





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class       __A     (						a    ):
				__A      =				["""image_processor""", """tokenizer"""]
				__A      =				"""LayoutLMv3ImageProcessor"""
				__A      =				("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")


				def __init__(		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , **UpperCAmelCase_							):
								lowerCamelCase	 =None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
												    """ instead."""   , UpperCAmelCase_   , )
												lowerCamelCase	 =kwargs.pop("""feature_extractor"""							)

								lowerCamelCase	 =image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
												raise ValueError("""You need to specify an `image_processor`."""							)
								if tokenizer is None:
												raise ValueError("""You need to specify a `tokenizer`."""							)

								super().__init__(UpperCAmelCase_   , UpperCAmelCase_							)


				def __call__(		self   , UpperCAmelCase_   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = True   , UpperCAmelCase_ = False   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = 0   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = False   , UpperCAmelCase_ = False   , UpperCAmelCase_ = False   , UpperCAmelCase_ = False   , UpperCAmelCase_ = True   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
												raise ValueError(
												    """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""							)

								if self.image_processor.apply_ocr and (word_labels is not None):
												raise ValueError(
												    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""							)

								# first, apply the image processor
								lowerCamelCase	 =self.image_processor(images=UpperCAmelCase_   , return_tensors=UpperCAmelCase_							)

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
												if isinstance(UpperCAmelCase_   , UpperCAmelCase_							):
																lowerCamelCase	 =[text]  # add batch dimension (as the image processor always adds a batch dimension)
												lowerCamelCase	 =features["""words"""]

								lowerCamelCase	 =self.tokenizer(
								    text=text if text is not None else features["""words"""]   , text_pair=text_pair if text_pair is not None else None   , boxes=boxes if boxes is not None else features["""boxes"""]   , word_labels=UpperCAmelCase_   , add_special_tokens=UpperCAmelCase_   , padding=UpperCAmelCase_   , truncation=UpperCAmelCase_   , max_length=UpperCAmelCase_   , stride=UpperCAmelCase_   , pad_to_multiple_of=UpperCAmelCase_   , return_token_type_ids=UpperCAmelCase_   , return_attention_mask=UpperCAmelCase_   , return_overflowing_tokens=UpperCAmelCase_   , return_special_tokens_mask=UpperCAmelCase_   , return_offsets_mapping=UpperCAmelCase_   , return_length=UpperCAmelCase_   , verbose=UpperCAmelCase_   , return_tensors=UpperCAmelCase_   , **UpperCAmelCase_   , )

								# add pixel values
								lowerCamelCase	 =features.pop("""pixel_values"""							)
								if return_overflowing_tokens is True:
												lowerCamelCase	 =self.get_overflowing_images(UpperCAmelCase_   , encoded_inputs["""overflow_to_sample_mapping"""]							)
								lowerCamelCase	 =images

								return encoded_inputs


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								lowerCamelCase	 =[]
								for sample_idx in overflow_to_sample_mapping:
												images_with_overflow.append(images[sample_idx]							)

								if len(UpperCAmelCase_							) != len(UpperCAmelCase_							):
												raise ValueError(
												    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
												    f""" {len(UpperCAmelCase_							)} and {len(UpperCAmelCase_							)}"""							)

								return images_with_overflow


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								return self.tokenizer.batch_decode(*UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								return self.tokenizer.decode(*UpperCAmelCase_   , **UpperCAmelCase_							)


				@property
				def _snake_case       (		self							):
								return ["input_ids", "bbox", "attention_mask", "pixel_values"]


				@property
				def _snake_case       (		self							):
								warnings.warn(
								    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   , UpperCAmelCase_   , )
								return self.image_processor_class


				@property
				def _snake_case       (		self							):
								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   , UpperCAmelCase_   , )
								return self.image_processor












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__						:			Optional[int]									={
    '''169M''': 12,
    '''430M''': 24,
    '''1B5''': 24,
    '''3B''': 32,
    '''7B''': 32,
    '''14B''': 40,
}

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





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

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

								lowerCamelCase	 =weight
				return state_dict





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=None			,      _UpperCAmelCase=None			,      _UpperCAmelCase=False			,      _UpperCAmelCase=None				)						->  Tuple:
				# 1. If possible, build the tokenizer.
				if tokenizer_file is None:
								print("""No `--tokenizer_file` provided, we will use the default tokenizer."""				)
								lowerCamelCase	 =5_02_77
								lowerCamelCase	 =AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""				)
				else:
								lowerCamelCase	 =PreTrainedTokenizerFast(tokenizer_file=_UpperCAmelCase				)
								lowerCamelCase	 =len(_UpperCAmelCase				)
				tokenizer.save_pretrained(_UpperCAmelCase				)

				# 2. Build the config
				lowerCamelCase	 =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:
																lowerCamelCase	 =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}."""				)

				lowerCamelCase	 =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
				lowerCamelCase	 =hf_hub_download(_UpperCAmelCase			,      _UpperCAmelCase				)
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      map_location="""cpu"""				)
				lowerCamelCase	 =convert_state_dict(_UpperCAmelCase				)

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

				if index is not None:
								lowerCamelCase	 =os.path.join(_UpperCAmelCase			,      _UpperCAmelCase				)
								# Save the index as well
								with open(_UpperCAmelCase			,      """w"""			,      encoding="""utf-8"""				) as f:
												lowerCamelCase	 =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."""				)
								lowerCamelCase	 =list(shards.keys()				)

								del state_dict
								del shards
								gc.collect()

								for shard_file in shard_files:
												lowerCamelCase	 =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."""				)
								lowerCamelCase	 =AutoModelForCausalLM.from_pretrained(_UpperCAmelCase				)
								model.push_to_hub(_UpperCAmelCase			,      max_shard_size="""2GB"""				)
								tokenizer.push_to_hub(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=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__						:			List[Any]									=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,
							)





