





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  A							(			snake_case__   :						List[str] ,				snake_case__   :						List[Any]=False				)					->				int:
 '''simple docstring'''






 try:
  __snake_case         =			os.environ[key]
 except KeyError:
  # KEY isn't set, default to `default`.
  __snake_case         =			default
 else:
  # KEY is set, convert it to True or False.
  try:
   __snake_case         =			strtobool(lowerCamelCase_				)
  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


UpperCAmelCase__   : Any         =       parse_flag_from_env("RUN_SLOW", default=False)
UpperCAmelCase__   : str         =       parse_flag_from_env("RUN_REMOTE", default=False)
UpperCAmelCase__   : Union[str, Any]         =       parse_flag_from_env("RUN_LOCAL", default=True)
UpperCAmelCase__   : List[str]         =       parse_flag_from_env("RUN_PACKAGED", default=True)

# Compression
UpperCAmelCase__   : Any         =       pytest.mark.skipif(not config.LZ4_AVAILABLE, reason="test requires lz4")
UpperCAmelCase__   : Tuple         =       pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason="test requires py7zr")
UpperCAmelCase__   : str         =       pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason="test requires zstandard")

# Audio
UpperCAmelCase__   : str         =       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
UpperCAmelCase__   : Dict         =       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
UpperCAmelCase__   : Tuple         =       pytest.mark.skipif(
    config.DILL_VERSION <= version.parse("0.3.2"),
    reason="test requires dill>0.3.2 for cloudpickle compatibility",
)

# Windows
UpperCAmelCase__   : int         =       pytest.mark.skipif(
    sys.platform == "win32",
    reason="test should not be run on Windows",
)


def  A							(			snake_case__   :						Optional[Any]				)					->				str:
 '''simple docstring'''






 try:
  import faiss  # noqa
 except ImportError:
  __snake_case         =			unittest.skip('test requires faiss'				)(lowerCamelCase_				)
 return test_case


def  A							(			snake_case__   :						Any				)					->				List[Any]:
 '''simple docstring'''






 try:
  import regex  # noqa
 except ImportError:
  __snake_case         =			unittest.skip('test requires regex'				)(lowerCamelCase_				)
 return test_case


def  A							(			snake_case__   :						int				)					->				Optional[int]:
 '''simple docstring'''






 try:
  import elasticsearch  # noqa
 except ImportError:
  __snake_case         =			unittest.skip('test requires elasticsearch'				)(lowerCamelCase_				)
 return test_case


def  A							(			snake_case__   :						Any				)					->				Union[str, Any]:
 '''simple docstring'''






 try:
  import sqlalchemy  # noqa
 except ImportError:
  __snake_case         =			unittest.skip('test requires sqlalchemy'				)(lowerCamelCase_				)
 return test_case


def  A							(			snake_case__   :						List[Any]				)					->				str:
 '''simple docstring'''






 if not config.TORCH_AVAILABLE:
  __snake_case         =			unittest.skip('test requires PyTorch'				)(lowerCamelCase_				)
 return test_case


def  A							(			snake_case__   :						Optional[Any]				)					->				Optional[int]:
 '''simple docstring'''






 if not config.TF_AVAILABLE:
  __snake_case         =			unittest.skip('test requires TensorFlow'				)(lowerCamelCase_				)
 return test_case


def  A							(			snake_case__   :						List[str]				)					->				List[Any]:
 '''simple docstring'''






 if not config.JAX_AVAILABLE:
  __snake_case         =			unittest.skip('test requires JAX'				)(lowerCamelCase_				)
 return test_case


def  A							(			snake_case__   :						Any				)					->				Tuple:
 '''simple docstring'''






 if not config.PIL_AVAILABLE:
  __snake_case         =			unittest.skip('test requires Pillow'				)(lowerCamelCase_				)
 return test_case


def  A							(			snake_case__   :						Union[str, Any]				)					->				Any:
 '''simple docstring'''






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


def  A							(			snake_case__   :						Dict				)					->				List[str]:
 '''simple docstring'''






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


def  A							(			snake_case__   :						List[str]				)					->				List[str]:
 '''simple docstring'''






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


def  A							(			snake_case__   :						str				)					->				Dict:
 '''simple docstring'''






 def _require_spacy_model(snake_case__   :						Dict				):
  try:
   import spacy  # noqa F401

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

 return _require_spacy_model


def  A							(			snake_case__   :						Dict				)					->				Dict:
 '''simple docstring'''






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


def  A							(			snake_case__   :						Optional[Any]				)					->				int:
 '''simple docstring'''






