




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




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Union[str, Any]:
						_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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=None			,     snake_case=None			,     snake_case=False			,     snake_case=None       )						->				Optional[Any]:
						# 1. If possible, build the tokenizer.
						if tokenizer_file is None:
												print("""No `--tokenizer_file` provided, we will use the default tokenizer."""       )
												_UpperCAmelCase      =			5_0_2_7_7
												_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__":
						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,
						)





# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[str]:
						return 1 / (1 + np.exp(-z       ))




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Tuple:
						return (-y * np.log(snake_case       ) - (1 - y) * np.log(1 - h       )).mean()




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
						return np.sum(y * scores - np.log(1 + np.exp(snake_case       )       )       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=7_0_0_0_0       )						->				Tuple:
						_UpperCAmelCase      =			np.zeros(x.shape[1]       )

						for iterations in range(snake_case       ):
												_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
												_UpperCAmelCase      =			sigmoid_function(snake_case       )
												_UpperCAmelCase      =			np.dot(x.T			,     h - y       ) / y.size
												_UpperCAmelCase      =			theta - alpha * gradient  # updating the weights
												_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
												_UpperCAmelCase      =			sigmoid_function(snake_case       )
												_UpperCAmelCase      =			cost_function(snake_case			,     snake_case       )
												if iterations % 1_0_0 == 0:
																		print(f"loss: {j} \t"       )  # printing the loss after every 100 iterations
						return theta


# In[68]:

if __name__ == "__main__":
				a													=			datasets.load_iris()
				a													=			iris.data[:, :2]
				a													=			(iris.target != 0) * 1

				a													=			0.1
				a													=			logistic_reg(alpha, x, y, max_iterations=70000)
				print("theta: ", theta)  # printing the theta i.e our weights vector




				def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
										return sigmoid_function(
										    np.dot(snake_case			,     snake_case       )       )  # predicting the value of probability from the logistic regression algorithm

				plt.figure(figsize=(10, 6))
				plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
				plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
				((a)							,  (a))													=			(x[:, 0].min(), x[:, 0].max())
				((a)							,  (a))													=			(x[:, 1].min(), x[:, 1].max())
				((a)							,  (a))													=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
				a													=			np.c_[xxa.ravel(), xxa.ravel()]
				a													=			predict_prob(grid).reshape(xxa.shape)
				plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")

				plt.legend()

				plt.show()