





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


A    :					Optional[int] =      logging.get_logger(__name__)







def    __lowerCamelCase					(							__a   :int    ,     __a   :List[str]    ,     __a   :Optional[Any]    ,     __a   :Tuple=False    )		->				int:






		"""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:
				A__        =   os.path.abspath(__a    )
				logger.info(F'Loading PyTorch weights from {pt_path}'    )

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

				A__        =   convert_pytorch_state_dict_to_flax(__a    ,     __a    )
		else:
				# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
				A__        =   convert_pytorch_sharded_state_dict_to_flax(__a    ,     __a    )
		return flax_state_dict







def    __lowerCamelCase					(							__a   :Tuple[str]    ,     __a   :np.ndarray    ,     __a   :Dict[str, jnp.ndarray]    ,     __a   :str    ,     )		->				(Tuple[str], np.ndarray):






		"""simple docstring"""

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

		# layer norm
		A__        =   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
		A__        =   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
		A__        =   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
		A__        =   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
		A__        =   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    ):
				A__        =   pt_tensor.transpose(2    ,     3    ,     1    ,     0    )
				return renamed_pt_tuple_key, pt_tensor

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

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

		# old PyTorch layer norm bias
		A__        =   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
		A__        =   None
		if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
				A__        =   pt_tuple_key[-2] + """_g"""
		elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
				A__        =   pt_tuple_key[-2] + """_v"""
		if name is not None:
				A__        =   pt_tuple_key[:-3] + (name,)
				return renamed_pt_tuple_key, pt_tensor

		return pt_tuple_key, pt_tensor







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






		"""simple docstring"""

		A__        =   {k: v.numpy() for k, v in pt_state_dict.items()}

		A__        =   flax_model.base_model_prefix

		# use params dict if the model contains batch norm layers
		if "params" in flax_model.params:
				A__        =   flax_model.params["""params"""]
		else:
				A__        =   flax_model.params
		A__        =   flatten_dict(__a    )

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

		A__        =   {}

		A__        =   (model_prefix not in flax_model_params) and (
		    model_prefix in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
		)
		A__        =   (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():
				A__        =   tuple(pt_key.split("""."""    )    )

				# remove base model prefix if necessary
				A__        =   pt_tuple_key[0] == model_prefix
				if load_model_with_head_into_base_model and has_base_model_prefix:
						A__        =   pt_tuple_key[1:]

				# Correctly rename weight parameters
				A__      ,			A__        =   rename_key_and_reshape_tensor(
				    __a    ,     __a    ,     __a    ,     __a    )

				# add model prefix if necessary
				A__        =   (model_prefix,) + flax_key in random_flax_state_dict
				if load_base_model_into_model_with_head and require_base_model_prefix:
						A__        =   (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]:
								A__        =   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
						A__        =   jnp.asarray(__a    )

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

		return unflatten_dict(__a    )







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






		"""simple docstring"""

		import torch

		# Load the index
		A__        =   {}
		for shard_file in shard_filenames:
				# load using msgpack utils
				A__        =   torch.load(__a    )
				A__        =   {k: v.numpy() for k, v in pt_state_dict.items()}

				A__        =   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:
						A__        =   flax_model.params["""params"""]

						A__        =   flatten_dict(__a    )
						random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]    )    )
				else:
						A__        =   flax_model.params
						A__        =   flatten_dict(__a    )

				A__        =   (model_prefix not in flax_model_params) and (
				    model_prefix in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
				)
				A__        =   (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():
						A__        =   tuple(pt_key.split("""."""    )    )

						# remove base model prefix if necessary
						A__        =   pt_tuple_key[0] == model_prefix
						if load_model_with_head_into_base_model and has_base_model_prefix:
								A__        =   pt_tuple_key[1:]

						# Correctly rename weight parameters
						A__      ,			A__        =   rename_key_and_reshape_tensor(
						    __a    ,     __a    ,     __a    ,     __a    )
						# add model prefix if necessary
						A__        =   (model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
								A__        =   (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]:
										A__        =   jnp.asarray(__a    )
										continue
								if "var" in flax_key[-1]:
										A__        =   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
								A__        =   jnp.asarray(__a    )

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







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






		"""simple docstring"""

		A__        =   os.path.abspath(__a    )
		logger.info(F'Loading Flax weights from {flax_checkpoint_path}'    )

		# import correct flax class
		A__        =   getattr(__a    ,     """Flax""" + model.__class__.__name__    )

		# load flax weight dict
		with open(__a    ,     """rb"""    ) as state_f:
				try:
						A__        =   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    __lowerCamelCase					(							__a   :str    ,     __a   :Tuple    )		->				int:






		"""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
		A__        =   flatten_dict(jax.tree_util.tree_map(lambda __a    : 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."""    )
				A__        =   jax.tree_util.tree_map(
				    lambda __a    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params    ,     __a    )

		A__        =   flatten_dict(__a    )
		A__        =   pt_model.state_dict()

		A__        =   (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()}
		)
		A__        =   (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
		A__        =   []
		A__        =   set(pt_model_dict.keys()    )

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				A__        =   flax_key_tuple[0] == pt_model.base_model_prefix
				A__        =   """.""".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:
						A__        =   flax_key_tuple[1:]
				elif load_base_model_into_model_with_head and require_base_model_prefix:
						A__        =   (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
						A__        =   flax_key_tuple[:-1] + ("""weight""",)
						A__        =   jnp.transpose(__a    ,     (3, 2, 0, 1)    )
				elif flax_key_tuple[-1] == "kernel" and ".".join(__a    ) not in pt_model_dict:
						# linear layer
						A__        =   flax_key_tuple[:-1] + ("""weight""",)
						A__        =   flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
						A__        =   flax_key_tuple[:-1] + ("""weight""",)

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

				if "batch_stats" in flax_state:
						A__        =   """.""".join(flax_key_tuple[1:]    )  # Remove the params/batch_stats header
				else:
						A__        =   """.""".join(__a    )

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

				if flax_key in special_pt_names:
						A__        =   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
								A__        =   np.asarray(__a    ) if not isinstance(__a    ,     np.ndarray    ) else flax_tensor
								A__        =   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
		A__        =   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



