


'''simple docstring'''
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=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       a_				(	UpperCamelCase_						,  UpperCamelCase_=0 ):
							A_			    =		[]
							for old_item in old_list:
														A_			    =		old_item.replace("in_layers.0"						,  "norm1" )
														A_			    =		new_item.replace("in_layers.2"						,  "conv1" )

														A_			    =		new_item.replace("out_layers.0"						,  "norm2" )
														A_			    =		new_item.replace("out_layers.3"						,  "conv2" )

														A_			    =		new_item.replace("emb_layers.1"						,  "time_emb_proj" )
														A_			    =		new_item.replace("skip_connection"						,  "conv_shortcut" )

														A_			    =		shave_segments(UpperCamelCase_						,  n_shave_prefix_segments=UpperCamelCase_ )

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

							return mapping







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=0 ):
							A_			    =		[]
							for old_item in old_list:
														A_			    =		old_item

														A_			    =		new_item.replace("norm.weight"						,  "group_norm.weight" )
														A_			    =		new_item.replace("norm.bias"						,  "group_norm.bias" )

														A_			    =		new_item.replace("proj_out.weight"						,  "proj_attn.weight" )
														A_			    =		new_item.replace("proj_out.bias"						,  "proj_attn.bias" )

														A_			    =		shave_segments(UpperCamelCase_						,  n_shave_prefix_segments=UpperCamelCase_ )

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

							return mapping







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=None						,  UpperCamelCase_=None						,  UpperCamelCase_=None ):
							assert isinstance(UpperCamelCase_						,  UpperCamelCase_ ), "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():
																					A_			    =		old_checkpoint[path]
																					A_			    =		old_tensor.shape[0] // 3

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

																					A_			    =		old_tensor.shape[0] // config["num_head_channels"] // 3

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

																					A_			    =		query.reshape(UpperCamelCase_ )
																					A_			    =		key.reshape(UpperCamelCase_ )
																					A_			    =		value.reshape(UpperCamelCase_ )

							for path in paths:
														A_			    =		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
														A_			    =		new_path.replace("middle_block.0"						,  "mid_block.resnets.0" )
														A_			    =		new_path.replace("middle_block.1"						,  "mid_block.attentions.0" )
														A_			    =		new_path.replace("middle_block.2"						,  "mid_block.resnets.1" )

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												A_			    =		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:
																					A_			    =		old_checkpoint[path["old"]][:, :, 0]
														else:
																					A_			    =		old_checkpoint[path["old"]]







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		{}

							A_			    =		checkpoint["time_embed.0.weight"]
							A_			    =		checkpoint["time_embed.0.bias"]
							A_			    =		checkpoint["time_embed.2.weight"]
							A_			    =		checkpoint["time_embed.2.bias"]

							A_			    =		checkpoint["input_blocks.0.0.weight"]
							A_			    =		checkpoint["input_blocks.0.0.bias"]

							A_			    =		checkpoint["out.0.weight"]
							A_			    =		checkpoint["out.0.bias"]
							A_			    =		checkpoint["out.2.weight"]
							A_			    =		checkpoint["out.2.bias"]

							# Retrieves the keys for the input blocks only
							A_			    =		len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "input_blocks" in layer} )
							A_			    =		{
							    layer_id: [key for key in checkpoint if f"input_blocks.{layer_id}" in key]
							    for layer_id in range(UpperCamelCase_ )
							}

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

							# Retrieves the keys for the output blocks only
							A_			    =		len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "output_blocks" in layer} )
							A_			    =		{
							    layer_id: [key for key in checkpoint if f"output_blocks.{layer_id}" in key]
							    for layer_id in range(UpperCamelCase_ )
							}

							for i in range(1						,  UpperCamelCase_ ):
														A_			    =		(i - 1) // (config["num_res_blocks"] + 1)
														A_			    =		(i - 1) % (config["num_res_blocks"] + 1)

														A_			    =		[key for key in input_blocks[i] if f"input_blocks.{i}.0" in key]
														A_			    =		[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:
																					A_			    =		checkpoint[
																					    f"input_blocks.{i}.0.op.weight"
																					]
																					A_			    =		checkpoint[
																					    f"input_blocks.{i}.0.op.bias"
																					]
																					continue

														A_			    =		renew_resnet_paths(UpperCamelCase_ )
														A_			    =		{"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
														A_			    =		{"old": "resnets.2.op", "new": "downsamplers.0.op"}
														assign_to_checkpoint(
														    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  additional_replacements=[meta_path, resnet_op]						,  config=UpperCamelCase_ )

														if len(UpperCamelCase_ ):
																					A_			    =		renew_attention_paths(UpperCamelCase_ )
																					A_			    =		{
																					    "old": f"input_blocks.{i}.1",
																					    "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
																					}
																					A_			    =		{
																					    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(
																					    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  additional_replacements=[meta_path]						,  attention_paths_to_split=UpperCamelCase_						,  config=UpperCamelCase_						,  )

							A_			    =		middle_blocks[0]
							A_			    =		middle_blocks[1]
							A_			    =		middle_blocks[2]

							A_			    =		renew_resnet_paths(UpperCamelCase_ )
							assign_to_checkpoint(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  config=UpperCamelCase_ )

