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     UpperCamelCase						(	__lowercase						:			Optional[Any]     ):
		'''simple docstring'''




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

				if name != "head.weight":
						A_		:    Union[str, Any]         =				"""rwkv.""" + name

				A_		:    List[str]         =				weight
		return state_dict




def     UpperCamelCase						(	__lowercase						:			Optional[int]						,__lowercase						:			List[str]						,__lowercase						:			List[str]						,__lowercase						:			List[Any]=None						,__lowercase						:			List[Any]=None						,__lowercase						:			List[Any]=False						,__lowercase						:			str=None     ):
		'''simple docstring'''




		if tokenizer_file is None:
				print('No `--tokenizer_file` provided, we will use the default tokenizer.'     )
				A_		:    Any         =				5_02_77
				A_		:    Dict         =				AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'     )
		else:
				A_		:    List[Any]         =				PreTrainedTokenizerFast(tokenizer_file=lowerCamelCase_     )
				A_		:    Tuple         =				len(lowerCamelCase_     )
		tokenizer.save_pretrained(lowerCamelCase_     )

		# 2. Build the config
		A_		:    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:
								A_		:    Union[str, Any]         =				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}.'''     )

		A_		:    Dict         =				RwkvConfig(
		    vocab_size=lowerCamelCase_						,num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]						,hidden_size=HIDEN_SIZE_MAPPING[size]						,)
		config.save_pretrained(lowerCamelCase_     )

		# 3. Download model file then convert state_dict
		A_		:    List[str]         =				hf_hub_download(lowerCamelCase_						,lowerCamelCase_     )
		A_		:    Any         =				torch.load(lowerCamelCase_						,map_location='cpu'     )
		A_		:    Tuple         =				convert_state_dict(lowerCamelCase_     )

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

		if index is not None:
				A_		:    Union[str, Any]         =				os.path.join(lowerCamelCase_						,lowerCamelCase_     )
				# Save the index as well
				with open(lowerCamelCase_						,'w'						,encoding='utf-8'     ) as f:
						A_		:    Union[str, Any]         =				json.dumps(lowerCamelCase_						,indent=2						,sort_keys=lowerCamelCase_     ) + """\n"""
						f.write(lowerCamelCase_     )

				# 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.'     )
				A_		:    Any         =				list(shards.keys()     )

				del state_dict
				del shards
				gc.collect()

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

		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.'     )
				A_		:    Any         =				AutoModelForCausalLM.from_pretrained(lowerCamelCase_     )
				model.push_to_hub(lowerCamelCase_						,max_shard_size='2GB'     )
				tokenizer.push_to_hub(lowerCamelCase_     )


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,
	)







import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCAmelCase            =		{
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

_UpperCAmelCase            =		logging.get_logger(__name__)





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''mask2former'''
				lowerCamelCase_    = ['''swin''']
				lowerCamelCase_    = {'''hidden_size''': '''hidden_dim'''}
				def __init__(						self		,     lowercase = None		,     lowercase = 2_5_6		,     lowercase = 2_5_6		,     lowercase = 2_5_6		,     lowercase = 1_0_2_4		,     lowercase = "relu"		,     lowercase = 6		,     lowercase = 1_0		,     lowercase = 8		,     lowercase = 0.0		,     lowercase = 2_0_4_8		,     lowercase = False		,     lowercase = False		,     lowercase = 4		,     lowercase = 2_5_5		,     lowercase = 1_0_0		,     lowercase = 0.1		,     lowercase = 2.0		,     lowercase = 5.0		,     lowercase = 5.0		,     lowercase = 1_2_5_4_4		,     lowercase = 3.0		,     lowercase = 0.75		,     lowercase = 0.02		,     lowercase = 1.0		,     lowercase = True		,     lowercase = [4, 8, 1_6, 3_2]		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						if backbone_config is None:
								logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
								A_		:    List[str]         =				CONFIG_MAPPING['swin'](
								    image_size=2_2_4		,     in_channels=3		,     patch_size=4		,     embed_dim=9_6		,     depths=[2, 2, 1_8, 2]		,     num_heads=[3, 6, 1_2, 2_4]		,     window_size=7		,     drop_path_rate=0.3		,     use_absolute_embeddings=lowercase		,     out_features=['stage1', 'stage2', 'stage3', 'stage4']		,     )

						if isinstance(lowercase		,     lowercase ):
								A_		:    str         =				backbone_config.pop('model_type' )
								A_		:    List[str]         =				CONFIG_MAPPING[backbone_model_type]
								A_		:    Tuple         =				config_class.from_dict(lowercase )

						# verify that the backbone is supported
						if backbone_config.model_type not in self.backbones_supported:
								logger.warning_once(
								    F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
								    F'''Supported model types: {','.join(self.backbones_supported )}''' )

						A_		:    List[Any]         =				backbone_config
						A_		:    Optional[Any]         =				feature_size
						A_		:    int         =				mask_feature_size
						A_		:    Tuple         =				hidden_dim
						A_		:    Dict         =				encoder_feedforward_dim
						A_		:    int         =				activation_function
						A_		:    str         =				encoder_layers
						A_		:    Tuple         =				decoder_layers
						A_		:    Tuple         =				num_attention_heads
						A_		:    str         =				dropout
						A_		:    List[str]         =				dim_feedforward
						A_		:    List[str]         =				pre_norm
						A_		:    Tuple         =				enforce_input_projection
						A_		:    Dict         =				common_stride
						A_		:    Union[str, Any]         =				ignore_value
						A_		:    List[Any]         =				num_queries
						A_		:    List[Any]         =				no_object_weight
						A_		:    int         =				class_weight
						A_		:    int         =				mask_weight
						A_		:    Optional[Any]         =				dice_weight
						A_		:    int         =				train_num_points
						A_		:    Optional[int]         =				oversample_ratio
						A_		:    Tuple         =				importance_sample_ratio
						A_		:    Union[str, Any]         =				init_std
						A_		:    List[Any]         =				init_xavier_std
						A_		:    Optional[Any]         =				use_auxiliary_loss
						A_		:    Dict         =				feature_strides
						A_		:    List[Any]         =				output_auxiliary_logits
						A_		:    Optional[int]         =				decoder_layers

						super().__init__(**lowercase )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     **lowercase ):



						"""simple docstring"""





						return cls(
						    backbone_config=lowercase		,     **lowercase		,     )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				copy.deepcopy(self.__dict__ )
						A_		:    Optional[int]         =				self.backbone_config.to_dict()
						A_		:    Dict         =				self.__class__.model_type
						return output






