


'''simple docstring'''


import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

						if is_vision_available():
												from transformers import OneFormerImageProcessor
												from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
												from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
						from PIL import Image






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase="shi-labs/oneformer_demo"   ):
		with open(hf_hub_download(lowerCAmelCase__      , lowerCAmelCase__      , repo_type="dataset"   )      , "r"   ) as f:
				_UpperCAmelCase		:       str								=      json.load(lowerCAmelCase__   )
		_UpperCAmelCase		:       str								=      {}
		_UpperCAmelCase		:       Dict								=      []
		_UpperCAmelCase		:       Optional[Any]								=      []
		for key, info in class_info.items():
				_UpperCAmelCase		:       Union[str, Any]								=      info["name"]
				class_names.append(info["name"]   )
				if info["isthing"]:
						thing_ids.append(int(lowerCAmelCase__   )   )
		_UpperCAmelCase		:       Optional[Any]								=      thing_ids
		_UpperCAmelCase		:       str								=      class_names
		return metadata






class 	lowerCAmelCase__			(			unittest.TestCase	):




			def __init__(		self  :     Any	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     Any=7	,  lowerCamelCase__  :     int=3	,  lowerCamelCase__  :     Dict=30	,  lowerCamelCase__  :     Optional[Any]=4_00	,  lowerCamelCase__  :     Any=None	,  lowerCamelCase__  :     List[Any]=True	,  lowerCamelCase__  :     str=True	,  lowerCamelCase__  :     Optional[Any]=[0.5, 0.5, 0.5]	,  lowerCamelCase__  :     Tuple=[0.5, 0.5, 0.5]	,  lowerCamelCase__  :     List[str]=10	,  lowerCamelCase__  :     List[str]=False	,  lowerCamelCase__  :     Tuple=2_55	,  lowerCamelCase__  :     Tuple="shi-labs/oneformer_demo"	,  lowerCamelCase__  :     Tuple="ade20k_panoptic.json"	,  lowerCamelCase__  :     List[Any]=10	,  )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      parent
					_UpperCAmelCase		:       Optional[int]								=      batch_size
					_UpperCAmelCase		:       str								=      num_channels
					_UpperCAmelCase		:       Optional[Any]								=      min_resolution
					_UpperCAmelCase		:       int								=      max_resolution
					_UpperCAmelCase		:       Optional[Any]								=      do_resize
					_UpperCAmelCase		:       Union[str, Any]								=      {"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
					_UpperCAmelCase		:       Any								=      do_normalize
					_UpperCAmelCase		:       Optional[Any]								=      image_mean
					_UpperCAmelCase		:       Optional[Any]								=      image_std
					_UpperCAmelCase		:       int								=      class_info_file
					_UpperCAmelCase		:       str								=      prepare_metadata(snake_case__	,  snake_case__ )
					_UpperCAmelCase		:       List[str]								=      num_text
					_UpperCAmelCase		:       Tuple								=      repo_path

					# for the post_process_functions
					_UpperCAmelCase		:       Union[str, Any]								=      2
					_UpperCAmelCase		:       int								=      10
					_UpperCAmelCase		:       Dict								=      10
					_UpperCAmelCase		:       Tuple								=      3
					_UpperCAmelCase		:       Optional[Any]								=      4
					_UpperCAmelCase		:       str								=      num_labels
					_UpperCAmelCase		:       List[Any]								=      do_reduce_labels
					_UpperCAmelCase		:       List[str]								=      ignore_index




			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Dict:



					'''simple docstring'''







					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "num_labels": self.num_labels,
					    "do_reduce_labels": self.do_reduce_labels,
					    "ignore_index": self.ignore_index,
					    "class_info_file": self.class_info_file,
					    "metadata": self.metadata,
					    "num_text": self.num_text,
					}




			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     Union[str, Any]=False )   ->List[Any]:



					'''simple docstring'''







					if not batched:
							_UpperCAmelCase		:       Optional[int]								=      image_inputs[0]
							if isinstance(snake_case__	,  Image.Image ):
									_UpperCAmelCase					,    _UpperCAmelCase		:       Tuple								=      image.size
							else:
									_UpperCAmelCase					,    _UpperCAmelCase		:       Dict								=      image.shape[1], image.shape[2]
							if w < h:
									_UpperCAmelCase		:       Union[str, Any]								=      int(self.size["shortest_edge"] * h / w )
									_UpperCAmelCase		:       List[str]								=      self.size["shortest_edge"]
							elif w > h:
									_UpperCAmelCase		:       Tuple								=      self.size["shortest_edge"]
									_UpperCAmelCase		:       str								=      int(self.size["shortest_edge"] * w / h )
							else:
									_UpperCAmelCase		:       Any								=      self.size["shortest_edge"]
									_UpperCAmelCase		:       List[Any]								=      self.size["shortest_edge"]

