




from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block







@dataclass
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						42







class 						lowercase							(   nn.Module		):

     def __init__(  self		,     _a=3		,     _a=3		,     _a=("DownEncoderBlock2D",)		,     _a=(64,)		,     _a=2		,     _a=32		,     _a="silu"		,     _a=True		,     )						->							Any:
           super().__init__()
           _A							:     List[str]			=						layers_per_block

           _A							:     Optional[Any]			=						torch.nn.Convad(
               _a		,     block_out_channels[0]		,     kernel_size=3		,     stride=1		,     padding=1		,     )

           _A							:     str			=						None
           _A							:     int			=						nn.ModuleList([]				)

           # down
           _A							:     Tuple			=						block_out_channels[0]
           for i, down_block_type in enumerate(_a				):
                 _A							:     int			=						output_channel
                 _A							:     List[str]			=						block_out_channels[i]
                 _A							:     Optional[Any]			=						i == len(_a				) - 1

                 _A							:     Tuple			=						get_down_block(
                     _a		,     num_layers=self.layers_per_block		,     in_channels=_a		,     out_channels=_a		,     add_downsample=not is_final_block		,     resnet_eps=1e-6		,     downsample_padding=0		,     resnet_act_fn=_a		,     resnet_groups=_a		,     attention_head_dim=_a		,     temb_channels=_a		,     )
                 self.down_blocks.append(_a				)

           # mid
           _A							:     List[Any]			=						UNetMidBlockaD(
               in_channels=block_out_channels[-1]		,     resnet_eps=1e-6		,     resnet_act_fn=_a		,     output_scale_factor=1		,     resnet_time_scale_shift="""default"""		,     attention_head_dim=block_out_channels[-1]		,     resnet_groups=_a		,     temb_channels=_a		,     )

           # out
           _A							:     Any			=						nn.GroupNorm(num_channels=block_out_channels[-1]		,     num_groups=_a		,     eps=1e-6				)
           _A							:     Optional[Any]			=						nn.SiLU()

           _A							:     List[str]			=						2 * out_channels if double_z else out_channels
           _A							:     List[Any]			=						nn.Convad(block_out_channels[-1]		,     _a		,     3		,     padding=1				)

           _A							:     Any			=						False



     def 					a__	(  self		,     _a				)						->							List[str]:
           _A							:     Any			=						x
           _A							:     Optional[Any]			=						self.conv_in(_a				)

           if self.training and self.gradient_checkpointing:

                 def create_custom_forward(_a				):
                       def custom_forward(*_a				):
                             return module(*_a				)

                       return custom_forward

                 # down
                 if is_torch_version(""">="""		,     """1.11.0"""				):
                       for down_block in self.down_blocks:
                             _A							:     int			=						torch.utils.checkpoint.checkpoint(
                                 create_custom_forward(_a				)		,     _a		,     use_reentrant=_a				)
                       # middle
                       _A							:     str			=						torch.utils.checkpoint.checkpoint(
                           create_custom_forward(self.mid_block				)		,     _a		,     use_reentrant=_a				)
                 else:
                       for down_block in self.down_blocks:
                             _A							:     str			=						torch.utils.checkpoint.checkpoint(create_custom_forward(_a				)		,     _a				)
                       # middle
                       _A							:     Any			=						torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block				)		,     _a				)

           else:
                 # down
                 for down_block in self.down_blocks:
                       _A							:     List[Any]			=						down_block(_a				)

                 # middle
                 _A							:     Dict			=						self.mid_block(_a				)

           # post-process
           _A							:     Union[str, Any]			=						self.conv_norm_out(_a				)
           _A							:     List[str]			=						self.conv_act(_a				)
           _A							:     str			=						self.conv_out(_a				)

           return sample







class 						lowercase							(   nn.Module		):

     def __init__(  self		,     _a=3		,     _a=3		,     _a=("UpDecoderBlock2D",)		,     _a=(64,)		,     _a=2		,     _a=32		,     _a="silu"		,     _a="group"		,     )						->							Optional[int]:
           super().__init__()
           _A							:     Tuple			=						layers_per_block

           _A							:     Any			=						nn.Convad(
               _a		,     block_out_channels[-1]		,     kernel_size=3		,     stride=1		,     padding=1		,     )

           _A							:     Optional[Any]			=						None
           _A							:     str			=						nn.ModuleList([]				)

           _A							:     Optional[int]			=						in_channels if norm_type == """spatial""" else None

           # mid
           _A							:     List[str]			=						UNetMidBlockaD(
               in_channels=block_out_channels[-1]		,     resnet_eps=1e-6		,     resnet_act_fn=_a		,     output_scale_factor=1		,     resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type		,     attention_head_dim=block_out_channels[-1]		,     resnet_groups=_a		,     temb_channels=_a		,     )

           # up
           _A							:     List[str]			=						list(reversed(_a				)				)
           _A							:     List[Any]			=						reversed_block_out_channels[0]
           for i, up_block_type in enumerate(_a				):
                 _A							:     Any			=						output_channel
                 _A							:     List[str]			=						reversed_block_out_channels[i]

                 _A							:     Optional[Any]			=						i == len(_a				) - 1

                 _A							:     List[str]			=						get_up_block(
                     _a		,     num_layers=self.layers_per_block + 1		,     in_channels=_a		,     out_channels=_a		,     prev_output_channel=_a		,     add_upsample=not is_final_block		,     resnet_eps=1e-6		,     resnet_act_fn=_a		,     resnet_groups=_a		,     attention_head_dim=_a		,     temb_channels=_a		,     resnet_time_scale_shift=_a		,     )
                 self.up_blocks.append(_a				)
                 _A							:     Optional[int]			=						output_channel

           # out
           if norm_type == "spatial":
                 _A							:     Dict			=						SpatialNorm(block_out_channels[0]		,     _a				)
           else:
                 _A							:     List[str]			=						nn.GroupNorm(num_channels=block_out_channels[0]		,     num_groups=_a		,     eps=1e-6				)
           _A							:     List[str]			=						nn.SiLU()
           _A							:     str			=						nn.Convad(block_out_channels[0]		,     _a		,     3		,     padding=1				)

           _A							:     int			=						False



     def 					a__	(  self		,     _a		,     _a=None				)						->							str:
           _A							:     Union[str, Any]			=						z
           _A							:     Dict			=						self.conv_in(_a				)

           _A							:     str			=						next(iter(self.up_blocks.parameters()				)				).dtype
           if self.training and self.gradient_checkpointing:

                 def create_custom_forward(_a				):
                       def custom_forward(*_a				):
                             return module(*_a				)

                       return custom_forward

                 if is_torch_version(""">="""		,     """1.11.0"""				):
                       # middle
                       _A							:     str			=						torch.utils.checkpoint.checkpoint(
                           create_custom_forward(self.mid_block				)		,     _a		,     _a		,     use_reentrant=_a				)
                       _A							:     Dict			=						sample.to(_a				)

                       # up
                       for up_block in self.up_blocks:
                             _A							:     Tuple			=						torch.utils.checkpoint.checkpoint(
                                 create_custom_forward(_a				)		,     _a		,     _a		,     use_reentrant=_a				)
                 else:
                       # middle
                       _A							:     Optional[int]			=						torch.utils.checkpoint.checkpoint(
                           create_custom_forward(self.mid_block				)		,     _a		,     _a				)
                       _A							:     Optional[int]			=						sample.to(_a				)

                       # up
                       for up_block in self.up_blocks:
                             _A							:     Tuple			=						torch.utils.checkpoint.checkpoint(create_custom_forward(_a				)		,     _a		,     _a				)
           else:
                 # middle
                 _A							:     Tuple			=						self.mid_block(_a		,     _a				)
                 _A							:     Dict			=						sample.to(_a				)

                 # up
                 for up_block in self.up_blocks:
                       _A							:     Dict			=						up_block(_a		,     _a				)

        # post-process
           if latent_embeds is None:
                 _A							:     List[Any]			=						self.conv_norm_out(_a				)
           else:
                 _A							:     int			=						self.conv_norm_out(_a		,     _a				)
           _A							:     Tuple			=						self.conv_act(_a				)
           _A							:     Tuple			=						self.conv_out(_a				)

           return sample







class 						lowercase							(   nn.Module		):

     def __init__(  self		,     _a		,     _a		,     _a		,     _a=None		,     _a="random"		,     _a=False		,     _a=True				)						->							Union[str, Any]:
           super().__init__()
           _A							:     List[str]			=						n_e
           _A							:     List[str]			=						vq_embed_dim
           _A							:     int			=						beta
           _A							:     Optional[Any]			=						legacy

           _A							:     int			=						nn.Embedding(self.n_e		,     self.vq_embed_dim				)
           self.embedding.weight.data.uniform_(-1.0 / self.n_e		,     1.0 / self.n_e				)

