

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

lowerCAmelCase_       	=	{
    '''169M''': 7_68,
    '''430M''': 10_24,
    '''1B5''': 20_48,
    '''3B''': 25_60,
    '''7B''': 40_96,
    '''14B''': 51_20,
}


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

												if name != "head.weight":
																		snake_case_   =	'''rwkv.''' + name

												snake_case_   =	weight
						return state_dict


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None  ,		SCREAMING_SNAKE_CASE__=None  ,		SCREAMING_SNAKE_CASE__=False  ,		SCREAMING_SNAKE_CASE__=None       ):
						# 1. If possible, build the tokenizer.
						if tokenizer_file is None:
												print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''       )
												snake_case_   =	50277
												snake_case_   =	AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''       )
						else:
												snake_case_   =	PreTrainedTokenizerFast(tokenizer_file=SCREAMING_SNAKE_CASE__       )
												snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__       )

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

						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
						snake_case_   =	hf_hub_download(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
						snake_case_   =	torch.load(SCREAMING_SNAKE_CASE__  ,		map_location='''cpu'''       )
						snake_case_   =	convert_state_dict(SCREAMING_SNAKE_CASE__       )

						# 4. Split in shards and save
						snake_case_,    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:
												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:
																		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.'''       )
												snake_case_   =	list(shards.keys()       )

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		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.'''       )
												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__":
					lowerCAmelCase_       	=	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_       	=	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 numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A     :   Union[str, Any]			    =			imread(R"digital_image_processing/image_data/lena_small.jpg")
A     :   Optional[Any]			    =			cvtColor(img, COLOR_BGR2GRAY)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			cn.convert_to_negative(_UpperCamelCase      )
			# assert negative_img array for at least one True
			assert negative_img.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			with Image.open("digital_image_processing/image_data/lena_small.jpg"      ) as img:
						# Work around assertion for response
						assert str(cc.change_contrast(_UpperCamelCase    , 110      )      ).startswith(
						    "<PIL.Image.Image image mode=RGB size=100x100 at"      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			canny.gen_gaussian_kernel(9    , sigma=1.4      )
			# Assert ambiguous array
			assert resp.all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			imread("digital_image_processing/image_data/lena_small.jpg"    , 0      )
			# assert ambiguous array for all == True
			assert canny_img.all()
			__lowerCAmelCase				    =			canny.canny(_UpperCamelCase      )
			# assert canny array for at least one True
			assert canny_array.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert gg.gaussian_filter(_UpperCamelCase    , 5    , sigma=0.9      ).all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]      )
			__lowerCAmelCase				    =			conv.img_convolve(_UpperCamelCase    , _UpperCamelCase      ).astype(_UpperCamelCase      )
			assert res.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert med.median_filter(_UpperCamelCase    , 3      ).any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase    ,	__lowerCAmelCase				    =			sob.sobel_filter(_UpperCamelCase      )
			assert grad.any() and theta.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			sp.make_sepia(_UpperCamelCase    , 20      )
			assert sepia.all()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			bs.Burkes(imread(_UpperCamelCase    , 1      )    , 120      )
			burkes.process()
			assert burkes.output_img.any()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    , ):

			'''simple docstring'''




			__lowerCAmelCase				    =			rs.NearestNeighbour(imread(_UpperCamelCase    , 1      )    , 400    , 200      )
			nn.process()
			assert nn.output.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"digital_image_processing/image_data/lena.jpg"

			# Reading the image and converting it to grayscale.
			__lowerCAmelCase				    =			imread(_UpperCamelCase    , 0      )

			# Test for get_neighbors_pixel function() return not None
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			image[x_coordinate][y_coordinate]

			__lowerCAmelCase				    =			lbp.get_neighbors_pixel(
			    _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			__lowerCAmelCase				    =			np.zeros((image.shape[0], image.shape[1])      )

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0    , image.shape[0]      ):
						for j in range(0    , image.shape[1]      ):
									__lowerCAmelCase				    =			lbp.local_binary_value(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert lbp_image.any()




