






import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__UpperCAmelCase								=						logging.get_logger(__name__)




def      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							List[str]     ,       __snake_case    :							Optional[Any]     ,       __snake_case    :							Tuple=False  ):


					'''simple docstring'''


					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    'Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'
										    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
										    ' instructions.'  )
										raise

					if not is_sharded:
										UpperCAmelCase_    :      List[str]            =						os.path.abspath(__a  )
										logger.info(F"Loading PyTorch weights from {pt_path}"  )

										UpperCAmelCase_    :      Dict            =						torch.load(__a     ,       map_location='cpu'  )
										logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()  ):,} parameters."  )

										UpperCAmelCase_    :      Dict            =						convert_pytorch_state_dict_to_flax(__a     ,       __a  )
					else:
										# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
										UpperCAmelCase_    :      Optional[Any]            =						convert_pytorch_sharded_state_dict_to_flax(__a     ,       __a  )
					return flax_state_dict




def      lowercase__      (     __snake_case    :							Tuple[str]     ,       __snake_case    :							np.ndarray     ,       __snake_case    :							Dict[str, jnp.ndarray]     ,       __snake_case    :							str     ,       ):


					'''simple docstring'''


					def is_key_or_prefix_key_in_dict(__snake_case    :							Tuple[str]  ) -> bool:
										return len(set(__a  ) & {key, (model_prefix,) + key}  ) > 0

					# layer norm
					UpperCAmelCase_    :      Union[str, Any]            =						pt_tuple_key[:-1] + ('scale',)
					if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__a  ):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer mean
					UpperCAmelCase_    :      Tuple            =						pt_tuple_key[:-1] + ('mean',)
					if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__a  ):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer var
					UpperCAmelCase_    :      str            =						pt_tuple_key[:-1] + ('var',)
					if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__a  ):
										return renamed_pt_tuple_key, pt_tensor

					# embedding
					UpperCAmelCase_    :      List[Any]            =						pt_tuple_key[:-1] + ('embedding',)
					if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__a  ):
										return renamed_pt_tuple_key, pt_tensor

					# conv layer
					UpperCAmelCase_    :      int            =						pt_tuple_key[:-1] + ('kernel',)
					if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__a  ):
										UpperCAmelCase_    :      List[Any]            =						pt_tensor.transpose(2     ,       3     ,       1     ,       0  )
										return renamed_pt_tuple_key, pt_tensor

					# linear layer
					UpperCAmelCase_    :      Dict            =						pt_tuple_key[:-1] + ('kernel',)
					if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__a  ):
										UpperCAmelCase_    :      str            =						pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm weight
					UpperCAmelCase_    :      List[str]            =						pt_tuple_key[:-1] + ('weight',)
					if pt_tuple_key[-1] == "gamma":
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm bias
					UpperCAmelCase_    :      Optional[int]            =						pt_tuple_key[:-1] + ('bias',)
					if pt_tuple_key[-1] == "beta":
										return renamed_pt_tuple_key, pt_tensor

					# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
					UpperCAmelCase_    :      List[Any]            =						None
					if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
										UpperCAmelCase_    :      int            =						pt_tuple_key[-2] + '_g'
					elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
										UpperCAmelCase_    :      Dict            =						pt_tuple_key[-2] + '_v'
					if name is not None:
										UpperCAmelCase_    :      Optional[int]            =						pt_tuple_key[:-3] + (name,)
										return renamed_pt_tuple_key, pt_tensor

					return pt_tuple_key, pt_tensor




def      lowercase__      (     __snake_case    :							Dict     ,       __snake_case    :							Optional[Any]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[str]            =						{k: v.numpy() for k, v in pt_state_dict.items()}

					UpperCAmelCase_    :      str            =						flax_model.base_model_prefix

					# use params dict if the model contains batch norm layers
					if "params" in flax_model.params:
										UpperCAmelCase_    :      str            =						flax_model.params['params']
					else:
										UpperCAmelCase_    :      Optional[Any]            =						flax_model.params
					UpperCAmelCase_    :      int            =						flatten_dict(__a  )

					# add batch_stats keys,values to dict
					if "batch_stats" in flax_model.params:
										UpperCAmelCase_    :      Dict            =						flatten_dict(flax_model.params['batch_stats']  )
										random_flax_state_dict.update(__a  )

