



'''simple docstring'''






import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
						import faiss

@require_faiss
class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	def      A	(						self			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Dict 							=		tempfile.mkdtemp()
								UpperCAmelCase :       List[Any] 							=		8

								# DPR tok
								UpperCAmelCase :       Optional[int] 							=		[
								    '''[UNK]''',
								    '''[CLS]''',
								    '''[SEP]''',
								    '''[PAD]''',
								    '''[MASK]''',
								    '''want''',
								    '''##want''',
								    '''##ed''',
								    '''wa''',
								    '''un''',
								    '''runn''',
								    '''##ing''',
								    ''',''',
								    '''low''',
								    '''lowest''',
								]
								UpperCAmelCase :       Optional[int] 							=		os.path.join(self.tmpdirname		,			'''dpr_tokenizer'''		)
								os.makedirs(__snake_case		,			exist_ok=__snake_case		)
								UpperCAmelCase :       List[Any] 							=		os.path.join(__snake_case		,			DPR_VOCAB_FILES_NAMES['''vocab_file''']		)
								with open(self.vocab_file		,			'''w'''		,			encoding='''utf-8'''		) as vocab_writer:
															vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]		)		)

								# BART tok
								UpperCAmelCase :       Optional[Any] 							=		[
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''\u0120''',
								    '''\u0120l''',
								    '''\u0120n''',
								    '''\u0120lo''',
								    '''\u0120low''',
								    '''er''',
								    '''\u0120lowest''',
								    '''\u0120newer''',
								    '''\u0120wider''',
								    '''<unk>''',
								]
								UpperCAmelCase :       Any 							=		dict(zip(__snake_case		,			range(len(__snake_case		)		)		)		)
								UpperCAmelCase :       List[str] 							=		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
								UpperCAmelCase :       str 							=		{'''unk_token''': '''<unk>'''}

								UpperCAmelCase :       List[str] 							=		os.path.join(self.tmpdirname		,			'''bart_tokenizer'''		)
								os.makedirs(__snake_case		,			exist_ok=__snake_case		)
								UpperCAmelCase :       Tuple 							=		os.path.join(__snake_case		,			BART_VOCAB_FILES_NAMES['''vocab_file''']		)
								UpperCAmelCase :       Optional[Any] 							=		os.path.join(__snake_case		,			BART_VOCAB_FILES_NAMES['''merges_file''']		)
								with open(self.vocab_file		,			'''w'''		,			encoding='''utf-8'''		) as fp:
															fp.write(json.dumps(__snake_case		) + '''\n'''		)
								with open(self.merges_file		,			'''w'''		,			encoding='''utf-8'''		) as fp:
															fp.write('''\n'''.join(__snake_case		)		)







	def      A	(						self			:     str		)			->		DPRQuestionEncoderTokenizer:
								return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,			'''dpr_tokenizer'''		)		)







	def      A	(						self			:     List[Any]		)			->		DPRContextEncoderTokenizer:
								return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,			'''dpr_tokenizer'''		)		)







	def      A	(						self			:     Dict		)			->		BartTokenizer:
								return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,			'''bart_tokenizer'''		)		)







	def      A	(						self			:     List[str]		)			->		Dict:
								shutil.rmtree(self.tmpdirname		)







	def      A	(						self			:     List[str]		)			->		List[str]:
								UpperCAmelCase :       Union[str, Any] 							=		Dataset.from_dict(
								    {
								        '''id''': ['''0''', '''1'''],
								        '''text''': ['''foo''', '''bar'''],
								        '''title''': ['''Foo''', '''Bar'''],
								        '''embeddings''': [np.ones(self.retrieval_vector_size		), 2 * np.ones(self.retrieval_vector_size		)],
								    }		)
								dataset.add_faiss_index('''embeddings'''		,			string_factory='''Flat'''		,			metric_type=faiss.METRIC_INNER_PRODUCT		)
								return dataset







