
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class   UpperCamelCase_      (      nn.Module		):







						'''simple docstring'''



						def __init__(			self							:Dict		,    lowerCAmelCase__							:int = 16		,    lowerCAmelCase__							:int = 88		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:int = 1		,    lowerCAmelCase__							:float = 0.0		,    lowerCAmelCase__							:int = 32		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:bool = False		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:str = "geglu"		,    lowerCAmelCase__							:Optional[int] = None		,    )  ->Dict:
										super().__init__()
										lowercase         =      nn.ModuleList(
										    [
										        TransformeraDModel(
										            num_attention_heads=lowerCAmelCase__		,    attention_head_dim=lowerCAmelCase__		,    in_channels=lowerCAmelCase__		,    num_layers=lowerCAmelCase__		,    dropout=lowerCAmelCase__		,    norm_num_groups=lowerCAmelCase__		,    cross_attention_dim=lowerCAmelCase__		,    attention_bias=lowerCAmelCase__		,    sample_size=lowerCAmelCase__		,    num_vector_embeds=lowerCAmelCase__		,    activation_fn=lowerCAmelCase__		,    num_embeds_ada_norm=lowerCAmelCase__		,    )
										        for _ in range(2     )
										    ]     )

										# Variables that can be set by a pipeline:

										# The ratio of transformer1 to transformer2's output states to be combined during inference
										lowercase         =      0.5

										# The shape of `encoder_hidden_states` is expected to be
										# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
										lowercase         =      [77, 257]

										# Which transformer to use to encode which condition.
										# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
										lowercase         =      [1, 0]





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:Optional[int]=None		,    lowerCAmelCase__							:str=None		,    lowerCAmelCase__							:bool = True		,    )  ->List[str]:
										lowercase         =      hidden_states

										lowercase         =      []
										lowercase         =      0
										# attention_mask is not used yet
										for i in range(2     ):
														# for each of the two transformers, pass the corresponding condition tokens
														lowercase         =      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
														lowercase         =      self.transformer_index_for_condition[i]
														lowercase         =      self.transformers[transformer_index](
														    lowerCAmelCase__		,    encoder_hidden_states=lowerCAmelCase__		,    timestep=lowerCAmelCase__		,    cross_attention_kwargs=lowerCAmelCase__		,    return_dict=lowerCAmelCase__		,    )[0]
														encoded_states.append(encoded_state - input_states     )
														tokens_start += self.condition_lengths[i]

										lowercase         =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
										lowercase         =      output_states + input_states

										if not return_dict:
														return (output_states,)

										return TransformeraDModelOutput(sample=lowerCAmelCase__     )


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_snake_case      :							Tuple     	=    False
_snake_case      :							Optional[int]     	=    True
_snake_case      :							Any     	=    False


if __name__ == "__main__":
						_snake_case      :							Any     	=    argparse.ArgumentParser()

						parser.add_argument(
						    "--repo_path",
						    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      :							Tuple     	=    parser.parse_args()

						_snake_case      :							Optional[Any]     	=    {
						    "image_size": "sample_size",
						    "num_res_blocks": "layers_per_block",
						    "block_channels": "block_out_channels",
						    "down_blocks": "down_block_types",
						    "up_blocks": "up_block_types",
						    "downscale_freq_shift": "freq_shift",
						    "resnet_num_groups": "norm_num_groups",
						    "resnet_act_fn": "act_fn",
						    "resnet_eps": "norm_eps",
						    "num_head_channels": "attention_head_dim",
						}

						_snake_case      :							Tuple     	=    {
						    "time_steps": "time_proj",
						    "mid": "mid_block",
						    "downsample_blocks": "down_blocks",
						    "upsample_blocks": "up_blocks",
						}

						_snake_case      :							Any     	=    "" if has_file(args.repo_path, "config.json") else "unet"

						with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
												_snake_case      :							List[Any]     	=    reader.read()
												_snake_case      :							List[Any]     	=    json.loads(text)

						if do_only_config:
												for key in config_parameters_to_change.keys():
																		config.pop(key, None)

						if has_file(args.repo_path, "config.json"):
												_snake_case      :							List[Any]     	=    UNetaDModel(**config)
						else:
												_snake_case      :							int     	=    UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
												_snake_case      :							Optional[Any]     	=    class_name(**config)

						if do_only_config:
												model.save_config(os.path.join(args.repo_path, subfolder))

						_snake_case      :							Any     	=    dict(model.config)

						if do_only_renaming:
												for key, value in config_parameters_to_change.items():
																		if key in config:
																								_snake_case      :							Any     	=    config[key]
																								del config[key]

												_snake_case      :							List[str]     	=    [k.replace("UNetRes", "") for k in config["down_block_types"]]
												_snake_case      :							Any     	=    [k.replace("UNetRes", "") for k in config["up_block_types"]]

						if do_only_weights:
												_snake_case      :							Optional[int]     	=    torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

												_snake_case      :							Any     	=    {}
												for param_key, param_value in state_dict.items():
																		if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
																								continue
																		_snake_case      :							List[Any]     	=    False
																		for key, new_key in key_parameters_to_change.items():
																								if not has_changed and param_key.split(".")[0] == key:
																														_snake_case      :							Dict     	=    param_value
																														_snake_case      :							Any     	=    True
																		if not has_changed:
																								_snake_case      :							int     	=    param_value

												model.load_state_dict(new_state_dict)
												model.save_pretrained(os.path.join(args.repo_path, subfolder))
