





'''simple docstring'''



import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Dict     =    None
	a      :			Optional[int]     =    None


	@property
	def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				return self.feat_extract_tester.prepare_feat_extract_dict()


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							self.feature_extraction_class(**self.feat_extract_dict	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''feature_size'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''sampling_rate'''	)	)
				self.assertTrue(hasattr(_lowerCamelCase       ,'''padding_value'''	)	)


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







				'''simple docstring'''





				__lowercase			  =							self.feat_extract_tester.prepare_inputs_for_common()
				__lowercase			  =							self.feature_extraction_class(**self.feat_extract_dict	)
				__lowercase			  =							feat_extract.model_input_names[0]

				__lowercase			  =							BatchFeature({input_name: speech_inputs}	)

				self.assertTrue(all(len(_lowerCamelCase	) == len(_lowerCamelCase	) for x, y in zip(_lowerCamelCase       ,processed_features[input_name]	)	)	)

				__lowercase			  =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase	)
				__lowercase			  =							BatchFeature({input_name: speech_inputs}       ,tensor_type='''np'''	)

				__lowercase			  =							processed_features[input_name]

				if len(batch_features_input.shape	) < 3:
							__lowercase			  =							batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.feature_size)	)


	@require_torch
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase	)
				__lowercase			  =							self.feature_extraction_class(**self.feat_extract_dict	)
				__lowercase			  =							feat_extract.model_input_names[0]

				__lowercase			  =							BatchFeature({input_name: speech_inputs}       ,tensor_type='''pt'''	)

				__lowercase			  =							processed_features[input_name]

				if len(batch_features_input.shape	) < 3:
							__lowercase			  =							batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.feature_size)	)


	@require_tf
	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCamelCase	)
				__lowercase			  =							self.feature_extraction_class(**self.feat_extract_dict	)
				__lowercase			  =							feat_extract.model_input_names[0]

				__lowercase			  =							BatchFeature({input_name: speech_inputs}       ,tensor_type='''tf'''	)

				__lowercase			  =							processed_features[input_name]

				if len(batch_features_input.shape	) < 3:
							__lowercase			  =							batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.feature_size)	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase=False	)							->							Dict:







				'''simple docstring'''





				def _inputs_have_equal_length(_lowerCamelCase	):
							__lowercase			  =							len(input[0]	)
							for input_slice in input[1:]:
										if len(_lowerCamelCase	) != length:
													return False
							return True

				def _inputs_are_equal(_lowerCamelCase       ,_lowerCamelCase	):
							if len(_lowerCamelCase	) != len(_lowerCamelCase	):
										return False

							for input_slice_a, input_slice_a in zip(_lowerCamelCase       ,_lowerCamelCase	):
										if not np.allclose(np.asarray(_lowerCamelCase	)       ,np.asarray(_lowerCamelCase	)       ,atol=1E-3	):
													return False
							return True

				__lowercase			  =							self.feature_extraction_class(**self.feat_extract_dict	)
				__lowercase			  =							self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase	)
				__lowercase			  =							feat_extract.model_input_names[0]

				__lowercase			  =							BatchFeature({input_name: speech_inputs}	)

				__lowercase			  =							self.feat_extract_tester.seq_length_diff
				__lowercase			  =							self.feat_extract_tester.max_seq_length + pad_diff
				__lowercase			  =							self.feat_extract_tester.min_seq_length
				__lowercase			  =							self.feat_extract_tester.batch_size
				__lowercase			  =							self.feat_extract_tester.feature_size

				# test padding for List[int] + numpy
				__lowercase			  =							feat_extract.pad(_lowerCamelCase       ,padding=_lowerCamelCase	)
				__lowercase			  =							input_a[input_name]

				__lowercase			  =							feat_extract.pad(_lowerCamelCase       ,padding='''longest'''	)
				__lowercase			  =							input_a[input_name]

				__lowercase			  =							feat_extract.pad(_lowerCamelCase       ,padding='''max_length'''       ,max_length=len(speech_inputs[-1]	)	)
				__lowercase			  =							input_a[input_name]

