
from typing import TYPE_CHECKING

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


UpperCAmelCase__		:     int     			=      {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     Optional[int]     			=      ['DebertaTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     List[str]     			=      [
       'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DebertaForMaskedLM',
       'DebertaForQuestionAnswering',
       'DebertaForSequenceClassification',
       'DebertaForTokenClassification',
       'DebertaModel',
       'DebertaPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     List[str]     			=      [
       'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFDebertaForMaskedLM',
       'TFDebertaForQuestionAnswering',
       'TFDebertaForSequenceClassification',
       'TFDebertaForTokenClassification',
       'TFDebertaModel',
       'TFDebertaPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

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





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


UpperCAmelCase__		:     Dict     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Union[str, Any]     			=      {
    'caidas/swin2sr-classicalsr-x2-64': (
        'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
    ),
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    '''swin2sr'''

   __UpperCamelCase		:    str 				=    {
       '''hidden_size''': '''embed_dim''',
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }


   def __init__(					self     :							List[str]			,			lowerCAmelCase_     :							int=6_4			,			lowerCAmelCase_     :							Optional[Any]=1			,			lowerCAmelCase_     :							Dict=3			,			lowerCAmelCase_     :							Dict=1_8_0			,			lowerCAmelCase_     :							Union[str, Any]=[6, 6, 6, 6, 6, 6]			,			lowerCAmelCase_     :							Tuple=[6, 6, 6, 6, 6, 6]			,			lowerCAmelCase_     :							int=8			,			lowerCAmelCase_     :							Any=2.0			,			lowerCAmelCase_     :							Union[str, Any]=True			,			lowerCAmelCase_     :							Dict=0.0			,			lowerCAmelCase_     :							Optional[int]=0.0			,			lowerCAmelCase_     :							int=0.1			,			lowerCAmelCase_     :							Any="gelu"			,			lowerCAmelCase_     :							List[str]=False			,			lowerCAmelCase_     :							str=0.02			,			lowerCAmelCase_     :							Optional[Any]=1e-5			,			lowerCAmelCase_     :							int=2			,			lowerCAmelCase_     :							int=1.0			,			lowerCAmelCase_     :							Any="1conv"			,			lowerCAmelCase_     :							List[str]="pixelshuffle"			,			**lowerCAmelCase_     :							str			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  List[str]					=       image_size
      _A:  Any					=       patch_size
      _A:  Any					=       num_channels
      _A:  Union[str, Any]					=       embed_dim
      _A:  int					=       depths
      _A:  List[Any]					=       len(lowerCAmelCase_     )
      _A:  int					=       num_heads
      _A:  Any					=       window_size
      _A:  Optional[int]					=       mlp_ratio
      _A:  int					=       qkv_bias
      _A:  List[Any]					=       hidden_dropout_prob
      _A:  List[str]					=       attention_probs_dropout_prob
      _A:  List[Any]					=       drop_path_rate
      _A:  Any					=       hidden_act
      _A:  List[str]					=       use_absolute_embeddings
      _A:  Tuple					=       layer_norm_eps
      _A:  str					=       initializer_range
      _A:  int					=       upscale
      _A:  int					=       img_range
      _A:  Optional[Any]					=       resi_connection
      _A:  int					=       upsampler