           _A							:     Tuple			=						remap
           if self.remap is not None:
                 self.register_buffer("""used"""		,     torch.tensor(np.load(self.remap				)				)				)
                 _A							:     Optional[Any]			=						self.used.shape[0]
                 _A							:     Dict			=						unknown_index  # "random" or "extra" or integer
                 if self.unknown_index == "extra":
                       _A							:     List[str]			=						self.re_embed
                       _A							:     str			=						self.re_embed + 1
                 print(
                     F'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
                     F'''Using {self.unknown_index} for unknown indices.'''				)
           else:
                 _A							:     Optional[Any]			=						n_e

           _A							:     str			=						sane_index_shape

     def 					a__	(  self		,     _a				)						->							List[str]:
           _A							:     Union[str, Any]			=						inds.shape
           assert len(_a				) > 1
           _A							:     Dict			=						inds.reshape(ishape[0]		,     -1				)
           _A							:     Any			=						self.used.to(_a				)
           _A							:     Union[str, Any]			=						(inds[:, :, None] == used[None, None, ...]).long()
           _A							:     Optional[int]			=						match.argmax(-1				)
           _A							:     Dict			=						match.sum(2				) < 1
           if self.unknown_index == "random":
                 _A							:     Dict			=						torch.randint(0		,     self.re_embed		,     size=new[unknown].shape				).to(device=new.device				)
           else:
                 _A							:     Tuple			=						self.unknown_index
           return new.reshape(_a				)

     def 					a__	(  self		,     _a				)						->							Optional[int]:
           _A							:     Dict			=						inds.shape
           assert len(_a				) > 1
           _A							:     Union[str, Any]			=						inds.reshape(ishape[0]		,     -1				)
           _A							:     List[Any]			=						self.used.to(_a				)
           if self.re_embed > self.used.shape[0]:  # extra token
                 _A							:     Union[str, Any]			=						0  # simply set to zero
           _A							:     int			=						torch.gather(used[None, :][inds.shape[0] * [0], :]		,     1		,     _a				)
           return back.reshape(_a				)

     def 					a__	(  self		,     _a				)						->							str:
           # reshape z -> (batch, height, width, channel) and flatten
           _A							:     Dict			=						z.permute(0		,     2		,     3		,     1				).contiguous()
           _A							:     List[str]			=						z.view(-1		,     self.vq_embed_dim				)

           # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
           _A							:     Tuple			=						torch.argmin(torch.cdist(_a		,     self.embedding.weight				)		,     dim=1				)

           _A							:     List[Any]			=						self.embedding(_a				).view(z.shape				)
           _A							:     Optional[int]			=						None
           _A							:     int			=						None

           # compute loss for embedding
           if not self.legacy:
                 _A							:     Any			=						self.beta * torch.mean((z_q.detach() - z) ** 2				) + torch.mean((z_q - z.detach()) ** 2				)
           else:
                 _A							:     Union[str, Any]			=						torch.mean((z_q.detach() - z) ** 2				) + self.beta * torch.mean((z_q - z.detach()) ** 2				)

           # preserve gradients
           _A							:     int			=						z + (z_q - z).detach()

           # reshape back to match original input shape
           _A							:     Dict			=						z_q.permute(0		,     3		,     1		,     2				).contiguous()

           if self.remap is not None:
                 _A							:     Dict			=						min_encoding_indices.reshape(z.shape[0]		,     -1				)  # add batch axis
                 _A							:     List[Any]			=						self.remap_to_used(_a				)
                 _A							:     str			=						min_encoding_indices.reshape(-1		,     1				)  # flatten

           if self.sane_index_shape:
                 _A							:     List[Any]			=						min_encoding_indices.reshape(z_q.shape[0]		,     z_q.shape[2]		,     z_q.shape[3]				)

           return z_q, loss, (perplexity, min_encodings, min_encoding_indices)



     def 					a__	(  self		,     _a		,     _a				)						->							Dict:
           # shape specifying (batch, height, width, channel)
           if self.remap is not None:
                 _A							:     Optional[int]			=						indices.reshape(shape[0]		,     -1				)  # add batch axis
                 _A							:     str			=						self.unmap_to_all(_a				)
                 _A							:     Optional[Any]			=						indices.reshape(-1				)  # flatten again

