace('proj_out.weight'		,		'proj_attn.weight'		)
		__magic_name__	:   List[Any]					     =					new_item.replace('proj_out.bias'		,		'proj_attn.bias'		)

		__magic_name__	:   Any					     =					shave_segments(lowerCAmelCase		,		n_shave_prefix_segments=lowerCAmelCase		)

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

	return mapping





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     int=None		,		lowerCAmelCase    :     List[Any]=None		,		lowerCAmelCase    :     Optional[Any]=None		):





	"""simple docstring"""







	assert isinstance(lowerCAmelCase		,		lowerCAmelCase		), "Paths should be a list of dicts containing 'old' and 'new' keys."

	# Splits the attention layers into three variables.
	if attention_paths_to_split is not None:
		for path, path_map in attention_paths_to_split.items():
			__magic_name__	:   str					     =					old_checkpoint[path]
			__magic_name__	:   Union[str, Any]					     =					old_tensor.shape[0] // 3

			__magic_name__	:   int					     =					(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

			__magic_name__	:   List[str]					     =					old_tensor.shape[0] // config['num_head_channels'] // 3

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

			__magic_name__	:   Tuple					     =					query.reshape(lowerCAmelCase		)
			__magic_name__	:   Tuple					     =					key.reshape(lowerCAmelCase		)
			__magic_name__	:   Tuple					     =					value.reshape(lowerCAmelCase		)

	for path in paths:
		__magic_name__	:   Any					     =					path['new']

		# These have already been assigned
		if attention_paths_to_split is not None and new_path in attention_paths_to_split:
			continue

		# Global renaming happens here
		__magic_name__	:   int					     =					new_path.replace('middle_block.0'		,		'mid_block.resnets.0'		)
		__magic_name__	:   Optional[int]					     =					new_path.replace('middle_block.1'		,		'mid_block.attentions.0'		)
		__magic_name__	:   Tuple					     =					new_path.replace('middle_block.2'		,		'mid_block.resnets.1'		)

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

        # proj_attn.weight has to be converted from conv 1D to linear
		if "proj_attn.weight" in new_path:
			__magic_name__	:   Union[str, Any]					     =					old_checkpoint[path['old']][:, :, 0]
		else:
			__magic_name__	:   Union[str, Any]					     =					old_checkpoint[path['old']]





def lowerCamelCase      (      lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__	:   int					     =					{}

	__magic_name__	:   Tuple					     =					checkpoint['time_embed.0.weight']
	__magic_name__	:   List[str]					     =					checkpoint['time_embed.0.bias']
	__magic_name__	:   Optional[Any]					     =					checkpoint['time_embed.2.weight']
	__magic_name__	:   Optional[Any]					     =					checkpoint['time_embed.2.bias']

	__magic_name__	:   str					     =					checkpoint['input_blocks.0.0.weight']
	__magic_name__	:   Optional[Any]					     =					checkpoint['input_blocks.0.0.bias']

	__magic_name__	:   Optional[Any]					     =					checkpoint['out.0.weight']
	__magic_name__	:   Tuple					     =					checkpoint['out.0.bias']
	__magic_name__	:   Optional[Any]					     =					checkpoint['out.2.weight']
	__magic_name__	:   Optional[int]					     =					checkpoint['out.2.bias']

	# Retrieves the keys for the input blocks only
	__magic_name__	:   Optional[int]					     =					len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'input_blocks' in layer}		)
	__magic_name__	:   Optional[int]					     =					{
	    layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
	    for layer_id in range(lowerCAmelCase		)
	}

	# Retrieves the keys for the middle blocks only
	__magic_name__	:   Tuple					     =					len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'middle_block' in layer}		)
	__magic_name__	:   Tuple					     =					{
	    layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
	    for layer_id in range(lowerCAmelCase		)
	}

	# Retrieves the keys for the output blocks only
	__magic_name__	:   int					     =					len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'output_blocks' in layer}		)
	__magic_name__	:   Union[str, Any]					     =					{
	    layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
	    for layer_id in range(lowerCAmelCase		)
	}

	for i in range(1		,		lowerCAmelCase		):
		__magic_name__	:   int					     =					(i - 1) // (config['num_res_blocks'] + 1)
		__magic_name__	:   Dict					     =					(i - 1) % (config['num_res_blocks'] + 1)

		__magic_name__	:   str					     =					[key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
		__magic_name__	:   int					     =					[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

		if f'input_blocks.{i}.0.op.weight' in checkpoint:
			__magic_name__	:   List[str]					     =					checkpoint[
			    f'input_blocks.{i}.0.op.weight'
			]
			__magic_name__	:   Tuple					     =					checkpoint[
			    f'input_blocks.{i}.0.op.bias'
			]
			continue

