



'''simple docstring'''


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase_      :				List[str]   					=    False
lowercase_      :				Tuple   					=    True
lowercase_      :				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_      :				Tuple   					=    parser.parse_args()

						lowercase_      :				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''',
						}

						lowercase_      :				List[str]   					=    {
						    '''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_      :				Dict   					=    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_      :				str   					=    UNetaDModel(**config)
						else:
												lowercase_      :				List[str]   					=    UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
												lowercase_      :				List[str]   					=    class_name(**config)

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

						lowercase_      :				List[Any]   					=    dict(model.config)

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

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

						if do_only_weights:
												lowercase_      :				Optional[Any]   					=    torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

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

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







'''simple docstring'''


# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


lowercase_      :				List[str]   					=    '''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
						import torch

						print('''Torch version:''', torch.__version__)
						print('''Cuda available:''', torch.cuda.is_available())
						print('''Cuda version:''', torch.version.cuda)
						print('''CuDNN version:''', torch.backends.cudnn.version())
						print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
						print('''Torch version:''', None)

try:
						import transformers

						print('''transformers version:''', transformers.__version__)
except ImportError:
						print('''transformers version:''', None)


