





import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch

import pyarrow as pa
import pytest
import requests
from packaging import version

from datasets import config


if config.PY_VERSION < version.parse('''3.8'''):
	import importlib_metadata
else:
	import importlib.metadata as importlib_metadata






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=False			)		->						List[str]:






				"""simple docstring"""


				try:
								snake_case_						:   Dict           =  os.environ[key]
				except KeyError:
								# KEY isn't set, default to `default`.
								snake_case_						:   str           =  default
				else:
								# KEY is set, convert it to True or False.
								try:
												snake_case_						:   int           =  strtobool(_UpperCamelCase			)
								except ValueError:
												# More values are supported, but let's keep the message simple.
												raise ValueError(f'''If set, {key} must be yes or no.'''			)
				return _value


lowerCAmelCase_    =    parse_flag_from_env('''RUN_SLOW''', default=False)
lowerCAmelCase_    =    parse_flag_from_env('''RUN_REMOTE''', default=False)
lowerCAmelCase_    =    parse_flag_from_env('''RUN_LOCAL''', default=True)
lowerCAmelCase_    =    parse_flag_from_env('''RUN_PACKAGED''', default=True)

# Compression
lowerCAmelCase_    =    pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='''test requires lz4''')
lowerCAmelCase_    =    pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='''test requires py7zr''')
lowerCAmelCase_    =    pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='''test requires zstandard''')

# Audio
lowerCAmelCase_    =    pytest.mark.skipif(
    # On Windows and OS X, soundfile installs sndfile
    find_spec('''soundfile''') is None or version.parse(importlib_metadata.version('''soundfile''')) < version.parse('''0.12.0'''),
    reason='''test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ''',
)

# Beam
lowerCAmelCase_    =    pytest.mark.skipif(
    not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('''0.3.2'''),
    reason='''test requires apache-beam and a compatible dill version''',
)

# Dill-cloudpickle compatibility
lowerCAmelCase_    =    pytest.mark.skipif(
    config.DILL_VERSION <= version.parse('''0.3.2'''),
    reason='''test requires dill>0.3.2 for cloudpickle compatibility''',
)

# Windows
lowerCAmelCase_    =    pytest.mark.skipif(
    sys.platform == '''win32''',
    reason='''test should not be run on Windows''',
)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


				try:
								import faiss  # noqa
				except ImportError:
								snake_case_						:   int           =  unittest.skip('''test requires faiss'''			)(_UpperCamelCase			)
				return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				try:
								import regex  # noqa
				except ImportError:
								snake_case_						:   Union[str, Any]           =  unittest.skip('''test requires regex'''			)(_UpperCamelCase			)
				return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				try:
								import elasticsearch  # noqa
				except ImportError:
								snake_case_						:   Any           =  unittest.skip('''test requires elasticsearch'''			)(_UpperCamelCase			)
				return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				try:
								import sqlalchemy  # noqa
				except ImportError:
								snake_case_						:   Union[str, Any]           =  unittest.skip('''test requires sqlalchemy'''			)(_UpperCamelCase			)
				return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				if not config.TORCH_AVAILABLE:
								snake_case_						:   Optional[int]           =  unittest.skip('''test requires PyTorch'''			)(_UpperCamelCase			)
				return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				if not config.TF_AVAILABLE:
								snake_case_						:   Dict           =  unittest.skip('''test requires TensorFlow'''			)(_UpperCamelCase			)
				return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				if not config.JAX_AVAILABLE:
								snake_case_						:   List[str]           =  unittest.skip('''test requires JAX'''			)(_UpperCamelCase			)
				return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				if not config.PIL_AVAILABLE:
								snake_case_						:   Union[str, Any]           =  unittest.skip('''test requires Pillow'''			)(_UpperCamelCase			)
				return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				try:
								import transformers  # noqa F401
				except ImportError:
								return unittest.skip('''test requires transformers'''			)(_UpperCamelCase			)
				else:
								return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				try:
								import tiktoken  # noqa F401
				except ImportError:
								return unittest.skip('''test requires tiktoken'''			)(_UpperCamelCase			)
				else:
								return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				try:
								import spacy  # noqa F401
				except ImportError:
								return unittest.skip('''test requires spacy'''			)(_UpperCamelCase			)
				else:
								return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				def _require_spacy_model(_UpperCamelCase			):
								try:
												import spacy  # noqa F401

