


'''simple docstring'''






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  _snake_case       (					A			,   A			,   A			,   A=False	)						->   Dict:
							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:
														lowerCAmelCase__	       =     os.path.abspath(A	)
														logger.info(F"""Loading PyTorch weights from {pt_path}"""	)

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

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


def  _snake_case       (					A			,   A			,   A			,   A			,   )						->   (Tuple[str], np.ndarray):

							def is_key_or_prefix_key_in_dict(A	) -> bool:
														return len(set(A	) & {key, (model_prefix,) + key}	) > 0

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

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

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

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

							return pt_tuple_key, pt_tensor


def  _snake_case       (					A			,   A	)						->   List[str]:
							# convert pytorch tensor to numpy
							lowerCAmelCase__	       =     {k: v.numpy() for k, v in pt_state_dict.items()}

							lowerCAmelCase__	       =     flax_model.base_model_prefix

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

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

							lowerCAmelCase__	       =     {}

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

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

														# Correctly rename weight parameters
														lowerCAmelCase__       , lowerCAmelCase__	       =     rename_key_and_reshape_tensor(
														    A			,   A			,   A			,   A	)

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

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

							return unflatten_dict(A	)


def  _snake_case       (					A			,   A	)						->   Optional[Any]:
							import torch

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

														lowerCAmelCase__	       =     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:
																					lowerCAmelCase__	       =     flax_model.params['''params''']

																					lowerCAmelCase__	       =     flatten_dict(A	)
																					random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']	)	)
														else:
																					lowerCAmelCase__	       =     flax_model.params
																					lowerCAmelCase__	       =     flatten_dict(A	)

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

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

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

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


def  _snake_case       (					A			,   A	)						->   int:
							lowerCAmelCase__	       =     os.path.abspath(A	)
							logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""	)

							# import correct flax class
							lowerCAmelCase__	       =     getattr(A			,   '''Flax''' + model.__class__.__name__	)

							# load flax weight dict
							with open(A			,   '''rb'''	) as state_f:
														try:
																					lowerCAmelCase__	       =     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  _snake_case       (					A			,   A	)						->   List[Any]:

							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
							lowerCAmelCase__	       =     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.'''	)
														lowerCAmelCase__	       =     jax.tree_util.tree_map(
														    lambda A	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params			,   A	)

							lowerCAmelCase__	       =     flatten_dict(A	)
							lowerCAmelCase__	       =     pt_model.state_dict()

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

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

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

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

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

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








'''simple docstring'''






import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Dict     						=				""
							lowercase__      :       str     						=				(
							    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
							)
							lowercase__      :       str     						=				None  # compression type in fsspec. ex: "gzip"
							lowercase__      :       str     						=				None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz







							def __init__(		self ,   lowerCamelCase_ = "" ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   **lowerCamelCase_       )	->							Any:
														super().__init__(self ,   **lowerCamelCase_       )
														# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
														lowerCAmelCase__	       =     fsspec.open(
														    lowerCamelCase_ ,   mode='''rb''' ,   protocol=lowerCamelCase_ ,   compression=self.compression ,   client_kwargs={
														        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
														        '''trust_env''': True,  # Enable reading proxy env variables.
														        **(target_options or {}).pop('''client_kwargs''' ,   {}       ),  # To avoid issues if it was already passed.
														    } ,   **(target_options or {}) ,   )
														lowerCAmelCase__	       =     os.path.basename(self.file.path.split('''::'''       )[0]       )
														lowerCAmelCase__	       =     (
														    self.compressed_name[: self.compressed_name.rindex('''.'''       )]
														    if '''.''' in self.compressed_name
														    else self.compressed_name
														)
														lowerCAmelCase__	       =     None







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls ,   lowerCamelCase_       )	->							Any:
														# compressed file paths are always relative to the archive root
														return super()._strip_protocol(lowerCamelCase_       ).lstrip('''/'''       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														if self.dir_cache is None:
																					lowerCAmelCase__	       =     {**self.file.fs.info(self.file.path       ), '''name''': self.uncompressed_name}
																					lowerCAmelCase__	       =     {f['''name''']: f}







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							List[Any]:
														return self.file.open().read()







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = "rb" ,   lowerCamelCase_=None ,   lowerCamelCase_=True ,   lowerCamelCase_=None ,   **lowerCamelCase_ ,   )	->							List[str]:
														lowerCAmelCase__	       =     self._strip_protocol(lowerCamelCase_       )
														if mode != "rb":
																					raise ValueError(F"""Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"""       )
														return self.file.open()




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				"bz2"
							lowercase__      :       str     						=				"bz2"
							lowercase__      :       Optional[int]     						=				".bz2"




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Any     						=				"gzip"
							lowercase__      :       int     						=				"gzip"
							lowercase__      :       int     						=				".gz"




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Tuple     						=				"lz4"
							lowercase__      :       Optional[Any]     						=				"lz4"
							lowercase__      :       int     						=				".lz4"




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Optional[int]     						=				"xz"
							lowercase__      :       str     						=				"xz"
							lowercase__      :       List[Any]     						=				".xz"




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Union[str, Any]     						=				"zstd"
							lowercase__      :       Union[str, Any]     						=				"zstd"
							lowercase__      :       Dict     						=				".zst"







							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = "rb" ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = DEFAULT_BLOCK_SIZE ,   **lowerCamelCase_ ,   )	->							List[Any]:
														super().__init__(
														    fo=lowerCamelCase_ ,   mode=lowerCamelCase_ ,   target_protocol=lowerCamelCase_ ,   target_options=lowerCamelCase_ ,   block_size=lowerCamelCase_ ,   **lowerCamelCase_ ,   )
														# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
														#
														# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
														#     out.close = close
														# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
														#
														# see https://github.com/intake/filesystem_spec/issues/725
														lowerCAmelCase__	       =     self.file.__enter__




														class 		a__    :





																					'''simple docstring'''






																					def __init__(		self ,   lowerCamelCase_       )	->							List[str]:
																												lowerCAmelCase__	       =     file_







																					def __enter__(		self       )	->							Tuple:
																												self._file.__enter__()
																												return self







																					def __exit__(		self ,   *lowerCamelCase_ ,   **lowerCamelCase_       )	->							str:
																												self._file.__exit__(*lowerCamelCase_ ,   **lowerCamelCase_       )







																					def __iter__(		self       )	->							Any:
																												return iter(self._file       )







																					def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
																												return next(self._file       )







																					def __getattr__(		self ,   lowerCamelCase_       )	->							str:
																												return getattr(self._file ,   lowerCamelCase_       )

														def fixed_enter(*lowerCamelCase_ ,   **lowerCamelCase_       ):
																					return WrappedFile(_enter(*lowerCamelCase_ ,   **lowerCamelCase_       )       )

														lowerCAmelCase__	       =     fixed_enter