'''simple docstring'''







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCamelCase_      	=	False
UpperCamelCase_      	=	True
UpperCamelCase_      	=	False


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

		UpperCamelCase_      	=	parser.parse_args()

		UpperCamelCase_      	=	{
		    '''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''',
		}

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

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

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

		UpperCamelCase_      	=	dict(model.config)

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

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

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

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

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







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase_      	=	{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	['''BloomTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BloomForCausalLM''',
		    '''BloomModel''',
		    '''BloomPreTrainedModel''',
		    '''BloomForSequenceClassification''',
		    '''BloomForTokenClassification''',
		    '''BloomForQuestionAnswering''',
		]

if TYPE_CHECKING:
		from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bloom_fast import BloomTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bloom import (
				    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BloomForCausalLM,
				    BloomForQuestionAnswering,
				    BloomForSequenceClassification,
				    BloomForTokenClassification,
				    BloomModel,
				    BloomPreTrainedModel,
				)

else:
		import sys

		UpperCamelCase_      	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