					else:
							_UpperCAmelCase		:       List[Any]								=      []
							for image in image_inputs:
									_UpperCAmelCase					,    _UpperCAmelCase		:       List[Any]								=      self.get_expected_values([image] )
									expected_values.append((expected_height, expected_width) )
							_UpperCAmelCase		:       str								=      max(snake_case__	,  key=lambda lowerCamelCase__ : item[0] )[0]
							_UpperCAmelCase		:       Optional[Any]								=      max(snake_case__	,  key=lambda lowerCamelCase__ : item[1] )[1]

					return expected_height, expected_width






			def 							lowerCAmelCase__    (		self  :     Tuple )   ->str:



					'''simple docstring'''







					return OneFormerForUniversalSegmentationOutput(
					    # +1 for null class
					    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) )	,  masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) )	,  )






@require_torch
@require_vision
class 	lowerCAmelCase__			(			__a    ,       unittest.TestCase	):
			lowerCAmelCase :	List[str]          =     OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
			# only for test_image_processing_common.test_image_proc_to_json_string
			lowerCAmelCase :	List[str]          =     image_processing_class




			def 							lowerCAmelCase__    (		self  :     int )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      OneFormerImageProcessorTester(self )




			@property
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Optional[Any]:



					'''simple docstring'''







					return self.image_processing_tester.prepare_image_processor_dict()




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(snake_case__	,  "image_mean" ) )
					self.assertTrue(hasattr(snake_case__	,  "image_std" ) )
					self.assertTrue(hasattr(snake_case__	,  "do_normalize" ) )
					self.assertTrue(hasattr(snake_case__	,  "do_resize" ) )
					self.assertTrue(hasattr(snake_case__	,  "size" ) )
					self.assertTrue(hasattr(snake_case__	,  "ignore_index" ) )
					self.assertTrue(hasattr(snake_case__	,  "class_info_file" ) )
					self.assertTrue(hasattr(snake_case__	,  "num_text" ) )
					self.assertTrue(hasattr(snake_case__	,  "repo_path" ) )
					self.assertTrue(hasattr(snake_case__	,  "metadata" ) )
					self.assertTrue(hasattr(snake_case__	,  "do_reduce_labels" ) )




			def 							lowerCAmelCase__    (		self  :     str )   ->int:



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__    (		self  :     str )   ->int:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					_UpperCAmelCase		:       List[Any]								=      prepare_image_inputs(self.image_processing_tester	,  equal_resolution=snake_case__ )
					for image in image_inputs:
							self.assertIsInstance(snake_case__	,  Image.Image )

					# Test not batched input
					_UpperCAmelCase		:       str								=      image_processor(image_inputs[0]	,  ["semantic"]	,  return_tensors="pt" ).pixel_values

					_UpperCAmelCase					,    _UpperCAmelCase		:       List[Any]								=      self.image_processing_tester.get_expected_values(snake_case__ )

					self.assertEqual(
					    encoded_images.shape	,  (1, self.image_processing_tester.num_channels, expected_height, expected_width)	,  )

					# Test batched
					_UpperCAmelCase					,    _UpperCAmelCase		:       int								=      self.image_processing_tester.get_expected_values(snake_case__	,  batched=snake_case__ )

					_UpperCAmelCase		:       List[str]								=      image_processor(
					    snake_case__	,  ["semantic"] * len(snake_case__ )	,  return_tensors="pt" ).pixel_values
					self.assertEqual(
					    encoded_images.shape	,  (
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,  )




			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					_UpperCAmelCase		:       Tuple								=      prepare_image_inputs(self.image_processing_tester	,  equal_resolution=snake_case__	,  numpify=snake_case__ )
					for image in image_inputs:
							self.assertIsInstance(snake_case__	,  np.ndarray )

					# Test not batched input
					_UpperCAmelCase		:       Optional[Any]								=      image_processor(image_inputs[0]	,  ["semantic"]	,  return_tensors="pt" ).pixel_values

					_UpperCAmelCase					,    _UpperCAmelCase		:       List[str]								=      self.image_processing_tester.get_expected_values(snake_case__ )

					self.assertEqual(
					    encoded_images.shape	,  (1, self.image_processing_tester.num_channels, expected_height, expected_width)	,  )

					# Test batched
					_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[Any]								=      self.image_processing_tester.get_expected_values(snake_case__	,  batched=snake_case__ )

					_UpperCAmelCase		:       str								=      image_processor(
					    snake_case__	,  ["semantic"] * len(snake_case__ )	,  return_tensors="pt" ).pixel_values
					self.assertEqual(
					    encoded_images.shape	,  (
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,  )




			def 							lowerCAmelCase__    (		self  :     Any )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					_UpperCAmelCase		:       Any								=      prepare_image_inputs(self.image_processing_tester	,  equal_resolution=snake_case__	,  torchify=snake_case__ )
					for image in image_inputs:
							self.assertIsInstance(snake_case__	,  torch.Tensor )