					UpperCAmelCase_    :      Tuple            =						{}

					UpperCAmelCase_    :      int            =						(model_prefix not in flax_model_params) and (
					    model_prefix in {k.split('.'  )[0] for k in pt_state_dict.keys()}
					)
					UpperCAmelCase_    :      Union[str, Any]            =						(model_prefix in flax_model_params) and (
					    model_prefix not in {k.split('.'  )[0] for k in pt_state_dict.keys()}
					)

					# Need to change some parameters name to match Flax names
					for pt_key, pt_tensor in pt_state_dict.items():
										UpperCAmelCase_    :      Tuple            =						tuple(pt_key.split('.'  )  )

										# remove base model prefix if necessary
										UpperCAmelCase_    :      List[str]            =						pt_tuple_key[0] == model_prefix
										if load_model_with_head_into_base_model and has_base_model_prefix:
															UpperCAmelCase_    :      int            =						pt_tuple_key[1:]

										# Correctly rename weight parameters
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						rename_key_and_reshape_tensor(
										    __a     ,       __a     ,       __a     ,       __a  )

										# add model prefix if necessary
										UpperCAmelCase_    :      List[str]            =						(model_prefix,) + flax_key in random_flax_state_dict
										if load_base_model_into_model_with_head and require_base_model_prefix:
															UpperCAmelCase_    :      List[str]            =						(model_prefix,) + flax_key

										if flax_key in random_flax_state_dict:
															if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																				raise ValueError(
																				    F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
																				    F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."  )

        # add batch stats if the model contains batchnorm layers
										if "batch_stats" in flax_model.params:
															if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																				UpperCAmelCase_    :      Optional[Any]            =						jnp.asarray(__a  )
																				continue
															# remove num_batches_tracked key
															if "num_batches_tracked" in flax_key[-1]:
																				flax_state_dict.pop(__a     ,       __a  )
																				continue

															# also add unexpected weight so that warning is thrown
															UpperCAmelCase_    :      int            =						jnp.asarray(__a  )

										else:
															# also add unexpected weight so that warning is thrown
															UpperCAmelCase_    :      Any            =						jnp.asarray(__a  )

					return unflatten_dict(__a  )




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							int  ):


					'''simple docstring'''


					import torch

					# Load the index
					UpperCAmelCase_    :      int            =						{}
					for shard_file in shard_filenames:
										# load using msgpack utils
										UpperCAmelCase_    :      Optional[Any]            =						torch.load(__a  )
										UpperCAmelCase_    :      Optional[Any]            =						{k: v.numpy() for k, v in pt_state_dict.items()}

										UpperCAmelCase_    :      Dict            =						flax_model.base_model_prefix

										# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
										if "batch_stats" in flax_model.params:
															UpperCAmelCase_    :      Optional[Any]            =						flax_model.params['params']

															UpperCAmelCase_    :      List[str]            =						flatten_dict(__a  )
															random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']  )  )
										else:
															UpperCAmelCase_    :      Dict            =						flax_model.params
															UpperCAmelCase_    :      Optional[Any]            =						flatten_dict(__a  )

										UpperCAmelCase_    :      Optional[int]            =						(model_prefix not in flax_model_params) and (
										    model_prefix in {k.split('.'  )[0] for k in pt_state_dict.keys()}
										)
										UpperCAmelCase_    :      int            =						(model_prefix in flax_model_params) and (
										    model_prefix not in {k.split('.'  )[0] for k in pt_state_dict.keys()}
										)
										# Need to change some parameters name to match Flax names
										for pt_key, pt_tensor in pt_state_dict.items():
															UpperCAmelCase_    :      Any            =						tuple(pt_key.split('.'  )  )

															# remove base model prefix if necessary
															UpperCAmelCase_    :      Union[str, Any]            =						pt_tuple_key[0] == model_prefix
															if load_model_with_head_into_base_model and has_base_model_prefix:
																				UpperCAmelCase_    :      int            =						pt_tuple_key[1:]

															# Correctly rename weight parameters
															UpperCAmelCase_       ,  UpperCAmelCase_    :      Dict            =						rename_key_and_reshape_tensor(
															    __a     ,       __a     ,       __a     ,       __a  )
															# add model prefix if necessary
															UpperCAmelCase_    :      Any            =						(model_prefix,) + flax_key in random_flax_state_dict
															if load_base_model_into_model_with_head and require_base_model_prefix:
																				UpperCAmelCase_    :      int            =						(model_prefix,) + flax_key

															if flax_key in random_flax_state_dict:
																				if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																									raise ValueError(
																									    F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
																									    F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."  )