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


def  A							(			snake_case__   :						Optional[Any]				)					->				Optional[int]:
 '''simple docstring'''






 if not _run_slow_tests or _run_slow_tests == 0:
  __snake_case         =			unittest.skip('test is slow'				)(lowerCamelCase_				)
 return test_case


def  A							(			snake_case__   :						Any				)					->				Tuple:
 '''simple docstring'''






 if not _run_local_tests or _run_local_tests == 0:
  __snake_case         =			unittest.skip('test is local'				)(lowerCamelCase_				)
 return test_case


def  A							(			snake_case__   :						Dict				)					->				Dict:
 '''simple docstring'''






 if not _run_packaged_tests or _run_packaged_tests == 0:
  __snake_case         =			unittest.skip('test is packaged'				)(lowerCamelCase_				)
 return test_case


def  A							(			snake_case__   :						Optional[int]				)					->				Tuple:
 '''simple docstring'''






 if not _run_remote_tests or _run_remote_tests == 0:
  __snake_case         =			unittest.skip('test requires remote'				)(lowerCamelCase_				)
 return test_case


def  A							(			*snake_case__   :						Union[str, Any]				)					->				str:
 '''simple docstring'''






 def decorate(cls   :						int				):
  for name, fn in cls.__dict__.items():
   if callable(lowerCamelCase_				) and name.startswith('test'				):
    for decorator in decorators:
     __snake_case         =			decorator(lowerCamelCase_				)
    setattr(cls ,				lowerCamelCase_ ,				lowerCamelCase_				)
  return cls

 return decorate






class 							__lowercase			(     SCREAMING_SNAKE_CASE__       ):
      pass






class 							__lowercase			(     SCREAMING_SNAKE_CASE__       ):
      __UpperCAmelCase		      =						0
      __UpperCAmelCase		      =						1
      __UpperCAmelCase		      =						2






@contextmanager
def  A							(			snake_case__   :						int=OfflineSimulationMode.CONNECTION_FAILS ,				snake_case__   :						Dict=1e-16				)					->				Optional[Any]:
 '''simple docstring'''
 __snake_case         =			requests.Session().request

 def timeout_request(snake_case__   :						Tuple ,				snake_case__   :						List[Any] ,				snake_case__   :						str ,				**snake_case__   :						List[Any]				):
  # Change the url to an invalid url so that the connection hangs
  __snake_case         =			"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         =			timeout
  try:
   return online_request(lowerCamelCase_ ,				lowerCamelCase_ ,				**lowerCamelCase_				)
  except Exception as e:
   # The following changes in the error are just here to make the offline timeout error prettier
   __snake_case         =			url
   __snake_case         =			e.args[0]
   __snake_case         =			(max_retry_error.args[0].replace('10.255.255.1' ,				f"OfflineMock[{url}]"				),)
   __snake_case         =			(max_retry_error,)
   raise

 def raise_connection_error(snake_case__   :						Tuple ,				snake_case__   :						str ,				**snake_case__   :						int				):
  raise requests.ConnectionError('Offline mode is enabled.' ,				request=lowerCamelCase_				)

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


@contextmanager
def  A							(			*snake_case__   :						List[str] ,				**snake_case__   :						Any				)					->				Optional[Any]:
 '''simple docstring'''
 __snake_case         =			str(Path().resolve()				)
 with tempfile.TemporaryDirectory(*lowerCamelCase_ ,				**lowerCamelCase_				) as tmp_dir:
  try:
   os.chdir(lowerCamelCase_				)
   yield
  finally:
   os.chdir(lowerCamelCase_				)


@contextmanager
def  A							(			)					->				List[str]:
 '''simple docstring'''
 import gc

 gc.collect()
 __snake_case         =			pa.total_allocated_bytes()
 yield
 assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."


@contextmanager
def  A							(			)					->				List[Any]:
 '''simple docstring'''
 import gc

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


def  A							(			snake_case__   :						Tuple ,				snake_case__   :						Dict				)					->				Dict:
 '''simple docstring'''
 return deepcopy(lowerCamelCase_				).integers(0 ,				100 ,				10				).tolist() == deepcopy(lowerCamelCase_				).integers(0 ,				100 ,				10				).tolist()


def  A							(			snake_case__   :						List[Any]				)					->				List[Any]:
 '''simple docstring'''
 import decorator
 from requests.exceptions import HTTPError

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

 return decorator.decorator(_wrapper ,				lowerCamelCase_				)






class 							__lowercase			:






      def __init__(		self			,							lowercase_			,							lowercase_			,							lowercase_)     ->       Optional[int]:
       __snake_case         =			returncode
       __snake_case         =			stdout
       __snake_case         =			stderr






async def  A							(			snake_case__   :						str ,				snake_case__   :						Optional[Any]				)					->				int:
 '''simple docstring'''






 while True:
  __snake_case         =			await stream.readline()
  if line:
   callback(lowerCamelCase_				)
  else:
   break


async def  A							(			snake_case__   :						Any ,				snake_case__   :						Dict=None ,				snake_case__   :						Union[str, Any]=None ,				snake_case__   :						int=None ,				snake_case__   :						str=False ,				snake_case__   :						Optional[Any]=False				)					->				Dict:
 '''simple docstring'''