					# Test not batched input
					_UpperCAmelCase		:       List[str]								=      image_processor(image_inputs[0]	,  ["semantic"]	,  return_tensors="pt" ).pixel_values

					_UpperCAmelCase					,    _UpperCAmelCase		:       Tuple								=      self.image_processing_tester.get_expected_values(snake_case__ )

					self.assertEqual(
					    encoded_images.shape	,  (1, self.image_processing_tester.num_channels, expected_height, expected_width)	,  )

					# Test batched
					_UpperCAmelCase					,    _UpperCAmelCase		:       Any								=      self.image_processing_tester.get_expected_values(snake_case__	,  batched=snake_case__ )

					_UpperCAmelCase		:       List[Any]								=      image_processor(
					    snake_case__	,  ["semantic"] * len(snake_case__ )	,  return_tensors="pt" ).pixel_values
					self.assertEqual(
					    encoded_images.shape	,  (
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,  )




			def 							lowerCAmelCase__    (		self  :     List[Any]	,  lowerCamelCase__  :     Any=False	,  lowerCamelCase__  :     Dict=False	,  lowerCamelCase__  :     Union[str, Any]="np" )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.image_processing_class(**self.image_processor_dict )
					# prepare image and target
					_UpperCAmelCase		:       Tuple								=      self.image_processing_tester.num_labels
					_UpperCAmelCase		:       Optional[Any]								=      None
					_UpperCAmelCase		:       Optional[int]								=      None
					_UpperCAmelCase		:       int								=      prepare_image_inputs(self.image_processing_tester	,  equal_resolution=snake_case__ )
					if with_segmentation_maps:
							_UpperCAmelCase		:       Optional[Any]								=      num_labels
							if is_instance_map:
									_UpperCAmelCase		:       Optional[int]								=      list(range(snake_case__ ) ) * 2
									_UpperCAmelCase		:       int								=      dict(enumerate(snake_case__ ) )
							_UpperCAmelCase		:       Union[str, Any]								=      [
							    np.random.randint(0	,  high * 2	,  (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
							]
							if segmentation_type == "pil":
									_UpperCAmelCase		:       str								=      [Image.fromarray(snake_case__ ) for annotation in annotations]

					_UpperCAmelCase		:       Optional[Any]								=      image_processor(
					    snake_case__	,  ["semantic"] * len(snake_case__ )	,  snake_case__	,  return_tensors="pt"	,  instance_id_to_semantic_id=snake_case__	,  pad_and_return_pixel_mask=snake_case__	,  )

					return inputs




			def 							lowerCAmelCase__    (		self  :     Dict )   ->List[str]:



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Union[str, Any]:



					'''simple docstring'''







					def common(lowerCamelCase__  :     Optional[int]=False	,  lowerCamelCase__  :     Dict=None ):
							_UpperCAmelCase		:       Union[str, Any]								=      self.comm_get_image_processor_inputs(
							    with_segmentation_maps=snake_case__	,  is_instance_map=snake_case__	,  segmentation_type=snake_case__ )

							_UpperCAmelCase		:       List[Any]								=      inputs["mask_labels"]
							_UpperCAmelCase		:       Optional[Any]								=      inputs["class_labels"]
							_UpperCAmelCase		:       str								=      inputs["pixel_values"]
							_UpperCAmelCase		:       Optional[Any]								=      inputs["text_inputs"]

							# check the batch_size
							for mask_label, class_label, text_input in zip(snake_case__	,  snake_case__	,  snake_case__ ):
									self.assertEqual(mask_label.shape[0]	,  class_label.shape[0] )
									# this ensure padding has happened
									self.assertEqual(mask_label.shape[1:]	,  pixel_values.shape[2:] )
									self.assertEqual(len(snake_case__ )	,  self.image_processing_tester.num_text )

					common()
					common(is_instance_map=snake_case__ )
					common(is_instance_map=snake_case__	,  segmentation_type="pil" )
					common(is_instance_map=snake_case__	,  segmentation_type="pil" )




			def 							lowerCAmelCase__    (		self  :     List[str] )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      np.zeros((20, 50) )
					_UpperCAmelCase		:       Dict								=      1
					_UpperCAmelCase		:       Any								=      1
					_UpperCAmelCase		:       Union[str, Any]								=      1

