
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__							:							Tuple									=			{
    "169M": 1_2,
    "430M": 2_4,
    "1B5": 2_4,
    "3B": 3_2,
    "7B": 3_2,
    "14B": 4_0,
}

lowercase__							:							Tuple									=			{
    "169M": 7_6_8,
    "430M": 1_0_2_4,
    "1B5": 2_0_4_8,
    "3B": 2_5_6_0,
    "7B": 4_0_9_6,
    "14B": 5_1_2_0,
}




def 	A_ (							snake_case							:   str )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			list(state_dict.keys() )
						for name in state_dict_keys:
												__UpperCamelCase       =			state_dict.pop(snake_case )
												# 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''' ,      snake_case )
												# ffn -> feed_forward
												__UpperCamelCase       =			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''' ):
																		__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 	A_ (							snake_case							:   Any ,      snake_case							:   List[str] ,      snake_case							:   List[Any] ,      snake_case							:   Tuple=None ,      snake_case							:   Tuple=None ,      snake_case							:   Union[str, Any]=False ,      snake_case							:   Dict=None )      ->    int:



						'''simple docstring'''

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

						# 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=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
						__UpperCamelCase       =			hf_hub_download(snake_case ,      snake_case )
						__UpperCamelCase       =			torch.load(snake_case ,      map_location='''cpu''' )
						__UpperCamelCase       =			convert_state_dict(snake_case )

						# 4. Split in shards and save
						__UpperCamelCase  ,							__UpperCamelCase       =			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:
												__UpperCamelCase       =			os.path.join(snake_case ,      snake_case )
												# Save the index as well
												with open(snake_case ,      '''w''' ,      encoding='''utf-8''' ) as f:
																		__UpperCamelCase       =			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.''' )
												__UpperCamelCase       =			list(shards.keys() )

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		__UpperCamelCase       =			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.''' )
												__UpperCamelCase       =			AutoModelForCausalLM.from_pretrained(snake_case )
												model.push_to_hub(snake_case ,      max_shard_size='''2GB''' )
												tokenizer.push_to_hub(snake_case )


if __name__ == "__main__":
				lowercase__							:							Tuple									=			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,
				)






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
				import jax






@require_flax
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  FlaxAutoencoderKL



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			4
													__UpperCamelCase       =			3
													__UpperCamelCase       =			(32, 32)

													__UpperCamelCase       =			jax.random.PRNGKey(0			)
													__UpperCamelCase       =			jax.random.uniform(SCREAMING_SNAKE_CASE_     ,	((batch_size, num_channels) + sizes)			)

													return {"sample": image, "prng_key": prng_key}



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			{
													    '''block_out_channels''': [32, 64],
													    '''in_channels''': 3,
													    '''out_channels''': 3,
													    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
													    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
													    '''latent_channels''': 4,
													}
													__UpperCamelCase       =			self.dummy_input
													return init_dict, inputs_dict




