


"""simple docstring"""







import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

a__				:							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'''),
]

a__				:							List[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'''),
]

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

a__				:							Union[str, Any]						=     '''mid_block.attentions.0.'''
a__				:							Any						=     '''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    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


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


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

a__				:							Union[str, Any]						=     [
    # (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__				:							int						=     F"encoder.down_blocks.{i}.resnets.{j}."
				a__				:							Optional[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__				:							str						=     F"down.{i}.downsample."
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

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


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




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					return w.reshape(*w.shape  ,						1  ,						1   )




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_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:
															__SCREAMING_SNAKE_CASE						   =				v.replace(lowerCAmelCase_  ,						lowerCAmelCase_   )
										__SCREAMING_SNAKE_CASE						   =				v
					for k, v in mapping.items():
										if "attentions" in k:
															for sd_part, hf_part in vae_conversion_map_attn:
																				__SCREAMING_SNAKE_CASE						   =				v.replace(lowerCAmelCase_  ,						lowerCAmelCase_   )
															__SCREAMING_SNAKE_CASE						   =				v
					__SCREAMING_SNAKE_CASE						   =				{v: vae_state_dict[k] for k, v in mapping.items()}
					__SCREAMING_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"""   )
																				__SCREAMING_SNAKE_CASE						   =				reshape_weight_for_sd(lowerCAmelCase_   )
					return new_state_dict


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


a__				:							str						=     [
    # (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__				:							Tuple						=     {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
a__				:							str						=     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    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				{}
					__SCREAMING_SNAKE_CASE						   =				{}
					__SCREAMING_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"   )
										):
															__SCREAMING_SNAKE_CASE						   =				k[: -len(".q_proj.weight"   )]
															__SCREAMING_SNAKE_CASE						   =				k[-len("q_proj.weight"   )]
															if k_pre not in capture_qkv_weight:
																				__SCREAMING_SNAKE_CASE						   =				[None, None, None]
															__SCREAMING_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"   )
										):
															__SCREAMING_SNAKE_CASE						   =				k[: -len(".q_proj.bias"   )]
															__SCREAMING_SNAKE_CASE						   =				k[-len("q_proj.bias"   )]
															if k_pre not in capture_qkv_bias:
																				__SCREAMING_SNAKE_CASE						   =				[None, None, None]
															__SCREAMING_SNAKE_CASE						   =				v
															continue

										__SCREAMING_SNAKE_CASE						   =				textenc_pattern.sub(lambda lowerCAmelCase_   : protected[re.escape(m.group(0   )   )]  ,						lowerCAmelCase_   )
										__SCREAMING_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"   )
										__SCREAMING_SNAKE_CASE						   =				textenc_pattern.sub(lambda lowerCAmelCase_   : protected[re.escape(m.group(0   )   )]  ,						lowerCAmelCase_   )
										__SCREAMING_SNAKE_CASE						   =				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"   )
										__SCREAMING_SNAKE_CASE						   =				textenc_pattern.sub(lambda lowerCAmelCase_   : protected[re.escape(m.group(0   )   )]  ,						lowerCAmelCase_   )
										__SCREAMING_SNAKE_CASE						   =				torch.cat(lowerCAmelCase_   )

					return new_state_dict




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					return text_enc_dict


if __name__ == "__main__":
		a__				:							Optional[int]						=     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__				:							Any						=     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__				:							Any						=     osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
		a__				:							Any						=     osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
		a__				:							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__				:							Union[str, Any]						=     load_file(unet_path, device='''cpu''')
		else:
				a__				:							str						=     osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
				a__				:							Union[str, Any]						=     torch.load(unet_path, map_location='''cpu''')

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

		# Convert the UNet model
		a__				:							Tuple						=     convert_unet_state_dict(unet_state_dict)
		a__				:							List[str]						=     {'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

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

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

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

'''simple docstring'''

import requests

__a: str          =   """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 # fetching a list of articles in json format
 lowercase__				:    Optional[Any]  =					requests.get(_NEWS_API + bbc_news_api_key     ).json()
 # each article in the list is a dict
 for i, article in enumerate(bbc_news_page['''articles''']		,     1     ):
  print(F"""{i}.) {article['title']}"""     )


if __name__ == "__main__":
  fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")