				__lowercase			  =							feat_extract.pad(_lowerCamelCase       ,padding='''longest'''       ,return_tensors='''np'''	)
				__lowercase			  =							input_a[input_name]

				# max_length parameter has to be provided when setting `padding="max_length"`
				with self.assertRaises(_lowerCamelCase	):
							feat_extract.pad(_lowerCamelCase       ,padding='''max_length'''	)[input_name]

				__lowercase			  =							feat_extract.pad(
				    _lowerCamelCase       ,padding='''max_length'''       ,max_length=_lowerCamelCase       ,return_tensors='''np'''	)
				__lowercase			  =							input_a[input_name]

				self.assertFalse(_inputs_have_equal_length(_lowerCamelCase	)	)
				self.assertTrue(_inputs_have_equal_length(_lowerCamelCase	)	)
				self.assertTrue(_inputs_have_equal_length(_lowerCamelCase	)	)
				self.assertTrue(_inputs_are_equal(_lowerCamelCase       ,_lowerCamelCase	)	)
				self.assertTrue(len(input_a[0]	) == pad_min_length	)
				self.assertTrue(len(input_a[1]	) == pad_min_length + pad_diff	)
				self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]	))	)
				self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)	)

				if feature_size > 1:
							self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size	)

				# test padding for `pad_to_multiple_of` for List[int] + numpy
				__lowercase			  =							feat_extract.pad(_lowerCamelCase       ,pad_to_multiple_of=10	)
				__lowercase			  =							input_a[input_name]

				__lowercase			  =							feat_extract.pad(_lowerCamelCase       ,padding='''longest'''       ,pad_to_multiple_of=10	)
				__lowercase			  =							input_a[input_name]

				__lowercase			  =							feat_extract.pad(
				    _lowerCamelCase       ,padding='''max_length'''       ,pad_to_multiple_of=10       ,max_length=_lowerCamelCase	)
				__lowercase			  =							input_a[input_name]

				__lowercase			  =							feat_extract.pad(
				    _lowerCamelCase       ,padding='''max_length'''       ,pad_to_multiple_of=10       ,max_length=_lowerCamelCase       ,return_tensors='''np'''       ,)
				__lowercase			  =							input_a[input_name]

				self.assertTrue(all(len(_lowerCamelCase	) % 10 == 0 for x in input_a	)	)
				self.assertTrue(_inputs_are_equal(_lowerCamelCase       ,_lowerCamelCase	)	)

				__lowercase			  =							pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
				self.assertTrue(all(len(_lowerCamelCase	) == expected_mult_pad_length for x in input_a	)	)
				self.assertEqual(input_a.shape[:2]       ,(batch_size, expected_mult_pad_length)	)

				if feature_size > 1:
							self.assertTrue(input_a.shape[2] == feature_size	)

				# Check padding value is correct
				__lowercase			  =							(np.ones(self.feat_extract_tester.feature_size	) * feat_extract.padding_value).sum()
				self.assertTrue(
				    abs(np.asarray(input_a[0]	)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)	)
				    < 1E-3	)
				self.assertTrue(
				    abs(
				        np.asarray(input_a[1]	)[pad_min_length + pad_diff :].sum()
				        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)	)
				    < 1E-3	)
				self.assertTrue(
				    abs(
				        np.asarray(input_a[2]	)[pad_min_length + 2 * pad_diff :].sum()
				        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)	)
				    < 1E-3	)
				self.assertTrue(
				    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)	) < 1E-3	)
				self.assertTrue(
				    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)	)
				    < 1E-3	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase=False	)							->							Optional[Any]:







				'''simple docstring'''





				def _inputs_have_equal_length(_lowerCamelCase	):
							__lowercase			  =							len(input[0]	)
							for input_slice in input[1:]:
										if len(_lowerCamelCase	) != length:
													return False
							return True

				def _inputs_are_equal(_lowerCamelCase       ,_lowerCamelCase	):
							if len(_lowerCamelCase	) != len(_lowerCamelCase	):
										return False

							for input_slice_a, input_slice_a in zip(_lowerCamelCase       ,_lowerCamelCase	):
										if not np.allclose(np.asarray(_lowerCamelCase	)       ,np.asarray(_lowerCamelCase	)       ,atol=1E-3	):
													return False
							return True

