
"""simple docstring"""
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowercase		:						Any =					'pcuenq/k-upscaler'


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				*, lowerCamelCase_	,				lowerCamelCase_ )     ->			List[str]:


					'''simple docstring'''
					__a:						Union[str, Any]		    =					{
					    # 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     lowerCamelCase_    (	lowerCamelCase_	,				*, lowerCamelCase_	,				lowerCamelCase_ )     ->			Optional[Any]:


					'''simple docstring'''
					__a    ,				__a    ,				__a:						Optional[int]		    =					checkpoint[f"""{attention_prefix}.qkv_proj.weight"""].chunk(3	,				dim=0 )
					__a    ,				__a    ,				__a:						Optional[int]		    =					checkpoint[f"""{attention_prefix}.qkv_proj.bias"""].chunk(3	,				dim=0 )
					__a:						List[str]		    =					{
					    # 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     lowerCamelCase_    (	lowerCamelCase_	,				*, lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			List[Any]:


					'''simple docstring'''
					__a    ,				__a:						Optional[Any]		    =					checkpoint[f"""{attention_prefix}.kv_proj.weight"""].chunk(2	,				dim=0 )
					__a    ,				__a:						str		    =					checkpoint[f"""{attention_prefix}.kv_proj.bias"""].chunk(2	,				dim=0 )

					__a:						List[Any]		    =					{
					    # 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     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Tuple:


					'''simple docstring'''
					__a:						Any		    =					"""inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
					__a:						List[Any]		    =					f"""{block_prefix}.{block_idx}"""

					__a:						Optional[int]		    =					{}

					if not hasattr(lowerCamelCase_	,				"""attentions""" ):
										__a:						Union[str, Any]		    =					1  # resnet only
					elif not block.attentions[0].add_self_attention:
										__a:						Tuple		    =					2  # resnet -> cross-attention
					else:
										__a:						Optional[int]		    =					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}"
										__a:						Tuple		    =					f"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
										__a:						str		    =					n * resnet_idx if block_type == """up""" else n * resnet_idx + 1
										__a:						int		    =					f"""{block_prefix}.{idx}""" if block_type == """up""" else f"""{block_prefix}.{idx}"""

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

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

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

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

					return diffusers_checkpoint


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			Dict:


					'''simple docstring'''
					__a:						List[Any]		    =					{}

					# 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(lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				"""down""" ) )

					# up_blocks
					for up_block_idx, up_block in enumerate(model.up_blocks ):
										diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				"""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     lowerCamelCase_    (	lowerCamelCase_ )     ->			List[str]:


					'''simple docstring'''
					__a:						Union[str, Any]		    =					original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
					__a:						int		    =					original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

					__a:						str		    =					original_config["""channels"""]

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

					__a:						Optional[int]		    =					original_config["""mapping_cond_dim"""]
					__a:						Tuple		    =					original_config["""cross_cond_dim"""]

					__a:						Dict		    =					[]
					__a:						str		    =					[]
					for s, c in zip(original_config["""self_attn_depths"""]	,				original_config["""cross_attn_depths"""] ):
										if s:
															__a:						int		    =					"""self"""
															__a:						Optional[Any]		    =					"""cross""" if c else None
										elif c:
															__a:						str		    =					"""cross"""
															__a:						Optional[int]		    =					None
										else:
															__a:						List[str]		    =					None
															__a:						Tuple		    =					None
										attna_types.append(lowerCamelCase_ )
										attna_types.append(lowerCamelCase_ )

					__a:						Optional[int]		    =					UNetaDConditionModel(
					    in_channels=lowerCamelCase_	,				out_channels=lowerCamelCase_	,				down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""")	,				mid_block_type=lowerCamelCase_	,				up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")	,				block_out_channels=lowerCamelCase_	,				layers_per_block=lowerCamelCase_	,				act_fn="""gelu"""	,				norm_num_groups=lowerCamelCase_	,				cross_attention_dim=lowerCamelCase_	,				attention_head_dim=64	,				time_cond_proj_dim=lowerCamelCase_	,				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     lowerCamelCase_    (	lowerCamelCase_ )     ->			Dict:


					'''simple docstring'''
					__a:						Optional[Any]		    =					torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )

					__a:						List[Any]		    =					huggingface_hub.hf_hub_download(lowerCamelCase_	,				"""config_laion_text_cond_latent_upscaler_2.json""" )
					__a:						int		    =					huggingface_hub.hf_hub_download(
					    lowerCamelCase_	,				"""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""" )
					__a:						Dict		    =					K.config.load_config(open(lowerCamelCase_ ) )["""model"""]
					__a:						List[str]		    =					unet_model_from_original_config(lowerCamelCase_ )

					__a:						Union[str, Any]		    =					torch.load(lowerCamelCase_	,				map_location=lowerCamelCase_ )["""model_ema"""]
					__a:						str		    =					unet_to_diffusers_checkpoint(lowerCamelCase_	,				lowerCamelCase_ )

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


if __name__ == "__main__":
			lowercase		:						str =					argparse.ArgumentParser()

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

			main(args)


"""simple docstring"""
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    _snake_case  (						unittest.TestCase       ):






							@slow
							def 							__lowercase  (					self						:			Optional[int]    ):






												"""simple docstring"""



												__a:						Any		    =					CamembertModel.from_pretrained("""camembert-base"""    )
												model.to(snake_case__    )

												__a:						str		    =					torch.tensor(
												    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]						, device=snake_case__						, dtype=torch.long						, )  # J'aime le camembert !
												with torch.no_grad():
																	__a:						str		    =					model(snake_case__    )["""last_hidden_state"""]
												__a:						Optional[Any]		    =					torch.Size((1, 1_0, 7_6_8)    )
												self.assertEqual(output.shape						, snake_case__    )
												# compare the actual values for a slice.
												__a:						Union[str, Any]		    =					torch.tensor(
												    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]						, device=snake_case__						, dtype=torch.float						, )
												# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
												# camembert.eval()
												# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

												self.assertTrue(torch.allclose(output[:, :3, :3]						, snake_case__						, atol=1e-4    )    )
