


'''simple docstring'''



import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


_lowercase      :      Optional[int]            =					{
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

_lowercase      :      str            =					{
    """169M""": 768,
    """430M""": 1024,
    """1B5""": 2048,
    """3B""": 2560,
    """7B""": 4096,
    """14B""": 5120,
}


def 				lowerCamelCase__				(	A       :      Optional[int]					):




      '''simple docstring'''






      UpperCAmelCase					    =     list(state_dict.keys()					)
      for name in state_dict_keys:
            UpperCAmelCase					    =     state_dict.pop(__UpperCamelCase					)
            # emb -> embedding
            if name.startswith('''emb.'''					):
                  UpperCAmelCase					    =     name.replace('''emb.'''				,       '''embeddings.'''					)
            # ln_0 -> pre_ln (only present at block 0)
            if name.startswith('''blocks.0.ln0'''					):
                  UpperCAmelCase					    =     name.replace('''blocks.0.ln0'''				,       '''blocks.0.pre_ln'''					)
            # att -> attention
            UpperCAmelCase					    =     re.sub(R'''blocks\.(\d+)\.att'''				,       R'''blocks.\1.attention'''				,       __UpperCamelCase					)
            # ffn -> feed_forward
            UpperCAmelCase					    =     re.sub(R'''blocks\.(\d+)\.ffn'''				,       R'''blocks.\1.feed_forward'''				,       __UpperCamelCase					)
            # time_mix_k -> time_mix_key and reshape
            if name.endswith('''.time_mix_k'''					):
                  UpperCAmelCase					    =     name.replace('''.time_mix_k'''				,       '''.time_mix_key'''					)
            # time_mix_v -> time_mix_value and reshape
            if name.endswith('''.time_mix_v'''					):
                  UpperCAmelCase					    =     name.replace('''.time_mix_v'''				,       '''.time_mix_value'''					)
            # time_mix_r -> time_mix_key and reshape
            if name.endswith('''.time_mix_r'''					):
                  UpperCAmelCase					    =     name.replace('''.time_mix_r'''				,       '''.time_mix_receptance'''					)

            if name != "head.weight":
                  UpperCAmelCase					    =     '''rwkv.''' + name

            UpperCAmelCase					    =     weight
      return state_dict


def 				lowerCamelCase__				(	A       :      Optional[int]				,       A       :      Any				,       A       :      Optional[int]				,       A       :      List[str]=None				,       A       :      str=None				,       A       :      Optional[int]=False				,       A       :      int=None					):




      '''simple docstring'''






      if tokenizer_file is None:
            print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''					)
            UpperCAmelCase					    =     5_02_77
            UpperCAmelCase					    =     AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''					)
      else:
            UpperCAmelCase					    =     PreTrainedTokenizerFast(tokenizer_file=__UpperCamelCase					)
            UpperCAmelCase					    =     len(__UpperCamelCase					)
      tokenizer.save_pretrained(__UpperCamelCase					)

      # 2. Build the config
      UpperCAmelCase					    =     list(NUM_HIDDEN_LAYERS_MAPPING.keys()					)
      if size is None:
            # Try to infer size from the checkpoint name
            for candidate in possible_sizes:
                  if candidate in checkpoint_file:
                        UpperCAmelCase					    =     candidate
                        break
            if size is None:
                  raise ValueError('''Could not infer the size, please provide it with the `--size` argument.'''					)
      if size not in possible_sizes:
            raise ValueError(f"""`size` should be one of {possible_sizes}, got {size}."""					)

      UpperCAmelCase					    =     RwkvConfig(
          vocab_size=__UpperCamelCase				,       num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]				,       hidden_size=HIDEN_SIZE_MAPPING[size]				,       )
      config.save_pretrained(__UpperCamelCase					)

      # 3. Download model file then convert state_dict
      UpperCAmelCase					    =     hf_hub_download(__UpperCamelCase				,       __UpperCamelCase					)
      UpperCAmelCase					    =     torch.load(__UpperCamelCase				,       map_location='''cpu'''					)
      UpperCAmelCase					    =     convert_state_dict(__UpperCamelCase					)

      # 4. Split in shards and save
      UpperCAmelCase    ,    UpperCAmelCase					    =     shard_checkpoint(__UpperCamelCase					)
      for shard_file, shard in shards.items():
            torch.save(__UpperCamelCase				,       os.path.join(__UpperCamelCase				,       __UpperCamelCase					)					)

      if index is not None:
            UpperCAmelCase					    =     os.path.join(__UpperCamelCase				,       __UpperCamelCase					)
            # Save the index as well
            with open(__UpperCamelCase				,       '''w'''				,       encoding='''utf-8'''					) as f:
                  UpperCAmelCase					    =     json.dumps(__UpperCamelCase				,       indent=2				,       sort_keys=__UpperCamelCase					) + '''\n'''
                  f.write(__UpperCamelCase					)

            # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
            print(
                '''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.'''					)
            UpperCAmelCase					    =     list(shards.keys()					)

            del state_dict
            del shards
            gc.collect()

            for shard_file in shard_files:
                  UpperCAmelCase					    =     torch.load(os.path.join(__UpperCamelCase				,       __UpperCamelCase					)					)
                  torch.save({k: v.cpu().clone() for k, v in state_dict.items()}				,       os.path.join(__UpperCamelCase				,       __UpperCamelCase					)					)

      del state_dict
      gc.collect()

      if push_to_hub:
            if model_name is None:
                  raise ValueError('''Please provide a `model_name` to push the model to the Hub.'''					)
            UpperCAmelCase					    =     AutoModelForCausalLM.from_pretrained(__UpperCamelCase					)
            model.push_to_hub(__UpperCamelCase				,       max_shard_size='''2GB'''					)
            tokenizer.push_to_hub(__UpperCamelCase					)


if __name__ == "__main__":
 _lowercase      :      Union[str, Any]            =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
 )
 parser.add_argument(
     """--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
 )
 parser.add_argument(
     """--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
 )
 parser.add_argument(
     """--tokenizer_file""",
     default=None,
     type=str,
     help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
 )
 parser.add_argument(
     """--size""",
     default=None,
     type=str,
     help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
 )
 parser.add_argument(
     """--push_to_hub""",
     action="""store_true""",
     help="""Push to the Hub the converted model.""",
 )
 parser.add_argument(
     """--model_name""",
     default=None,
     type=str,
     help="""Name of the pushed model on the Hub, including the username / organization.""",
 )

