


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_lowercase       :      Dict	=	False
_lowercase       :      List[Any]	=	True
_lowercase       :      Optional[Any]	=	False


if __name__ == "__main__":
				_lowercase       :      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.")

				_lowercase       :      Dict	=	parser.parse_args()

				_lowercase       :      List[str]	=	{
				    "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",
				}

				_lowercase       :      Union[str, Any]	=	{
				    "time_steps": "time_proj",
				    "mid": "mid_block",
				    "downsample_blocks": "down_blocks",
				    "upsample_blocks": "up_blocks",
				}

				_lowercase       :      Optional[int]	=	"" 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:
								_lowercase       :      str	=	reader.read()
								_lowercase       :      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"):
								_lowercase       :      Tuple	=	UNetaDModel(**config)
				else:
								_lowercase       :      Any	=	UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
								_lowercase       :      Dict	=	class_name(**config)

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

				_lowercase       :      Dict	=	dict(model.config)

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

								_lowercase       :      str	=	[k.replace("UNetRes", "") for k in config["down_block_types"]]
								_lowercase       :      Tuple	=	[k.replace("UNetRes", "") for k in config["up_block_types"]]

				if do_only_weights:
								_lowercase       :      List[str]	=	torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

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

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



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= ['image_processor', 'tokenizer']
				lowerCAmelCase 		= 'ViltImageProcessor'
				lowerCAmelCase 		= ('BertTokenizer', 'BertTokenizerFast')

				def __init__(						self				,   a__=None				,   a__=None				,   **a__   )	->				Any:
					A								=				None
					if "feature_extractor" in kwargs:
						warnings.warn(
						    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
						    """ instead."""				,   a__				,   )
						A								=				kwargs.pop("""feature_extractor"""   )

					A								=				image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
						raise ValueError("""You need to specify an `image_processor`."""   )
					if tokenizer is None:
						raise ValueError("""You need to specify a `tokenizer`."""   )

					super().__init__(a__				,   a__   )
					A								=				self.image_processor

				def __call__(						self				,   a__				,   a__ = None				,   a__ = True				,   a__ = False				,   a__ = None				,   a__ = None				,   a__ = 0				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = True				,   a__ = None				,   **a__				,   )	->				BatchEncoding:
					A								=				self.tokenizer(
					    text=a__				,   add_special_tokens=a__				,   padding=a__				,   truncation=a__				,   max_length=a__				,   stride=a__				,   pad_to_multiple_of=a__				,   return_token_type_ids=a__				,   return_attention_mask=a__				,   return_overflowing_tokens=a__				,   return_special_tokens_mask=a__				,   return_offsets_mapping=a__				,   return_length=a__				,   verbose=a__				,   return_tensors=a__				,   **a__				,   )
					# add pixel_values + pixel_mask
					A								=				self.image_processor(a__				,   return_tensors=a__   )
					encoding.update(a__   )

					return encoding

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				Union[str, Any]:
					return self.tokenizer.batch_decode(*a__				,   **a__   )

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				Any:
					return self.tokenizer.decode(*a__				,   **a__   )

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				self.tokenizer.model_input_names
					A								=				self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )

				@property
				def      _UpperCAmelCase					(						self   )	->				str:
					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,   a__				,   )
					return self.image_processor_class

				@property
				def      _UpperCAmelCase					(						self   )	->				str:
					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,   a__				,   )
					return self.image_processor



