



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

lowerCAmelCase__		:					List[str]							={
    '''169M''': 768,
    '''430M''': 1024,
    '''1B5''': 2048,
    '''3B''': 2560,
    '''7B''': 4096,
    '''14B''': 5120,
}






def      __lowercase       (			a__       )    ->			Optional[int]:
							__SCREAMING_SNAKE_CASE									= list(state_dict.keys()       )
							for name in state_dict_keys:
														__SCREAMING_SNAKE_CASE									= state_dict.pop(a_       )
														# 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'							,       a_       )
														# ffn -> feed_forward
														__SCREAMING_SNAKE_CASE									= re.sub(R'blocks\.(\d+)\.ffn'							,       R'blocks.\1.feed_forward'							,       a_       )
														# 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      __lowercase       (			a__							,       a__							,       a__							,       a__=None							,       a__=None							,       a__=False							,       a__=None       )    ->			Any:
							# 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_02_77
														__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'       )
							else:
														__SCREAMING_SNAKE_CASE									= PreTrainedTokenizerFast(tokenizer_file=a_       )
														__SCREAMING_SNAKE_CASE									= len(a_       )
							tokenizer.save_pretrained(a_       )

							# 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=a_							,       num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]							,       hidden_size=HIDEN_SIZE_MAPPING[size]							,       )
							config.save_pretrained(a_       )

							# 3. Download model file then convert state_dict
							__SCREAMING_SNAKE_CASE									= hf_hub_download(a_							,       a_       )
							__SCREAMING_SNAKE_CASE									= torch.load(a_							,       map_location='cpu'       )
							__SCREAMING_SNAKE_CASE									= convert_state_dict(a_       )

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

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

														# 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(a_							,       a_       )       )
																					torch.save({k: v.cpu().clone() for k, v in state_dict.items()}							,       os.path.join(a_							,       a_       )       )

							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(a_       )
														model.push_to_hub(a_							,       max_shard_size='2GB'       )
														tokenizer.push_to_hub(a_       )


if __name__ == "__main__":
			lowerCAmelCase__		:					Optional[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.''',
			)

			lowerCAmelCase__		:					Tuple							=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 itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCAmelCase__		:					Dict							=random.Random()

if is_torch_available():
			import torch






def      __lowercase       (			a__							,       a__=1.0							,       a__=None							,       a__=None       )    ->			Any:
							if rng is None:
														__SCREAMING_SNAKE_CASE									= global_rng

							__SCREAMING_SNAKE_CASE									= []
							for batch_idx in range(shape[0]       ):
														values.append([]       )
														for _ in range(shape[1]       ):
																					values[-1].append(rng.random() * scale       )

							return values

class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A=7				,							_A=400				,							_A=2_000				,							_A=1				,							_A=0.0				,							_A=16_000				,							_A=True				,							_A=True				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= parent
													__SCREAMING_SNAKE_CASE									= batch_size
													__SCREAMING_SNAKE_CASE									= min_seq_length
													__SCREAMING_SNAKE_CASE									= max_seq_length
													__SCREAMING_SNAKE_CASE									= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
													__SCREAMING_SNAKE_CASE									= feature_size
													__SCREAMING_SNAKE_CASE									= padding_value
													__SCREAMING_SNAKE_CASE									= sampling_rate
													__SCREAMING_SNAKE_CASE									= return_attention_mask
													__SCREAMING_SNAKE_CASE									= do_normalize






						def 	_A     (   self		):







													'''simple docstring'''

													return {
													    "feature_size": self.feature_size,
													    "padding_value": self.padding_value,
													    "sampling_rate": self.sampling_rate,
													    "return_attention_mask": self.return_attention_mask,
													    "do_normalize": self.do_normalize,
													}






						def 	_A     (   self				,							_A=False				,							_A=False		):