	def      A	(						self			:     Tuple		)			->		Union[str, Any]:
								UpperCAmelCase :       str 							=		self.get_dummy_dataset()
								UpperCAmelCase :       Optional[int] 							=		RagConfig(
								    retrieval_vector_size=self.retrieval_vector_size		,			question_encoder=DPRConfig().to_dict()		,			generator=BartConfig().to_dict()		,			)
								with patch('''transformers.models.rag.retrieval_rag.load_dataset'''		) as mock_load_dataset:
															UpperCAmelCase :       List[str] 							=		dataset
															UpperCAmelCase :       str 							=		RagRetriever(
															    __snake_case		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			)
								return retriever







	def      A	(						self			:     List[Any]		,			__snake_case			:     bool		)			->		List[str]:
								UpperCAmelCase :       Optional[int] 							=		self.get_dummy_dataset()
								UpperCAmelCase :       Dict 							=		RagConfig(
								    retrieval_vector_size=self.retrieval_vector_size		,			question_encoder=DPRConfig().to_dict()		,			generator=BartConfig().to_dict()		,			index_name='''custom'''		,			)
								if from_disk:
															UpperCAmelCase :       int 							=		os.path.join(self.tmpdirname		,			'''dataset'''		)
															UpperCAmelCase :       Optional[Any] 							=		os.path.join(self.tmpdirname		,			'''index.faiss'''		)
															dataset.get_index('''embeddings'''		).save(os.path.join(self.tmpdirname		,			'''index.faiss'''		)		)
															dataset.drop_index('''embeddings'''		)
															dataset.save_to_disk(os.path.join(self.tmpdirname		,			'''dataset'''		)		)
															del dataset
															UpperCAmelCase :       str 							=		RagRetriever(
															    __snake_case		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			)
								else:
															UpperCAmelCase :       Union[str, Any] 							=		RagRetriever(
															    __snake_case		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			index=CustomHFIndex(config.retrieval_vector_size		,			__snake_case		)		,			)
								return retriever







	def      A	(						self			:     Optional[Any]		)			->		Optional[Any]:
								UpperCAmelCase :       Optional[int] 							=		Dataset.from_dict(
								    {
								        '''id''': ['''0''', '''1'''],
								        '''text''': ['''foo''', '''bar'''],
								        '''title''': ['''Foo''', '''Bar'''],
								        '''embeddings''': [np.ones(self.retrieval_vector_size + 1		), 2 * np.ones(self.retrieval_vector_size + 1		)],
								    }		)
								dataset.add_faiss_index('''embeddings'''		,			string_factory='''Flat'''		,			metric_type=faiss.METRIC_INNER_PRODUCT		)

								UpperCAmelCase :       Dict 							=		os.path.join(self.tmpdirname		,			'''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''		)
								dataset.save_faiss_index('''embeddings'''		,			index_file_name + '''.index.dpr'''		)
								pickle.dump(dataset['''id''']		,			open(index_file_name + '''.index_meta.dpr'''		,			'''wb'''		)		)

								UpperCAmelCase :       List[Any] 							=		os.path.join(self.tmpdirname		,			'''psgs_w100.tsv.pkl'''		)
								UpperCAmelCase :       int 							=		{sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
								pickle.dump(__snake_case		,			open(__snake_case		,			'''wb'''		)		)

								UpperCAmelCase :       List[str] 							=		RagConfig(
								    retrieval_vector_size=self.retrieval_vector_size		,			question_encoder=DPRConfig().to_dict()		,			generator=BartConfig().to_dict()		,			index_name='''legacy'''		,			index_path=self.tmpdirname		,			)
								UpperCAmelCase :       Dict 							=		RagRetriever(
								    __snake_case		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		)
								return retriever







	def      A	(						self			:     Dict		)			->		Optional[Any]:
								UpperCAmelCase :       List[Any] 							=		1
								UpperCAmelCase :       Optional[int] 							=		self.get_dummy_canonical_hf_index_retriever()
								UpperCAmelCase :       Union[str, Any] 							=		np.array(
								    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Any 							=		retriever.retrieve(__snake_case		,			n_docs=__snake_case		)
								self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
								self.assertEqual(len(__snake_case		)		,			2		)
								self.assertEqual(sorted(doc_dicts[0]		)		,			['''embeddings''', '''id''', '''text''', '''title''']		)
								self.assertEqual(len(doc_dicts[0]['''id''']		)		,			__snake_case		)
								self.assertEqual(doc_dicts[0]['''id'''][0]		,			'''1'''		)  # max inner product is reached with second doc
								self.assertEqual(doc_dicts[1]['''id'''][0]		,			'''0'''		)  # max inner product is reached with first doc
								self.assertListEqual(doc_ids.tolist()		,			[[1], [0]]		)







	def      A	(						self			:     Any		)			->		List[str]:
								UpperCAmelCase :       List[Any] 							=		self.get_dummy_canonical_hf_index_retriever()
								with tempfile.TemporaryDirectory() as tmp_dirname:
															with patch('''transformers.models.rag.retrieval_rag.load_dataset'''		) as mock_load_dataset:
																						UpperCAmelCase :       List[Any] 							=		self.get_dummy_dataset()
																						retriever.save_pretrained(__snake_case		)
																						UpperCAmelCase :       Tuple 							=		RagRetriever.from_pretrained(__snake_case		)
																						self.assertIsInstance(__snake_case		,			__snake_case		)
																						UpperCAmelCase :       List[Any] 							=		np.array(
																						    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
																						UpperCAmelCase :       Optional[int] 							=		retriever.retrieve(__snake_case		,			n_docs=1		)
																						self.assertTrue(out is not None		)







	def      A	(						self			:     Union[str, Any]		)			->		Any:
								UpperCAmelCase :       Any 							=		1
								UpperCAmelCase :       Optional[Any] 							=		self.get_dummy_custom_hf_index_retriever(from_disk=__snake_case		)
								UpperCAmelCase :       str 							=		np.array(
								    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		retriever.retrieve(__snake_case		,			n_docs=__snake_case		)
								self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
								self.assertEqual(len(__snake_case		)		,			2		)
								self.assertEqual(sorted(doc_dicts[0]		)		,			['''embeddings''', '''id''', '''text''', '''title''']		)
								self.assertEqual(len(doc_dicts[0]['''id''']		)		,			__snake_case		)
								self.assertEqual(doc_dicts[0]['''id'''][0]		,			'''1'''		)  # max inner product is reached with second doc
								self.assertEqual(doc_dicts[1]['''id'''][0]		,			'''0'''		)  # max inner product is reached with first doc
								self.assertListEqual(doc_ids.tolist()		,			[[1], [0]]		)







	def      A	(						self			:     Tuple		)			->		Any:
								UpperCAmelCase :       Dict 							=		self.get_dummy_custom_hf_index_retriever(from_disk=__snake_case		)
								with tempfile.TemporaryDirectory() as tmp_dirname:
															retriever.save_pretrained(__snake_case		)
															UpperCAmelCase :       Dict 							=		RagRetriever.from_pretrained(__snake_case		)
															self.assertIsInstance(__snake_case		,			__snake_case		)
															UpperCAmelCase :       Tuple 							=		np.array(
															    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
															UpperCAmelCase :       List[Any] 							=		retriever.retrieve(__snake_case		,			n_docs=1		)
															self.assertTrue(out is not None		)







	def      A	(						self			:     Any		)			->		Optional[Any]:
								UpperCAmelCase :       Optional[int] 							=		1
								UpperCAmelCase :       int 							=		self.get_dummy_custom_hf_index_retriever(from_disk=__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		np.array(
								    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       int 							=		retriever.retrieve(__snake_case		,			n_docs=__snake_case		)
								self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
								self.assertEqual(len(__snake_case		)		,			2		)
								self.assertEqual(sorted(doc_dicts[0]		)		,			['''embeddings''', '''id''', '''text''', '''title''']		)
								self.assertEqual(len(doc_dicts[0]['''id''']		)		,			__snake_case		)
								self.assertEqual(doc_dicts[0]['''id'''][0]		,			'''1'''		)  # max inner product is reached with second doc
								self.assertEqual(doc_dicts[1]['''id'''][0]		,			'''0'''		)  # max inner product is reached with first doc
								self.assertListEqual(doc_ids.tolist()		,			[[1], [0]]		)







