
import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

A     :	Dict    = [
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

A     :	List[Any]    = [
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

A     :	List[Any]    = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
					# loop over downblocks/upblocks

					for j in range(2):
										# loop over resnets/attentions for downblocks
										A     :	Dict    = F"down_blocks.{i}.resnets.{j}."
										A     :	int    = F"input_blocks.{3*i + j + 1}.0."
										unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

										if i < 3:
															# no attention layers in down_blocks.3
															A     :	List[Any]    = F"down_blocks.{i}.attentions.{j}."
															A     :	Optional[int]    = F"input_blocks.{3*i + j + 1}.1."
															unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

					for j in range(3):
										# loop over resnets/attentions for upblocks
										A     :	Optional[int]    = F"up_blocks.{i}.resnets.{j}."
										A     :	List[Any]    = F"output_blocks.{3*i + j}.0."
										unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

										if i > 0:
															# no attention layers in up_blocks.0
															A     :	Optional[int]    = F"up_blocks.{i}.attentions.{j}."
															A     :	Dict    = F"output_blocks.{3*i + j}.1."
															unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

					if i < 3:
										# no downsample in down_blocks.3
										A     :	Dict    = F"down_blocks.{i}.downsamplers.0.conv."
										A     :	Dict    = F"input_blocks.{3*(i+1)}.0.op."
										unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

										# no upsample in up_blocks.3
										A     :	str    = F"up_blocks.{i}.upsamplers.0."
										A     :	int    = F"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
										unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

A     :	Tuple    = 'mid_block.attentions.0.'
A     :	List[str]    = 'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
					A     :	List[Any]    = F"mid_block.resnets.{j}."
					A     :	List[Any]    = F"middle_block.{2*j}."
					unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def     __lowerCAmelCase			(			a__  )       ->  Tuple:
				# buyer beware: this is a *brittle* function,
				# and correct output requires that all of these pieces interact in
				# the exact order in which I have arranged them.
				__a										=    {k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								__a										=    sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																__a										=    v.replace(a__						,    a__  )
												__a										=    v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												__a										=    v.replace(a__						,    a__  )
								__a										=    v
				__a										=    {v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


# ================#
# VAE Conversion #
# ================#

A     :	str    = [
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
					# down_blocks have two resnets
					for j in range(2):
										A     :	Union[str, Any]    = F"encoder.down_blocks.{i}.resnets.{j}."
										A     :	int    = F"encoder.down.{i}.block.{j}."
										vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

					if i < 3:
										A     :	List[str]    = F"down_blocks.{i}.downsamplers.0."
										A     :	List[Any]    = F"down.{i}.downsample."
										vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

										A     :	Any    = F"up_blocks.{i}.upsamplers.0."
										A     :	List[Any]    = F"up.{3-i}.upsample."
										vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

					# up_blocks have three resnets
					# also, up blocks in hf are numbered in reverse from sd
					for j in range(3):
										A     :	str    = F"decoder.up_blocks.{i}.resnets.{j}."
										A     :	Union[str, Any]    = F"decoder.up.{3-i}.block.{j}."
										vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
					A     :	str    = F"mid_block.resnets.{i}."
					A     :	Tuple    = F"mid.block_{i+1}."
					vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


A     :	Optional[Any]    = [
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]


def     __lowerCAmelCase			(			a__  )       ->  Dict:
				# convert HF linear weights to SD conv2d weights
				return w.reshape(*w.shape						,    1						,    1  )


def     __lowerCAmelCase			(			a__  )       ->  List[Any]:
				__a										=    {k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												__a										=    v.replace(a__						,    a__  )
								__a										=    v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																__a										=    v.replace(a__						,    a__  )
												__a										=    v
				__a										=    {v: vae_state_dict[k] for k, v in mapping.items()}
				__a										=    ['''q''', '''k''', '''v''', '''proj_out''']
				for k, v in new_state_dict.items():
								for weight_name in weights_to_convert:
												if F"""mid.attn_1.{weight_name}.weight""" in k:
																print(F"""Reshaping {k} for SD format"""  )
																__a										=    reshape_weight_for_sd(a__  )
				return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


A     :	Any    = [
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
A     :	Optional[int]    = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A     :	Dict    = re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
A     :	int    = {'q': 0, 'k': 1, 'v': 2}


def     __lowerCAmelCase			(			a__  )       ->  Tuple:
				__a										=    {}
				__a										=    {}
				__a										=    {}
				for k, v in text_enc_dict.items():
								if (
								    k.endswith('''.self_attn.q_proj.weight'''  )
								    or k.endswith('''.self_attn.k_proj.weight'''  )
								    or k.endswith('''.self_attn.v_proj.weight'''  )
								):
												__a										=    k[: -len('''.q_proj.weight'''  )]
												__a										=    k[-len('''q_proj.weight'''  )]
												if k_pre not in capture_qkv_weight:
																__a										=    [None, None, None]
												__a										=    v
												continue

								if (
								    k.endswith('''.self_attn.q_proj.bias'''  )
								    or k.endswith('''.self_attn.k_proj.bias'''  )
								    or k.endswith('''.self_attn.v_proj.bias'''  )
								):
												__a										=    k[: -len('''.q_proj.bias'''  )]
												__a										=    k[-len('''q_proj.bias'''  )]
												if k_pre not in capture_qkv_bias:
																__a										=    [None, None, None]
												__a										=    v
												continue