				__lowercase			  =							self.feature_extraction_class(**self.feat_extract_dict	)
				__lowercase			  =							self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCamelCase	)
				__lowercase			  =							feat_extract.model_input_names[0]

				__lowercase			  =							BatchFeature({input_name: speech_inputs}	)

				# truncate to smallest
				__lowercase			  =							feat_extract.pad(
				    _lowerCamelCase       ,padding='''max_length'''       ,max_length=len(speech_inputs[0]	)       ,truncation=_lowerCamelCase	)
				__lowercase			  =							input_a[input_name]

				__lowercase			  =							feat_extract.pad(_lowerCamelCase       ,padding='''max_length'''       ,max_length=len(speech_inputs[0]	)	)
				__lowercase			  =							input_a[input_name]

				self.assertTrue(_inputs_have_equal_length(_lowerCamelCase	)	)
				self.assertFalse(_inputs_have_equal_length(_lowerCamelCase	)	)

				# truncate to smallest with np
				__lowercase			  =							feat_extract.pad(
				    _lowerCamelCase       ,padding='''max_length'''       ,max_length=len(speech_inputs[0]	)       ,return_tensors='''np'''       ,truncation=_lowerCamelCase       ,)
				__lowercase			  =							input_a[input_name]

				__lowercase			  =							feat_extract.pad(
				    _lowerCamelCase       ,padding='''max_length'''       ,max_length=len(speech_inputs[0]	)       ,return_tensors='''np'''	)
				__lowercase			  =							input_a[input_name]

				self.assertTrue(_inputs_have_equal_length(_lowerCamelCase	)	)
				self.assertTrue(input_a.shape[1] == len(speech_inputs[0]	)	)

				# since truncation forces padding to be smaller than longest input
				# function can't return `np.ndarray`, but has to return list
				self.assertFalse(_inputs_have_equal_length(_lowerCamelCase	)	)

				# truncate to middle
				__lowercase			  =							feat_extract.pad(
				    _lowerCamelCase       ,padding='''max_length'''       ,max_length=len(speech_inputs[1]	)       ,truncation=_lowerCamelCase       ,return_tensors='''np'''       ,)
				__lowercase			  =							input_a[input_name]

				__lowercase			  =							feat_extract.pad(
				    _lowerCamelCase       ,padding='''max_length'''       ,max_length=len(speech_inputs[1]	)       ,truncation=_lowerCamelCase	)
				__lowercase			  =							input_a[input_name]

				__lowercase			  =							feat_extract.pad(
				    _lowerCamelCase       ,padding='''max_length'''       ,max_length=len(speech_inputs[1]	)       ,return_tensors='''np'''	)
				__lowercase			  =							input_a[input_name]

				self.assertTrue(input_a.shape[1] == len(speech_inputs[1]	)	)
				self.assertTrue(_inputs_have_equal_length(_lowerCamelCase	)	)
				self.assertTrue(_inputs_have_equal_length(_lowerCamelCase	)	)
				self.assertTrue(_inputs_are_equal(_lowerCamelCase       ,_lowerCamelCase	)	)

				# since truncation forces padding to be smaller than longest input
				# function can't return `np.ndarray`, but has to return list
				self.assertFalse(_inputs_have_equal_length(_lowerCamelCase	)	)
				self.assertTrue(len(input_a[-1]	) == len(speech_inputs[-1]	)	)

				# padding has to be max_length when setting `truncation=True`
				with self.assertRaises(_lowerCamelCase	):
							feat_extract.pad(_lowerCamelCase       ,truncation=_lowerCamelCase	)[input_name]

				# padding has to be max_length when setting `truncation=True`
				with self.assertRaises(_lowerCamelCase	):
							feat_extract.pad(_lowerCamelCase       ,padding='''longest'''       ,truncation=_lowerCamelCase	)[input_name]

