






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase=1):
			if n_shave_prefix_segments >= 0:
						return ".".join(path.split('.')[n_shave_prefix_segments:])
			else:
						return ".".join(path.split('.')[:n_shave_prefix_segments])






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase=0):
			SCREAMING_SNAKE_CASE						= []
			for old_item in old_list:
						SCREAMING_SNAKE_CASE						= old_item.replace('in_layers.0'							,       'norm1')
						SCREAMING_SNAKE_CASE						= new_item.replace('in_layers.2'							,       'conv1')

						SCREAMING_SNAKE_CASE						= new_item.replace('out_layers.0'							,       'norm2')
						SCREAMING_SNAKE_CASE						= new_item.replace('out_layers.3'							,       'conv2')

						SCREAMING_SNAKE_CASE						= new_item.replace('emb_layers.1'							,       'time_emb_proj')
						SCREAMING_SNAKE_CASE						= new_item.replace('skip_connection'							,       'conv_shortcut')

						SCREAMING_SNAKE_CASE						= shave_segments(_UpperCAmelCase							,       n_shave_prefix_segments=_UpperCAmelCase)

						mapping.append({'old': old_item, 'new': new_item})

			return mapping






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase=0):
			SCREAMING_SNAKE_CASE						= []
			for old_item in old_list:
						SCREAMING_SNAKE_CASE						= old_item

						SCREAMING_SNAKE_CASE						= new_item.replace('norm.weight'							,       'group_norm.weight')
						SCREAMING_SNAKE_CASE						= new_item.replace('norm.bias'							,       'group_norm.bias')

						SCREAMING_SNAKE_CASE						= new_item.replace('proj_out.weight'							,       'proj_attn.weight')
						SCREAMING_SNAKE_CASE						= new_item.replace('proj_out.bias'							,       'proj_attn.bias')

						SCREAMING_SNAKE_CASE						= shave_segments(_UpperCAmelCase							,       n_shave_prefix_segments=_UpperCAmelCase)

						mapping.append({'old': old_item, 'new': new_item})

			return mapping






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=None							,       _UpperCAmelCase=None							,       _UpperCAmelCase=None):
			assert isinstance(_UpperCAmelCase							,       _UpperCAmelCase), "Paths should be a list of dicts containing 'old' and 'new' keys."

			# Splits the attention layers into three variables.
			if attention_paths_to_split is not None:
						for path, path_map in attention_paths_to_split.items():
									SCREAMING_SNAKE_CASE						= old_checkpoint[path]
									SCREAMING_SNAKE_CASE						= old_tensor.shape[0] // 3

									SCREAMING_SNAKE_CASE						= (-1, channels) if len(old_tensor.shape) == 3 else (-1)

									SCREAMING_SNAKE_CASE						= old_tensor.shape[0] // config['num_head_channels'] // 3

									SCREAMING_SNAKE_CASE						= old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= old_tensor.split(channels // num_heads							,       dim=1)

									SCREAMING_SNAKE_CASE						= query.reshape(_UpperCAmelCase)
									SCREAMING_SNAKE_CASE						= key.reshape(_UpperCAmelCase)
									SCREAMING_SNAKE_CASE						= value.reshape(_UpperCAmelCase)

			for path in paths:
						SCREAMING_SNAKE_CASE						= path['new']

						# These have already been assigned
						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						# Global renaming happens here
						SCREAMING_SNAKE_CASE						= new_path.replace('middle_block.0'							,       'mid_block.resnets.0')
						SCREAMING_SNAKE_CASE						= new_path.replace('middle_block.1'							,       'mid_block.attentions.0')
						SCREAMING_SNAKE_CASE						= new_path.replace('middle_block.2'							,       'mid_block.resnets.1')

						if additional_replacements is not None:
									for replacement in additional_replacements:
												SCREAMING_SNAKE_CASE						= new_path.replace(replacement['old']							,       replacement['new'])

        # proj_attn.weight has to be converted from conv 1D to linear
						if "proj_attn.weight" in new_path:
									SCREAMING_SNAKE_CASE						= old_checkpoint[path['old']][:, :, 0]
						else:
									SCREAMING_SNAKE_CASE						= old_checkpoint[path['old']]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= {}

			SCREAMING_SNAKE_CASE						= checkpoint['time_embed.0.weight']
			SCREAMING_SNAKE_CASE						= checkpoint['time_embed.0.bias']
			SCREAMING_SNAKE_CASE						= checkpoint['time_embed.2.weight']
			SCREAMING_SNAKE_CASE						= checkpoint['time_embed.2.bias']