												spacy.load(_UpperCamelCase			)
								except ImportError:
												return unittest.skip('''test requires spacy'''			)(_UpperCamelCase			)
								except OSError:
												return unittest.skip('''test requires spacy model \'{}\''''.format(_UpperCamelCase			)			)(_UpperCamelCase			)
								else:
												return test_case

				return _require_spacy_model






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				try:
								import pyspark  # noqa F401
				except ImportError:
								return unittest.skip('''test requires pyspark'''			)(_UpperCamelCase			)
				else:
								return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				try:
								import joblibspark  # noqa F401
				except ImportError:
								return unittest.skip('''test requires joblibspark'''			)(_UpperCamelCase			)
				else:
								return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				if not _run_slow_tests or _run_slow_tests == 0:
								snake_case_						:   int           =  unittest.skip('''test is slow'''			)(_UpperCamelCase			)
				return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				if not _run_local_tests or _run_local_tests == 0:
								snake_case_						:   Union[str, Any]           =  unittest.skip('''test is local'''			)(_UpperCamelCase			)
				return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				if not _run_packaged_tests or _run_packaged_tests == 0:
								snake_case_						:   Optional[Any]           =  unittest.skip('''test is packaged'''			)(_UpperCamelCase			)
				return test_case






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				if not _run_remote_tests or _run_remote_tests == 0:
								snake_case_						:   Optional[Any]           =  unittest.skip('''test requires remote'''			)(_UpperCamelCase			)
				return test_case






def 					lowerCamelCase_						(    *_UpperCamelCase			)		->						str:






				"""simple docstring"""


				def decorate(cls			):
								for name, fn in cls.__dict__.items():
												if callable(_UpperCamelCase			) and name.startswith('''test'''			):
																for decorator in decorators:
																				snake_case_						:   List[Any]           =  decorator(_UpperCamelCase			)
																setattr(cls			,    _UpperCamelCase			,    _UpperCamelCase			)
								return cls

				return decorate



class  __lowerCAmelCase	(      _a					):
			pass




class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Tuple	      =				0
			lowerCamelCase_					:					Optional[Any]	      =				1
			lowerCamelCase_					:					List[str]	      =				2






@contextmanager
def 					lowerCamelCase_						(    _UpperCamelCase=OfflineSimulationMode.CONNECTION_FAILS			,    _UpperCamelCase=1E-16			)		->						Any:






				"""simple docstring"""


				snake_case_						:   Tuple           =  requests.Session().request

				def timeout_request(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    **_UpperCamelCase			):
								# Change the url to an invalid url so that the connection hangs
								snake_case_						:   Optional[Any]           =  '''https://10.255.255.1'''
								if kwargs.get('''timeout'''			) is None:
												raise RequestWouldHangIndefinitelyError(
												    f'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.'''			)
								snake_case_						:   Tuple           =  timeout
								try:
												return online_request(_UpperCamelCase			,    _UpperCamelCase			,    **_UpperCamelCase			)
								except Exception as e:
												# The following changes in the error are just here to make the offline timeout error prettier
												snake_case_						:   Union[str, Any]           =  url
												snake_case_						:   Optional[int]           =  e.args[0]
												snake_case_						:   Optional[Any]           =  (max_retry_error.args[0].replace('''10.255.255.1'''			,    f'''OfflineMock[{url}]'''			),)
												snake_case_						:   Any           =  (max_retry_error,)
												raise

				def raise_connection_error(_UpperCamelCase			,    _UpperCamelCase			,    **_UpperCamelCase			):
								raise requests.ConnectionError('''Offline mode is enabled.'''			,    request=_UpperCamelCase			)

				if mode is OfflineSimulationMode.CONNECTION_FAILS:
								with patch('''requests.Session.send'''			,    _UpperCamelCase			):
												yield
				elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
								# inspired from https://stackoverflow.com/a/904609
								with patch('''requests.Session.request'''			,    _UpperCamelCase			):
												yield
				elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
								with patch('''datasets.config.HF_DATASETS_OFFLINE'''			,    _UpperCamelCase			):
												yield
				else:
								raise ValueError('''Please use a value from the OfflineSimulationMode enum.'''			)