            # add batch stats if the model contains batchnorm layers
															if "batch_stats" in flax_model.params:
																				if "mean" in flax_key[-1]:
																									UpperCAmelCase_    :      List[str]            =						jnp.asarray(__a  )
																									continue
																				if "var" in flax_key[-1]:
																									UpperCAmelCase_    :      Union[str, Any]            =						jnp.asarray(__a  )
																									continue
																				# remove num_batches_tracked key
																				if "num_batches_tracked" in flax_key[-1]:
																									flax_state_dict.pop(__a     ,       __a  )
																									continue

																				# also add unexpected weight so that warning is thrown
																				UpperCAmelCase_    :      Optional[Any]            =						jnp.asarray(__a  )

															else:
																				# also add unexpected weight so that warning is thrown
																				UpperCAmelCase_    :      Any            =						jnp.asarray(__a  )
					return unflatten_dict(__a  )




def      lowercase__      (     __snake_case    :							Union[str, Any]     ,       __snake_case    :							Dict  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[int]            =						os.path.abspath(__a  )
					logger.info(F"Loading Flax weights from {flax_checkpoint_path}"  )

					# import correct flax class
					UpperCAmelCase_    :      Optional[Any]            =						getattr(__a     ,       'Flax' + model.__class__.__name__  )

					# load flax weight dict
					with open(__a     ,       'rb'  ) as state_f:
										try:
															UpperCAmelCase_    :      Any            =						from_bytes(__a     ,       state_f.read()  )
										except UnpicklingError:
															raise EnvironmentError(F"Unable to convert {flax_checkpoint_path} to Flax deserializable object. "  )

					return load_flax_weights_in_pytorch_model(__a     ,       __a  )






def      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							Tuple  ):


					'''simple docstring'''


					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    'Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'
										    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
										    ' instructions.'  )
										raise

					# check if we have bf16 weights
					UpperCAmelCase_    :      List[str]            =						flatten_dict(jax.tree_util.tree_map(lambda __snake_case  : x.dtype == jnp.bfloataa     ,       __a  )  ).values()
					if any(__a  ):
										# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
										    'before loading those in PyTorch model.'  )
										UpperCAmelCase_    :      List[Any]            =						jax.tree_util.tree_map(
										    lambda __snake_case  : params.astype(np.floataa  ) if params.dtype == jnp.bfloataa else params     ,       __a  )

					UpperCAmelCase_    :      Optional[int]            =						flatten_dict(__a  )
					UpperCAmelCase_    :      int            =						pt_model.state_dict()

					UpperCAmelCase_    :      Tuple            =						(pt_model.base_model_prefix in flax_state) and (
					    pt_model.base_model_prefix not in {k.split('.'  )[0] for k in pt_model_dict.keys()}
					)
					UpperCAmelCase_    :      Union[str, Any]            =						(pt_model.base_model_prefix not in flax_state) and (
					    pt_model.base_model_prefix in {k.split('.'  )[0] for k in pt_model_dict.keys()}
					)

					# keep track of unexpected & missing keys
					UpperCAmelCase_    :      Tuple            =						[]
					UpperCAmelCase_    :      Dict            =						set(pt_model_dict.keys()  )

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										UpperCAmelCase_    :      List[str]            =						flax_key_tuple[0] == pt_model.base_model_prefix
										UpperCAmelCase_    :      Union[str, Any]            =						'.'.join((pt_model.base_model_prefix,) + flax_key_tuple  ) in pt_model_dict

										# adapt flax_key to prepare for loading from/to base model only
										if load_model_with_head_into_base_model and has_base_model_prefix:
															UpperCAmelCase_    :      str            =						flax_key_tuple[1:]
										elif load_base_model_into_model_with_head and require_base_model_prefix:
															UpperCAmelCase_    :      Optional[int]            =						(pt_model.base_model_prefix,) + flax_key_tuple

