





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_	:		Dict         =   logging.get_logger(__name__)

lowerCamelCase_	:		Optional[Any]         =   {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					int             =  """swiftformer"""

      def __init__(	self   , snake_case_=3   , snake_case_=[3, 3, 6, 4]   , snake_case_=[4_8, 5_6, 1_1_2, 2_2_0]   , snake_case_=4   , snake_case_=[True, True, True, True]   , snake_case_="gelu"   , snake_case_=3   , snake_case_=2   , snake_case_=1   , snake_case_=0.0   , snake_case_=True   , snake_case_=1E-5   , snake_case_=1E-5   , **snake_case_   , ):

        """simple docstring"""

        super().__init__(**snake_case_    )
        A_    :			Optional[Any]									=    num_channels
        A_    :			Dict									=    depths
        A_    :			str									=    embed_dims
        A_    :			Optional[Any]									=    mlp_ratio
        A_    :			str									=    downsamples
        A_    :			Tuple									=    hidden_act
        A_    :			Tuple									=    down_patch_size
        A_    :			Optional[int]									=    down_stride
        A_    :			List[Any]									=    down_pad
        A_    :			List[str]									=    drop_path_rate
        A_    :			List[Any]									=    use_layer_scale
        A_    :			Union[str, Any]									=    layer_scale_init_value
        A_    :			str									=    batch_norm_eps


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					int             =  version.parse("""1.11"""						)

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]    )

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 1E-4







from typing import TYPE_CHECKING

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


snake_case_   :	Dict							      ={'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[Any]							      =['''T5Tokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Any							      =['''T5TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[int]							      =[
					    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''T5EncoderModel''',
					    '''T5ForConditionalGeneration''',
					    '''T5Model''',
					    '''T5PreTrainedModel''',
					    '''load_tf_weights_in_t5''',
					    '''T5ForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[Any]							      =[
					    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFT5EncoderModel''',
					    '''TFT5ForConditionalGeneration''',
					    '''TFT5Model''',
					    '''TFT5PreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Tuple							      =[
					    '''FlaxT5EncoderModel''',
					    '''FlaxT5ForConditionalGeneration''',
					    '''FlaxT5Model''',
					    '''FlaxT5PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta import TaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta_fast import TaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ta import (
										    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TaEncoderModel,
										    TaForConditionalGeneration,
										    TaForQuestionAnswering,
										    TaModel,
										    TaPreTrainedModel,
										    load_tf_weights_in_ta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ta import (
										    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTaEncoderModel,
										    TFTaForConditionalGeneration,
										    TFTaModel,
										    TFTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_ta import (
										    FlaxTaEncoderModel,
										    FlaxTaForConditionalGeneration,
										    FlaxTaModel,
										    FlaxTaPreTrainedModel,
										)


else:
					import sys

					snake_case_   :	List[Any]							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