					_UpperCAmelCase		:       str								=      binary_mask_to_rle(snake_case__ )
					self.assertEqual(len(snake_case__ )	,  4 )
					self.assertEqual(rle[0]	,  21 )
					self.assertEqual(rle[1]	,  45 )




			def 							lowerCAmelCase__    (		self  :     Dict )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes	,  max_seq_length=77	,  task_seq_length=77	,  class_info_file="ade20k_panoptic.json"	,  num_text=self.image_processing_tester.num_text	,  repo_path="shi-labs/oneformer_demo"	,  )
					_UpperCAmelCase		:       Any								=      self.image_processing_tester.get_fake_oneformer_outputs()

					_UpperCAmelCase		:       Tuple								=      fature_extractor.post_process_semantic_segmentation(snake_case__ )

					self.assertEqual(len(snake_case__ )	,  self.image_processing_tester.batch_size )
					self.assertEqual(
					    segmentation[0].shape	,  (
					        self.image_processing_tester.height,
					        self.image_processing_tester.width,
					    )	,  )

					_UpperCAmelCase		:       str								=      [(1, 4) for i in range(self.image_processing_tester.batch_size )]
					_UpperCAmelCase		:       Any								=      fature_extractor.post_process_semantic_segmentation(snake_case__	,  target_sizes=snake_case__ )

					self.assertEqual(segmentation[0].shape	,  target_sizes[0] )




			def 							lowerCAmelCase__    (		self  :     Any )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes	,  max_seq_length=77	,  task_seq_length=77	,  class_info_file="ade20k_panoptic.json"	,  num_text=self.image_processing_tester.num_text	,  repo_path="shi-labs/oneformer_demo"	,  )
					_UpperCAmelCase		:       Tuple								=      self.image_processing_tester.get_fake_oneformer_outputs()
					_UpperCAmelCase		:       Optional[int]								=      image_processor.post_process_instance_segmentation(snake_case__	,  threshold=0 )

					self.assertTrue(len(snake_case__ ) == self.image_processing_tester.batch_size )
					for el in segmentation:
							self.assertTrue("segmentation" in el )
							self.assertTrue("segments_info" in el )
							self.assertEqual(type(el["segments_info"] )	,  snake_case__ )
							self.assertEqual(
							    el["segmentation"].shape	,  (self.image_processing_tester.height, self.image_processing_tester.width) )






			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes	,  max_seq_length=77	,  task_seq_length=77	,  class_info_file="ade20k_panoptic.json"	,  num_text=self.image_processing_tester.num_text	,  repo_path="shi-labs/oneformer_demo"	,  )
					_UpperCAmelCase		:       str								=      self.image_processing_tester.get_fake_oneformer_outputs()
					_UpperCAmelCase		:       Any								=      image_processor.post_process_panoptic_segmentation(snake_case__	,  threshold=0 )

					self.assertTrue(len(snake_case__ ) == self.image_processing_tester.batch_size )
					for el in segmentation:
							self.assertTrue("segmentation" in el )
							self.assertTrue("segments_info" in el )
							self.assertEqual(type(el["segments_info"] )	,  snake_case__ )
							self.assertEqual(
							    el["segmentation"].shape	,  (self.image_processing_tester.height, self.image_processing_tester.width) )







'''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


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

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






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

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

				_UpperCAmelCase		:       Optional[Any]								=      weight
		return state_dict






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase=None      , __lowerCAmelCase=None      , __lowerCAmelCase=False      , __lowerCAmelCase=None   ):
		# 1. If possible, build the tokenizer.
		if tokenizer_file is None:
				print("No `--tokenizer_file` provided, we will use the default tokenizer."   )
				_UpperCAmelCase		:       str								=      50_277
				_UpperCAmelCase		:       Optional[Any]								=      AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"   )
		else:
				_UpperCAmelCase		:       Tuple								=      PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase   )
				_UpperCAmelCase		:       List[Any]								=      len(__lowerCAmelCase   )
		tokenizer.save_pretrained(__lowerCAmelCase   )

		# 2. Build the config
		_UpperCAmelCase		:       Optional[int]								=      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:
								_UpperCAmelCase		:       Optional[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}."""   )

		_UpperCAmelCase		:       Any								=      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
		_UpperCAmelCase		:       str								=      hf_hub_download(__lowerCAmelCase      , __lowerCAmelCase   )
		_UpperCAmelCase		:       Optional[int]								=      torch.load(__lowerCAmelCase      , map_location="cpu"   )
		_UpperCAmelCase		:       Any								=      convert_state_dict(__lowerCAmelCase   )

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

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

				del state_dict
				del shards
				gc.collect()

				for shard_file in shard_files:
						_UpperCAmelCase		:       Union[str, Any]								=      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."   )
				_UpperCAmelCase		:       Optional[Any]								=      AutoModelForCausalLM.from_pretrained(__lowerCAmelCase   )
				model.push_to_hub(__lowerCAmelCase      , max_shard_size="2GB"   )
				tokenizer.push_to_hub(__lowerCAmelCase   )


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,
						)



