






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase								=		logging.get_logger(__name__)

UpperCamelCase								=		{
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class 				lowerCAmelCase_	(		__snake_case			):
				_UpperCamelCase					:       Optional[int]							       = "fnet"


				def __init__(					self ,						_lowerCAmelCase=3_2_0_0_0 ,						_lowerCAmelCase=7_6_8 ,						_lowerCAmelCase=1_2 ,						_lowerCAmelCase=3_0_7_2 ,						_lowerCAmelCase="gelu_new" ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=5_1_2 ,						_lowerCAmelCase=4 ,						_lowerCAmelCase=0.02 ,						_lowerCAmelCase=1E-12 ,						_lowerCAmelCase=False ,						_lowerCAmelCase=5_1_2 ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=1 ,						_lowerCAmelCase=2 ,						**_lowerCAmelCase ,						):
											super().__init__(pad_token_id=_lowerCAmelCase ,						bos_token_id=_lowerCAmelCase ,						eos_token_id=_lowerCAmelCase ,						**_lowerCAmelCase      )

											_lowercase							:   str 							=   vocab_size
											_lowercase							:   Optional[int] 							=   max_position_embeddings
											_lowercase							:   Optional[Any] 							=   hidden_size
											_lowercase							:   Tuple 							=   num_hidden_layers
											_lowercase							:   List[str] 							=   intermediate_size
											_lowercase							:   List[str] 							=   hidden_act
											_lowercase							:   Optional[Any] 							=   hidden_dropout_prob
											_lowercase							:   Any 							=   initializer_range
											_lowercase							:   Any 							=   type_vocab_size
											_lowercase							:   Optional[int] 							=   layer_norm_eps
											_lowercase							:   Dict 							=   use_tpu_fourier_optimizations
											_lowercase							:   Any 							=   tpu_short_seq_length










# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a__		     =     {
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     ['''EfficientNetImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     [
	    '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''EfficientNetForImageClassification''',
	    '''EfficientNetModel''',
	    '''EfficientNetPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_efficientnet import (
	    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    EfficientNetConfig,
	    EfficientNetOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_efficientnet import EfficientNetImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_efficientnet import (
		    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    EfficientNetForImageClassification,
		    EfficientNetModel,
		    EfficientNetPreTrainedModel,
		)

else:
	import sys

	a__		     =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)