           # get quantized latent vectors
           _A							:     Optional[int]			=						self.embedding(_a				)

           if shape is not None:
                 _A							:     Tuple			=						z_q.view(_a				)
                 # reshape back to match original input shape
                 _A							:     str			=						z_q.permute(0		,     3		,     1		,     2				).contiguous()

           return z_q







class 						lowercase							(   UpperCamelCase__		):

     def __init__(  self		,     _a		,     _a=False				)						->							List[str]:
           _A							:     str			=						parameters
           _A			,   _A							:     Dict			=						torch.chunk(_a		,     2		,     dim=1				)
           _A							:     List[Any]			=						torch.clamp(self.logvar		,     -30.0		,     20.0				)
           _A							:     Optional[int]			=						deterministic
           _A							:     List[str]			=						torch.exp(0.5 * self.logvar				)
           _A							:     Dict			=						torch.exp(self.logvar				)
           if self.deterministic:
                 _A							:     str			=						torch.zeros_like(
                     self.mean		,     device=self.parameters.device		,     dtype=self.parameters.dtype				)

     def 					a__	(  self		,     _a = None				)						->							torch.FloatTensor:
           # make sure sample is on the same device as the parameters and has same dtype
           _A							:     Optional[int]			=						randn_tensor(
               self.mean.shape		,     generator=_a		,     device=self.parameters.device		,     dtype=self.parameters.dtype				)
           _A							:     Optional[Any]			=						self.mean + self.std * sample
           return x

     def 					a__	(  self		,     _a=None				)						->							int:
           if self.deterministic:
                 return torch.Tensor([0.0]				)
           else:
                 if other is None:
                       return 0.5 * torch.sum(torch.pow(self.mean		,     2				) + self.var - 1.0 - self.logvar		,     dim=[1, 2, 3]				)
                 else:
                       return 0.5 * torch.sum(
                           torch.pow(self.mean - other.mean		,     2				) / other.var
                           + self.var / other.var
                           - 1.0
                           - self.logvar
                           + other.logvar		,     dim=[1, 2, 3]		,     )

     def 					a__	(  self		,     _a		,     _a=[1, 2, 3]				)						->							Dict:
           if self.deterministic:
                 return torch.Tensor([0.0]				)
           _A							:     Dict			=						np.log(2.0 * np.pi				)
           return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean		,     2				) / self.var		,     dim=_a				)



     def 					a__	(  self				)						->							int:
           return self.mean







import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case										=       logging.get_logger(__name__)

_snake_case										=       [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 				lowerCAmelCase_	(     snake_case_     ):
      if k == "embeddings.weight":
            return "shared.weight"

      for parlai_name, hf_name in PATTERNS:
            _A							:     str			=						k.replace(snake_case_,snake_case_     )

      if k.startswith("""encoder"""     ):
            _A							:     Optional[Any]			=						k.replace(""".attn""",""".self_attn"""     )
            _A							:     Dict			=						k.replace("""norm1""","""self_attn_layer_norm"""     )
            _A							:     Optional[Any]			=						k.replace("""norm2""","""final_layer_norm"""     )
      elif k.startswith("""decoder"""     ):
            _A							:     str			=						k.replace("""norm1""","""self_attn_layer_norm"""     )
            _A							:     Any			=						k.replace("""norm2""","""encoder_attn_layer_norm"""     )
            _A							:     Optional[int]			=						k.replace("""norm3""","""final_layer_norm"""     )
      return k







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[Any]			=						[
          """model.encoder.layernorm_embedding.weight""",
          """model.encoder.layernorm_embedding.bias""",
          """model.decoder.layernorm_embedding.weight""",
          """model.decoder.layernorm_embedding.bias""",
      ]
      for k in keys:
            _A							:     str			=						sd.pop(snake_case_     )
            _A							:     Optional[int]			=						k.replace("""layernorm_embedding""","""layer_norm"""     )
            assert new_k not in sd
            _A							:     Optional[int]			=						v


_snake_case										=       ["START"]







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )
      _A							:     List[Any]			=						model["""model"""]
      _A							:     Optional[Any]			=						BlenderbotConfig.from_json_file(snake_case_     )
      _A							:     List[str]			=						BlenderbotForConditionalGeneration(snake_case_     )
      _A							:     Tuple			=						m.model.state_dict().keys()
      _A							:     Any			=						[]
      _A							:     Dict			=						{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

            _A							:     Optional[int]			=						rename_state_dict_key(snake_case_     )
            if new_k not in valid_keys:
                  failures.append([k, new_k]     )
            else:
                  _A							:     Dict			=						v
      if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
            rename_layernorm_keys(snake_case_     )
      m.model.load_state_dict(snake_case_,strict=snake_case_     )
      m.half()
      m.save_pretrained(snake_case_     )


if __name__ == "__main__":
  _snake_case										=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
  parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
  parser.add_argument(
      "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
  )
  _snake_case										=       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

