
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


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

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



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

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

												_SCREAMING_SNAKE_CASE				= weight
						return state_dict



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]	,			_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=None	,			_SCREAMING_SNAKE_CASE		:   List[str]=None	,			_SCREAMING_SNAKE_CASE		:   List[Any]=False	,			_SCREAMING_SNAKE_CASE		:   Dict=None  ):
						# 1. If possible, build the tokenizer.
						if tokenizer_file is None:
												print("No `--tokenizer_file` provided, we will use the default tokenizer."  )
												_SCREAMING_SNAKE_CASE				= 5_0277
												_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"  )
						else:
												_SCREAMING_SNAKE_CASE				= PreTrainedTokenizerFast(tokenizer_file=_SCREAMING_SNAKE_CASE  )
												_SCREAMING_SNAKE_CASE				= len(_SCREAMING_SNAKE_CASE  )
						tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE  )

						# 2. Build the config
						_SCREAMING_SNAKE_CASE				= 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:
																								_SCREAMING_SNAKE_CASE				= 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}.'  )

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

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

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

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

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		_SCREAMING_SNAKE_CASE				= torch.load(os.path.join(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )  )
																		torch.save({k: v.cpu().clone() for k, v in state_dict.items()}	,			os.path.join(_SCREAMING_SNAKE_CASE	,			_SCREAMING_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."  )
												_SCREAMING_SNAKE_CASE				= AutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE  )
												model.push_to_hub(_SCREAMING_SNAKE_CASE	,			max_shard_size="2GB"  )
												tokenizer.push_to_hub(_SCREAMING_SNAKE_CASE  )


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

						_snake_case				:   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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import GLPNImageProcessor


class 			lowerCAmelCase (				unittest.TestCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=7					,						UpperCamelCase=3					,						UpperCamelCase=18					,						UpperCamelCase=30					,						UpperCamelCase=400					,						UpperCamelCase=True					,						UpperCamelCase=32					,						UpperCamelCase=True					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= num_channels
								_SCREAMING_SNAKE_CASE				= image_size
								_SCREAMING_SNAKE_CASE				= min_resolution
								_SCREAMING_SNAKE_CASE				= max_resolution
								_SCREAMING_SNAKE_CASE				= do_resize
								_SCREAMING_SNAKE_CASE				= size_divisor
								_SCREAMING_SNAKE_CASE				= do_rescale




		def     lowercase       (				self      ):
								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}


@require_torch
@require_vision
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Dict       =					GLPNImageProcessor if is_vision_available() else None




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= GLPNImageProcessingTester(self      )




		@property
		def     lowercase       (				self      ):
								return self.image_processor_tester.prepare_image_processor_dict()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_resize"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"size_divisor"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"resample"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_rescale"      )      )




		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						Image.Image      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random numpy tensors
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase					,						numpify=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						np.ndarray      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase					,						torchify=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						torch.Tensor      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )