






import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


snake_case_   :	Union[str, Any]							      ='''pcuenq/k-upscaler'''



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     *, lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							{
		    # 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      UpperCAmelCase						( lowerCAmelCase__					,     *, lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A ,   __A ,   __A    =							checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3					,     dim=0	)
		__A ,   __A ,   __A    =							checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3					,     dim=0	)
		__A    =							{
		    # 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      UpperCAmelCase						( lowerCAmelCase__					,     *, lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A ,   __A    =							checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2					,     dim=0	)
		__A ,   __A    =							checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2					,     dim=0	)

		__A    =							{
		    # 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      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							"inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
		__A    =							F"""{block_prefix}.{block_idx}"""

		__A    =							{}

		if not hasattr(lowerCAmelCase__					,     "attentions"	):
				__A    =							1  # resnet only
		elif not block.attentions[0].add_self_attention:
				__A    =							2  # resnet -> cross-attention
		else:
				__A    =							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    =							F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
				__A    =							n * resnet_idx if block_type == "up" else n * resnet_idx + 1
				__A    =							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    =							F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
						__A    =							n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
						__A    =							F"""{block_prefix}.{idx}"""
						__A    =							F"""{block_prefix}.{idx }"""
						__A    =							1 if not attention.add_self_attention else 2
						__A    =							(
						    n * attention_idx + cross_attention_index
						    if block_type == "up"
						    else n * attention_idx + cross_attention_index + 1
						)
						__A    =							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      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							{}

		# 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      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							original_config["input_channels"] + original_config["unet_cond_dim"]
		__A    =							original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

		__A    =							original_config["channels"]

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

		__A    =							original_config["mapping_cond_dim"]
		__A    =							original_config["cross_cond_dim"]

		__A    =							[]
		__A    =							[]
		for s, c in zip(original_config["self_attn_depths"]					,     original_config["cross_attn_depths"]	):
				if s:
						__A    =							"self"
						__A    =							"cross" if c else None
				elif c:
						__A    =							"cross"
						__A    =							None
				else:
						__A    =							None
						__A    =							None
				attna_types.append(lowerCAmelCase__	)
				attna_types.append(lowerCAmelCase__	)

		__A    =							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      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							torch.device("cuda" if torch.cuda.is_available() else "cpu"	)

		__A    =							huggingface_hub.hf_hub_download(lowerCAmelCase__					,     "config_laion_text_cond_latent_upscaler_2.json"	)
		__A    =							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    =							K.config.load_config(open(lowerCAmelCase__	)	)["model"]
		__A    =							unet_model_from_original_config(lowerCAmelCase__	)

		__A    =							torch.load(lowerCAmelCase__					,     map_location=lowerCAmelCase__	)["model_ema"]
		__A    =							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__":
					snake_case_   :	Optional[Any]							      =argparse.ArgumentParser()

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

					main(args)





'''simple docstring'''

import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


_UpperCAmelCase					:			List[str]				     =					logging.getLogger(__name__)


class    a__					(			__A   ):







				"""simple docstring"""



				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase=None							,      __lowercase=None		):
										if current_layer == 0:
																__lowerCAmelCase											=					self.embedding_hidden_mapping_in(__lowercase		)
										else:
																__lowerCAmelCase											=					hidden_states[0]

										__lowerCAmelCase											=					int(self.config.num_hidden_layers / self.config.num_hidden_groups		)

										# Index of the hidden group
										__lowerCAmelCase											=					int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)		)

										__lowerCAmelCase											=					self.albert_layer_groups[group_idx](
										    __lowercase							,      __lowercase							,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]							,      )
										__lowerCAmelCase											=					layer_group_output[0]

										return (hidden_states,)


@add_start_docstrings(
    'The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'				,    __A				,    )
class    a__					(			__A   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										super().__init__(__lowercase		)

										__lowerCAmelCase											=					AlbertTransformerWithPabee(__lowercase		)

										self.init_weights()
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0

										__lowerCAmelCase											=					0




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					threshold




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					patience




				def        _snake_case	(self		):
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.inference_layers_num / self.inference_instances_num
										__lowerCAmelCase											=					(
										    F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
										    F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
										)
										print(__lowercase		)




				@add_start_docstrings_to_model_forward(__lowercase		)
				def        _snake_case	(self							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=False							,      ):

										if input_ids is not None and inputs_embeds is not None:
																raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''		)
										elif input_ids is not None:
																__lowerCAmelCase											=					input_ids.size()
										elif inputs_embeds is not None:
																__lowerCAmelCase											=					inputs_embeds.size()[:-1]
										else:
																raise ValueError('''You have to specify either input_ids or inputs_embeds'''		)

										__lowerCAmelCase											=					input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
																__lowerCAmelCase											=					torch.ones(__lowercase							,      device=__lowercase		)
										if token_type_ids is None:
																__lowerCAmelCase											=					torch.zeros(__lowercase							,      dtype=torch.long							,      device=__lowercase		)

