





"""simple docstring"""



import os

import pytest
from attr import dataclass


a_ =			"""us-east-1"""  # defaults region






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     42
 _lowerCamelCase        =     """arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _lowerCamelCase        =     {
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 5_00,
     """save_steps""": 55_00,
 }
 _lowerCamelCase        =     {**hyperparameters, """max_steps""": 10_00}

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.framework == "pytorch":
         return [
             {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
             {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
             {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
         ]
     else:
         return [
             {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
             {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
             {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
         ]

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return F"""{self.framework}-transfromers-test"""

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return F"""./tests/sagemaker/scripts/{self.framework}"""







 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.framework == "pytorch":
         return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
     else:
         return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope='''class'''						)
def 		__lowercase     (   snake_case_  :  int						)   ->Tuple:



    '''simple docstring'''







    __A    :						str						 =     SageMakerTestEnvironment(framework=request.cls.framework						)











"""simple docstring"""



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


a_ =			logging.get_logger(__name__)

a_ =			{
    """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 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """nat"""

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

 def __init__(    self					,						__lowerCamelCase=4					,						__lowerCamelCase=3					,						__lowerCamelCase=64					,						__lowerCamelCase=[3, 4, 6, 5]					,						__lowerCamelCase=[2, 4, 8, 16]					,						__lowerCamelCase=7					,						__lowerCamelCase=3.0					,						__lowerCamelCase=True					,						__lowerCamelCase=0.0					,						__lowerCamelCase=0.0					,						__lowerCamelCase=0.1					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-5					,						__lowerCamelCase=0.0					,						__lowerCamelCase=None					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     __A    :						Union[str, Any]						 =     patch_size
     __A    :						Optional[Any]						 =     num_channels
     __A    :						Tuple						 =     embed_dim
     __A    :						Dict						 =     depths
     __A    :						str						 =     len(__lowerCamelCase  )
     __A    :						Optional[Any]						 =     num_heads
     __A    :						str						 =     kernel_size
     __A    :						Any						 =     mlp_ratio
     __A    :						Optional[int]						 =     qkv_bias
     __A    :						str						 =     hidden_dropout_prob
     __A    :						Any						 =     attention_probs_dropout_prob
     __A    :						int						 =     drop_path_rate
     __A    :						int						 =     hidden_act
     __A    :						Any						 =     layer_norm_eps
     __A    :						Tuple						 =     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
     __A    :						int						 =     int(embed_dim * 2 ** (len(__lowerCamelCase  ) - 1)  )
     __A    :						Union[str, Any]						 =     layer_scale_init_value
     __A    :						List[str]						 =     ['''stem'''] + [F"""stage{idx}""" for idx in range(1					,						len(__lowerCamelCase  ) + 1  )]
     __A ,       __A    :						Any						 =     get_aligned_output_features_output_indices(
         out_features=__lowerCamelCase					,						out_indices=__lowerCamelCase					,						stage_names=self.stage_names  )