@contextmanager
def 					lowerCamelCase_						(    *_UpperCamelCase			,    **_UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				snake_case_						:   List[Any]           =  str(Path().resolve()			)
				with tempfile.TemporaryDirectory(*_UpperCamelCase			,    **_UpperCamelCase			) as tmp_dir:
								try:
												os.chdir(_UpperCamelCase			)
												yield
								finally:
												os.chdir(_UpperCamelCase			)






@contextmanager
def 					lowerCamelCase_						(    )		->						Union[str, Any]:






				"""simple docstring"""


				import gc

				gc.collect()
				snake_case_						:   Optional[Any]           =  pa.total_allocated_bytes()
				yield
				assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."






@contextmanager
def 					lowerCamelCase_						(    )		->						int:






				"""simple docstring"""


				import gc

				gc.collect()
				snake_case_						:   Any           =  pa.total_allocated_bytes()
				yield
				assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				return deepcopy(_UpperCamelCase			).integers(0			,    100			,    10			).tolist() == deepcopy(_UpperCamelCase			).integers(0			,    100			,    10			).tolist()






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				import decorator
				from requests.exceptions import HTTPError

				def _wrapper(_UpperCamelCase			,    *_UpperCamelCase			,    **_UpperCamelCase			):
								try:
												return func(*_UpperCamelCase			,    **_UpperCamelCase			)
								except HTTPError as err:
												if str(_UpperCamelCase			).startswith('''500'''			) or str(_UpperCamelCase			).startswith('''502'''			):
																pytest.xfail(str(_UpperCamelCase			)			)
												raise err

				return decorator.decorator(_wrapper			,    _UpperCamelCase			)



class  __lowerCAmelCase	:






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  returncode
							snake_case_						:   Union[str, Any]           =  stdout
							snake_case_						:   Any           =  stderr






async def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				while True:
								snake_case_						:   List[str]           =  await stream.readline()
								if line:
												callback(_UpperCamelCase			)
								else:
												break






async def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=False			,    _UpperCamelCase=False			)		->						_RunOutput:






				"""simple docstring"""


				if echo:
								print('''\nRunning: '''			,    ''' '''.join(_UpperCamelCase			)			)

				snake_case_						:   Optional[int]           =  await asyncio.create_subprocess_exec(
				    cmd[0]			,    *cmd[1:]			,    stdin=_UpperCamelCase			,    stdout=asyncio.subprocess.PIPE			,    stderr=asyncio.subprocess.PIPE			,    env=_UpperCamelCase			,    )

				# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
				# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
				#
				# If it starts hanging, will need to switch to the following code. The problem is that no data
				# will be seen until it's done and if it hangs for example there will be no debug info.
				# out, err = await p.communicate()
				# return _RunOutput(p.returncode, out, err)

				snake_case_						:   Any           =  []
				snake_case_						:   Optional[Any]           =  []

				def tee(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase=""			):
								snake_case_						:   Tuple           =  line.decode('''utf-8'''			).rstrip()
								sink.append(_UpperCamelCase			)
								if not quiet:
												print(_UpperCamelCase			,    _UpperCamelCase			,    file=_UpperCamelCase			)

    # XXX: the timeout doesn't seem to make any difference here
				await asyncio.wait(
				    [
				        _read_stream(p.stdout			,    lambda _UpperCamelCase			: tee(_UpperCamelCase			,    _UpperCamelCase			,    sys.stdout			,    label='''stdout:'''			)			),
				        _read_stream(p.stderr			,    lambda _UpperCamelCase			: tee(_UpperCamelCase			,    _UpperCamelCase			,    sys.stderr			,    label='''stderr:'''			)			),
				    ]			,    timeout=_UpperCamelCase			,    )
				return _RunOutput(await p.wait()			,    _UpperCamelCase			,    _UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=180			,    _UpperCamelCase=False			,    _UpperCamelCase=True			)		->						_RunOutput:






				"""simple docstring"""


				snake_case_						:   int           =  asyncio.get_event_loop()
				snake_case_						:   Any           =  loop.run_until_complete(
				    _stream_subprocess(_UpperCamelCase			,    env=_UpperCamelCase			,    stdin=_UpperCamelCase			,    timeout=_UpperCamelCase			,    quiet=_UpperCamelCase			,    echo=_UpperCamelCase			)			)

				snake_case_						:   int           =  ''' '''.join(_UpperCamelCase			)
				if result.returncode > 0:
								snake_case_						:   Dict           =  '''\n'''.join(result.stderr			)
								raise RuntimeError(
								    f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
								    f'''The combined stderr from workers follows:\n{stderr}'''			)

				# check that the subprocess actually did run and produced some output, should the test rely on
				# the remote side to do the testing
				if not result.stdout and not result.stderr:
								raise RuntimeError(f'''\'{cmd_str}\' produced no output.'''			)

				return result






def 					lowerCamelCase_						(    )		->						Dict:






				"""simple docstring"""


				snake_case_						:   Optional[int]           =  os.environ.get('''PYTEST_XDIST_WORKER'''			,    '''gw0'''			)
				snake_case_						:   Union[str, Any]           =  re.sub(R'''^gw'''			,    ''''''			,    _UpperCamelCase			,    0			,    re.M			)
				return int(_UpperCamelCase			)






def 					lowerCamelCase_						(    )		->						Tuple:






				"""simple docstring"""


				snake_case_						:   Dict           =  29_500
				snake_case_						:   Optional[int]           =  pytest_xdist_worker_id()
				return port + uniq_delta







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'''
	)
	lowerCAmelCase_    =    None
lowerCAmelCase_    =    {
    '''7B''': 1_1_0_0_8,
    '''13B''': 1_3_8_2_4,
    '''30B''': 1_7_9_2_0,
    '''65B''': 2_2_0_1_6,
    '''70B''': 2_8_6_7_2,
}
lowerCAmelCase_    =    {
    '''7B''': 1,
    '''7Bf''': 1,
    '''13B''': 2,
    '''13Bf''': 2,
    '''30B''': 4,
    '''65B''': 8,
    '''70B''': 8,
    '''70Bf''': 8,
}






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=1			,    _UpperCamelCase=256			)		->						Optional[int]:






				"""simple docstring"""


				return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3			)			) + multiple_of - 1) // multiple_of)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				with open(_UpperCamelCase			,    '''r'''			) as f:
								return json.load(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				with open(_UpperCamelCase			,    '''w'''			) as f:
								json.dump(_UpperCamelCase			,    _UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase=True			)		->						Optional[Any]:






				"""simple docstring"""


				os.makedirs(_UpperCamelCase			,    exist_ok=_UpperCamelCase			)
				snake_case_						:   int           =  os.path.join(_UpperCamelCase			,    '''tmp'''			)
				os.makedirs(_UpperCamelCase			,    exist_ok=_UpperCamelCase			)

				snake_case_						:   Dict           =  read_json(os.path.join(_UpperCamelCase			,    '''params.json'''			)			)
				snake_case_						:   Tuple           =  NUM_SHARDS[model_size]
				snake_case_						:   Optional[Any]           =  params['''n_layers''']
				snake_case_						:   int           =  params['''n_heads''']
				snake_case_						:   Dict           =  n_heads // num_shards
				snake_case_						:   List[Any]           =  params['''dim''']
				snake_case_						:   str           =  dim // n_heads
				snake_case_						:   Any           =  10_000.0
				snake_case_						:   Any           =  1.0 / (base ** (torch.arange(0			,    _UpperCamelCase			,    2			).float() / dims_per_head))

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

				# permute for sliced rotary
				def permute(_UpperCamelCase			,    _UpperCamelCase=n_heads			,    _UpperCamelCase=dim			,    _UpperCamelCase=dim			):
								return w.view(_UpperCamelCase			,    dima // n_heads // 2			,    2			,    _UpperCamelCase			).transpose(1			,    2			).reshape(_UpperCamelCase			,    _UpperCamelCase			)