										__lowerCAmelCase											=					attention_mask.unsqueeze(1		).unsqueeze(2		)
										__lowerCAmelCase											=					extended_attention_mask.to(dtype=self.dtype		)  # fp16 compatibility
										__lowerCAmelCase											=					(1.0 - extended_attention_mask) * -1_0_0_0_0.0
										__lowerCAmelCase											=					self.get_head_mask(__lowercase							,      self.config.num_hidden_layers		)

										__lowerCAmelCase											=					self.embeddings(
										    __lowercase							,      position_ids=__lowercase							,      token_type_ids=__lowercase							,      inputs_embeds=__lowercase		)
										__lowerCAmelCase											=					embedding_output

										if self.training:
																__lowerCAmelCase											=					[]
																for i in range(self.config.num_hidden_layers		):
																						__lowerCAmelCase											=					self.encoder.adaptive_forward(
																						    __lowercase							,      current_layer=__lowercase							,      attention_mask=__lowercase							,      head_mask=__lowercase							,      )

																						__lowerCAmelCase											=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
																						__lowerCAmelCase											=					output_layers[i](output_dropout(__lowercase		)		)
																						res.append(__lowercase		)
										elif self.patience == 0:  # Use all layers for inference
																__lowerCAmelCase											=					self.encoder(__lowercase							,      __lowercase							,      head_mask=__lowercase		)
																__lowerCAmelCase											=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
																__lowerCAmelCase											=					[output_layers[self.config.num_hidden_layers - 1](__lowercase		)]
										else:
																__lowerCAmelCase											=					0
																__lowerCAmelCase											=					None
																__lowerCAmelCase											=					0
																for i in range(self.config.num_hidden_layers		):
																						calculated_layer_num += 1
																						__lowerCAmelCase											=					self.encoder.adaptive_forward(
																						    __lowercase							,      current_layer=__lowercase							,      attention_mask=__lowercase							,      head_mask=__lowercase							,      )

																						__lowerCAmelCase											=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
																						__lowerCAmelCase											=					output_layers[i](__lowercase		)
																						if regression:
																												__lowerCAmelCase											=					logits.detach()
																												if patient_result is not None:
																																		__lowerCAmelCase											=					patient_result.detach()
																												if (patient_result is not None) and torch.abs(patient_result - labels		) < self.regression_threshold:
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase											=					0
																						else:
																												__lowerCAmelCase											=					logits.detach().argmax(dim=1		)
																												if patient_result is not None:
																																		__lowerCAmelCase											=					patient_result.detach().argmax(dim=1		)
																												if (patient_result is not None) and torch.all(labels.eq(__lowercase		)		):
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase											=					0

																						__lowerCAmelCase											=					logits
																						if patient_counter == self.patience:
																												break
																__lowerCAmelCase											=					[patient_result]
																self.inference_layers_num += calculated_layer_num
																self.inference_instances_num += 1

										return res


@add_start_docstrings(
    'Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '				,    __A				,    )
class    a__					(			__A   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										super().__init__(__lowercase		)
										__lowerCAmelCase											=					config.num_labels

										__lowerCAmelCase											=					AlbertModelWithPabee(__lowercase		)
										__lowerCAmelCase											=					nn.Dropout(config.classifier_dropout_prob		)
										__lowerCAmelCase											=					nn.ModuleList(
										    [nn.Linear(config.hidden_size							,      self.config.num_labels		) for _ in range(config.num_hidden_layers		)]		)

										self.init_weights()




				@add_start_docstrings_to_model_forward(__lowercase		)
				def        _snake_case	(self							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      ):
										__lowerCAmelCase											=					self.albert(
										    input_ids=__lowercase							,      attention_mask=__lowercase							,      token_type_ids=__lowercase							,      position_ids=__lowercase							,      head_mask=__lowercase							,      inputs_embeds=__lowercase							,      output_dropout=self.dropout							,      output_layers=self.classifiers							,      regression=self.num_labels == 1							,      )

										__lowerCAmelCase											=					(logits[-1],)

										if labels is not None:
																__lowerCAmelCase											=					None
																__lowerCAmelCase											=					0
																for ix, logits_item in enumerate(__lowercase		):
																						if self.num_labels == 1:
																												#  We are doing regression
																												__lowerCAmelCase											=					MSELoss()
																												__lowerCAmelCase											=					loss_fct(logits_item.view(-1		)							,      labels.view(-1		)		)
																						else:
																												__lowerCAmelCase											=					CrossEntropyLoss()
																												__lowerCAmelCase											=					loss_fct(logits_item.view(-1							,      self.num_labels		)							,      labels.view(-1		)		)
																						if total_loss is None:
																												__lowerCAmelCase											=					loss
																						else:
																												total_loss += loss * (ix + 1)
																						total_weights += ix + 1
																__lowerCAmelCase											=					(total_loss / total_weights,) + outputs

										return outputs

