

"""simple docstring"""





from typing import TYPE_CHECKING

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


_a							=							{
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a							=							['BertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a							=							[
       'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BertForMaskedLM',
       'BertForMultipleChoice',
       'BertForNextSentencePrediction',
       'BertForPreTraining',
       'BertForQuestionAnswering',
       'BertForSequenceClassification',
       'BertForTokenClassification',
       'BertLayer',
       'BertLMHeadModel',
       'BertModel',
       'BertPreTrainedModel',
       'load_tf_weights_in_bert',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a							=							[
       'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFBertEmbeddings',
       'TFBertForMaskedLM',
       'TFBertForMultipleChoice',
       'TFBertForNextSentencePrediction',
       'TFBertForPreTraining',
       'TFBertForQuestionAnswering',
       'TFBertForSequenceClassification',
       'TFBertForTokenClassification',
       'TFBertLMHeadModel',
       'TFBertMainLayer',
       'TFBertModel',
       'TFBertPreTrainedModel',
   ]
try:
   if not is_tensorflow_text_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a							=							['TFBertTokenizer']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a							=							[
       'FlaxBertForCausalLM',
       'FlaxBertForMaskedLM',
       'FlaxBertForMultipleChoice',
       'FlaxBertForNextSentencePrediction',
       'FlaxBertForPreTraining',
       'FlaxBertForQuestionAnswering',
       'FlaxBertForSequenceClassification',
       'FlaxBertForTokenClassification',
       'FlaxBertModel',
       'FlaxBertPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
   from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bert_fast import BertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bert import (
          BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BertForMaskedLM,
          BertForMultipleChoice,
          BertForNextSentencePrediction,
          BertForPreTraining,
          BertForQuestionAnswering,
          BertForSequenceClassification,
          BertForTokenClassification,
          BertLayer,
          BertLMHeadModel,
          BertModel,
          BertPreTrainedModel,
          load_tf_weights_in_bert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bert import (
          TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFBertEmbeddings,
          TFBertForMaskedLM,
          TFBertForMultipleChoice,
          TFBertForNextSentencePrediction,
          TFBertForPreTraining,
          TFBertForQuestionAnswering,
          TFBertForSequenceClassification,
          TFBertForTokenClassification,
          TFBertLMHeadModel,
          TFBertMainLayer,
          TFBertModel,
          TFBertPreTrainedModel,
      )

   try:
      if not is_tensorflow_text_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bert_tf import TFBertTokenizer

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bert import (
          FlaxBertForCausalLM,
          FlaxBertForMaskedLM,
          FlaxBertForMultipleChoice,
          FlaxBertForNextSentencePrediction,
          FlaxBertForPreTraining,
          FlaxBertForQuestionAnswering,
          FlaxBertForSequenceClassification,
          FlaxBertForTokenClassification,
          FlaxBertModel,
          FlaxBertPreTrainedModel,
      )

else:
   import sys

   _a							=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""simple docstring"""





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


_a							=							logging.get_logger(__name__)

_a							=							{
    'caidas/swin2sr-classicalsr-x2-64': (
        'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
    ),
}




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : List[Any]       		=     "swin2sr"

 __UpperCAmelCase     : List[Any]       		=     {
     "hidden_size": "embed_dim",
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }


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

    __lowercase								=						image_size
    __lowercase								=						patch_size
    __lowercase								=						num_channels
    __lowercase								=						embed_dim
    __lowercase								=						depths
    __lowercase								=						len(UpperCAmelCase__     )
    __lowercase								=						num_heads
    __lowercase								=						window_size
    __lowercase								=						mlp_ratio
    __lowercase								=						qkv_bias
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						drop_path_rate
    __lowercase								=						hidden_act
    __lowercase								=						use_absolute_embeddings
    __lowercase								=						layer_norm_eps
    __lowercase								=						initializer_range
    __lowercase								=						upscale
    __lowercase								=						img_range
    __lowercase								=						resi_connection
    __lowercase								=						upsampler




