




'''simple docstring'''





import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a		:  List[Any]   			=			'''pcuenq/k-upscaler'''





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  *, UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       Optional[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 UpperCAmelCase						(					UpperCAmelCase_      ,  *, UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a					,   _a					,   _a							:       str       					=			checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3      ,  dim=0	)
    _a					,   _a					,   _a							:       str       					=			checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3      ,  dim=0	)
    _a							:       Tuple       					=			{
        # 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						(					UpperCAmelCase_      ,  *, UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a					,   _a							:       Union[str, 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[str]       					=			{
        # 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						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       str       					=			"""inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
    _a							:       str       					=			F'''{block_prefix}.{block_idx}'''

    _a							:       Union[str, Any]       					=			{}

    if not hasattr(UpperCAmelCase_      ,  """attentions"""	):
        _a							:       Dict       					=			1  # resnet only
    elif not block.attentions[0].add_self_attention:
        _a							:       int       					=			2  # resnet -> cross-attention
    else:
        _a							:       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}"
        _a							:       Optional[int]       					=			F'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
        _a							:       List[Any]       					=			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(
                UpperCAmelCase_      ,  UpperCAmelCase_      ,  diffusers_resnet_prefix=UpperCAmelCase_      ,  resnet_prefix=UpperCAmelCase_	)	)

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

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

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

    return diffusers_checkpoint





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       str       					=			{}

    # 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(UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  """down"""	)	)

    # up_blocks
    for up_block_idx, up_block in enumerate(model.up_blocks	):
        diffusers_checkpoint.update(block_to_diffusers_checkpoint(UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  """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						(					UpperCAmelCase_	):
    _a							:       List[Any]       					=			original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
    _a							:       Union[str, Any]       					=			original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

    _a							:       Union[str, Any]       					=			original_config["""channels"""]

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

    _a							:       Dict       					=			original_config["""mapping_cond_dim"""]
    _a							:       Optional[Any]       					=			original_config["""cross_cond_dim"""]

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

    _a							:       str       					=			UNetaDConditionModel(
        in_channels=UpperCAmelCase_      ,  out_channels=UpperCAmelCase_      ,  down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""")      ,  mid_block_type=UpperCAmelCase_      ,  up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")      ,  block_out_channels=UpperCAmelCase_      ,  layers_per_block=UpperCAmelCase_      ,  act_fn="""gelu"""      ,  norm_num_groups=UpperCAmelCase_      ,  cross_attention_dim=UpperCAmelCase_      ,  attention_head_dim=64      ,  time_cond_proj_dim=UpperCAmelCase_      ,  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						(					UpperCAmelCase_	):
    _a							:       Optional[Any]       					=			torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""	)

    _a							:       int       					=			huggingface_hub.hf_hub_download(UpperCAmelCase_      ,  """config_laion_text_cond_latent_upscaler_2.json"""	)
    _a							:       Optional[int]       					=			huggingface_hub.hf_hub_download(
        UpperCAmelCase_      ,  """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							:       Any       					=			K.config.load_config(open(UpperCAmelCase_	)	)["""model"""]
    _a							:       str       					=			unet_model_from_original_config(UpperCAmelCase_	)

    _a							:       Tuple       					=			torch.load(UpperCAmelCase_      ,  map_location=UpperCAmelCase_	)["""model_ema"""]
    _a							:       Union[str, Any]       					=			unet_to_diffusers_checkpoint(UpperCAmelCase_      ,  UpperCAmelCase_	)

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


if __name__ == "__main__":
  a		:  Dict   			=			argparse.ArgumentParser()

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

  main(args)








'''simple docstring'''





import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


a		:  List[str]   			=			logging.get_logger(__name__)

a		:  Optional[Any]   			=			[
    '''yjernite/retribert-base-uncased''',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]




class     UpperCamelCase					(  snake_case__					):
  a    :List[Any]      					=				RetriBertConfig
  a    :int      					=				None
  a    :List[Any]      					=				'retribert'



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      if isinstance(__lowerCAmelCase		,    nn.Linear   ):
          module.weight.data.normal_(mean=0.0		,    std=self.config.initializer_range   )
          if module.bias is not None:
              module.bias.data.zero_()
      elif isinstance(__lowerCAmelCase		,    nn.Embedding   ):
          module.weight.data.normal_(mean=0.0		,    std=self.config.initializer_range   )
          if module.padding_idx is not None:
              module.weight.data[module.padding_idx].zero_()
      elif isinstance(__lowerCAmelCase		,    nn.LayerNorm   ):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0   )






a		:  Union[str, Any]   			=			R'''