	def      A	(						self			:     List[Any]		)			->		Optional[Any]:
								UpperCAmelCase :       Dict 							=		self.get_dummy_custom_hf_index_retriever(from_disk=__snake_case		)
								with tempfile.TemporaryDirectory() as tmp_dirname:
															retriever.save_pretrained(__snake_case		)
															UpperCAmelCase :       Optional[Any] 							=		RagRetriever.from_pretrained(__snake_case		)
															self.assertIsInstance(__snake_case		,			__snake_case		)
															UpperCAmelCase :       Union[str, Any] 							=		np.array(
															    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
															UpperCAmelCase :       List[Any] 							=		retriever.retrieve(__snake_case		,			n_docs=1		)
															self.assertTrue(out is not None		)







	def      A	(						self			:     Optional[Any]		)			->		List[Any]:
								UpperCAmelCase :       List[str] 							=		1
								UpperCAmelCase :       Union[str, Any] 							=		self.get_dummy_legacy_index_retriever()
								UpperCAmelCase :       Optional[Any] 							=		np.array(
								    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		retriever.retrieve(__snake_case		,			n_docs=__snake_case		)
								self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
								self.assertEqual(len(__snake_case		)		,			2		)
								self.assertEqual(sorted(doc_dicts[0]		)		,			['''text''', '''title''']		)
								self.assertEqual(len(doc_dicts[0]['''text''']		)		,			__snake_case		)
								self.assertEqual(doc_dicts[0]['''text'''][0]		,			'''bar'''		)  # max inner product is reached with second doc
								self.assertEqual(doc_dicts[1]['''text'''][0]		,			'''foo'''		)  # max inner product is reached with first doc
								self.assertListEqual(doc_ids.tolist()		,			[[1], [0]]		)







	def      A	(						self			:     List[str]		)			->		List[str]:
								UpperCAmelCase :       int 							=		self.get_dummy_legacy_index_retriever()
								with tempfile.TemporaryDirectory() as tmp_dirname:
															retriever.save_pretrained(__snake_case		)
															UpperCAmelCase :       int 							=		RagRetriever.from_pretrained(__snake_case		)
															self.assertIsInstance(__snake_case		,			__snake_case		)
															UpperCAmelCase :       int 							=		np.array(
															    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
															UpperCAmelCase :       int 							=		retriever.retrieve(__snake_case		,			n_docs=1		)
															self.assertTrue(out is not None		)







	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def      A	(						self			:     int		)			->		Tuple:
								import torch

								UpperCAmelCase :       List[str] 							=		1
								UpperCAmelCase :       List[str] 							=		self.get_dummy_canonical_hf_index_retriever()
								UpperCAmelCase :       str 							=		[[5, 7], [10, 11]]
								UpperCAmelCase :       List[Any] 							=		np.array(
								    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
								UpperCAmelCase :       Optional[int] 							=		retriever(__snake_case		,			__snake_case		,			prefix=retriever.config.generator.prefix		,			n_docs=__snake_case		)
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       List[Any] 							=		(
								    out['''context_input_ids'''],
								    out['''context_attention_mask'''],
								    out['''retrieved_doc_embeds'''],
								)
								self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
								self.assertIsInstance(__snake_case		,			__snake_case		)
								self.assertIsInstance(__snake_case		,			__snake_case		)
								self.assertIsInstance(__snake_case		,			np.ndarray		)

								UpperCAmelCase :       Tuple 							=		retriever(
								    __snake_case		,			__snake_case		,			prefix=retriever.config.generator.prefix		,			n_docs=__snake_case		,			return_tensors='''pt'''		,			)
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[int] 							=		(  # noqa: F841
								    out['''context_input_ids'''],
								    out['''context_attention_mask'''],
								    out['''retrieved_doc_embeds'''],
								    out['''doc_ids'''],
								)
								self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
								self.assertIsInstance(__snake_case		,			torch.Tensor		)
								self.assertIsInstance(__snake_case		,			torch.Tensor		)
								self.assertIsInstance(__snake_case		,			torch.Tensor		)







