

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

A											=     [
    # (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											=     [
    # (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											=     []
# 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											=     f"""down_blocks.{i}.resnets.{j}."""
				A											=     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											=     f"""down_blocks.{i}.attentions.{j}."""
						A											=     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											=     f"""up_blocks.{i}.resnets.{j}."""
				A											=     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											=     f"""up_blocks.{i}.attentions.{j}."""
						A											=     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											=     f"""down_blocks.{i}.downsamplers.0.conv."""
				A											=     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											=     f"""up_blocks.{i}.upsamplers.0."""
				A											=     f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

A											=     'mid_block.attentions.0.'
A											=     'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		A											=     f"""mid_block.resnets.{j}."""
		A											=     f"""middle_block.{2*j}."""
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def a(lowercase__	):





	'''simple docstring'''
	# 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.
	snake_case_      		= {k: k for k in unet_state_dict.keys()}
	for sd_name, hf_name in unet_conversion_map:
		snake_case_      		= sd_name
	for k, v in mapping.items():
		if "resnets" in k:
			for sd_part, hf_part in unet_conversion_map_resnet:
				snake_case_      		= v.replace(lowercase__			, lowercase__	)
			snake_case_      		= v
	for k, v in mapping.items():
		for sd_part, hf_part in unet_conversion_map_layer:
			snake_case_      		= v.replace(lowercase__			, lowercase__	)
		snake_case_      		= v
	snake_case_      		= {v: unet_state_dict[k] for k, v in mapping.items()}
	return new_state_dict


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

A											=     [
    # (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											=     f"""encoder.down_blocks.{i}.resnets.{j}."""
				A											=     f"""encoder.down.{i}.block.{j}."""
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				A											=     f"""down_blocks.{i}.downsamplers.0."""
				A											=     f"""down.{i}.downsample."""
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				A											=     f"""up_blocks.{i}.upsamplers.0."""
				A											=     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											=     f"""decoder.up_blocks.{i}.resnets.{j}."""
				A											=     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											=     f"""mid_block.resnets.{i}."""
		A											=     f"""mid.block_{i+1}."""
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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





def a(lowercase__	):





	'''simple docstring'''
	# convert HF linear weights to SD conv2d weights
	return w.reshape(*w.shape			, 1			, 1	)





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= {k: k for k in vae_state_dict.keys()}
	for k, v in mapping.items():
		for sd_part, hf_part in vae_conversion_map:
			snake_case_      		= v.replace(lowercase__			, lowercase__	)
		snake_case_      		= v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				snake_case_      		= v.replace(lowercase__			, lowercase__	)
			snake_case_      		= v
	snake_case_      		= {v: vae_state_dict[k] for k, v in mapping.items()}
	snake_case_      		= ['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"""	)
				snake_case_      		= reshape_weight_for_sd(lowercase__	)
	return new_state_dict


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


A											=     [
    # (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											=     {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A											=     re.compile('|'.join(protected.keys()))

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





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= {}
	snake_case_      		= {}
	snake_case_      		= {}
	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'	)
		):
			snake_case_      		= k[: -len('.q_proj.weight'	)]
			snake_case_      		= k[-len('q_proj.weight'	)]
			if k_pre not in capture_qkv_weight:
				snake_case_      		= [None, None, None]
			snake_case_      		= 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'	)
		):
			snake_case_      		= k[: -len('.q_proj.bias'	)]
			snake_case_      		= k[-len('q_proj.bias'	)]
			if k_pre not in capture_qkv_bias:
				snake_case_      		= [None, None, None]
			snake_case_      		= v
			continue

		snake_case_      		= textenc_pattern.sub(lambda lowercase__	: protected[re.escape(m.group(0	)	)]			, lowercase__	)
		snake_case_      		= 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'	)
		snake_case_      		= textenc_pattern.sub(lambda lowercase__	: protected[re.escape(m.group(0	)	)]			, lowercase__	)
		snake_case_      		= torch.cat(lowercase__	)

	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'	)
		snake_case_      		= textenc_pattern.sub(lambda lowercase__	: protected[re.escape(m.group(0	)	)]			, lowercase__	)
		snake_case_      		= torch.cat(lowercase__	)

	return new_state_dict





def a(lowercase__	):





	'''simple docstring'''
	return text_enc_dict


if __name__ == "__main__":
		A											=     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											=     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											=     osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
		A											=     osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
		A											=     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											=     load_file(unet_path, device='cpu')
		else:
				A											=     osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
				A											=     torch.load(unet_path, map_location='cpu')

		if osp.exists(vae_path):
				A											=     load_file(vae_path, device='cpu')
		else:
				A											=     osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
				A											=     torch.load(vae_path, map_location='cpu')

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

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

		# Convert the VAE model
		A											=     convert_vae_state_dict(vae_state_dict)
		A											=     {'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											=     '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											=     {'transformer.' + k: v for k, v in text_enc_dict.items()}
				A											=     convert_text_enc_state_dict_vaa(text_enc_dict)
				A											=     {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
		else:
				A											=     convert_text_enc_state_dict(text_enc_dict)
				A											=     {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

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

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



from typing import TYPE_CHECKING

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


A											=     {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ASTForAudioClassification',
		    'ASTModel',
		    'ASTPreTrainedModel',
		]

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['ASTFeatureExtractor']

if TYPE_CHECKING:
		from .configuration_audio_spectrogram_transformer import (
		    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ASTConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ASTForAudioClassification,
				    ASTModel,
				    ASTPreTrainedModel,
				)

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
		import sys

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