 _lowercase      :      List[str]            =					parser.parse_args()
 convert_rmkv_checkpoint_to_hf_format(
     args.repo_id,
     args.checkpoint_file,
     args.output_dir,
     size=args.size,
     tokenizer_file=args.tokenizer_file,
     push_to_hub=args.push_to_hub,
     model_name=args.model_name,
 )









'''simple docstring'''



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			str				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=lowerCAmelCase				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

																UpperCAmelCase					    =     FlaxTransformeraDModel(
																    in_channels=self.out_channels				,   n_heads=self.num_attention_heads				,   d_head=self.out_channels // self.num_attention_heads				,   depth=1				,   use_linear_projection=self.use_linear_projection				,   only_cross_attention=self.only_cross_attention				,   use_memory_efficient_attention=self.use_memory_efficient_attention				,   dtype=self.dtype				,   )
																attentions.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets
										UpperCAmelCase					    =     attentions

										if self.add_downsample:
																UpperCAmelCase					    =     FlaxDownsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			List[Any]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			List[str]=True				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     ()

										for resnet, attn in zip(self.resnets				,   self.attentions				):
																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																UpperCAmelCase					    =     attn(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																output_states += (hidden_states,)

										if self.add_downsample:
																UpperCAmelCase					    =     self.downsamplers_a(lowerCAmelCase				)
																output_states += (hidden_states,)

										return hidden_states, output_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			List[str]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=lowerCAmelCase				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)
										UpperCAmelCase					    =     resnets

										if self.add_downsample:
																UpperCAmelCase					    =     FlaxDownsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			List[str]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			List[str]=True				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     ()

										for resnet in self.resnets:
																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																output_states += (hidden_states,)

										if self.add_downsample:
																UpperCAmelCase					    =     self.downsamplers_a(lowerCAmelCase				)
																output_states += (hidden_states,)

										return hidden_states, output_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			List[str]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if (i == self.num_layers - 1) else self.out_channels
																UpperCAmelCase					    =     self.prev_output_channel if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

																UpperCAmelCase					    =     FlaxTransformeraDModel(
																    in_channels=self.out_channels				,   n_heads=self.num_attention_heads				,   d_head=self.out_channels // self.num_attention_heads				,   depth=1				,   use_linear_projection=self.use_linear_projection				,   only_cross_attention=self.only_cross_attention				,   use_memory_efficient_attention=self.use_memory_efficient_attention				,   dtype=self.dtype				,   )
																attentions.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets
										UpperCAmelCase					    =     attentions

										if self.add_upsample:
																UpperCAmelCase					    =     FlaxUpsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Union[str, Any]=True				)->					Optional[int]:

										"""simple docstring"""




										for resnet, attn in zip(self.resnets				,   self.attentions				):
																# pop res hidden states
																UpperCAmelCase					    =     res_hidden_states_tuple[-1]
																UpperCAmelCase					    =     res_hidden_states_tuple[:-1]
																UpperCAmelCase					    =     jnp.concatenate((hidden_states, res_hidden_states)				,   axis=-1				)

																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																UpperCAmelCase					    =     attn(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)

										if self.add_upsample:
																UpperCAmelCase					    =     self.upsamplers_a(lowerCAmelCase				)

										return hidden_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			Optional[int]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if (i == self.num_layers - 1) else self.out_channels
																UpperCAmelCase					    =     self.prev_output_channel if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets

										if self.add_upsample:
																UpperCAmelCase					    =     FlaxUpsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict=True				)->					Tuple:

										"""simple docstring"""




										for resnet in self.resnets:
																# pop res hidden states
																UpperCAmelCase					    =     res_hidden_states_tuple[-1]
																UpperCAmelCase					    =     res_hidden_states_tuple[:-1]
																UpperCAmelCase					    =     jnp.concatenate((hidden_states, res_hidden_states)				,   axis=-1				)

																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)

										if self.add_upsample:
																UpperCAmelCase					    =     self.upsamplers_a(lowerCAmelCase				)

										return hidden_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			int				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     [
										    FlaxResnetBlockaD(
										        in_channels=self.in_channels				,   out_channels=self.in_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
										]

										UpperCAmelCase					    =     []

										for _ in range(self.num_layers				):
																UpperCAmelCase					    =     FlaxTransformeraDModel(
																    in_channels=self.in_channels				,   n_heads=self.num_attention_heads				,   d_head=self.in_channels // self.num_attention_heads				,   depth=1				,   use_linear_projection=self.use_linear_projection				,   use_memory_efficient_attention=self.use_memory_efficient_attention				,   dtype=self.dtype				,   )
																attentions.append(lowerCAmelCase				)

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=self.in_channels				,   out_channels=self.in_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets
										UpperCAmelCase					    =     attentions





				def __call__(		self      :			Dict				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Any=True				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.resnets[0](lowerCAmelCase				,   lowerCAmelCase				)
										for attn, resnet in zip(self.attentions				,   self.resnets[1:]				):
																UpperCAmelCase					    =     attn(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)

										return hidden_states





