
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


__lowerCAmelCase					:			str			      =		{
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

__lowerCAmelCase					:			Any			      =		{
    """169M""": 768,
    """430M""": 1_024,
    """1B5""": 2_048,
    """3B""": 2_560,
    """7B""": 4_096,
    """14B""": 5_120,
}


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Dict:
    __lowercase		:			Tuple	    =       list(state_dict.keys()	)
    for name in state_dict_keys:
        __lowercase		:			List[Any]	    =       state_dict.pop(__lowerCAmelCase	)
        # emb -> embedding
        if name.startswith('''emb.'''	):
            __lowercase		:			Dict	    =       name.replace('''emb.'''	,					'''embeddings.'''	)
        # ln_0 -> pre_ln (only present at block 0)
        if name.startswith('''blocks.0.ln0'''	):
            __lowercase		:			str	    =       name.replace('''blocks.0.ln0'''	,					'''blocks.0.pre_ln'''	)
        # att -> attention
        __lowercase		:			Union[str, Any]	    =       re.sub(R'''blocks\.(\d+)\.att'''	,					R'''blocks.\1.attention'''	,					__lowerCAmelCase	)
        # ffn -> feed_forward
        __lowercase		:			Dict	    =       re.sub(R'''blocks\.(\d+)\.ffn'''	,					R'''blocks.\1.feed_forward'''	,					__lowerCAmelCase	)
        # time_mix_k -> time_mix_key and reshape
        if name.endswith('''.time_mix_k'''	):
            __lowercase		:			Optional[Any]	    =       name.replace('''.time_mix_k'''	,					'''.time_mix_key'''	)
        # time_mix_v -> time_mix_value and reshape
        if name.endswith('''.time_mix_v'''	):
            __lowercase		:			str	    =       name.replace('''.time_mix_v'''	,					'''.time_mix_value'''	)
        # time_mix_r -> time_mix_key and reshape
        if name.endswith('''.time_mix_r'''	):
            __lowercase		:			List[str]	    =       name.replace('''.time_mix_r'''	,					'''.time_mix_receptance'''	)

        if name != "head.weight":
            __lowercase		:			Optional[int]	    =       '''rwkv.''' + name

        __lowercase		:			List[str]	    =       weight
    return state_dict


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase=None	,					__lowerCAmelCase=None	,					__lowerCAmelCase=False	,					__lowerCAmelCase=None	) ->						List[str]:
    # 1. If possible, build the tokenizer.
    if tokenizer_file is None:
        print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''	)
        __lowercase		:			Optional[Any]	    =       50_277
        __lowercase		:			Tuple	    =       AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''	)
    else:
        __lowercase		:			Union[str, Any]	    =       PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase	)
        __lowercase		:			Optional[int]	    =       len(__lowerCAmelCase	)
    tokenizer.save_pretrained(__lowerCAmelCase	)

    # 2. Build the config
    __lowercase		:			Any	    =       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		:			Optional[int]	    =       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		:			Any	    =       RwkvConfig(
        vocab_size=__lowerCAmelCase	,					num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]	,					hidden_size=HIDEN_SIZE_MAPPING[size]	,					)
    config.save_pretrained(__lowerCAmelCase	)

    # 3. Download model file then convert state_dict
    __lowercase		:			Any	    =       hf_hub_download(__lowerCAmelCase	,					__lowerCAmelCase	)
    __lowercase		:			Union[str, Any]	    =       torch.load(__lowerCAmelCase	,					map_location='''cpu'''	)
    __lowercase		:			Tuple	    =       convert_state_dict(__lowerCAmelCase	)

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

    if index is not None:
        __lowercase		:			Union[str, Any]	    =       os.path.join(__lowerCAmelCase	,					__lowerCAmelCase	)
        # Save the index as well
        with open(__lowerCAmelCase	,					'''w'''	,					encoding='''utf-8'''	) as f:
            __lowercase		:			Optional[Any]	    =       json.dumps(__lowerCAmelCase	,					indent=2	,					sort_keys=__lowerCAmelCase	) + '''\n'''
            f.write(__lowerCAmelCase	)

        # 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		:			Any	    =       list(shards.keys()	)

        del state_dict
        del shards
        gc.collect()

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

    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		:			Any	    =       AutoModelForCausalLM.from_pretrained(__lowerCAmelCase	)
        model.push_to_hub(__lowerCAmelCase	,					max_shard_size='''2GB'''	)
        tokenizer.push_to_hub(__lowerCAmelCase	)


if __name__ == "__main__":
 __lowerCAmelCase					:			List[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.",
 )