			SCREAMING_SNAKE_CASE						= checkpoint['input_blocks.0.0.weight']
			SCREAMING_SNAKE_CASE						= checkpoint['input_blocks.0.0.bias']

			SCREAMING_SNAKE_CASE						= checkpoint['out.0.weight']
			SCREAMING_SNAKE_CASE						= checkpoint['out.0.bias']
			SCREAMING_SNAKE_CASE						= checkpoint['out.2.weight']
			SCREAMING_SNAKE_CASE						= checkpoint['out.2.bias']

			# Retrieves the keys for the input blocks only
			SCREAMING_SNAKE_CASE						= len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'input_blocks' in layer})
			SCREAMING_SNAKE_CASE						= {
			    layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
			    for layer_id in range(_UpperCAmelCase)
			}

			# Retrieves the keys for the middle blocks only
			SCREAMING_SNAKE_CASE						= len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'middle_block' in layer})
			SCREAMING_SNAKE_CASE						= {
			    layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
			    for layer_id in range(_UpperCAmelCase)
			}

			# Retrieves the keys for the output blocks only
			SCREAMING_SNAKE_CASE						= len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'output_blocks' in layer})
			SCREAMING_SNAKE_CASE						= {
			    layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
			    for layer_id in range(_UpperCAmelCase)
			}

			for i in range(1							,       _UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= (i - 1) // (config['num_res_blocks'] + 1)
						SCREAMING_SNAKE_CASE						= (i - 1) % (config['num_res_blocks'] + 1)

						SCREAMING_SNAKE_CASE						= [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
						SCREAMING_SNAKE_CASE						= [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]

						if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
									SCREAMING_SNAKE_CASE						= checkpoint[
									    F'''input_blocks.{i}.0.op.weight'''
									]
									SCREAMING_SNAKE_CASE						= checkpoint[
									    F'''input_blocks.{i}.0.op.bias'''
									]
									continue

						SCREAMING_SNAKE_CASE						= renew_resnet_paths(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= {'old': F'''input_blocks.{i}.0''', 'new': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
						SCREAMING_SNAKE_CASE						= {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
						assign_to_checkpoint(
						    _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       additional_replacements=[meta_path, resnet_op]							,       config=_UpperCAmelCase)

						if len(_UpperCAmelCase):
									SCREAMING_SNAKE_CASE						= renew_attention_paths(_UpperCAmelCase)
									SCREAMING_SNAKE_CASE						= {
									    'old': F'''input_blocks.{i}.1''',
									    'new': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
									}
									SCREAMING_SNAKE_CASE						= {
									    F'''input_blocks.{i}.1.qkv.bias''': {
									        'key': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
									        'query': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
									        'value': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
									    },
									    F'''input_blocks.{i}.1.qkv.weight''': {
									        'key': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
									        'query': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
									        'value': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
									    },
									}
									assign_to_checkpoint(
									    _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       additional_replacements=[meta_path]							,       attention_paths_to_split=_UpperCAmelCase							,       config=_UpperCAmelCase							,       )

			SCREAMING_SNAKE_CASE						= middle_blocks[0]
			SCREAMING_SNAKE_CASE						= middle_blocks[1]
			SCREAMING_SNAKE_CASE						= middle_blocks[2]

			SCREAMING_SNAKE_CASE						= renew_resnet_paths(_UpperCAmelCase)
			assign_to_checkpoint(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       config=_UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= renew_resnet_paths(_UpperCAmelCase)
			assign_to_checkpoint(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       config=_UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= renew_attention_paths(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= {
			    'middle_block.1.qkv.bias': {
			        'key': 'mid_block.attentions.0.key.bias',
			        'query': 'mid_block.attentions.0.query.bias',
			        'value': 'mid_block.attentions.0.value.bias',
			    },
			    'middle_block.1.qkv.weight': {
			        'key': 'mid_block.attentions.0.key.weight',
			        'query': 'mid_block.attentions.0.query.weight',
			        'value': 'mid_block.attentions.0.value.weight',
			    },
			}
			assign_to_checkpoint(
			    _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       attention_paths_to_split=_UpperCAmelCase							,       config=_UpperCAmelCase)

			for i in range(_UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= i // (config['num_res_blocks'] + 1)
						SCREAMING_SNAKE_CASE						= i % (config['num_res_blocks'] + 1)
						SCREAMING_SNAKE_CASE						= [shave_segments(_UpperCAmelCase							,       2) for name in output_blocks[i]]
						SCREAMING_SNAKE_CASE						= {}

						for layer in output_block_layers:
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= layer.split('.')[0], shave_segments(_UpperCAmelCase							,       1)
									if layer_id in output_block_list:
												output_block_list[layer_id].append(_UpperCAmelCase)
									else:
												SCREAMING_SNAKE_CASE						= [layer_name]

						if len(_UpperCAmelCase) > 1:
									SCREAMING_SNAKE_CASE						= [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
									SCREAMING_SNAKE_CASE						= [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