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

 __snake_case         =			await asyncio.create_subprocess_exec(
     cmd[0] ,				*cmd[1:] ,				stdin=lowerCamelCase_ ,				stdout=asyncio.subprocess.PIPE ,				stderr=asyncio.subprocess.PIPE ,				env=lowerCamelCase_ ,				)

 # 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         =			[]
 __snake_case         =			[]

 def tee(snake_case__   :						Union[str, Any] ,				snake_case__   :						str ,				snake_case__   :						Dict ,				snake_case__   :						Union[str, Any]=""				):
  __snake_case         =			line.decode('utf-8'				).rstrip()
  sink.append(lowerCamelCase_				)
  if not quiet:
   print(lowerCamelCase_ ,				lowerCamelCase_ ,				file=lowerCamelCase_				)

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


def  A							(			snake_case__   :						Optional[int] ,				snake_case__   :						Union[str, Any]=None ,				snake_case__   :						Any=None ,				snake_case__   :						List[str]=180 ,				snake_case__   :						Dict=False ,				snake_case__   :						Optional[Any]=True				)					->				str:
 '''simple docstring'''
 __snake_case         =			asyncio.get_event_loop()
 __snake_case         =			loop.run_until_complete(
     _stream_subprocess(lowerCamelCase_ ,				env=lowerCamelCase_ ,				stdin=lowerCamelCase_ ,				timeout=lowerCamelCase_ ,				quiet=lowerCamelCase_ ,				echo=lowerCamelCase_				)				)

 __snake_case         =			" ".join(lowerCamelCase_				)
 if result.returncode > 0:
  __snake_case         =			"\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  A							(			)					->				Dict:
 '''simple docstring'''
 __snake_case         =			os.environ.get('PYTEST_XDIST_WORKER' ,				'gw0'				)
 __snake_case         =			re.sub(r'^gw' ,				'' ,				lowerCamelCase_ ,				0 ,				re.M				)
 return int(lowerCamelCase_				)







def  A							(			)					->				int:
 '''simple docstring'''
 __snake_case         =			2_9500
 __snake_case         =			pytest_xdist_worker_id()
 return port + uniq_delta






"""simple docstring"""







import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
												# expert layer
												snake_case   :			List[str]						    = flax_key_tuple[:-1] + ("weight",)
												snake_case   :			Union[str, Any]						    = torch.permute(lowerCamelCase_     ,		(0, 2, 1) )
						elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_ ):
												# linear layer
												snake_case   :			int						    = flax_key_tuple[:-1] + ("weight",)
												snake_case   :			Dict						    = flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
												snake_case   :			Any						    = flax_key_tuple[:-1] + ("weight",)

						return flax_key_tuple, flax_tensor

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						if "metadata" in layer:
												snake_case   :			Dict						    = layer.split("metadata" )
												snake_case   :			Optional[Any]						    = "".join(split_layer[0] )[:-1]
												snake_case   :			Any						    = [tuple(("metadata" + split_layer[1]).split("/" ) )]
						elif "kvstore" in layer:
												snake_case   :			List[str]						    = layer.split("kvstore" )
												snake_case   :			Tuple						    = "".join(split_layer[0] )[:-1]
												snake_case   :			Union[str, Any]						    = [tuple(("kvstore" + split_layer[1]).split("/" ) )]

						else:
												snake_case   :			List[Any]						    = layer.split("/" )
												snake_case   :			Union[str, Any]						    = "/".join(split_layer[:-1] )
												snake_case   :			int						    = (split_layer[-1],)

						if "kvstore/path" in layer:
												snake_case   :			str						    = f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
						elif "kvstore/driver" in layer:
												snake_case   :			Tuple						    = "file"
						else:
												snake_case   :			int						    = checkpoint_info[layer]

						return curr_real_layer_name, split_layer, content

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Dict ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = rename_keys(lowerCamelCase_ )
						snake_case   :			str						    = {}
						for k, v in current_block.items():
												snake_case   :			List[str]						    = v
						snake_case   :			List[str]						    = new_current_block
						torch.save(lowerCamelCase_     ,		lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Dict     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							str = WEIGHTS_NAME ):
						"""simple docstring"""






						snake_case   :			List[str]						    = convert_file_size_to_int(lowerCamelCase_ )
						snake_case   :			List[Any]						    = []
						snake_case   :			Dict						    = {}
						snake_case   :			str						    = 0
						snake_case   :			List[str]						    = 0

						os.makedirs(lowerCamelCase_     ,		exist_ok=lowerCamelCase_ )
						with gfile.GFile(switch_checkpoint_path + "/checkpoint"     ,		"rb" ) as fp:
												snake_case   :			List[Any]						    = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
												snake_case   :			Union[str, Any]						    = flatten_dict(lowerCamelCase_     ,		sep="/" )

