
'''simple docstring'''
from typing import Dict

from .base import GenericTensor, Pipeline


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							def 	lowercase							(self  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  )					->       Optional[int]:
														if tokenize_kwargs is None:
																					_snake_case       =  {}

														if truncation is not None:
																					if "truncation" in tokenize_kwargs:
																												raise ValueError(
																												    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""  )
																					_snake_case       =  truncation

														_snake_case       =  tokenize_kwargs

														_snake_case       =  {}
														if return_tensors is not None:
																					_snake_case       =  return_tensors

														return preprocess_params, {}, postprocess_params

							def 	lowercase							(self  ,  UpperCAmelCase  ,  **UpperCAmelCase  )					->       Dict[str, GenericTensor]:
														_snake_case       =  self.framework
														_snake_case       =  self.tokenizer(UpperCAmelCase  ,  return_tensors=UpperCAmelCase  ,  **UpperCAmelCase  )
														return model_inputs

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Dict:
														_snake_case       =  self.model(**UpperCAmelCase  )
														return model_outputs

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=False  )					->       List[Any]:
														# [0] is the first available tensor, logits or last_hidden_state.
														if return_tensors:
																					return model_outputs[0]
														if self.framework == "pt":
																					return model_outputs[0].tolist()
														elif self.framework == "tf":
																					return model_outputs[0].numpy().tolist()





							def __call__(self  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       Union[str, Any]:
														return super().__call__(*UpperCAmelCase  ,  **UpperCAmelCase  )

'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	{
    '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       _lowerCAmelCase   (   __snake_case					,							__snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"nat"

							lowerCAmelCase_ 						=						{
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}

							def __init__(self  ,  UpperCAmelCase=4  ,  UpperCAmelCase=3  ,  UpperCAmelCase=64  ,  UpperCAmelCase=[3, 4, 6, 5]  ,  UpperCAmelCase=[2, 4, 8, 16]  ,  UpperCAmelCase=7  ,  UpperCAmelCase=3.0  ,  UpperCAmelCase=True  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-5  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  ,  )					->       str:
														super().__init__(**UpperCAmelCase  )

														_snake_case       =  patch_size
														_snake_case       =  num_channels
														_snake_case       =  embed_dim
														_snake_case       =  depths
														_snake_case       =  len(UpperCAmelCase  )
														_snake_case       =  num_heads
														_snake_case       =  kernel_size
														_snake_case       =  mlp_ratio
														_snake_case       =  qkv_bias
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  drop_path_rate
														_snake_case       =  hidden_act
														_snake_case       =  layer_norm_eps
														_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
														_snake_case       =  int(embed_dim * 2 ** (len(UpperCAmelCase  ) - 1)  )
														_snake_case       =  layer_scale_init_value
														_snake_case       =  ["""stem"""] + [f"""stage{idx}""" for idx in range(1  ,  len(UpperCAmelCase  ) + 1  )]
														_snake_case,							_snake_case       =  get_aligned_output_features_output_indices(
														    out_features=UpperCAmelCase  ,  out_indices=UpperCAmelCase  ,  stage_names=self.stage_names  )