
'''simple docstring'''







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


_snake_case				:   Dict     =				{
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

_snake_case				:   Tuple     =				{
    """169M""": 768,
    """430M""": 1_024,
    """1B5""": 2_048,
    """3B""": 2_560,
    """7B""": 4_096,
    """14B""": 5_120,
}



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

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

												_SCREAMING_SNAKE_CASE				= weight
						return state_dict



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]	,			_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=None	,			_SCREAMING_SNAKE_CASE		:   List[str]=None	,			_SCREAMING_SNAKE_CASE		:   List[Any]=False	,			_SCREAMING_SNAKE_CASE		:   Dict=None  ):
						# 1. If possible, build the tokenizer.
						if tokenizer_file is None:
												print("No `--tokenizer_file` provided, we will use the default tokenizer."  )
												_SCREAMING_SNAKE_CASE				= 5_0277
												_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"  )
						else:
												_SCREAMING_SNAKE_CASE				= PreTrainedTokenizerFast(tokenizer_file=_SCREAMING_SNAKE_CASE  )
												_SCREAMING_SNAKE_CASE				= len(_SCREAMING_SNAKE_CASE  )
						tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE  )

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

						_SCREAMING_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
						_SCREAMING_SNAKE_CASE				= hf_hub_download(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= torch.load(_SCREAMING_SNAKE_CASE	,			map_location="cpu"  )
						_SCREAMING_SNAKE_CASE				= convert_state_dict(_SCREAMING_SNAKE_CASE  )

						# 4. Split in shards and save
						_SCREAMING_SNAKE_CASE,							_SCREAMING_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:
												_SCREAMING_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:
																		_SCREAMING_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."  )
												_SCREAMING_SNAKE_CASE				= list(shards.keys()  )

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		_SCREAMING_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."  )
												_SCREAMING_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__":
						_snake_case				:   Any     =				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.""",
						)

						_snake_case				:   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,
						)

'''simple docstring'''







from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
						import torch

if is_tf_available():
						import tensorflow as tf


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Optional[int]       =					["""image_processor"""]
		a							:					Tuple       =					"""SamImageProcessor"""




		def __init__(				self					,						UpperCamelCase      ):
								super().__init__(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.image_processor
								_SCREAMING_SNAKE_CASE				= -10
								_SCREAMING_SNAKE_CASE				= self.image_processor.size["longest_edge"]




		def __call__(				self					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= self.image_processor(
								    UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)

								# pop arguments that are not used in the foward but used nevertheless
								_SCREAMING_SNAKE_CASE				= encoding_image_processor["original_sizes"]

								if hasattr(UpperCamelCase					,						"numpy"      ):  # Checks if Torch or TF tensor
														_SCREAMING_SNAKE_CASE				= original_sizes.numpy()

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._check_and_preprocess_points(
								    input_points=UpperCamelCase					,						input_labels=UpperCamelCase					,						input_boxes=UpperCamelCase					,						)

								_SCREAMING_SNAKE_CASE				= self._normalize_and_convert(
								    UpperCamelCase					,						UpperCamelCase					,						input_points=UpperCamelCase					,						input_labels=UpperCamelCase					,						input_boxes=UpperCamelCase					,						return_tensors=UpperCamelCase					,						)

								return encoding_image_processor




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase="pt"					,						):
								if input_points is not None:
														if len(UpperCamelCase      ) != len(UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						original_sizes[0]      ) for point in input_points
																				]
														else:
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						UpperCamelCase      )
																				    for point, original_size in zip(UpperCamelCase					,						UpperCamelCase      )
																				]
														# check that all arrays have the same shape
														if not all(point.shape == input_points[0].shape for point in input_points      ):
																				if input_labels is not None:
																										_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._pad_points_and_labels(UpperCamelCase					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

								if input_labels is not None:
														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

								if input_boxes is not None:
														if len(UpperCamelCase      ) != len(UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						original_sizes[0]					,						is_bounding_box=UpperCamelCase      )
																				    for box in input_boxes
																				]
														else:
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						UpperCamelCase					,						is_bounding_box=UpperCamelCase      )
																				    for box, original_size in zip(UpperCamelCase					,						UpperCamelCase      )
																				]
														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

