






'''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


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

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



def 				snake_case       (  a_				:			Tuple						)							->					Dict:





      """simple docstring"""

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

            if name != "head.weight":
                  UpperCamelCase_					: Tuple            =				"""rwkv.""" + name

            UpperCamelCase_					: Union[str, Any]            =				weight
      return state_dict



def 				snake_case       (  a_				:			Tuple     , a_				:			Union[str, Any]     , a_				:			int     , a_				:			str=None     , a_				:			int=None     , a_				:			Dict=False     , a_				:			List[str]=None						)							->					Tuple:





      """simple docstring"""

      if tokenizer_file is None:
            print("""No `--tokenizer_file` provided, we will use the default tokenizer."""						)
            UpperCamelCase_					: List[str]            =				50_277
            UpperCamelCase_					: str            =				AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""						)
      else:
            UpperCamelCase_					: Any            =				PreTrainedTokenizerFast(tokenizer_file=__a						)
            UpperCamelCase_					: int            =				len(__a						)
      tokenizer.save_pretrained(__a						)

      # 2. Build the config
      UpperCamelCase_					: Dict            =				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_					: Dict            =				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_					: Dict            =				RwkvConfig(
          vocab_size=__a     , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]     , hidden_size=HIDEN_SIZE_MAPPING[size]     , )
      config.save_pretrained(__a						)

      # 3. Download model file then convert state_dict
      UpperCamelCase_					: List[Any]            =				hf_hub_download(__a     , __a						)
      UpperCamelCase_					: Union[str, Any]            =				torch.load(__a     , map_location="""cpu"""						)
      UpperCamelCase_					: int            =				convert_state_dict(__a						)

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

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

            # 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_					: int            =				list(shards.keys()						)

            del state_dict
            del shards
            gc.collect()

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

      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_					: Union[str, Any]            =				AutoModelForCausalLM.from_pretrained(__a						)
            model.push_to_hub(__a     , max_shard_size="""2GB"""						)
            tokenizer.push_to_hub(__a						)


if __name__ == "__main__":
      UpperCamelCase      					=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.",
      )

      UpperCamelCase      					=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 logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='%(message)s')



def 							_lowerCamelCase			(			__a     ):
		return input_array.reshape((input_array.size, 1)     )



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			np.nan
		for i in range(__a     ):
				SCREAMING_SNAKE_CASE_    			=			features[:, labels == i]
				SCREAMING_SNAKE_CASE_    			=			data.mean(1     )
				# Centralize the data of class i
				SCREAMING_SNAKE_CASE_    			=			data - column_reshape(__a     )
				if i > 0:
						# If covariance_sum is not None
						covariance_sum += np.dot(__a,				centered_data.T     )
				else:
						# If covariance_sum is np.nan (i.e. first loop)
						SCREAMING_SNAKE_CASE_    			=			np.dot(__a,				centered_data.T     )

		return covariance_sum / features.shape[1]



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			features.mean(1     )
		SCREAMING_SNAKE_CASE_    			=			np.nan
		for i in range(__a     ):
				SCREAMING_SNAKE_CASE_    			=			features[:, labels == i]
				SCREAMING_SNAKE_CASE_    			=			data.shape[1]
				SCREAMING_SNAKE_CASE_    			=			data.mean(1     )
				if i > 0:
						# If covariance_sum is not None
						covariance_sum += device_data * np.dot(
						    column_reshape(__a     ) - column_reshape(__a     ),				(column_reshape(__a     ) - column_reshape(__a     )).T,				)
				else:
						# If covariance_sum is np.nan (i.e. first loop)
						SCREAMING_SNAKE_CASE_    			=			device_data * np.dot(
						    column_reshape(__a     ) - column_reshape(__a     ),				(column_reshape(__a     ) - column_reshape(__a     )).T,				)

		return covariance_sum / features.shape[1]



def 							_lowerCamelCase			(			__a,				__a     ):

		# Check if the features have been loaded
		if features.any():
				SCREAMING_SNAKE_CASE_    			=			features.mean(1     )
				# Center the dataset
				SCREAMING_SNAKE_CASE_    			=			features - np.reshape(__a,				(data_mean.size, 1)     )
				SCREAMING_SNAKE_CASE_    			=			np.dot(__a,				centered_data.T     ) / features.shape[1]
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			np.linalg.eigh(__a     )
				# Take all the columns in the reverse order (-1), and then takes only the first
				SCREAMING_SNAKE_CASE_    			=			eigenvectors[:, ::-1][:, 0:dimensions]
				# Project the database on the new space
				SCREAMING_SNAKE_CASE_    			=			np.dot(filtered_eigenvectors.T,				__a     )
				logging.info('''Principal Component Analysis computed'''     )

				return projected_data
		else:
				logging.basicConfig(level=logging.ERROR,				format='''%(message)s''',				force=__a     )
				logging.error('''Dataset empty'''     )
				raise AssertionError



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a     ):
		assert classes > dimensions

		# Check if features have been already loaded
		if features.any:
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			eigh(
				    covariance_between_classes(__a,				__a,				__a     ),				covariance_within_classes(__a,				__a,				__a     ),				)
				SCREAMING_SNAKE_CASE_    			=			eigenvectors[:, ::-1][:, :dimensions]
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			np.linalg.svd(__a     )
				SCREAMING_SNAKE_CASE_    			=			svd_matrix[:, 0:dimensions]
				SCREAMING_SNAKE_CASE_    			=			np.dot(filtered_svd_matrix.T,				__a     )
				logging.info('''Linear Discriminant Analysis computed'''     )

				return projected_data
		else:
				logging.basicConfig(level=logging.ERROR,				format='''%(message)s''',				force=__a     )
				logging.error('''Dataset empty'''     )
				raise AssertionError



def 							_lowerCamelCase			(			):
		# Create dummy dataset with 2 classes and 3 features
		SCREAMING_SNAKE_CASE_    			=			np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]     )
		SCREAMING_SNAKE_CASE_    			=			np.array([0, 0, 0, 1, 1]     )
		SCREAMING_SNAKE_CASE_    			=			2
		SCREAMING_SNAKE_CASE_    			=			2

		# Assert that the function raises an AssertionError if dimensions > classes
		with pytest.raises(__a     ) as error_info:
				SCREAMING_SNAKE_CASE_    			=			linear_discriminant_analysis(
				    __a,				__a,				__a,				__a     )
				if isinstance(__a,				np.ndarray     ):
						raise AssertionError(
						    '''Did not raise AssertionError for dimensions > classes'''     )
				assert error_info.type is AssertionError



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     )
		SCREAMING_SNAKE_CASE_    			=			2
		SCREAMING_SNAKE_CASE_    			=			np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]     )

		with pytest.raises(__a     ) as error_info:
				SCREAMING_SNAKE_CASE_    			=			principal_component_analysis(__a,				__a     )
				if not np.allclose(__a,				__a     ):
						raise AssertionError
				assert error_info.type is AssertionError


if __name__ == "__main__":
						import doctest

						doctest.testmod()