	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def      A	(						self			:     Optional[int]		)			->		Optional[Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.get_dpr_ctx_encoder_tokenizer()
								UpperCAmelCase :       List[Any] 							=		1
								UpperCAmelCase :       Optional[int] 							=		self.get_dummy_custom_hf_index_retriever(from_disk=__snake_case		)
								retriever.set_ctx_encoder_tokenizer(__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		[[5, 7], [10, 11]]
								UpperCAmelCase :       Optional[int] 							=		np.array(
								    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
								UpperCAmelCase :       Optional[int] 							=		retriever(__snake_case		,			__snake_case		,			prefix=retriever.config.generator.prefix		,			n_docs=__snake_case		)

								self.assertEqual(
								    len(__snake_case		)		,			6		)  # check whether the retriever output consist of 6 attributes including tokenized docs
								self.assertEqual(
								    all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')		)		,			__snake_case		)  # check for doc token related keys in dictionary.










'''simple docstring'''






import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
						from transformers import LlamaTokenizerFast
except ImportError as e:
						warnings.warn(e)
						warnings.warn(
						    "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
						)
						UpperCamelCase__:   str								=			None
UpperCamelCase__:   int								=			{
    "7B": 11008,
    "13B": 13824,
    "30B": 17920,
    "65B": 22016,
    "70B": 28672,
}
UpperCamelCase__:   List[Any]								=			{
    "7B": 1,
    "7Bf": 1,
    "13B": 2,
    "13Bf": 2,
    "30B": 4,
    "65B": 8,
    "70B": 8,
    "70Bf": 8,
}


def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Tuple=1   ,      _lowerCAmelCase  :						List[Any]=256  )      ->   Optional[Any]:
							return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3  )  ) + multiple_of - 1) // multiple_of)


def  snake_case_     (					_lowerCAmelCase  :						List[str]  )      ->   str:
							with open(_lowerCAmelCase   ,      '''r'''  ) as f:
														return json.load(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						Any  )      ->   Optional[Any]:
							with open(_lowerCAmelCase   ,      '''w'''  ) as f:
														json.dump(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Any=True  )      ->   List[Any]:
							os.makedirs(_lowerCAmelCase   ,      exist_ok=_lowerCAmelCase  )
							UpperCAmelCase :       int 							=		os.path.join(_lowerCAmelCase   ,      '''tmp'''  )
							os.makedirs(_lowerCAmelCase   ,      exist_ok=_lowerCAmelCase  )

							UpperCAmelCase :       List[str] 							=		read_json(os.path.join(_lowerCAmelCase   ,      '''params.json'''  )  )
							UpperCAmelCase :       str 							=		NUM_SHARDS[model_size]
							UpperCAmelCase :       Any 							=		params['''n_layers''']
							UpperCAmelCase :       str 							=		params['''n_heads''']
							UpperCAmelCase :       Any 							=		n_heads // num_shards
							UpperCAmelCase :       List[str] 							=		params['''dim''']
							UpperCAmelCase :       Optional[Any] 							=		dim // n_heads
							UpperCAmelCase :       str 							=		1_0_0_0_0.0
							UpperCAmelCase :       Optional[int] 							=		1.0 / (base ** (torch.arange(0   ,      _lowerCAmelCase   ,      2  ).float() / dims_per_head))

							if "n_kv_heads" in params:
														UpperCAmelCase :       Tuple 							=		params['''n_kv_heads''']  # for GQA / MQA
														UpperCAmelCase :       Optional[int] 							=		n_heads_per_shard // num_key_value_heads
														UpperCAmelCase :       Optional[Any] 							=		dim // num_key_value_heads
							else:  # compatibility with other checkpoints
														UpperCAmelCase :       List[str] 							=		n_heads
														UpperCAmelCase :       Optional[int] 							=		n_heads_per_shard
														UpperCAmelCase :       List[str] 							=		dim

