

"""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


_a											=							{
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

_a											=							{
    '169M': 7_68,
    '430M': 10_24,
    '1B5': 20_48,
    '3B': 25_60,
    '7B': 40_96,
    '14B': 51_20,
}




def   _A  (  UpperCamelCase_					:	str)     ->						Any:
   '''simple docstring'''


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

      if name != "head.weight":
         __lowercase								=						"rwkv." + name

      __lowercase								=						weight
   return state_dict




def   _A  (  UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	Union[str, Any]=None,      UpperCamelCase_					:	Union[str, Any]=None,      UpperCamelCase_					:	List[str]=False,      UpperCamelCase_					:	List[str]=None)     ->						str:
   '''simple docstring'''


   if tokenizer_file is None:
      print("No `--tokenizer_file` provided, we will use the default tokenizer.")
      __lowercase								=						50277
      __lowercase								=						AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
   else:
      __lowercase								=						PreTrainedTokenizerFast(tokenizer_file=snake_case_)
      __lowercase								=						len(snake_case_)
   tokenizer.save_pretrained(snake_case_)

   # 2. Build the config
   __lowercase								=						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:
            __lowercase								=						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}.""")

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

   # 3. Download model file then convert state_dict
   __lowercase								=						hf_hub_download(snake_case_,      snake_case_)
   __lowercase								=						torch.load(snake_case_,      map_location="cpu")
   __lowercase								=						convert_state_dict(snake_case_)

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

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

      # 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.")
      __lowercase								=						list(shards.keys())

      del state_dict
      del shards
      gc.collect()

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

   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.")
      __lowercase								=						AutoModelForCausalLM.from_pretrained(snake_case_)
      model.push_to_hub(snake_case_,      max_shard_size="2GB")
      tokenizer.push_to_hub(snake_case_)


if __name__ == "__main__":
   _a											=							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.',
   )

   _a											=							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"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 @slow
 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base"     )

    __lowercase								=						tf.convert_to_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]], dtype=tf.intaa, )  # J'aime le camembert !"

    __lowercase								=						model(UpperCAmelCase__     )["last_hidden_state"]
    __lowercase								=						tf.TensorShape((1, 1_0, 7_6_8)     )
    self.assertEqual(output.shape, UpperCAmelCase__     )
    # compare the actual values for a slice.
    __lowercase								=						tf.convert_to_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]]], dtype=tf.floataa, )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1E-4     )     )