		__magic_name__	:   Optional[int]					     =					renew_resnet_paths(lowerCAmelCase		)
		__magic_name__	:   int					     =					{'old': f'input_blocks.{i}.0', 'new': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
		__magic_name__	:   Optional[int]					     =					{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
		assign_to_checkpoint(
		    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		additional_replacements=[meta_path, resnet_op]		,		config=lowerCAmelCase		)

		if len(lowerCAmelCase		):
			__magic_name__	:   int					     =					renew_attention_paths(lowerCAmelCase		)
			__magic_name__	:   int					     =					{
			    'old': f'input_blocks.{i}.1',
			    'new': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
			}
			__magic_name__	:   Optional[int]					     =					{
			    f'input_blocks.{i}.1.qkv.bias': {
			        'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
			        'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
			        'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
			    },
			    f'input_blocks.{i}.1.qkv.weight': {
			        'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
			        'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
			        'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
			    },
			}
			assign_to_checkpoint(
			    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		additional_replacements=[meta_path]		,		attention_paths_to_split=lowerCAmelCase		,		config=lowerCAmelCase		,		)

	__magic_name__	:   Optional[int]					     =					middle_blocks[0]
	__magic_name__	:   Union[str, Any]					     =					middle_blocks[1]
	__magic_name__	:   Any					     =					middle_blocks[2]

	__magic_name__	:   Optional[int]					     =					renew_resnet_paths(lowerCAmelCase		)
	assign_to_checkpoint(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		config=lowerCAmelCase		)

	__magic_name__	:   List[str]					     =					renew_resnet_paths(lowerCAmelCase		)
	assign_to_checkpoint(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		config=lowerCAmelCase		)

	__magic_name__	:   List[str]					     =					renew_attention_paths(lowerCAmelCase		)
	__magic_name__	:   Optional[Any]					     =					{
	    'middle_block.1.qkv.bias': {
	        'key': 'mid_block.attentions.0.key.bias',
	        'query': 'mid_block.attentions.0.query.bias',
	        'value': 'mid_block.attentions.0.value.bias',
	    },
	    'middle_block.1.qkv.weight': {
	        'key': 'mid_block.attentions.0.key.weight',
	        'query': 'mid_block.attentions.0.query.weight',
	        'value': 'mid_block.attentions.0.value.weight',
	    },
	}
	assign_to_checkpoint(
	    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		attention_paths_to_split=lowerCAmelCase		,		config=lowerCAmelCase		)

	for i in range(lowerCAmelCase		):
		__magic_name__	:   Optional[Any]					     =					i // (config['num_res_blocks'] + 1)
		__magic_name__	:   Optional[int]					     =					i % (config['num_res_blocks'] + 1)
		__magic_name__	:   int					     =					[shave_segments(lowerCAmelCase		,		2		) for name in output_blocks[i]]
		__magic_name__	:   Any					     =					{}

		for layer in output_block_layers:
			__magic_name__     ,    __magic_name__	:   int					     =					layer.split('.'		)[0], shave_segments(lowerCAmelCase		,		1		)
			if layer_id in output_block_list:
				output_block_list[layer_id].append(lowerCAmelCase		)
			else:
				__magic_name__	:   Union[str, Any]					     =					[layer_name]

		if len(lowerCAmelCase		) > 1:
			__magic_name__	:   Any					     =					[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
			__magic_name__	:   List[str]					     =					[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

			__magic_name__	:   Any					     =					renew_resnet_paths(lowerCAmelCase		)
			__magic_name__	:   Dict					     =					renew_resnet_paths(lowerCAmelCase		)

			__magic_name__	:   str					     =					{'old': f'output_blocks.{i}.0', 'new': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
			assign_to_checkpoint(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		additional_replacements=[meta_path]		,		config=lowerCAmelCase		)

			if ["conv.weight", "conv.bias"] in output_block_list.values():
				__magic_name__	:   List[str]					     =					list(output_block_list.values()		).index(['conv.weight', 'conv.bias']		)
				__magic_name__	:   str					     =					checkpoint[
				    f'output_blocks.{i}.{index}.conv.weight'
				]
				__magic_name__	:   List[Any]					     =					checkpoint[
				    f'output_blocks.{i}.{index}.conv.bias'
				]

