



"""simple docstring"""






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__snake_case				:  Union[str, Any] =       False
__snake_case				:  List[Any] =       True
__snake_case				:  Union[str, Any] =       False


if __name__ == "__main__":
	__snake_case				:  Tuple =       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				:  Any =       parser.parse_args()

	__snake_case				:  List[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				:  int =       {
	    'time_steps': 'time_proj',
	    'mid': 'mid_block',
	    'downsample_blocks': 'down_blocks',
	    'upsample_blocks': 'up_blocks',
	}

	__snake_case				:  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:
		__snake_case				:  Union[str, Any] =       reader.read()
		__snake_case				:  Union[str, 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				:  Optional[Any] =       UNetaDModel(**config)
	else:
		__snake_case				:  Any =       UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
		__snake_case				:  Union[str, Any] =       class_name(**config)

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

	__snake_case				:  List[str] =       dict(model.config)

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

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

	if do_only_weights:
		__snake_case				:  int =       torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

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

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




"""simple docstring"""






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@register_to_config
	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						float			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						bool = False			,     )							->			Any:

				"""simple docstring"""

				super().__init__()

				__lowerCAmelCase :    str				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    str				=			False

				__lowerCAmelCase :    Optional[Any]				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaConfig(
				    vocab_size=_SCREAMING_SNAKE_CASE			,     d_model=_SCREAMING_SNAKE_CASE			,     num_heads=_SCREAMING_SNAKE_CASE			,     d_kv=_SCREAMING_SNAKE_CASE			,     d_ff=_SCREAMING_SNAKE_CASE			,     dropout_rate=_SCREAMING_SNAKE_CASE			,     feed_forward_proj=_SCREAMING_SNAKE_CASE			,     is_decoder=_SCREAMING_SNAKE_CASE			,     is_encoder_decoder=_SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    str				=			nn.ModuleList()
				for lyr_num in range(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    int				=			TaBlock(_SCREAMING_SNAKE_CASE)
							self.encoders.append(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaLayerNorm(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)







	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			self.token_embedder(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			encoder_input_tokens.shape[1]
				__lowerCAmelCase :    List[Any]				=			torch.arange(_SCREAMING_SNAKE_CASE			,     device=encoder_input_tokens.device)
				x += self.position_encoding(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[Any]				=			self.dropout_pre(_SCREAMING_SNAKE_CASE)

				# inverted the attention mask
				__lowerCAmelCase :    List[Any]				=			encoder_input_tokens.size()
				__lowerCAmelCase :    Any				=			self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				for lyr in self.encoders:
							__lowerCAmelCase :    Union[str, Any]				=			lyr(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)[0]
				__lowerCAmelCase :    int				=			self.layer_norm(_SCREAMING_SNAKE_CASE)

				return self.dropout_post(_SCREAMING_SNAKE_CASE), encoder_inputs_mask