							A_			    =		renew_resnet_paths(UpperCamelCase_ )
							assign_to_checkpoint(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  config=UpperCamelCase_ )

							A_			    =		renew_attention_paths(UpperCamelCase_ )
							A_			    =		{
							    "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(
							    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  attention_paths_to_split=UpperCamelCase_						,  config=UpperCamelCase_ )

							for i in range(UpperCamelCase_ ):
														A_			    =		i // (config["num_res_blocks"] + 1)
														A_			    =		i % (config["num_res_blocks"] + 1)
														A_			    =		[shave_segments(UpperCamelCase_						,  2 ) for name in output_blocks[i]]
														A_			    =		{}

														for layer in output_block_layers:
																					A_    ,					A_			    =		layer.split("." )[0], shave_segments(UpperCamelCase_						,  1 )
																					if layer_id in output_block_list:
																												output_block_list[layer_id].append(UpperCamelCase_ )
																					else:
																												A_			    =		[layer_name]

														if len(UpperCamelCase_ ) > 1:
																					A_			    =		[key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
																					A_			    =		[key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

																					A_			    =		renew_resnet_paths(UpperCamelCase_ )
																					A_			    =		renew_resnet_paths(UpperCamelCase_ )

																					A_			    =		{"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
																					assign_to_checkpoint(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  additional_replacements=[meta_path]						,  config=UpperCamelCase_ )

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

																												# Clear attentions as they have been attributed above.
																												if len(UpperCamelCase_ ) == 2:
																																			A_			    =		[]

																					if len(UpperCamelCase_ ):
																												A_			    =		renew_attention_paths(UpperCamelCase_ )
																												A_			    =		{
																												    "old": f"output_blocks.{i}.1",
																												    "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
																												}
																												A_			    =		{
																												    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(
																												    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  additional_replacements=[meta_path]						,  attention_paths_to_split=to_split if any("qkv" in key for key in attentions ) else None						,  config=UpperCamelCase_						,  )
														else:
																					A_			    =		renew_resnet_paths(UpperCamelCase_						,  n_shave_prefix_segments=1 )
																					for path in resnet_0_paths:
																												A_			    =		".".join(["output_blocks", str(UpperCamelCase_ ), path["old"]] )
																												A_			    =		".".join(["up_blocks", str(UpperCamelCase_ ), "resnets", str(UpperCamelCase_ ), path["new"]] )

																												A_			    =		checkpoint[old_path]

							return new_checkpoint


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				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.''')

							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		parser.parse_args()

							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														__SCREAMING_SNAKE_CASE					:				List[str]									=		json.loads(f.read())

							__SCREAMING_SNAKE_CASE					:				Tuple									=		convert_ldm_checkpoint(checkpoint, config)

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

							__SCREAMING_SNAKE_CASE					:				Tuple									=		UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

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

														__SCREAMING_SNAKE_CASE					:				Optional[int]									=		LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)









'''simple docstring'''
import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE					:				List[Any]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Tuple									=		[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]







def       a_				(	UpperCamelCase_ ):
							if k == "embeddings.weight":
														return "shared.weight"

							for parlai_name, hf_name in PATTERNS:
														A_			    =		k.replace(UpperCamelCase_						,  UpperCamelCase_ )

							if k.startswith("encoder" ):
														A_			    =		k.replace(".attn"						,  ".self_attn" )
														A_			    =		k.replace("norm1"						,  "self_attn_layer_norm" )
														A_			    =		k.replace("norm2"						,  "final_layer_norm" )
							elif k.startswith("decoder" ):
														A_			    =		k.replace("norm1"						,  "self_attn_layer_norm" )
														A_			    =		k.replace("norm2"						,  "encoder_attn_layer_norm" )
														A_			    =		k.replace("norm3"						,  "final_layer_norm" )
							return k







def       a_				(	UpperCamelCase_ ):
							A_			    =		[
							    "model.encoder.layernorm_embedding.weight",
							    "model.encoder.layernorm_embedding.bias",
							    "model.decoder.layernorm_embedding.weight",
							    "model.decoder.layernorm_embedding.bias",
							]
							for k in keys:
														A_			    =		sd.pop(UpperCamelCase_ )
														A_			    =		k.replace("layernorm_embedding"						,  "layer_norm" )
														assert new_k not in sd
														A_			    =		v


__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		['''START''']







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		torch.load(UpperCamelCase_						,  map_location="cpu" )
							A_			    =		model["model"]
							A_			    =		BlenderbotConfig.from_json_file(UpperCamelCase_ )
							A_			    =		BlenderbotForConditionalGeneration(UpperCamelCase_ )
							A_			    =		m.model.state_dict().keys()
							A_			    =		[]
							A_			    =		{}
							for k, v in sd.items():
														if k in IGNORE_KEYS:
																					continue

														A_			    =		rename_state_dict_key(UpperCamelCase_ )
														if new_k not in valid_keys:
																					failures.append([k, new_k] )
														else:
																					A_			    =		v
							if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
														rename_layernorm_keys(UpperCamelCase_ )
							m.model.load_state_dict(UpperCamelCase_						,  strict=UpperCamelCase_ )
							m.half()
							m.save_pretrained(UpperCamelCase_ )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
							parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
							parser.add_argument(
							    '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
							)
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)





