




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase__            = False
lowerCamelCase__            = True
lowerCamelCase__            = False


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

		lowerCamelCase__            = parser.parse_args()

		lowerCamelCase__            = {
		    '''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''',
		}

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

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

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

		lowerCamelCase__            = dict(model.config)

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

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

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

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

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











from typing import TYPE_CHECKING

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


_lowerCamelCase 					=      {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase 					=      ['SpeechEncoderDecoderModel']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase 					=      ['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

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