
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_						:     List[str]	       =							{
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

UpperCamelCase_						:     Union[str, Any]	       =							{
    """169M""": 768,
    """430M""": 1024,
    """1B5""": 2048,
    """3B""": 2560,
    """7B""": 4096,
    """14B""": 5120,
}



def 		UpperCamelCase     (       _UpperCAmelCase  :   Union[str, Any]  )   ->			Optional[Any]:






						'''simple docstring'''

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

												if name != "head.weight":
																		_lowercase       :	List[str]           =   "rwkv." + name

												_lowercase       :	Any           =   weight
						return state_dict



def 		UpperCamelCase     (       _UpperCAmelCase  :   int ,			_UpperCAmelCase  :   Tuple ,			_UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   Tuple=None ,			_UpperCAmelCase  :   Union[str, Any]=None ,			_UpperCAmelCase  :   Any=False ,			_UpperCAmelCase  :   Optional[int]=None  )   ->			Union[str, Any]:






						'''simple docstring'''

						if tokenizer_file is None:
												print("No `--tokenizer_file` provided, we will use the default tokenizer."  )
												_lowercase       :	int           =   5_0277
												_lowercase       :	Tuple           =   AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"  )
						else:
												_lowercase       :	Tuple           =   PreTrainedTokenizerFast(tokenizer_file=_UpperCAmelCase  )
												_lowercase       :	int           =   len(_UpperCAmelCase  )
						tokenizer.save_pretrained(_UpperCAmelCase  )

						# 2. Build the config
						_lowercase       :	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:
																								_lowercase       :	List[str]           =   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}."""  )

						_lowercase       :	Any           =   RwkvConfig(
						    vocab_size=_UpperCAmelCase ,			num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] ,			hidden_size=HIDEN_SIZE_MAPPING[size] ,			)
						config.save_pretrained(_UpperCAmelCase  )

						# 3. Download model file then convert state_dict
						_lowercase       :	Tuple           =   hf_hub_download(_UpperCAmelCase ,			_UpperCAmelCase  )
						_lowercase       :	Optional[int]           =   torch.load(_UpperCAmelCase ,			map_location="cpu"  )
						_lowercase       :	str           =   convert_state_dict(_UpperCAmelCase  )

						# 4. Split in shards and save
						_lowercase      ,    _lowercase       :	Optional[Any]           =   shard_checkpoint(_UpperCAmelCase  )
						for shard_file, shard in shards.items():
												torch.save(_UpperCAmelCase ,			os.path.join(_UpperCAmelCase ,			_UpperCAmelCase  )  )

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

												# 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."  )
												_lowercase       :	List[Any]           =   list(shards.keys()  )

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		_lowercase       :	List[str]           =   torch.load(os.path.join(_UpperCAmelCase ,			_UpperCAmelCase  )  )
																		torch.save({k: v.cpu().clone() for k, v in state_dict.items()} ,			os.path.join(_UpperCAmelCase ,			_UpperCAmelCase  )  )

						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."  )
												_lowercase       :	Any           =   AutoModelForCausalLM.from_pretrained(_UpperCAmelCase  )
												model.push_to_hub(_UpperCAmelCase ,			max_shard_size="2GB"  )
												tokenizer.push_to_hub(_UpperCAmelCase  )


if __name__ == "__main__":
							UpperCamelCase_						:     int	       =							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_						:     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 torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


UpperCamelCase_						:     int	       =							logging.get_logger(__name__)



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   Dict  )   ->			Any:






						'''simple docstring'''

						_lowercase       :	List[Any]           =   nn.functional.normalize(_UpperCAmelCase  )
						_lowercase       :	List[str]           =   nn.functional.normalize(_UpperCAmelCase  )
						return torch.mm(_UpperCAmelCase ,			normalized_text_embeds.t()  )




class   __lowercase						(						__snake_case       ):
	_A       =   CLIPConfig

	_A       =   ["CLIPEncoderLayer"]






	def __init__(self				: List[str]      ,	snake_case				: CLIPConfig    ) ->			Optional[int]:
							super().__init__(snake_case    )

							_lowercase       :	Tuple           =   CLIPVisionModel(config.vision_config    )
							_lowercase       :	Any           =   nn.Linear(config.vision_config.hidden_size      ,	config.projection_dim      ,	bias=snake_case    )