 __lowerCAmelCase					:			Union[str, Any]			      =		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,
 )


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 						__lowerCAmelCase	(	lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			Dict        =    None
      A__	:			Any        =    BloomTokenizerFast
      A__	:			Optional[Any]        =    BloomTokenizerFast
      A__	:			List[Any]        =    True
      A__	:			int        =    False
      A__	:			Any        =    '''tokenizer_file'''
      A__	:			Optional[int]        =    {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




      def 		snake_case_			(      self		:       Any				):
          super().setUp()
          __lowercase		:			int	    =       BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''				)
          tokenizer.save_pretrained(self.tmpdirname				)




      def 		snake_case_			(      self		:       Dict				,       **_snake_case		:       Any				):
          kwargs.update(self.special_tokens_map				)
          return BloomTokenizerFast.from_pretrained(self.tmpdirname				,       **_snake_case				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Dict	    =       self.get_rust_tokenizer()

          __lowercase		:			Optional[int]	    =       ['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
          __lowercase		:			Union[str, Any]	    =       [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

          __lowercase		:			Optional[int]	    =       tokenizer.batch_encode_plus(_snake_case				)['''input_ids''']
          self.assertListEqual(_snake_case				,       _snake_case				)

          __lowercase		:			List[Any]	    =       tokenizer.batch_decode(_snake_case				)
          self.assertListEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       str				,       _snake_case		:       int=6				):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
                  __lowercase		:			Dict	    =       self.rust_tokenizer_class.from_pretrained(_snake_case				,       **_snake_case				)
                  # tokenizer_r.pad_token = None # Hotfixing padding = None
                  # Simple input
                  __lowercase		:			List[Any]	    =       '''This is a simple input'''
                  __lowercase		:			List[str]	    =       ['''This is a simple input 1''', '''This is a simple input 2''']
                  __lowercase		:			Optional[Any]	    =       ('''This is a simple input''', '''This is a pair''')
                  __lowercase		:			Any	    =       [
                      ('''This is a simple input 1''', '''This is a simple input 2'''),
                      ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                  ]

                  # Simple input tests
                  try:
                      tokenizer_r.encode(_snake_case				,       max_length=_snake_case				)
                      tokenizer_r.encode_plus(_snake_case				,       max_length=_snake_case				)

                      tokenizer_r.batch_encode_plus(_snake_case				,       max_length=_snake_case				)
                      tokenizer_r.encode(_snake_case				,       max_length=_snake_case				)
                      tokenizer_r.batch_encode_plus(_snake_case				,       max_length=_snake_case				)
                  except ValueError:
                      self.fail('''Bloom Tokenizer should be able to deal with padding'''				)

                  __lowercase		:			Dict	    =       None  # Hotfixing padding = None
                  self.assertRaises(_snake_case				,       tokenizer_r.encode				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				)

                  # Simple input
                  self.assertRaises(_snake_case				,       tokenizer_r.encode_plus				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				)

                  # Simple input
                  self.assertRaises(
                      _snake_case				,       tokenizer_r.batch_encode_plus				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				,       )

                  # Pair input
                  self.assertRaises(_snake_case				,       tokenizer_r.encode				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				)

                  # Pair input
                  self.assertRaises(_snake_case				,       tokenizer_r.encode_plus				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				)

                  # Pair input
                  self.assertRaises(
                      _snake_case				,       tokenizer_r.batch_encode_plus				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				,       )




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			List[Any]	    =       self.get_rust_tokenizer()
          __lowercase		:			int	    =       load_dataset('''xnli'''				,       '''all_languages'''				,       split='''test'''				,       streaming=_snake_case				)

          __lowercase		:			Optional[Any]	    =       next(iter(_snake_case				)				)['''premise''']  # pick up one data
          __lowercase		:			List[str]	    =       list(sample_data.values()				)

          __lowercase		:			List[str]	    =       list(map(tokenizer.encode				,       _snake_case				)				)
          __lowercase		:			Tuple	    =       [tokenizer.decode(_snake_case				,       clean_up_tokenization_spaces=_snake_case				) for x in output_tokens]
          self.assertListEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
          # any sequence length constraints. This test of the parent class will fail since it relies on the
          # maximum sequence length of the positoonal embeddings.
          self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)				,       1				)
          self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)				,       1				)