				# padding has to be max_length when setting `truncation=True`
				with self.assertRaises(_lowerCamelCase	):
							feat_extract.pad(_lowerCamelCase       ,padding='''longest'''       ,truncation=_lowerCamelCase	)[input_name]

				# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
				with self.assertRaises(_lowerCamelCase	):
							feat_extract.pad(_lowerCamelCase       ,padding='''max_length'''       ,truncation=_lowerCamelCase	)[input_name]

				# test truncation for `pad_to_multiple_of` for List[int] + numpy
				__lowercase			  =							12
				__lowercase			  =							feat_extract.pad(
				    _lowerCamelCase       ,padding='''max_length'''       ,max_length=len(speech_inputs[0]	)       ,pad_to_multiple_of=_lowerCamelCase       ,truncation=_lowerCamelCase       ,)
				__lowercase			  =							input_a[input_name]

				__lowercase			  =							feat_extract.pad(
				    _lowerCamelCase       ,padding='''max_length'''       ,max_length=len(speech_inputs[0]	)       ,pad_to_multiple_of=_lowerCamelCase       ,)
				__lowercase			  =							input_a[input_name]

				# retrieve expected_length as multiple of pad_to_multiple_of
				__lowercase			  =							len(speech_inputs[0]	)
				if expected_length % pad_to_multiple_of != 0:
							__lowercase			  =							((len(speech_inputs[0]	) // pad_to_multiple_of) + 1) * pad_to_multiple_of

				self.assertTrue(len(input_a[0]	) == expected_length	)
				self.assertTrue(_inputs_have_equal_length(_lowerCamelCase	)	)
				self.assertFalse(_inputs_have_equal_length(_lowerCamelCase	)	)


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







				'''simple docstring'''





				self._check_padding(numpify=_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				self._check_padding(numpify=_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							Optional[int]:







				'''simple docstring'''





				self._check_truncation(numpify=_lowerCamelCase	)


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







				'''simple docstring'''





				self._check_truncation(numpify=_lowerCamelCase	)


	@require_torch
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				__lowercase			  =							self.feature_extraction_class(**self.feat_extract_dict	)
				__lowercase			  =							self.feat_extract_tester.prepare_inputs_for_common()
				__lowercase			  =							feat_extract.model_input_names[0]

				__lowercase			  =							BatchFeature({input_name: speech_inputs}	)

				__lowercase			  =							feat_extract.pad(_lowerCamelCase       ,padding='''longest'''       ,return_tensors='''np'''	)[input_name]
				__lowercase			  =							feat_extract.pad(_lowerCamelCase       ,padding='''longest'''       ,return_tensors='''pt'''	)[input_name]

				self.assertTrue(abs(input_np.astype(np.floataa	).sum() - input_pt.numpy().astype(np.floataa	).sum()	) < 1E-2	)


	@require_tf
	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							self.feature_extraction_class(**self.feat_extract_dict	)
				__lowercase			  =							self.feat_extract_tester.prepare_inputs_for_common()
				__lowercase			  =							feat_extract.model_input_names[0]

				__lowercase			  =							BatchFeature({input_name: speech_inputs}	)

				__lowercase			  =							feat_extract.pad(_lowerCamelCase       ,padding='''longest'''       ,return_tensors='''np'''	)[input_name]
				__lowercase			  =							feat_extract.pad(_lowerCamelCase       ,padding='''longest'''       ,return_tensors='''tf'''	)[input_name]

				self.assertTrue(abs(input_np.astype(np.floataa	).sum() - input_tf.numpy().astype(np.floataa	).sum()	) < 1E-2	)


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







				'''simple docstring'''





				__lowercase			  =							self.feat_extract_dict
				__lowercase			  =							True
				__lowercase			  =							self.feature_extraction_class(**_lowerCamelCase	)
				__lowercase			  =							self.feat_extract_tester.prepare_inputs_for_common()
				__lowercase			  =							[len(_lowerCamelCase	) for x in speech_inputs]
				__lowercase			  =							feat_extract.model_input_names[0]