								__a										=    textenc_pattern.sub(lambda a__  : protected[re.escape(m.group(0  )  )]						,    a__  )
								__a										=    v

				for k_pre, tensors in capture_qkv_weight.items():
								if None in tensors:
												raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''  )
								__a										=    textenc_pattern.sub(lambda a__  : protected[re.escape(m.group(0  )  )]						,    a__  )
								__a										=    torch.cat(a__  )

				for k_pre, tensors in capture_qkv_bias.items():
								if None in tensors:
												raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''  )
								__a										=    textenc_pattern.sub(lambda a__  : protected[re.escape(m.group(0  )  )]						,    a__  )
								__a										=    torch.cat(a__  )

				return new_state_dict


def     __lowerCAmelCase			(			a__  )       ->  Any:
				return text_enc_dict


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

					parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
					parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
					parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
					parser.add_argument(
					    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
					)

					A     :	List[str]    = parser.parse_args()

					assert args.model_path is not None, "Must provide a model path!"

					assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

					# Path for safetensors
					A     :	int    = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
					A     :	Union[str, Any]    = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
					A     :	Union[str, Any]    = osp.join(args.model_path, 'text_encoder', 'model.safetensors')

					# Load models from safetensors if it exists, if it doesn't pytorch
					if osp.exists(unet_path):
										A     :	Tuple    = load_file(unet_path, device='cpu')
					else:
										A     :	List[Any]    = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
										A     :	List[str]    = torch.load(unet_path, map_location='cpu')

					if osp.exists(vae_path):
										A     :	Optional[int]    = load_file(vae_path, device='cpu')
					else:
										A     :	Dict    = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
										A     :	List[Any]    = torch.load(vae_path, map_location='cpu')

					if osp.exists(text_enc_path):
										A     :	int    = load_file(text_enc_path, device='cpu')
					else:
										A     :	Optional[Any]    = osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
										A     :	Optional[Any]    = torch.load(text_enc_path, map_location='cpu')

					# Convert the UNet model
					A     :	Union[str, Any]    = convert_unet_state_dict(unet_state_dict)
					A     :	Optional[Any]    = {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

					# Convert the VAE model
					A     :	Any    = convert_vae_state_dict(vae_state_dict)
					A     :	List[Any]    = {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

					# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
					A     :	List[str]    = 'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

					if is_vaa_model:
										# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
										A     :	Dict    = {'transformer.' + k: v for k, v in text_enc_dict.items()}
										A     :	str    = convert_text_enc_state_dict_vaa(text_enc_dict)
										A     :	Union[str, Any]    = {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
					else:
										A     :	int    = convert_text_enc_state_dict(text_enc_dict)
										A     :	Optional[Any]    = {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

					# Put together new checkpoint
					A     :	List[Any]    = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
					if args.half:
										A     :	Dict    = {k: v.half() for k, v in state_dict.items()}

					if args.use_safetensors:
										save_file(state_dict, args.checkpoint_path)
					else:
										A     :	str    = {'state_dict': state_dict}
										torch.save(state_dict, args.checkpoint_path)

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	Optional[int]    = {
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = ['WhisperTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	str    = [
					    'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'WhisperForConditionalGeneration',
					    'WhisperModel',
					    'WhisperPreTrainedModel',
					    'WhisperForAudioClassification',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Tuple    = [
					    'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFWhisperForConditionalGeneration',
					    'TFWhisperModel',
					    'TFWhisperPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = [
					    'FlaxWhisperForConditionalGeneration',
					    'FlaxWhisperModel',
					    'FlaxWhisperPreTrainedModel',
					    'FlaxWhisperForAudioClassification',
					]


if TYPE_CHECKING:
					from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
					from .feature_extraction_whisper import WhisperFeatureExtractor
					from .processing_whisper import WhisperProcessor
					from .tokenization_whisper import WhisperTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_whisper_fast import WhisperTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_whisper import (
										    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WhisperForAudioClassification,
										    WhisperForConditionalGeneration,
										    WhisperModel,
										    WhisperPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_whisper import (
										    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFWhisperForConditionalGeneration,
										    TFWhisperModel,
										    TFWhisperPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_whisper import (
										    FlaxWhisperForAudioClassification,
										    FlaxWhisperForConditionalGeneration,
										    FlaxWhisperModel,
										    FlaxWhisperPreTrainedModel,
										)

else:
					import sys

					A     :	Tuple    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)