



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE  :						Dict    							=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      {
    '''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_(   A__    ):





					'''simple docstring'''



					lowercase__   :       Any										= '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__=0.0_2				,       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__   )

									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				max_position_embeddings
									_lowerCamelCase        =				hidden_size
									_lowerCamelCase        =				num_hidden_layers
									_lowerCamelCase        =				intermediate_size
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				hidden_dropout_prob
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				type_vocab_size
									_lowerCamelCase        =				layer_norm_eps
									_lowerCamelCase        =				use_tpu_fourier_optimizations
									_lowerCamelCase        =				tpu_short_seq_length





"""simple docstring"""
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						str    							=      ['''VisionEncoderDecoderModel''']

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Dict    							=      ['''TFVisionEncoderDecoderModel''']

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
			from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