										# rename flax weights to PyTorch format
										if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__a  ) not in pt_model_dict:
															# conv layer
															UpperCAmelCase_    :      Tuple            =						flax_key_tuple[:-1] + ('weight',)
															UpperCAmelCase_    :      int            =						jnp.transpose(__a     ,       (3, 2, 0, 1)  )
										elif flax_key_tuple[-1] == "kernel" and ".".join(__a  ) not in pt_model_dict:
															# linear layer
															UpperCAmelCase_    :      Tuple            =						flax_key_tuple[:-1] + ('weight',)
															UpperCAmelCase_    :      Union[str, Any]            =						flax_tensor.T
										elif flax_key_tuple[-1] in ["scale", "embedding"]:
															UpperCAmelCase_    :      int            =						flax_key_tuple[:-1] + ('weight',)

										# adding batch stats from flax batch norm to pt
										elif "mean" in flax_key_tuple[-1]:
															UpperCAmelCase_    :      int            =						flax_key_tuple[:-1] + ('running_mean',)
										elif "var" in flax_key_tuple[-1]:
															UpperCAmelCase_    :      Dict            =						flax_key_tuple[:-1] + ('running_var',)

										if "batch_stats" in flax_state:
															UpperCAmelCase_    :      Tuple            =						'.'.join(flax_key_tuple[1:]  )  # Remove the params/batch_stats header
										else:
															UpperCAmelCase_    :      List[str]            =						'.'.join(__a  )

										# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
										UpperCAmelCase_    :      Optional[Any]            =						{}
										# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
										for key in pt_model_dict:
															UpperCAmelCase_    :      Union[str, Any]            =						key.split('.'  )
															UpperCAmelCase_    :      Tuple            =						None
															if key_components[-3::2] == ["parametrizations", "original0"]:
																				UpperCAmelCase_    :      List[Any]            =						key_components[-2] + '_g'
															elif key_components[-3::2] == ["parametrizations", "original1"]:
																				UpperCAmelCase_    :      Optional[Any]            =						key_components[-2] + '_v'
															if name is not None:
																				UpperCAmelCase_    :      List[str]            =						key_components[:-3] + [name]
																				UpperCAmelCase_    :      List[str]            =						'.'.join(__a  )
																				UpperCAmelCase_    :      Union[str, Any]            =						key

										if flax_key in special_pt_names:
															UpperCAmelCase_    :      Union[str, Any]            =						special_pt_names[flax_key]

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
																				    F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."  )
															else:
																				# add weight to pytorch dict
																				UpperCAmelCase_    :      Optional[int]            =						np.asarray(__a  ) if not isinstance(__a     ,       np.ndarray  ) else flax_tensor
																				UpperCAmelCase_    :      Union[str, Any]            =						torch.from_numpy(__a  )
																				# remove from missing keys
																				missing_keys.remove(__a  )
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(__a  )

					pt_model.load_state_dict(__a  )

					# re-transform missing_keys to list
					UpperCAmelCase_    :      List[str]            =						list(__a  )

					if len(__a  ) > 0:
										logger.warning(
										    'Some weights of the Flax model were not used when initializing the PyTorch model'
										    F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
										    F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
										    ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
										    F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
										    ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
										    ' FlaxBertForSequenceClassification model).'  )
					else:
										logger.warning(F"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"  )
					if len(__a  ) > 0:
										logger.warning(
										    F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
										    F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
										    ' use it for predictions and inference.'  )
					else:
										logger.warning(
										    F"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
										    'If your task is similar to the task the model of the checkpoint was trained on, '
										    F"you can already use {pt_model.__class__.__name__} for predictions without further training."  )

					return pt_model













# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile







def    __lowerCamelCase					(							__a   :List[str]    ,     __a   :List[Any]    ,     __a   :Union[str, Any]    ,     __a   :List[Any]    )		->				Dict:






		"""simple docstring"""

		A__        =   multiprocessing.Manager()
		A__        =   manager.list()

		A__        =   multiprocessing.Process(target=__a    ,     args=(check_program, result, timeout)    )
		p.start()
		p.join(timeout=timeout + 1    )
		if p.is_alive():
				p.kill()

		if not result:
				result.append("""timed out"""    )

		return {
		    "task_id": task_id,
		    "passed": result[0] == "passed",
		    "result": result[0],
		    "completion_id": completion_id,
		}







def    __lowerCamelCase					(							__a   :Optional[Any]    ,     __a   :Any    ,     __a   :List[Any]    )		->				Union[str, Any]:






