import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCamelCase    				=		'pcuenq/k-upscaler'

def      lowercase_							(     _lowerCamelCase				:			str						,      _lowerCamelCase				:			Any						,      *, _lowerCamelCase				:			Optional[int]						,      _lowerCamelCase				:			Any):
    lowercase__					:  int          =							{
        # norm1
        f'''{diffusers_resnet_prefix}.norm1.linear.weight''': checkpoint[f'''{resnet_prefix}.main.0.mapper.weight'''],
        f'''{diffusers_resnet_prefix}.norm1.linear.bias''': checkpoint[f'''{resnet_prefix}.main.0.mapper.bias'''],
        # conv1
        f'''{diffusers_resnet_prefix}.conv1.weight''': checkpoint[f'''{resnet_prefix}.main.2.weight'''],
        f'''{diffusers_resnet_prefix}.conv1.bias''': checkpoint[f'''{resnet_prefix}.main.2.bias'''],
        # norm2
        f'''{diffusers_resnet_prefix}.norm2.linear.weight''': checkpoint[f'''{resnet_prefix}.main.4.mapper.weight'''],
        f'''{diffusers_resnet_prefix}.norm2.linear.bias''': checkpoint[f'''{resnet_prefix}.main.4.mapper.bias'''],
        # conv2
        f'''{diffusers_resnet_prefix}.conv2.weight''': checkpoint[f'''{resnet_prefix}.main.6.weight'''],
        f'''{diffusers_resnet_prefix}.conv2.bias''': checkpoint[f'''{resnet_prefix}.main.6.bias'''],
    }

    if resnet.conv_shortcut is not None:
        rv.update(
            {
                f'''{diffusers_resnet_prefix}.conv_shortcut.weight''': checkpoint[f'''{resnet_prefix}.skip.weight'''],
            })

    return rv

def      lowercase_							(     _lowerCamelCase				:			Tuple						,      *, _lowerCamelCase				:			str						,      _lowerCamelCase				:			Union[str, Any]):
    lowercase__					:  int          =							checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3						,      dim=0)
    lowercase__					:  Union[str, Any]          =							checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3						,      dim=0)
    lowercase__					:  Union[str, Any]          =							{
        # norm
        f'''{diffusers_attention_prefix}.norm1.linear.weight''': checkpoint[f'''{attention_prefix}.norm_in.mapper.weight'''],
        f'''{diffusers_attention_prefix}.norm1.linear.bias''': checkpoint[f'''{attention_prefix}.norm_in.mapper.bias'''],
        # to_q
        f'''{diffusers_attention_prefix}.attn1.to_q.weight''': weight_q.squeeze(-1).squeeze(-1),
        f'''{diffusers_attention_prefix}.attn1.to_q.bias''': bias_q,
        # to_k
        f'''{diffusers_attention_prefix}.attn1.to_k.weight''': weight_k.squeeze(-1).squeeze(-1),
        f'''{diffusers_attention_prefix}.attn1.to_k.bias''': bias_k,
        # to_v
        f'''{diffusers_attention_prefix}.attn1.to_v.weight''': weight_v.squeeze(-1).squeeze(-1),
        f'''{diffusers_attention_prefix}.attn1.to_v.bias''': bias_v,
        # to_out
        f'''{diffusers_attention_prefix}.attn1.to_out.0.weight''': checkpoint[f'''{attention_prefix}.out_proj.weight''']
        .squeeze(-1)
        .squeeze(-1),
        f'''{diffusers_attention_prefix}.attn1.to_out.0.bias''': checkpoint[f'''{attention_prefix}.out_proj.bias'''],
    }

    return rv

def      lowercase_							(     _lowerCamelCase				:			str						,      *, _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			str						,      _lowerCamelCase				:			Optional[Any]):
    lowercase__					:  Optional[Any]          =							checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2						,      dim=0)
    lowercase__					:  Optional[Any]          =							checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2						,      dim=0)

    lowercase__					:  Dict          =							{
        # norm2 (ada groupnorm)
        f'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight''': checkpoint[
            f'''{attention_prefix}.norm_dec.mapper.weight'''
        ],
        f'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias''': checkpoint[
            f'''{attention_prefix}.norm_dec.mapper.bias'''
        ],
        # layernorm on encoder_hidden_state
        f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight''': checkpoint[
            f'''{attention_prefix}.norm_enc.weight'''
        ],
        f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias''': checkpoint[
            f'''{attention_prefix}.norm_enc.bias'''
        ],
        # to_q
        f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight''': checkpoint[
            f'''{attention_prefix}.q_proj.weight'''
        ]
        .squeeze(-1)
        .squeeze(-1),
        f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias''': checkpoint[
            f'''{attention_prefix}.q_proj.bias'''
        ],
        # to_k
        f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight''': weight_k.squeeze(-1).squeeze(-1),
        f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias''': bias_k,
        # to_v
        f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight''': weight_v.squeeze(-1).squeeze(-1),
        f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias''': bias_v,
        # to_out
        f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight''': checkpoint[
            f'''{attention_prefix}.out_proj.weight'''
        ]
        .squeeze(-1)
        .squeeze(-1),
        f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias''': checkpoint[
            f'''{attention_prefix}.out_proj.bias'''
        ],
    }