				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.)
								snake_case_						:   Optional[Any]           =  torch.load(os.path.join(_UpperCamelCase			,    '''consolidated.00.pth'''			)			,    map_location='''cpu'''			)
				else:
								# Sharded
								snake_case_						:   Union[str, Any]           =  [
								    torch.load(os.path.join(_UpperCamelCase			,    f'''consolidated.{i:02d}.pth'''			)			,    map_location='''cpu'''			)
								    for i in range(_UpperCamelCase			)
								]
				snake_case_						:   Optional[Any]           =  0
				snake_case_						:   str           =  {'''weight_map''': {}}
				for layer_i in range(_UpperCamelCase			):
								snake_case_						:   Optional[int]           =  f'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
								if model_size == "7B":
												# Unsharded
												snake_case_						:   str           =  {
												    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.

												snake_case_						:   Union[str, Any]           =  {
												    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(),
												}
												snake_case_						:   int           =  permute(
												    torch.cat(
												        [
												            loaded[i][f'''layers.{layer_i}.attention.wq.weight'''].view(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
												            for i in range(_UpperCamelCase			)
												        ]			,    dim=0			,    ).reshape(_UpperCamelCase			,    _UpperCamelCase			)			)
												snake_case_						:   Optional[int]           =  permute(
												    torch.cat(
												        [
												            loaded[i][f'''layers.{layer_i}.attention.wk.weight'''].view(
												                _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
												            for i in range(_UpperCamelCase			)
												        ]			,    dim=0			,    ).reshape(_UpperCamelCase			,    _UpperCamelCase			)			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    )
												snake_case_						:   int           =  torch.cat(
												    [
												        loaded[i][f'''layers.{layer_i}.attention.wv.weight'''].view(
												            _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
												        for i in range(_UpperCamelCase			)
												    ]			,    dim=0			,    ).reshape(_UpperCamelCase			,    _UpperCamelCase			)

												snake_case_						:   Optional[int]           =  torch.cat(
												    [loaded[i][f'''layers.{layer_i}.attention.wo.weight'''] for i in range(_UpperCamelCase			)]			,    dim=1			)
												snake_case_						:   Dict           =  torch.cat(
												    [loaded[i][f'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(_UpperCamelCase			)]			,    dim=0			)
												snake_case_						:   Union[str, Any]           =  torch.cat(
												    [loaded[i][f'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(_UpperCamelCase			)]			,    dim=1			)
												snake_case_						:   Optional[int]           =  torch.cat(
												    [loaded[i][f'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(_UpperCamelCase			)]			,    dim=0			)

								snake_case_						:   str           =  inv_freq
								for k, v in state_dict.items():
												snake_case_						:   Dict           =  filename
												param_count += v.numel()
								torch.save(_UpperCamelCase			,    os.path.join(_UpperCamelCase			,    _UpperCamelCase			)			)

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

				for k, v in state_dict.items():
								snake_case_						:   List[str]           =  filename
								param_count += v.numel()
				torch.save(_UpperCamelCase			,    os.path.join(_UpperCamelCase			,    _UpperCamelCase			)			)

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

				# 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.'''			)
				snake_case_						:   Union[str, Any]           =  LlamaForCausalLM.from_pretrained(_UpperCamelCase			,    torch_dtype=torch.floataa			,    low_cpu_mem_usage=_UpperCamelCase			)
				# Avoid saving this as part of the config.
				del model.config._name_or_path

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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
				print(f'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''			)
				snake_case_						:   Union[str, Any]           =  tokenizer_class(_UpperCamelCase			)
				tokenizer.save_pretrained(_UpperCamelCase			)






def 					lowerCamelCase_						(    )		->						int:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  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=_UpperCamelCase			,    help='''Whether or not to save using `safetensors`.'''			)
				snake_case_						:   Dict           =  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			,    )
				snake_case_						:   Dict           =  os.path.join(args.input_dir			,    '''tokenizer.model'''			)
				write_tokenizer(args.output_dir			,    _UpperCamelCase			)


if __name__ == "__main__":
	main()