							# permute for sliced rotary
							def permute(_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Optional[Any]=n_heads   ,      _lowerCAmelCase  :						int=dim   ,      _lowerCAmelCase  :						Dict=dim  ):
														return w.view(_lowerCAmelCase   ,      dima // n_heads // 2   ,      2   ,      _lowerCAmelCase  ).transpose(1   ,      2  ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )

							print(f"""Fetching all parameters from the checkpoint at {input_base_path}."""  )
							# Load weights
							if model_size == "7B":
														# Not sharded
														# (The sharded implementation would also work, but this is simpler.)
														UpperCAmelCase :       int 							=		torch.load(os.path.join(_lowerCAmelCase   ,      '''consolidated.00.pth'''  )   ,      map_location='''cpu'''  )
							else:
														# Sharded
														UpperCAmelCase :       Optional[Any] 							=		[
														    torch.load(os.path.join(_lowerCAmelCase   ,      f"""consolidated.{i:02d}.pth"""  )   ,      map_location='''cpu'''  )
														    for i in range(_lowerCAmelCase  )
														]
							UpperCAmelCase :       Any 							=		0
							UpperCAmelCase :       str 							=		{'''weight_map''': {}}
							for layer_i in range(_lowerCAmelCase  ):
														UpperCAmelCase :       Optional[Any] 							=		f"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"""
														if model_size == "7B":
																					# Unsharded
																					UpperCAmelCase :       Optional[int] 							=		{
																					    f"""model.layers.{layer_i}.self_attn.q_proj.weight""": permute(
																					        loaded[f"""layers.{layer_i}.attention.wq.weight"""]  ),
																					    f"""model.layers.{layer_i}.self_attn.k_proj.weight""": permute(
																					        loaded[f"""layers.{layer_i}.attention.wk.weight"""]  ),
																					    f"""model.layers.{layer_i}.self_attn.v_proj.weight""": loaded[f"""layers.{layer_i}.attention.wv.weight"""],
																					    f"""model.layers.{layer_i}.self_attn.o_proj.weight""": loaded[f"""layers.{layer_i}.attention.wo.weight"""],
																					    f"""model.layers.{layer_i}.mlp.gate_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w1.weight"""],
																					    f"""model.layers.{layer_i}.mlp.down_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w2.weight"""],
																					    f"""model.layers.{layer_i}.mlp.up_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w3.weight"""],
																					    f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[f"""layers.{layer_i}.attention_norm.weight"""],
																					    f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[f"""layers.{layer_i}.ffn_norm.weight"""],
																					}
														else:
																					# Sharded
																					# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
																					# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
																					# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

																					UpperCAmelCase :       List[str] 							=		{
																					    f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[0][
																					        f"""layers.{layer_i}.attention_norm.weight"""
																					    ].clone(),
																					    f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[0][
																					        f"""layers.{layer_i}.ffn_norm.weight"""
																					    ].clone(),
																					}
																					UpperCAmelCase :       Union[str, Any] 							=		permute(
																					    torch.cat(
																					        [
																					            loaded[i][f"""layers.{layer_i}.attention.wq.weight"""].view(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					            for i in range(_lowerCAmelCase  )
																					        ]   ,      dim=0   ,      ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )  )
																					UpperCAmelCase :       Optional[Any] 							=		permute(
																					    torch.cat(
																					        [
																					            loaded[i][f"""layers.{layer_i}.attention.wk.weight"""].view(
																					                _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					            for i in range(_lowerCAmelCase  )
																					        ]   ,      dim=0   ,      ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      )
																					UpperCAmelCase :       str 							=		torch.cat(
																					    [
																					        loaded[i][f"""layers.{layer_i}.attention.wv.weight"""].view(
																					            _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					        for i in range(_lowerCAmelCase  )
																					    ]   ,      dim=0   ,      ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )

																					UpperCAmelCase :       Optional[int] 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.attention.wo.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=1  )
																					UpperCAmelCase :       Any 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=0  )
																					UpperCAmelCase :       str 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=1  )
																					UpperCAmelCase :       Tuple 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=0  )

														UpperCAmelCase :       Any 							=		inv_freq
														for k, v in state_dict.items():
																					UpperCAmelCase :       List[Any] 							=		filename
																					param_count += v.numel()
														torch.save(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )  )

