

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


__A			:					Dict						=			logging.get_logger(__name__)

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





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "swin2sr"

   lowerCamelCase__				=      {
       "hidden_size": "embed_dim",
       "num_attention_heads": "num_heads",
       "num_hidden_layers": "num_layers",
   }





   def __init__(       self					:			Dict     , __lowerCamelCase					:			Optional[int]=64     , __lowerCamelCase					:			int=1     , __lowerCamelCase					:			Optional[int]=3     , __lowerCamelCase					:			Any=180     , __lowerCamelCase					:			str=[6, 6, 6, 6, 6, 6]     , __lowerCamelCase					:			Dict=[6, 6, 6, 6, 6, 6]     , __lowerCamelCase					:			Optional[Any]=8     , __lowerCamelCase					:			Any=2.0     , __lowerCamelCase					:			int=True     , __lowerCamelCase					:			Dict=0.0     , __lowerCamelCase					:			Dict=0.0     , __lowerCamelCase					:			Dict=0.1     , __lowerCamelCase					:			Tuple="gelu"     , __lowerCamelCase					:			List[str]=False     , __lowerCamelCase					:			Any=0.02     , __lowerCamelCase					:			Optional[int]=1e-5     , __lowerCamelCase					:			List[Any]=2     , __lowerCamelCase					:			List[str]=1.0     , __lowerCamelCase					:			Union[str, Any]="1conv"     , __lowerCamelCase					:			List[Any]="pixelshuffle"     , **__lowerCamelCase					:			Optional[Any]     , ):
       super().__init__(**__lowerCamelCase			)

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




"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A_						:    Optional[Any]				=      {
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Tuple				=      ['SqueezeBertTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    int				=      [
							    'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SqueezeBertForMaskedLM',
							    'SqueezeBertForMultipleChoice',
							    'SqueezeBertForQuestionAnswering',
							    'SqueezeBertForSequenceClassification',
							    'SqueezeBertForTokenClassification',
							    'SqueezeBertModel',
							    'SqueezeBertModule',
							    'SqueezeBertPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_squeezebert import (
							    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SqueezeBertConfig,
							    SqueezeBertOnnxConfig,
							)
							from .tokenization_squeezebert import SqueezeBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_squeezebert import (
														    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SqueezeBertForMaskedLM,
														    SqueezeBertForMultipleChoice,
														    SqueezeBertForQuestionAnswering,
														    SqueezeBertForSequenceClassification,
														    SqueezeBertForTokenClassification,
														    SqueezeBertModel,
														    SqueezeBertModule,
														    SqueezeBertPreTrainedModel,
														)

else:
							import sys

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