								if input_boxes is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# boxes batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= input_boxes.unsqueeze(1      ) if len(input_boxes.shape      ) != 3 else input_boxes
														elif return_tensors == "tf":
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(UpperCamelCase      )
																				# boxes batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= tf.expand_dims(UpperCamelCase					,						1      ) if len(input_boxes.shape      ) != 3 else input_boxes
														encoding_image_processor.update({"input_boxes": input_boxes}      )
								if input_points is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= input_points.unsqueeze(1      ) if len(input_points.shape      ) != 4 else input_points
														elif return_tensors == "tf":
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= tf.expand_dims(UpperCamelCase					,						1      ) if len(input_points.shape      ) != 4 else input_points
														encoding_image_processor.update({"input_points": input_points}      )
								if input_labels is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= input_labels.unsqueeze(1      ) if len(input_labels.shape      ) != 3 else input_labels
														elif return_tensors == "tf":
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= tf.expand_dims(UpperCamelCase					,						1      ) if len(input_labels.shape      ) != 3 else input_labels
														encoding_image_processor.update({"input_labels": input_labels}      )

								return encoding_image_processor




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= max([point.shape[0] for point in input_points]      )
								_SCREAMING_SNAKE_CASE				= []
								for i, point in enumerate(UpperCamelCase      ):
														if point.shape[0] != expected_nb_points:
																				_SCREAMING_SNAKE_CASE				= np.concatenate(
																				    [point, np.zeros((expected_nb_points - point.shape[0], 2)      ) + self.point_pad_value]					,						axis=0      )
																				_SCREAMING_SNAKE_CASE				= np.append(input_labels[i]					,						[self.point_pad_value]      )
														processed_input_points.append(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= processed_input_points
								return input_points, input_labels




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=False      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= original_size
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.image_processor._get_preprocess_shape(UpperCamelCase					,						longest_edge=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= deepcopy(UpperCamelCase      ).astype(UpperCamelCase      )

								if is_bounding_box:
														_SCREAMING_SNAKE_CASE				= coords.reshape(-1					,						2					,						2      )

								_SCREAMING_SNAKE_CASE				= coords[..., 0] * (new_w / old_w)
								_SCREAMING_SNAKE_CASE				= coords[..., 1] * (new_h / old_h)

								if is_bounding_box:
														_SCREAMING_SNAKE_CASE				= coords.reshape(-1					,						4      )

								return coords




		def     lowercase       (				self					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						):
								if input_points is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):  # Checks for TF or Torch tensor
																				_SCREAMING_SNAKE_CASE				= input_points.numpy().tolist()

														if not isinstance(UpperCamelCase					,						UpperCamelCase      ) or not isinstance(input_points[0]					,						UpperCamelCase      ):
																				raise ValueError("Input points must be a list of list of floating points."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ) for input_point in input_points]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if input_labels is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):
																				_SCREAMING_SNAKE_CASE				= input_labels.numpy().tolist()

														if not isinstance(UpperCamelCase					,						UpperCamelCase      ) or not isinstance(input_labels[0]					,						UpperCamelCase      ):
																				raise ValueError("Input labels must be a list of list integers."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ) for label in input_labels]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if input_boxes is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):
																				_SCREAMING_SNAKE_CASE				= input_boxes.numpy().tolist()

														if (
														    not isinstance(UpperCamelCase					,						UpperCamelCase      )
														    or not isinstance(input_boxes[0]					,						UpperCamelCase      )
														    or not isinstance(input_boxes[0][0]					,						UpperCamelCase      )
														):
																				raise ValueError("Input boxes must be a list of list of list of floating points."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ).astype(np.floataa      ) for box in input_boxes]
								else:
														_SCREAMING_SNAKE_CASE				= None

								return input_points, input_labels, input_boxes




		@property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processor.model_input_names
								return list(dict.fromkeys(UpperCamelCase      )      )




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.image_processor.post_process_masks(*UpperCamelCase					,						**UpperCamelCase      )