    return rv

def      lowercase_							(     _lowerCamelCase				:			str						,      _lowerCamelCase				:			Optional[int]						,      _lowerCamelCase				:			Optional[int]						,      _lowerCamelCase				:			List[Any]):
    lowercase__					:  Union[str, Any]          =							'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
    lowercase__					:  Tuple          =							f'''{block_prefix}.{block_idx}'''

    lowercase__					:  Dict          =							{}

    if not hasattr(_A						,      "attentions"):
        lowercase__					:  str          =							1  # resnet only
    elif not block.attentions[0].add_self_attention:
        lowercase__					:  Any          =							2  # resnet -> cross-attention
    else:
        lowercase__					:  List[Any]          =							3  # resnet -> self-attention -> cross-attention)

    for resnet_idx, resnet in enumerate(block.resnets):
        # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
        lowercase__					:  Tuple          =							f'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
        lowercase__					:  Optional[Any]          =							n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
        lowercase__					:  Tuple          =							f'''{block_prefix}.{idx}''' if block_type == 'up' else f'''{block_prefix}.{idx}'''

        diffusers_checkpoint.update(
            resnet_to_diffusers_checkpoint(
                _A						,      _A						,      diffusers_resnet_prefix=_A						,      resnet_prefix=_A))

    if hasattr(_A						,      "attentions"):
        for attention_idx, attention in enumerate(block.attentions):
            lowercase__					:  Optional[Any]          =							f'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
            lowercase__					:  Any          =							n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
            lowercase__					:  Dict          =							f'''{block_prefix}.{idx}'''
            lowercase__					:  Union[str, Any]          =							f'''{block_prefix}.{idx }'''
            lowercase__					:  Tuple          =							1 if not attention.add_self_attention else 2
            lowercase__					:  str          =							(
                n * attention_idx + cross_attention_index
                if block_type == 'up'
                else n * attention_idx + cross_attention_index + 1
            )
            lowercase__					:  List[Any]          =							f'''{block_prefix}.{idx }'''

            diffusers_checkpoint.update(
                cross_attn_to_diffusers_checkpoint(
                    _A						,      diffusers_attention_prefix=_A						,      diffusers_attention_index=2						,      attention_prefix=_A						,      ))

            if attention.add_self_attention is True:
                diffusers_checkpoint.update(
                    self_attn_to_diffusers_checkpoint(
                        _A						,      diffusers_attention_prefix=_A						,      attention_prefix=_A						,      ))

    return diffusers_checkpoint

def      lowercase_							(     _lowerCamelCase				:			Dict						,      _lowerCamelCase				:			Optional[int]):
    lowercase__					:  Tuple          =							{}

    # pre-processing
    diffusers_checkpoint.update(
        {
            "conv_in.weight": checkpoint["inner_model.proj_in.weight"],
            "conv_in.bias": checkpoint["inner_model.proj_in.bias"],
        })

    # timestep and class embedding
    diffusers_checkpoint.update(
        {
            "time_proj.weight": checkpoint["inner_model.timestep_embed.weight"].squeeze(-1),
            "time_embedding.linear_1.weight": checkpoint["inner_model.mapping.0.weight"],
            "time_embedding.linear_1.bias": checkpoint["inner_model.mapping.0.bias"],
            "time_embedding.linear_2.weight": checkpoint["inner_model.mapping.2.weight"],
            "time_embedding.linear_2.bias": checkpoint["inner_model.mapping.2.bias"],
            "time_embedding.cond_proj.weight": checkpoint["inner_model.mapping_cond.weight"],
        })

    # down_blocks
    for down_block_idx, down_block in enumerate(model.down_blocks):
        diffusers_checkpoint.update(block_to_diffusers_checkpoint(_A						,      _A						,      _A						,      "down"))

    # up_blocks
    for up_block_idx, up_block in enumerate(model.up_blocks):
        diffusers_checkpoint.update(block_to_diffusers_checkpoint(_A						,      _A						,      _A						,      "up"))

    # post-processing
    diffusers_checkpoint.update(
        {
            "conv_out.weight": checkpoint["inner_model.proj_out.weight"],
            "conv_out.bias": checkpoint["inner_model.proj_out.bias"],
        })

    return diffusers_checkpoint

def      lowercase_							(     _lowerCamelCase				:			Any):
    lowercase__					:  List[str]          =							original_config['input_channels'] + original_config['unet_cond_dim']
    lowercase__					:  Dict          =							original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

    lowercase__					:  str          =							original_config['channels']

    assert (
        len(set(original_config["depths"])) == 1
    ), "UNet2DConditionModel currently do not support blocks with different number of layers"
    lowercase__					:  List[str]          =							original_config['depths'][0]

    lowercase__					:  List[str]          =							original_config['mapping_cond_dim']
    lowercase__					:  Optional[Any]          =							original_config['cross_cond_dim']

