from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCamelCase       : Any      					=		logging.get_logger(__name__)

_lowerCamelCase       : Union[str, Any]      					=		{
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class     lowercase     (  UpperCamelCase__					,							UpperCamelCase__  ):
							lowercase__    :							Any										=				"""nat"""

							lowercase__    :							Tuple										=				{
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}




							def __init__(    self :       List[Any]						,				_UpperCamelCase :       Tuple=4						,				_UpperCamelCase :       int=3						,				_UpperCamelCase :       Union[str, Any]=64						,				_UpperCamelCase :       Optional[Any]=[3, 4, 6, 5]						,				_UpperCamelCase :       Tuple=[2, 4, 8, 16]						,				_UpperCamelCase :       Optional[int]=7						,				_UpperCamelCase :       Optional[int]=3.0						,				_UpperCamelCase :       int=True						,				_UpperCamelCase :       Dict=0.0						,				_UpperCamelCase :       Tuple=0.0						,				_UpperCamelCase :       List[Any]=0.1						,				_UpperCamelCase :       Optional[int]="gelu"						,				_UpperCamelCase :       Optional[Any]=0.0_2						,				_UpperCamelCase :       Optional[int]=1e-5						,				_UpperCamelCase :       List[str]=0.0						,				_UpperCamelCase :       List[str]=None						,				_UpperCamelCase :       Optional[int]=None						,				**_UpperCamelCase :       List[Any]						,				)       ->					Optional[Any]:



												'''simple docstring'''

												super().__init__(**__snake_case   )

												SCREAMING_SNAKE_CASE           =							patch_size
												SCREAMING_SNAKE_CASE           =							num_channels
												SCREAMING_SNAKE_CASE           =							embed_dim
												SCREAMING_SNAKE_CASE           =							depths
												SCREAMING_SNAKE_CASE           =							len(__snake_case   )
												SCREAMING_SNAKE_CASE           =							num_heads
												SCREAMING_SNAKE_CASE           =							kernel_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           =							layer_norm_eps
												SCREAMING_SNAKE_CASE           =							initializer_range
												# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
												# this indicates the channel dimension after the last stage of the model
												SCREAMING_SNAKE_CASE           =							int(embed_dim * 2 ** (len(__snake_case   ) - 1)   )
												SCREAMING_SNAKE_CASE           =							layer_scale_init_value
												SCREAMING_SNAKE_CASE           =							["""stem"""] + [F"stage{idx}" for idx in range(1						,				len(__snake_case   ) + 1   )]
												SCREAMING_SNAKE_CASE           =							get_aligned_output_features_output_indices(
												    out_features=__snake_case						,				out_indices=__snake_case						,				stage_names=self.stage_names   )










from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType




@dataclass
class 		__A   :
						UpperCamelCase      =       42
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""Translation"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def __call__(		self :Union[str, Any]			):
										'''simple docstring'''

										return pa.struct({lang: pa.string() for lang in sorted(self.languages			)}			)


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Value

										return {k: Value("""string"""			) for k in sorted(self.languages			)}




@dataclass
class 		__A   :
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""TranslationVariableLanguages"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=sorted(set(self.languages			)			) if self.languages else None
										__magic_name__						:						Optional[int]										=len(self.languages			) if self.languages else None

						def __call__(		self :List[str]			):
										'''simple docstring'''

										return pa.struct({"""language""": pa.list_(pa.string()			), """translation""": pa.list_(pa.string()			)}			)

						def      A__    (		self :str     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=set(self.languages			)
										if self.languages and set(__snake_case			) - lang_set:
														raise ValueError(
														    f"Some languages in example ({', '.join(sorted(set(__snake_case			) - lang_set			)			)}) are not in valid set ({', '.join(__snake_case			)})."			)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										__magic_name__						:						Any										=[]
										for lang, text in translation_dict.items():
														if isinstance(__snake_case     ,			__snake_case			):
																		translation_tuples.append((lang, text)			)
														else:
																		translation_tuples.extend([(lang, el) for el in text]			)

        # Ensure translations are in ascending order by language code.
										__magic_name__					,			__magic_name__						:						List[str]										=zip(*sorted(__snake_case			)			)

										return {"language": languages, "translation": translations}


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Sequence, Value

										return {
										    "language": Sequence(Value("""string"""			)			),
										    "translation": Sequence(Value("""string"""			)			),
										}




