



'''simple docstring'''

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


a							   =				False
a							   =				True
a							   =				False


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

	a							   =				parser.parse_args()

	a							   =				{
	    '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',
	}

	a							   =				{
	    'time_steps': 'time_proj',
	    'mid': 'mid_block',
	    'downsample_blocks': 'down_blocks',
	    'upsample_blocks': 'up_blocks',
	}

	a							   =				'' 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:
		a							   =				reader.read()
		a							   =				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'):
		a							   =				UNetaDModel(**config)
	else:
		a							   =				UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
		a							   =				class_name(**config)

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

	a							   =				dict(model.config)

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

		a							   =				[k.replace('UNetRes', '') for k in config['down_block_types']]
		a							   =				[k.replace('UNetRes', '') for k in config['up_block_types']]

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

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

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










"""simple docstring"""






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


lowercase__			=		logging.get_logger(__name__)

lowercase__			=		{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """dpr"""
			def __init__(    self    , lowercase=30522    , lowercase=768    , lowercase=12    , lowercase=12    , lowercase=3072    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=2    , lowercase=0.02    , lowercase=1E-12    , lowercase=0    , lowercase="absolute"    , lowercase = 0    , **lowercase    , ):
							super().__init__(pad_token_id=lowercase    , **lowercase    )

							_lowerCamelCase	:     int       =       vocab_size
							_lowerCamelCase	:     Optional[int]       =       hidden_size
							_lowerCamelCase	:     str       =       num_hidden_layers
							_lowerCamelCase	:     Optional[int]       =       num_attention_heads
							_lowerCamelCase	:     Tuple       =       hidden_act
							_lowerCamelCase	:     int       =       intermediate_size
							_lowerCamelCase	:     str       =       hidden_dropout_prob
							_lowerCamelCase	:     Tuple       =       attention_probs_dropout_prob
							_lowerCamelCase	:     Union[str, Any]       =       max_position_embeddings
							_lowerCamelCase	:     str       =       type_vocab_size
							_lowerCamelCase	:     int       =       initializer_range
							_lowerCamelCase	:     Optional[int]       =       layer_norm_eps
							_lowerCamelCase	:     Dict       =       projection_dim
							_lowerCamelCase	:     int       =       position_embedding_type