				__lowercase			  =							BatchFeature({input_name: speech_inputs}	)

				__lowercase			  =							feat_extract.pad(_lowerCamelCase       ,padding='''longest'''       ,return_tensors='''np'''	)
				self.assertIn('''attention_mask'''       ,_lowerCamelCase	)
				self.assertListEqual(list(processed.attention_mask.shape	)       ,list(processed[input_name].shape[:2]	)	)
				self.assertListEqual(processed.attention_mask.sum(-1	).tolist()       ,_lowerCamelCase	)


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







				'''simple docstring'''





				__lowercase			  =							self.feat_extract_dict
				__lowercase			  =							True
				__lowercase			  =							self.feature_extraction_class(**_lowerCamelCase	)
				__lowercase			  =							self.feat_extract_tester.prepare_inputs_for_common()
				__lowercase			  =							[len(_lowerCamelCase	) for x in speech_inputs]
				__lowercase			  =							feat_extract.model_input_names[0]

				__lowercase			  =							BatchFeature({input_name: speech_inputs}	)
				__lowercase			  =							min(_lowerCamelCase	)

				__lowercase			  =							feat_extract.pad(
				    _lowerCamelCase       ,padding='''max_length'''       ,max_length=_lowerCamelCase       ,truncation=_lowerCamelCase       ,return_tensors='''np'''	)
				self.assertIn('''attention_mask'''       ,_lowerCamelCase	)
				self.assertListEqual(
				    list(processed_pad.attention_mask.shape	)       ,[processed_pad[input_name].shape[0], max_length]	)
				self.assertListEqual(
				    processed_pad.attention_mask[:, :max_length].sum(-1	).tolist()       ,[max_length for x in speech_inputs]	)












'''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


_SCREAMING_SNAKE_CASE   =      {
    '''169M''': 1_2,
    '''430M''': 2_4,
    '''1B5''': 2_4,
    '''3B''': 3_2,
    '''7B''': 3_2,
    '''14B''': 4_0,
}

_SCREAMING_SNAKE_CASE   =      {
    '''169M''': 7_6_8,
    '''430M''': 1_0_2_4,
    '''1B5''': 2_0_4_8,
    '''3B''': 2_5_6_0,
    '''7B''': 4_0_9_6,
    '''14B''': 5_1_2_0,
}




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

						if name != "head.weight":
									__lowercase			  =							'''rwkv.''' + name

						__lowercase			  =							weight
			return state_dict




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[int]						,    lowerCamelCase_     :					Union[str, Any]						,    lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					List[Any]=None						,    lowerCamelCase_     :					List[Any]=None						,    lowerCamelCase_     :					Any=False						,    lowerCamelCase_     :					int=None			):
			# 1. If possible, build the tokenizer.
			if tokenizer_file is None:
						print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''			)
						__lowercase			  =							5_0_2_7_7
						__lowercase			  =							AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''			)
			else:
						__lowercase			  =							PreTrainedTokenizerFast(tokenizer_file=lowerCamelCase_			)
						__lowercase			  =							len(lowerCamelCase_			)
			tokenizer.save_pretrained(lowerCamelCase_			)

			# 2. Build the config
			__lowercase			  =							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			  =							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			  =							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
			__lowercase			  =							hf_hub_download(lowerCamelCase_						,    lowerCamelCase_			)
			__lowercase			  =							torch.load(lowerCamelCase_						,    map_location='''cpu'''			)
			__lowercase			  =							convert_state_dict(lowerCamelCase_			)

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

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

						del state_dict
						del shards
						gc.collect()

						for shard_file in shard_files:
									__lowercase			  =							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.'''			)
						__lowercase			  =							AutoModelForCausalLM.from_pretrained(lowerCamelCase_			)
						model.push_to_hub(lowerCamelCase_						,    max_shard_size='''2GB'''			)
						tokenizer.push_to_hub(lowerCamelCase_			)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE   =      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.''',
	)

	_SCREAMING_SNAKE_CASE   =      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,
	)