													'''simple docstring'''

													def _flatten(_A		):
																				return list(itertools.chain(*_A		)		)

													if equal_length:
																				__SCREAMING_SNAKE_CASE									= floats_list((self.batch_size, self.max_seq_length)		)
													else:
																				# make sure that inputs increase in size
																				__SCREAMING_SNAKE_CASE									= [
																				    _flatten(floats_list((x, self.feature_size)		)		)
																				    for x in range(self.min_seq_length				,							self.max_seq_length				,							self.seq_length_diff		)
																				]

													if numpify:
																				__SCREAMING_SNAKE_CASE									= [np.asarray(_A		) for x in speech_inputs]

													return speech_inputs







@require_torch
@require_torchaudio
class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  ASTFeatureExtractor






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= ASTFeatureExtractionTester(self		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
													# create three inputs of length 800, 1000, and 1200
													__SCREAMING_SNAKE_CASE									= [floats_list((1, x)		)[0] for x in range(800				,							1_400				,							200		)]
													__SCREAMING_SNAKE_CASE									= [np.asarray(_A		) for speech_input in speech_inputs]

													# Test not batched input
													__SCREAMING_SNAKE_CASE									= feat_extract(speech_inputs[0]				,							return_tensors='np'		).input_values
													__SCREAMING_SNAKE_CASE									= feat_extract(np_speech_inputs[0]				,							return_tensors='np'		).input_values
													self.assertTrue(np.allclose(_A				,							_A				,							atol=1e-3		)		)

													# Test batched
													__SCREAMING_SNAKE_CASE									= feat_extract(_A				,							padding=_A				,							return_tensors='np'		).input_values
													__SCREAMING_SNAKE_CASE									= feat_extract(_A				,							padding=_A				,							return_tensors='np'		).input_values
													for enc_seq_a, enc_seq_a in zip(_A				,							_A		):
																				self.assertTrue(np.allclose(_A				,							_A				,							atol=1e-3		)		)

													# Test 2-D numpy arrays are batched.
													__SCREAMING_SNAKE_CASE									= [floats_list((1, x)		)[0] for x in (800, 800, 800)]
													__SCREAMING_SNAKE_CASE									= np.asarray(_A		)
													__SCREAMING_SNAKE_CASE									= feat_extract(_A				,							return_tensors='np'		).input_values
													__SCREAMING_SNAKE_CASE									= feat_extract(_A				,							return_tensors='np'		).input_values
													for enc_seq_a, enc_seq_a in zip(_A				,							_A		):
																				self.assertTrue(np.allclose(_A				,							_A				,							atol=1e-3		)		)






						@require_torch
						def 	_A     (   self		):







													'''simple docstring'''

													import torch

													__SCREAMING_SNAKE_CASE									= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
													__SCREAMING_SNAKE_CASE									= np.random.rand(100		).astype(np.floataa		)
													__SCREAMING_SNAKE_CASE									= np_speech_inputs.tolist()

													for inputs in [py_speech_inputs, np_speech_inputs]:
																				__SCREAMING_SNAKE_CASE									= feature_extractor.pad([{'input_values': inputs}]				,							return_tensors='np'		)
																				self.assertTrue(np_processed.input_values.dtype == np.floataa		)
																				__SCREAMING_SNAKE_CASE									= feature_extractor.pad([{'input_values': inputs}]				,							return_tensors='pt'		)
																				self.assertTrue(pt_processed.input_values.dtype == torch.floataa		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													from datasets import load_dataset

													__SCREAMING_SNAKE_CASE									= load_dataset('hf-internal-testing/librispeech_asr_dummy'				,							'clean'				,							split='validation'		)
													# automatic decoding with librispeech
													__SCREAMING_SNAKE_CASE									= ds.sort('id'		).select(range(_A		)		)[:num_samples]['audio']

													return [x["array"] for x in speech_samples]






						@require_torch
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= torch.tensor(
													    [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
													     -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
													     -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
													     -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]		)
													# fmt: on

													__SCREAMING_SNAKE_CASE									= self._load_datasamples(1		)
													__SCREAMING_SNAKE_CASE									= ASTFeatureExtractor()
													__SCREAMING_SNAKE_CASE									= feature_extractor(_A				,							return_tensors='pt'		).input_values
													self.assertEquals(input_values.shape				,							(1, 1_024, 128)		)
													self.assertTrue(torch.allclose(input_values[0, 0, :30]				,							_A				,							atol=1e-4		)		)






