






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			:		Any   				=    logging.get_logger(__name__)





def 	lowerCAmelCase_		(lowercase__						:       Tuple     ,					lowercase__						:       Optional[int]     ,					lowercase__						:       List[Any]     ,					lowercase__						:       Optional[int]=False       )		->    Dict:


    '''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:
        lowerCAmelCase__      			=						os.path.abspath(lowercase__       )
        logger.info(f'Loading PyTorch weights from {pt_path}'       )

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





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


    '''simple docstring'''




    def is_key_or_prefix_key_in_dict(lowercase__						:       Tuple[str]       ) -> bool:
        return len(set(lowercase__       ) & {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(lowercase__       ):
        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(lowercase__       ):
        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(lowercase__       ):
        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(lowercase__       ):
        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(lowercase__       ):
        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(lowercase__       ):
        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 	lowerCAmelCase_		(lowercase__						:       Optional[int]     ,					lowercase__						:       List[str]       )		->    Tuple:


    '''simple docstring'''




    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(lowercase__       )

    # 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(lowercase__       )

    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(
            lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__       )

        # 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(lowercase__       )
                continue
            # remove num_batches_tracked key
            if "num_batches_tracked" in flax_key[-1]:
                flax_state_dict.pop(lowercase__     ,					lowercase__       )
                continue

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

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

    return unflatten_dict(lowercase__       )





def 	lowerCAmelCase_		(lowercase__						:       Any     ,					lowercase__						:       Union[str, Any]       )		->    Optional[Any]:


    '''simple docstring'''




    import torch

    # Load the index
    lowerCAmelCase__      			=						{}
    for shard_file in shard_filenames:
        # load using msgpack utils
        lowerCAmelCase__      			=						torch.load(lowercase__       )
        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(lowercase__       )
            random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']       )       )
        else:
            lowerCAmelCase__      			=						flax_model.params
            lowerCAmelCase__      			=						flatten_dict(lowercase__       )

        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(
                lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__       )
            # 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(lowercase__       )
                    continue
                if "var" in flax_key[-1]:
                    lowerCAmelCase__      			=						jnp.asarray(lowercase__       )
                    continue
                # remove num_batches_tracked key
                if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(lowercase__     ,					lowercase__       )
                    continue

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

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





def 	lowerCAmelCase_		(lowercase__						:       Dict     ,					lowercase__						:       Tuple       )		->    Union[str, Any]:


    '''simple docstring'''




    lowerCAmelCase__      			=						os.path.abspath(lowercase__       )
    logger.info(f'Loading Flax weights from {flax_checkpoint_path}'       )

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

    # load flax weight dict
    with open(lowercase__     ,					'''rb'''       ) as state_f:
        try:
            lowerCAmelCase__      			=						from_bytes(lowercase__     ,					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(lowercase__     ,					lowercase__       )





def 	lowerCAmelCase_		(lowercase__						:       Union[str, Any]     ,					lowercase__						:       Optional[Any]       )		->    Optional[Any]:


    '''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
    lowerCAmelCase__      			=						flatten_dict(jax.tree_util.tree_map(lambda lowercase__       : x.dtype == jnp.bfloataa     ,					lowercase__       )       ).values()
    if any(lowercase__       ):
        # 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 lowercase__       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params     ,					lowercase__       )

    lowerCAmelCase__      			=						flatten_dict(lowercase__       )
    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(lowercase__       ) not in pt_model_dict:
            # conv layer
            lowerCAmelCase__      			=						flax_key_tuple[:-1] + ('''weight''',)
            lowerCAmelCase__      			=						jnp.transpose(lowercase__     ,					(3, 2, 0, 1)       )
        elif flax_key_tuple[-1] == "kernel" and ".".join(lowercase__       ) 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(lowercase__       )

        # 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(lowercase__       )
                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(lowercase__       ) if not isinstance(lowercase__     ,					np.ndarray       ) else flax_tensor
                lowerCAmelCase__      			=						torch.from_numpy(lowercase__       )
                # remove from missing keys
                missing_keys.remove(lowercase__       )
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(lowercase__       )

    pt_model.load_state_dict(lowercase__       )

    # re-transform missing_keys to list
    lowerCAmelCase__      			=						list(lowercase__       )

    if len(lowercase__       ) > 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(lowercase__       ) > 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













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase			:		List[str]   				=    {
    "configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraOnnxConfig"],
    "tokenization_electra": ["ElectraTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    ["ElectraTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Optional[Any]   				=    [
			    "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "ElectraForCausalLM",
			    "ElectraForMaskedLM",
			    "ElectraForMultipleChoice",
			    "ElectraForPreTraining",
			    "ElectraForQuestionAnswering",
			    "ElectraForSequenceClassification",
			    "ElectraForTokenClassification",
			    "ElectraModel",
			    "ElectraPreTrainedModel",
			    "load_tf_weights_in_electra",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Union[str, Any]   				=    [
			    "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFElectraForMaskedLM",
			    "TFElectraForMultipleChoice",
			    "TFElectraForPreTraining",
			    "TFElectraForQuestionAnswering",
			    "TFElectraForSequenceClassification",
			    "TFElectraForTokenClassification",
			    "TFElectraModel",
			    "TFElectraPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Optional[int]   				=    [
			    "FlaxElectraForCausalLM",
			    "FlaxElectraForMaskedLM",
			    "FlaxElectraForMultipleChoice",
			    "FlaxElectraForPreTraining",
			    "FlaxElectraForQuestionAnswering",
			    "FlaxElectraForSequenceClassification",
			    "FlaxElectraForTokenClassification",
			    "FlaxElectraModel",
			    "FlaxElectraPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
			from .tokenization_electra import ElectraTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_electra_fast import ElectraTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_electra import (
						    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ElectraForCausalLM,
						    ElectraForMaskedLM,
						    ElectraForMultipleChoice,
						    ElectraForPreTraining,
						    ElectraForQuestionAnswering,
						    ElectraForSequenceClassification,
						    ElectraForTokenClassification,
						    ElectraModel,
						    ElectraPreTrainedModel,
						    load_tf_weights_in_electra,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_electra import (
						    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFElectraForMaskedLM,
						    TFElectraForMultipleChoice,
						    TFElectraForPreTraining,
						    TFElectraForQuestionAnswering,
						    TFElectraForSequenceClassification,
						    TFElectraForTokenClassification,
						    TFElectraModel,
						    TFElectraPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_electra import (
						    FlaxElectraForCausalLM,
						    FlaxElectraForMaskedLM,
						    FlaxElectraForMultipleChoice,
						    FlaxElectraForPreTraining,
						    FlaxElectraForQuestionAnswering,
						    FlaxElectraForSequenceClassification,
						    FlaxElectraForTokenClassification,
						    FlaxElectraModel,
						    FlaxElectraPreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		Any   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