				# Clear attentions as they have been attributed above.
				if len(lowerCAmelCase		) == 2:
					__magic_name__	:   int					     =					[]

			if len(lowerCAmelCase		):
				__magic_name__	:   int					     =					renew_attention_paths(lowerCAmelCase		)
				__magic_name__	:   Tuple					     =					{
				    'old': f'output_blocks.{i}.1',
				    'new': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
				}
				__magic_name__	:   Dict					     =					{
				    f'output_blocks.{i}.1.qkv.bias': {
				        'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
				        'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
				        'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
				    },
				    f'output_blocks.{i}.1.qkv.weight': {
				        'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
				        'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
				        'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
				    },
				}
				assign_to_checkpoint(
				    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		additional_replacements=[meta_path]		,		attention_paths_to_split=to_split if any('qkv' in key for key in attentions		) else None		,		config=lowerCAmelCase		,		)
		else:
			__magic_name__	:   str					     =					renew_resnet_paths(lowerCAmelCase		,		n_shave_prefix_segments=1		)
			for path in resnet_0_paths:
				__magic_name__	:   Any					     =					'.'.join(['output_blocks', str(lowerCAmelCase		), path['old']]		)
				__magic_name__	:   List[str]					     =					'.'.join(['up_blocks', str(lowerCAmelCase		), 'resnets', str(lowerCAmelCase		), path['new']]		)

				__magic_name__	:   List[str]					     =					checkpoint[old_path]

	return new_checkpoint


if __name__ == "__main__":
							lowerCAmelCase				:int        =      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.''')

							lowerCAmelCase				:str        =      parser.parse_args()

							lowerCAmelCase				:str        =      torch.load(args.checkpoint_path)

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

							lowerCAmelCase				:int        =      convert_ldm_checkpoint(checkpoint, config)

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

							lowerCAmelCase				:Optional[int]        =      UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														lowerCAmelCase				:Union[str, Any]        =      DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
														lowerCAmelCase				:int        =      VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

														lowerCAmelCase				:Tuple        =      LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)



'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase				:Any        =      logging.get_logger(__name__)

lowerCAmelCase				:int        =      {
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      List[str]				   =    """lxmert"""
		A_     :      int				   =    {}







		def __init__( self		:						int  ,					_A		:						List[Any]=30522  ,					_A		:						int=768  ,					_A		:						Any=12  ,					_A		:						Optional[Any]=9500  ,					_A		:						Any=1600  ,					_A		:						Tuple=400  ,					_A		:						str=3072  ,					_A		:						Tuple="gelu"  ,					_A		:						int=0.1  ,					_A		:						List[Any]=0.1  ,					_A		:						Dict=512  ,					_A		:						Dict=2  ,					_A		:						Tuple=0.02  ,					_A		:						List[Any]=1E-12  ,					_A		:						Optional[Any]=9  ,					_A		:						List[Any]=5  ,					_A		:						str=5  ,					_A		:						int=2048  ,					_A		:						Tuple=4  ,					_A		:						Tuple=6.67  ,					_A		:						Dict=True  ,					_A		:						str=True  ,					_A		:						str=True  ,					_A		:						Dict=True  ,					_A		:						str=True  ,					_A		:						int=True  ,					_A		:						Tuple=True  ,					**_A		:						int  ,					)  ->			str:
			__magic_name__	:   List[Any]					     =					vocab_size
			__magic_name__	:   List[Any]					     =					hidden_size
			__magic_name__	:   Optional[int]					     =					num_attention_heads
			__magic_name__	:   Dict					     =					hidden_act
			__magic_name__	:   Optional[Any]					     =					intermediate_size
			__magic_name__	:   str					     =					hidden_dropout_prob
			__magic_name__	:   List[str]					     =					attention_probs_dropout_prob
			__magic_name__	:   Tuple					     =					max_position_embeddings
			__magic_name__	:   Dict					     =					type_vocab_size
			__magic_name__	:   str					     =					initializer_range
			__magic_name__	:   str					     =					layer_norm_eps
			__magic_name__	:   Union[str, Any]					     =					num_qa_labels
			__magic_name__	:   str					     =					num_object_labels
			__magic_name__	:   List[str]					     =					num_attr_labels
			__magic_name__	:   Tuple					     =					l_layers
			__magic_name__	:   List[Any]					     =					x_layers
			__magic_name__	:   Optional[int]					     =					r_layers
			__magic_name__	:   Dict					     =					visual_feat_dim
			__magic_name__	:   Optional[int]					     =					visual_pos_dim
			__magic_name__	:   Optional[int]					     =					visual_loss_normalizer
			__magic_name__	:   int					     =					task_matched
			__magic_name__	:   Dict					     =					task_mask_lm
			__magic_name__	:   List[Any]					     =					task_obj_predict
			__magic_name__	:   str					     =					task_qa
			__magic_name__	:   List[Any]					     =					visual_obj_loss
			__magic_name__	:   Dict					     =					visual_attr_loss
			__magic_name__	:   int					     =					visual_feat_loss
			__magic_name__	:   Tuple					     =					{'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
			super().__init__(**_A      )