							_lowercase       :	int           =   nn.Parameter(torch.ones(17      ,	config.projection_dim    )      ,	requires_grad=snake_case    )
							_lowercase       :	str           =   nn.Parameter(torch.ones(3      ,	config.projection_dim    )      ,	requires_grad=snake_case    )

							_lowercase       :	Any           =   nn.Parameter(torch.ones(17    )      ,	requires_grad=snake_case    )
							_lowercase       :	Dict           =   nn.Parameter(torch.ones(3    )      ,	requires_grad=snake_case    )






	@torch.no_grad()
	def       _a(self				: int      ,	snake_case				: Tuple      ,	snake_case				: Optional[Any]    ) ->			Union[str, Any]:
							_lowercase       :	str           =   self.vision_model(snake_case    )[1]  # pooled_output
							_lowercase       :	List[Any]           =   self.visual_projection(snake_case    )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							_lowercase       :	str           =   cosine_distance(snake_case      ,	self.special_care_embeds    ).cpu().float().numpy()
							_lowercase       :	Optional[int]           =   cosine_distance(snake_case      ,	self.concept_embeds    ).cpu().float().numpy()

							_lowercase       :	List[str]           =   []
							_lowercase       :	int           =   image_embeds.shape[0]
							for i in range(snake_case    ):
													_lowercase       :	int           =   {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}

													# increase this value to create a stronger `nfsw` filter
													# at the cost of increasing the possibility of filtering benign images
													_lowercase       :	Optional[int]           =   0.0

													for concept_idx in range(len(special_cos_dist[0]    )    ):
																			_lowercase       :	Union[str, Any]           =   special_cos_dist[i][concept_idx]
																			_lowercase       :	Optional[int]           =   self.special_care_embeds_weights[concept_idx].item()
																			_lowercase       :	Optional[int]           =   round(concept_cos - concept_threshold + adjustment      ,	3    )
																			if result_img["special_scores"][concept_idx] > 0:
																									result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]}    )
																									_lowercase       :	Union[str, Any]           =   0.01

													for concept_idx in range(len(cos_dist[0]    )    ):
																			_lowercase       :	Tuple           =   cos_dist[i][concept_idx]
																			_lowercase       :	Union[str, Any]           =   self.concept_embeds_weights[concept_idx].item()
																			_lowercase       :	str           =   round(concept_cos - concept_threshold + adjustment      ,	3    )
																			if result_img["concept_scores"][concept_idx] > 0:
																									result_img["bad_concepts"].append(snake_case    )

													result.append(snake_case    )

							_lowercase       :	Dict           =   [len(res["bad_concepts"]    ) > 0 for res in result]

							return images, has_nsfw_concepts






	@torch.no_grad()
	def       _a(self				: str      ,	snake_case				: torch.FloatTensor      ,	snake_case				: torch.FloatTensor    ) ->			List[str]:
							_lowercase       :	Dict           =   self.vision_model(snake_case    )[1]  # pooled_output
							_lowercase       :	Any           =   self.visual_projection(snake_case    )

							_lowercase       :	Optional[Any]           =   cosine_distance(snake_case      ,	self.special_care_embeds    )
							_lowercase       :	Optional[Any]           =   cosine_distance(snake_case      ,	self.concept_embeds    )

							# increase this value to create a stronger `nsfw` filter
							# at the cost of increasing the possibility of filtering benign images
							_lowercase       :	Optional[int]           =   0.0

							_lowercase       :	Optional[int]           =   special_cos_dist - self.special_care_embeds_weights + adjustment
							# special_scores = special_scores.round(decimals=3)
							_lowercase       :	List[str]           =   torch.any(special_scores > 0      ,	dim=1    )
							_lowercase       :	List[Any]           =   special_care * 0.01
							_lowercase       :	Optional[Any]           =   special_adjustment.unsqueeze(1    ).expand(-1      ,	cos_dist.shape[1]    )

							_lowercase       :	Any           =   (cos_dist - self.concept_embeds_weights) + special_adjustment
							# concept_scores = concept_scores.round(decimals=3)
							_lowercase       :	List[Any]           =   torch.any(concept_scores > 0      ,	dim=1    )

							return images, has_nsfw_concepts