									SCREAMING_SNAKE_CASE						= renew_resnet_paths(_UpperCAmelCase)
									SCREAMING_SNAKE_CASE						= renew_resnet_paths(_UpperCAmelCase)

									SCREAMING_SNAKE_CASE						= {'old': F'''output_blocks.{i}.0''', 'new': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
									assign_to_checkpoint(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       additional_replacements=[meta_path]							,       config=_UpperCAmelCase)

									if ["conv.weight", "conv.bias"] in output_block_list.values():
												SCREAMING_SNAKE_CASE						= list(output_block_list.values()).index(['conv.weight', 'conv.bias'])
												SCREAMING_SNAKE_CASE						= checkpoint[
												    F'''output_blocks.{i}.{index}.conv.weight'''
												]
												SCREAMING_SNAKE_CASE						= checkpoint[
												    F'''output_blocks.{i}.{index}.conv.bias'''
												]

												# Clear attentions as they have been attributed above.
												if len(_UpperCAmelCase) == 2:
															SCREAMING_SNAKE_CASE						= []

									if len(_UpperCAmelCase):
												SCREAMING_SNAKE_CASE						= renew_attention_paths(_UpperCAmelCase)
												SCREAMING_SNAKE_CASE						= {
												    'old': F'''output_blocks.{i}.1''',
												    'new': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												SCREAMING_SNAKE_CASE						= {
												    F'''output_blocks.{i}.1.qkv.bias''': {
												        'key': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
												        'query': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
												        'value': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
												    },
												    F'''output_blocks.{i}.1.qkv.weight''': {
												        'key': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
												        'query': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
												        'value': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
												    },
												}
												assign_to_checkpoint(
												    _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       additional_replacements=[meta_path]							,       attention_paths_to_split=to_split if any('qkv' in key for key in attentions) else None							,       config=_UpperCAmelCase							,       )
						else:
									SCREAMING_SNAKE_CASE						= renew_resnet_paths(_UpperCAmelCase							,       n_shave_prefix_segments=1)
									for path in resnet_0_paths:
												SCREAMING_SNAKE_CASE						= '.'.join(['output_blocks', str(_UpperCAmelCase), path['old']])
												SCREAMING_SNAKE_CASE						= '.'.join(['up_blocks', str(_UpperCAmelCase), 'resnets', str(_UpperCAmelCase), path['new']])

												SCREAMING_SNAKE_CASE						= checkpoint[old_path]

			return new_checkpoint


if __name__ == "__main__":
				a_		:						Tuple             =    argparse.ArgumentParser()

				parser.add_argument(
				    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
				)

				parser.add_argument(
				    '--config_file',
				    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_		:						Union[str, Any]             =    parser.parse_args()

				a_		:						Optional[int]             =    torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								a_		:						Dict             =    json.loads(f.read())

				a_		:						Any             =    convert_ldm_checkpoint(checkpoint, config)

				if "ldm" in config:
								del config["ldm"]

				a_		:						str             =    UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								a_		:						int             =    DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
								a_		:						Any             =    VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

								a_		:						str             =    LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


a_		:						int             =    {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[Any]             =    ['SpeechT5Tokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[int]             =    [
				    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SpeechT5ForSpeechToText',
				    'SpeechT5ForSpeechToSpeech',
				    'SpeechT5ForTextToSpeech',
				    'SpeechT5Model',
				    'SpeechT5PreTrainedModel',
				    'SpeechT5HifiGan',
				]

if TYPE_CHECKING:
				from .configuration_speechta import (
				    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
				    SpeechTaConfig,
				    SpeechTaHifiGanConfig,
				)
				from .feature_extraction_speechta import SpeechTaFeatureExtractor
				from .processing_speechta import SpeechTaProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speechta import SpeechTaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speechta import (
								    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechTaForSpeechToSpeech,
								    SpeechTaForSpeechToText,
								    SpeechTaForTextToSpeech,
								    SpeechTaHifiGan,
								    SpeechTaModel,
								    SpeechTaPreTrainedModel,
								)

else:
				import sys

				a_		:						Dict             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




