




'''simple docstring'''







import requests

A     ='YOUR API KEY'





def 						snake_case_							(_a	:							int    ,   _a	:							List[Any] = giphy_api_key     ):
    UpperCAmelCase			       =		'''+'''.join(query.split()     )
    UpperCAmelCase			       =		F"https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"
    UpperCAmelCase			       =		requests.get(_a     ).json()['''data''']
    return [gif["url"] for gif in gifs]


if __name__ == "__main__":
   print('\n'.join(get_gifs('space ship')))





"""simple docstring"""






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowerCAmelCase__  :  Optional[int]						     =	[
    # (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'),
]

lowerCAmelCase__  :  str						     =	[
    # (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'),
]

lowerCAmelCase__  :  int						     =	[]
# 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
												lowerCAmelCase__  :  Dict						     =	F"""down_blocks.{i}.resnets.{j}."""
												lowerCAmelCase__  :  Union[str, Any]						     =	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
																		lowerCAmelCase__  :  Tuple						     =	F"""down_blocks.{i}.attentions.{j}."""
																		lowerCAmelCase__  :  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
												lowerCAmelCase__  :  Tuple						     =	F"""up_blocks.{i}.resnets.{j}."""
												lowerCAmelCase__  :  int						     =	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
																		lowerCAmelCase__  :  Any						     =	F"""up_blocks.{i}.attentions.{j}."""
																		lowerCAmelCase__  :  List[str]						     =	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
												lowerCAmelCase__  :  List[str]						     =	F"""down_blocks.{i}.downsamplers.0.conv."""
												lowerCAmelCase__  :  Union[str, Any]						     =	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
												lowerCAmelCase__  :  Dict						     =	F"""up_blocks.{i}.upsamplers.0."""
												lowerCAmelCase__  :  List[Any]						     =	F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
												unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCAmelCase__  :  str						     =	'mid_block.attentions.0.'
lowerCAmelCase__  :  Union[str, Any]						     =	'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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



def   a_						(					lowerCamelCase       ):
				# 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.
				UpperCAmelCase__             =							{k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								UpperCAmelCase__             =							sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																UpperCAmelCase__             =							v.replace(lowerCamelCase					,			lowerCamelCase       )
												UpperCAmelCase__             =							v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												UpperCAmelCase__             =							v.replace(lowerCamelCase					,			lowerCamelCase       )
								UpperCAmelCase__             =							v
				UpperCAmelCase__             =							{v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


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

lowerCAmelCase__  :  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):
												lowerCAmelCase__  :  List[str]						     =	F"""encoder.down_blocks.{i}.resnets.{j}."""
												lowerCAmelCase__  :  List[Any]						     =	F"""encoder.down.{i}.block.{j}."""
												vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

						if i < 3:
												lowerCAmelCase__  :  Dict						     =	F"""down_blocks.{i}.downsamplers.0."""
												lowerCAmelCase__  :  str						     =	F"""down.{i}.downsample."""
												vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

												lowerCAmelCase__  :  int						     =	F"""up_blocks.{i}.upsamplers.0."""
												lowerCAmelCase__  :  str						     =	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):
												lowerCAmelCase__  :  Dict						     =	F"""decoder.up_blocks.{i}.resnets.{j}."""
												lowerCAmelCase__  :  Optional[int]						     =	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):
						lowerCAmelCase__  :  Any						     =	F"""mid_block.resnets.{i}."""
						lowerCAmelCase__  :  Any						     =	F"""mid.block_{i+1}."""
						vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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



def   a_						(					lowerCamelCase       ):
				# convert HF linear weights to SD conv2d weights
				return w.reshape(*w.shape					,			1					,			1       )



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							{k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												UpperCAmelCase__             =							v.replace(lowerCamelCase					,			lowerCamelCase       )
								UpperCAmelCase__             =							v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																UpperCAmelCase__             =							v.replace(lowerCamelCase					,			lowerCamelCase       )
												UpperCAmelCase__             =							v
				UpperCAmelCase__             =							{v: vae_state_dict[k] for k, v in mapping.items()}
				UpperCAmelCase__             =							['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'''       )
																UpperCAmelCase__             =							reshape_weight_for_sd(lowerCamelCase       )
				return new_state_dict


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


lowerCAmelCase__  :  Optional[int]						     =	[
    # (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'),
]
lowerCAmelCase__  :  List[Any]						     =	{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCAmelCase__  :  int						     =	re.compile('|'.join(protected.keys()))

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



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							{}
				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'       )
								):
												UpperCAmelCase__             =							k[: -len('.q_proj.weight'       )]
												UpperCAmelCase__             =							k[-len('q_proj.weight'       )]
												if k_pre not in capture_qkv_weight:
																UpperCAmelCase__             =							[None, None, None]
												UpperCAmelCase__             =							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'       )
								):
												UpperCAmelCase__             =							k[: -len('.q_proj.bias'       )]
												UpperCAmelCase__             =							k[-len('q_proj.bias'       )]
												if k_pre not in capture_qkv_bias:
																UpperCAmelCase__             =							[None, None, None]
												UpperCAmelCase__             =							v
												continue

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

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

				return new_state_dict



def   a_						(					lowerCamelCase       ):
				return text_enc_dict


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

						lowerCAmelCase__  :  Optional[int]						     =	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
						lowerCAmelCase__  :  Tuple						     =	osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
						lowerCAmelCase__  :  List[str]						     =	osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
						lowerCAmelCase__  :  int						     =	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):
												lowerCAmelCase__  :  Union[str, Any]						     =	load_file(unet_path, device='cpu')
						else:
												lowerCAmelCase__  :  str						     =	osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
												lowerCAmelCase__  :  Dict						     =	torch.load(unet_path, map_location='cpu')

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

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

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

						# Convert the VAE model
						lowerCAmelCase__  :  List[Any]						     =	convert_vae_state_dict(vae_state_dict)
						lowerCAmelCase__  :  str						     =	{'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
						lowerCAmelCase__  :  List[Any]						     =	'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
												lowerCAmelCase__  :  Tuple						     =	{'transformer.' + k: v for k, v in text_enc_dict.items()}
												lowerCAmelCase__  :  List[str]						     =	convert_text_enc_state_dict_vaa(text_enc_dict)
												lowerCAmelCase__  :  str						     =	{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
						else:
												lowerCAmelCase__  :  Optional[Any]						     =	convert_text_enc_state_dict(text_enc_dict)
												lowerCAmelCase__  :  Optional[Any]						     =	{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

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

						if args.use_safetensors:
												save_file(state_dict, args.checkpoint_path)
						else:
												lowerCAmelCase__  :  List[str]						     =	{'state_dict': state_dict}
												torch.save(state_dict, args.checkpoint_path)