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''




@add_start_docstrings(
    'Bert Based model to embed queries or document for document retrieval.'			,		snake_case__			,		)
class     UpperCamelCase					(  snake_case__					):



  def __init__( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      super().__init__(__lowerCAmelCase   )
      _a							:       Tuple       					=			config.projection_dim

      _a							:       int       					=			BertModel(__lowerCAmelCase   )
      _a							:       int       					=			None if config.share_encoders else BertModel(__lowerCAmelCase   )
      _a							:       Optional[int]       					=			nn.Dropout(config.hidden_dropout_prob   )
      _a							:       Dict       					=			nn.Linear(config.hidden_size		,    config.projection_dim		,    bias=__lowerCAmelCase   )
      _a							:       Any       					=			nn.Linear(config.hidden_size		,    config.projection_dim		,    bias=__lowerCAmelCase   )

      _a							:       Tuple       					=			nn.CrossEntropyLoss(reduction="""mean"""   )

      # Initialize weights and apply final processing
      self.post_init()



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=-1		,    ):







      '''simple docstring'''


      if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
          return sent_encoder(__lowerCAmelCase		,    attention_mask=__lowerCAmelCase   )[1]
      else:
          # prepare implicit variables
          _a							:       Optional[int]       					=			input_ids.device
          _a							:       Optional[Any]       					=			input_ids.size()
          _a							:       str       					=			torch.zeros(__lowerCAmelCase		,    dtype=torch.long		,    device=__lowerCAmelCase   )
          _a							:       Optional[int]       					=			[None] * sent_encoder.config.num_hidden_layers
          _a							:       torch.Tensor       					=			sent_encoder.get_extended_attention_mask(
              __lowerCAmelCase		,    __lowerCAmelCase   )

          # define function for checkpointing
          def partial_encode(*__lowerCAmelCase   ):
              _a							:       Tuple       					=			sent_encoder.encoder(
                  inputs[0]		,    attention_mask=inputs[1]		,    head_mask=__lowerCAmelCase		,    )
              _a							:       List[str]       					=			encoder_outputs[0]
              _a							:       List[str]       					=			sent_encoder.pooler(__lowerCAmelCase   )
              return pooled_output

          # run embedding layer on everything at once
          _a							:       Optional[Any]       					=			sent_encoder.embeddings(
              input_ids=__lowerCAmelCase		,    position_ids=__lowerCAmelCase		,    token_type_ids=__lowerCAmelCase		,    inputs_embeds=__lowerCAmelCase   )
          # run encoding and pooling on one mini-batch at a time
          _a							:       Optional[int]       					=			[]
          for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size   )   ):
              _a							:       Union[str, Any]       					=			embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
              _a							:       Any       					=			extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
              _a							:       Optional[int]       					=			checkpoint.checkpoint(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
              pooled_output_list.append(__lowerCAmelCase   )
          return torch.cat(__lowerCAmelCase		,    dim=0   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase=None		,    __lowerCAmelCase=-1		,    ):







      '''simple docstring'''


      _a							:       Any       					=			self.embed_sentences_checkpointed(
          __lowerCAmelCase		,    __lowerCAmelCase		,    self.bert_query		,    __lowerCAmelCase		,    )
      return self.project_query(__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase=None		,    __lowerCAmelCase=-1		,    ):







      '''simple docstring'''


      _a							:       List[Any]       					=			self.embed_sentences_checkpointed(
          __lowerCAmelCase		,    __lowerCAmelCase		,    self.bert_query if self.bert_doc is None else self.bert_doc		,    __lowerCAmelCase		,    )
      return self.project_doc(__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = -1		,    ):







      '''simple docstring'''


      _a							:       str       					=			input_ids_query.device
      _a							:       Tuple       					=			self.embed_questions(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
      _a							:       List[str]       					=			self.embed_answers(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
      _a							:       Dict       					=			torch.mm(__lowerCAmelCase		,    a_reps.t()   )
      _a							:       str       					=			self.ce_loss(__lowerCAmelCase		,    torch.arange(compare_scores.shape[1]   ).to(__lowerCAmelCase   )   )
      _a							:       Any       					=			self.ce_loss(compare_scores.t()		,    torch.arange(compare_scores.shape[0]   ).to(__lowerCAmelCase   )   )
      _a							:       List[str]       					=			(loss_qa + loss_aq) / 2
      return loss