		"""simple docstring"""

		with create_tempdir():
				# These system calls are needed when cleaning up tempdir.
				import os
				import shutil

				A__        =   shutil.rmtree
				A__        =   os.rmdir
				A__        =   os.chdir

				# Disable functionalities that can make destructive changes to the test.
				reliability_guard()

				# Run program.
				try:
						A__        =   {}
						with swallow_io():
								with time_limit(__a    ):
										exec(__a    ,     __a    )
						result.append("""passed"""    )
				except TimeoutException:
						result.append("""timed out"""    )
				except BaseException as e:
						result.append(F'failed: {e}'    )

				# Needed for cleaning up.
				A__        =   rmtree
				A__        =   rmdir
				A__        =   chdir







@contextlib.contextmanager
def    __lowerCamelCase					(							__a   :List[str]    )		->				Dict:






		"""simple docstring"""

		def signal_handler(__a   :List[Any]    ,     __a   :Optional[Any]    ):
				raise TimeoutException("""Timed out!"""    )

		signal.setitimer(signal.ITIMER_REAL    ,     __a    )
		signal.signal(signal.SIGALRM    ,     __a    )
		try:
				yield
		finally:
				signal.setitimer(signal.ITIMER_REAL    ,     0    )







@contextlib.contextmanager
def    __lowerCamelCase					(							)		->				Union[str, Any]:






		"""simple docstring"""

		A__        =   WriteOnlyStringIO()
		with contextlib.redirect_stdout(__a    ):
				with contextlib.redirect_stderr(__a    ):
						with redirect_stdin(__a    ):
								yield







@contextlib.contextmanager
def    __lowerCamelCase					(							)		->				Dict:






		"""simple docstring"""

		with tempfile.TemporaryDirectory() as dirname:
				with chdir(__a    ):
						yield dirname


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			pass


class   A			(io.StringIO    ):
			'''simple docstring'''




			def    a_       (			self   :    Any							,	*__lowerCAmelCase   :    List[str]							,	**__lowerCAmelCase   :    str   )	->	Dict:







					"""simple docstring"""






					raise OSError






			def    a_       (			self   :    Optional[Any]							,	*__lowerCAmelCase   :    Any							,	**__lowerCAmelCase   :    Optional[int]   )	->	str:







					"""simple docstring"""






					raise OSError






			def    a_       (			self   :    Optional[Any]							,	*__lowerCAmelCase   :    Any							,	**__lowerCAmelCase   :    Any   )	->	int:







					"""simple docstring"""






					raise OSError







			def    a_       (			self   :    str							,	*__lowerCAmelCase   :    Any							,	**__lowerCAmelCase   :    Union[str, Any]   )	->	int:







					"""simple docstring"""






					return False


class   A			(contextlib._RedirectStream    ):  # type: ignore
			'''simple docstring'''




			__lowerCamelCase	:       Union[str, Any]							=    '''stdin'''







@contextlib.contextmanager
def    __lowerCamelCase					(							__a   :Union[str, Any]    )		->				List[str]:






		"""simple docstring"""

		if root == ".":
				yield
				return
		A__        =   os.getcwd()
		os.chdir(__a    )
		try:
				yield
		except BaseException as exc:
				raise exc
		finally:
				os.chdir(__a    )







def    __lowerCamelCase					(							__a   :Union[str, Any]=None    )		->				Dict:






		"""simple docstring"""

		if maximum_memory_bytes is not None:
				import resource

				resource.setrlimit(resource.RLIMIT_AS    ,     (maximum_memory_bytes, maximum_memory_bytes)    )
				resource.setrlimit(resource.RLIMIT_DATA    ,     (maximum_memory_bytes, maximum_memory_bytes)    )
				if not platform.uname().system == "Darwin":
						resource.setrlimit(resource.RLIMIT_STACK    ,     (maximum_memory_bytes, maximum_memory_bytes)    )

		faulthandler.disable()

		import builtins

		A__        =   None
		A__        =   None

		import os

		A__        =   """1"""

		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None

		import shutil

		A__        =   None
		A__        =   None
		A__        =   None

		import subprocess

		A__        =   None  # type: ignore

		A__        =   None

		import sys

		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None
		A__        =   None