						snake_case   :			Optional[int]						    = {}
						for layer in checkpoint_info.keys():
												snake_case    ,			snake_case    ,			snake_case   :			Union[str, Any]						    = get_key_and_tensorstore_dict(
												    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )
												if curr_real_layer_name in all_layers:
																		snake_case   :			str						    = content
												else:
																		snake_case   :			Any						    = {split_layer[-1]: content}

						for key in all_layers.keys():
												# open tensorstore file
												snake_case   :			Optional[Any]						    = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
												snake_case   :			Tuple						    = torch.tensor(lowerCamelCase_ )
												snake_case   :			str						    = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

												# use the renaming pattern from the small conversion scripts
												snake_case    ,			snake_case   :			Dict						    = rename_base_flax_keys(tuple(key.split("/" ) )     ,		lowerCamelCase_ )
												snake_case   :			Union[str, Any]						    = "/".join(lowerCamelCase_ )

												# If this weight is going to tip up over the maximal size, we split.
												if current_block_size + weight_size > max_shard_size:
																		snake_case   :			str						    = os.path.join(
																		    lowerCamelCase_     ,		weights_name.replace(".bin"     ,		f'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
																		rename_and_save_block(lowerCamelCase_     ,		lowerCamelCase_ )
																		sharded_state_dicts.append(current_block.keys() )
																		del current_block
																		snake_case   :			Any						    = {}
																		snake_case   :			Union[str, Any]						    = 0

												snake_case   :			Any						    = raw_weights.to(getattr(lowerCamelCase_     ,		lowerCamelCase_ ) )
												current_block_size += weight_size
												total_size += weight_size

						# Add the last block
						snake_case   :			List[Any]						    = os.path.join(lowerCamelCase_     ,		weights_name.replace(".bin"     ,		f'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
						rename_and_save_block(lowerCamelCase_     ,		lowerCamelCase_ )
						sharded_state_dicts.append(current_block.keys() )

						# If we only have one shard, we return it
						if len(lowerCamelCase_ ) == 1:
												return {weights_name: sharded_state_dicts[0]}, None

						# Otherwise, let's build the index
						snake_case   :			List[Any]						    = {}
						snake_case   :			Dict						    = {}
						for idx, shard in enumerate(lowerCamelCase_ ):
												snake_case   :			List[Any]						    = weights_name.replace(
												    ".bin"     ,		f'''-{idx+1:05d}-of-{len(lowerCamelCase_ ):05d}.bin''' )  # len(sharded_state_dicts):05d}
												snake_case   :			Tuple						    = os.path.join(lowerCamelCase_     ,		weights_name.replace(".bin"     ,		f'''-{idx+1:05d}-of-???.bin''' ) )
												os.rename(lowerCamelCase_     ,		os.path.join(lowerCamelCase_     ,		lowerCamelCase_ ) )
												snake_case   :			Union[str, Any]						    = shard
												for key in shard:
																		snake_case   :			List[Any]						    = shard_file

    # Add the metadata
						snake_case   :			Optional[int]						    = {"total_size": total_size}
						snake_case   :			List[str]						    = {"metadata": metadata, "weight_map": weight_map}

						with open(os.path.join(lowerCamelCase_     ,		lowerCamelCase_ )     ,		"w"     ,		encoding="utf-8" ) as f:
												snake_case   :			Tuple						    = json.dumps(lowerCamelCase_     ,		indent=2     ,		sort_keys=lowerCamelCase_ ) + "\n"
												f.write(lowerCamelCase_ )

						return metadata, index


if __name__ == "__main__":
			A		=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--switch_t5x_checkpoint_path',
			    default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
			    type=str,
			    required=False,
			    help='Path to a directory containing a folder per layer. Follows the original Google format.',
			)
			parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
			parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
			    type=str,
			    required=False,
			    help='Path to the output pytorch model.',
			)
			A		=						parser.parse_args()
			shard_on_the_fly(
			    args.switch_tax_checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.max_shard_size,
			    args.dtype,
			)

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

						snake_case   :			List[Any]						    = SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
						config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
						snake_case   :			List[Any]						    = SwitchTransformersForConditionalGeneration.from_pretrained(
						    "/home/arthur_huggingface_co/transformers/switch_converted"     ,		device_map="auto" )

						snake_case   :			Dict						    = TaTokenizer.from_pretrained("t5-small" )
						snake_case   :			Tuple						    = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

						snake_case   :			Dict						    = tokenizer(lowerCamelCase_     ,		return_tensors="pt" ).input_ids
						snake_case   :			Optional[int]						    = model.generate(lowerCamelCase_     ,		decoder_start_token_id=0 )
						print(tokenizer.decode(out[0] ) )