    lowercase__					:  List[str]          =							[]
    lowercase__					:  Any          =							[]
    for s, c in zip(original_config["self_attn_depths"]						,      original_config["cross_attn_depths"]):
        if s:
            lowercase__					:  List[Any]          =							'self'
            lowercase__					:  Dict          =							'cross' if c else None
        elif c:
            lowercase__					:  str          =							'cross'
            lowercase__					:  str          =							None
        else:
            lowercase__					:  Dict          =							None
            lowercase__					:  Dict          =							None
        attna_types.append(_A)
        attna_types.append(_A)

    lowercase__					:  Tuple          =							UNetaDConditionModel(
        in_channels=_A						,      out_channels=_A						,      down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")						,      mid_block_type=_A						,      up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")						,      block_out_channels=_A						,      layers_per_block=_A						,      act_fn="gelu"						,      norm_num_groups=_A						,      cross_attention_dim=_A						,      attention_head_dim=64						,      time_cond_proj_dim=_A						,      resnet_time_scale_shift="scale_shift"						,      time_embedding_type="fourier"						,      timestep_post_act="gelu"						,      conv_in_kernel=1						,      conv_out_kernel=1						,      )

    return unet

def      lowercase_							(     _lowerCamelCase				:			str):
    lowercase__					:  List[str]          =							torch.device("cuda" if torch.cuda.is_available() else "cpu")

    lowercase__					:  Optional[int]          =							huggingface_hub.hf_hub_download(_A						,      "config_laion_text_cond_latent_upscaler_2.json")
    lowercase__					:  List[str]          =							huggingface_hub.hf_hub_download(
        _A						,      "laion_text_cond_latent_upscaler_2_1_00470000_slim.pth")
    print(f'''loading original model configuration from {orig_config_path}''')
    print(f'''loading original model checkpoint from {orig_weights_path}''')

    print("converting to diffusers unet")
    lowercase__					:  Union[str, Any]          =							K.config.load_config(open(_A))['model']
    lowercase__					:  List[str]          =							unet_model_from_original_config(_A)

    lowercase__					:  Optional[Any]          =							torch.load(_A						,      map_location=_A)['model_ema']
    lowercase__					:  Tuple          =							unet_to_diffusers_checkpoint(_A						,      _A)

    model.load_state_dict(_A						,      strict=_A)
    model.save_pretrained(args.dump_path)
    print(f'''saving converted unet model in {args.dump_path}''')


if __name__ == "__main__":
      UpperCamelCase    				=		argparse.ArgumentParser()

      parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
      UpperCamelCase    				=		parser.parse_args()

      main(args)







'''simple docstring'''




import math
from collections.abc import Generator

def       __UpperCamelCase     (     _A     :					int					)     ->   Generator[int, None, None]:




	"""simple docstring"""





	lowerCAmelCase       :     Generator   				=			(i for i in range(1       , (max_n + 1)					))
	for i in (n for n in numbers if n > 1):
		for j in range(2       , _A					):
			if (i % j) == 0:
				break
		else:
			yield i

def       __UpperCamelCase     (     _A     :					int					)     ->   Generator[int, None, None]:




	"""simple docstring"""





	lowerCAmelCase       :     Generator   				=			(i for i in range(1       , (max_n + 1)					))
	for i in (n for n in numbers if n > 1):
		# only need to check for factors up to sqrt(i)
		lowerCAmelCase       :     List[str]   				=			int(math.sqrt(_A					)					) + 1
		for j in range(2       , _A					):
			if (i % j) == 0:
				break
		else:
			yield i

def       __UpperCamelCase     (     _A     :					int					)     ->   Generator[int, None, None]:




	"""simple docstring"""





	lowerCAmelCase       :     Generator   				=			(i for i in range(1       , (max_n + 1)       , 2					))
	# It's useless to test even numbers as they will not be prime
	if max_n > 2:
		yield 2  # Because 2 will not be tested, it's necessary to yield it now
	for i in (n for n in numbers if n > 1):
		lowerCAmelCase       :     int   				=			int(math.sqrt(_A					)					) + 1
		for j in range(3       , _A       , 2					):
			# As we removed the even numbers, we don't need them now
			if (i % j) == 0:
				break
		else:
			yield i

def       __UpperCamelCase     (     )     ->   Dict:




	"""simple docstring"""





	from timeit import timeit

	lowerCAmelCase       :     Tuple   				=			'from __main__ import slow_primes, primes, fast_primes'
	print(timeit('slow_primes(1_000_000_000_000)'       , setup=_A       , number=1_00_00_00					)					)
	print(timeit('primes(1_000_000_000_000)'       , setup=_A       , number=1_00_00_00					)					)
	print(timeit('fast_primes(1_000_000_000_000)'       , setup=_A       , number=1_00_00_00					)					)


if __name__ == "__main__":
				_lowerCAmelCase	:		Optional[Any]		  =						int(input('Calculate primes up to:\n>> ').strip())
				for ret in primes(number):
								print(ret)
				benchmark()