							UpperCAmelCase :       Optional[int] 							=		f"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"""
							if model_size == "7B":
														# Unsharded
														UpperCAmelCase :       str 							=		{
														    '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
														    '''model.norm.weight''': loaded['''norm.weight'''],
														    '''lm_head.weight''': loaded['''output.weight'''],
														}
							else:
														UpperCAmelCase :       Any 							=		{
														    '''model.norm.weight''': loaded[0]['''norm.weight'''],
														    '''model.embed_tokens.weight''': torch.cat(
														        [loaded[i]['''tok_embeddings.weight'''] for i in range(_lowerCAmelCase  )]   ,      dim=1  ),
														    '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(_lowerCAmelCase  )]   ,      dim=0  ),
														}

							for k, v in state_dict.items():
														UpperCAmelCase :       Optional[int] 							=		filename
														param_count += v.numel()
							torch.save(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )  )

							# Write configs
							UpperCAmelCase :       Union[str, Any] 							=		{'''total_size''': param_count * 2}
							write_json(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      '''pytorch_model.bin.index.json'''  )  )
							UpperCAmelCase :       int 							=		params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
							UpperCAmelCase :       Tuple 							=		params['''multiple_of'''] if '''multiple_of''' in params else 256
							UpperCAmelCase :       Any 							=		LlamaConfig(
							    hidden_size=_lowerCAmelCase   ,      intermediate_size=compute_intermediate_size(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )   ,      num_attention_heads=params['''n_heads''']   ,      num_hidden_layers=params['''n_layers''']   ,      rms_norm_eps=params['''norm_eps''']   ,      num_key_value_heads=_lowerCAmelCase   ,      )
							config.save_pretrained(_lowerCAmelCase  )

							# Make space so we can load the model properly now.
							del state_dict
							del loaded
							gc.collect()

							print('''Loading the checkpoint in a Llama model.'''  )
							UpperCAmelCase :       Optional[int] 							=		LlamaForCausalLM.from_pretrained(_lowerCAmelCase   ,      torch_dtype=torch.floataa   ,      low_cpu_mem_usage=_lowerCAmelCase  )
							# Avoid saving this as part of the config.
							del model.config._name_or_path

							print('''Saving in the Transformers format.'''  )
							model.save_pretrained(_lowerCAmelCase   ,      safe_serialization=_lowerCAmelCase  )
							shutil.rmtree(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Union[str, Any]  )      ->   List[str]:
							# Initialize the tokenizer based on the `spm` model
							UpperCAmelCase :       Dict 							=		LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
							print(f"""Saving a {tokenizer_class.__name__} to {tokenizer_path}."""  )
							UpperCAmelCase :       List[Any] 							=		tokenizer_class(_lowerCAmelCase  )
							tokenizer.save_pretrained(_lowerCAmelCase  )


def  snake_case_     (					)      ->   List[Any]:
							UpperCAmelCase :       int 							=		argparse.ArgumentParser()
							parser.add_argument(
							    '''--input_dir'''   ,      help='''Location of LLaMA weights, which contains tokenizer.model and model folders'''   ,      )
							parser.add_argument(
							    '''--model_size'''   ,      choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only''']   ,      )
							parser.add_argument(
							    '''--output_dir'''   ,      help='''Location to write HF model and tokenizer'''   ,      )
							parser.add_argument('''--safe_serialization'''   ,      type=_lowerCAmelCase   ,      help='''Whether or not to save using `safetensors`.'''  )
							UpperCAmelCase :       List[Any] 							=		parser.parse_args()
							if args.model_size != "tokenizer_only":
														write_model(
														    model_path=args.output_dir   ,      input_base_path=os.path.join(args.input_dir   ,      args.model_size  )   ,      model_size=args.model_size   ,      safe_serialization=args.safe_serialization   ,      )
							UpperCAmelCase :       Optional[int] 							=		os.path.join(args.input_dir   ,      '''tokenizer.model'''  )
							write_tokenizer(args.output_dir   ,      _lowerCAmelCase  )


if __name__ == "__main__":
						main()





