



"""simple docstring"""





import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
     import torch

     from transformers.generation import (
         MaxLengthCriteria,
         MaxNewTokensCriteria,
         MaxTimeCriteria,
         StoppingCriteriaList,
         validate_stopping_criteria,
     )



@require_torch
class    _lowerCAmelCase    (				unittest.TestCase		):





  """simple docstring"""






  def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						Union[str, Any]:







        '''simple docstring'''

        snake_case_   : Dict						=			3
        snake_case_   : str						=			2_5_0

        snake_case_   : Union[str, Any]						=			ids_tensor((batch_size, length)							,    lowerCAmelCase__   )
        snake_case_   : List[str]						=			torch.ones((batch_size, length)							,    device=lowerCAmelCase__							,    dtype=torch.float   ) / length
        return input_ids, scores





  def    UpperCAmelCase__							(					self   ) ->						List[Any]:







        '''simple docstring'''

        snake_case_			,		snake_case_   : Optional[int]						=			self._get_tensors(5   )

        snake_case_   : List[str]						=			StoppingCriteriaList(
            [
                MaxLengthCriteria(max_length=1_0   ),
                MaxTimeCriteria(max_time=0.1   ),
            ]   )

        self.assertFalse(criteria(lowerCAmelCase__							,    lowerCAmelCase__   )   )

        snake_case_			,		snake_case_   : List[Any]						=			self._get_tensors(9   )
        self.assertFalse(criteria(lowerCAmelCase__							,    lowerCAmelCase__   )   )

        snake_case_			,		snake_case_   : Dict						=			self._get_tensors(1_0   )
        self.assertTrue(criteria(lowerCAmelCase__							,    lowerCAmelCase__   )   )





  def    UpperCAmelCase__							(					self   ) ->						List[Any]:







        '''simple docstring'''

        snake_case_   : Any						=			MaxLengthCriteria(max_length=1_0   )

        snake_case_			,		snake_case_   : Optional[int]						=			self._get_tensors(5   )
        self.assertFalse(criteria(lowerCAmelCase__							,    lowerCAmelCase__   )   )

        snake_case_			,		snake_case_   : Dict						=			self._get_tensors(9   )
        self.assertFalse(criteria(lowerCAmelCase__							,    lowerCAmelCase__   )   )

        snake_case_			,		snake_case_   : Any						=			self._get_tensors(1_0   )
        self.assertTrue(criteria(lowerCAmelCase__							,    lowerCAmelCase__   )   )





  def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







        '''simple docstring'''

        snake_case_   : Union[str, Any]						=			MaxNewTokensCriteria(start_length=5							,    max_new_tokens=5   )

        snake_case_			,		snake_case_   : Union[str, Any]						=			self._get_tensors(5   )
        self.assertFalse(criteria(lowerCAmelCase__							,    lowerCAmelCase__   )   )

        snake_case_			,		snake_case_   : Union[str, Any]						=			self._get_tensors(9   )
        self.assertFalse(criteria(lowerCAmelCase__							,    lowerCAmelCase__   )   )

        snake_case_			,		snake_case_   : Union[str, Any]						=			self._get_tensors(1_0   )
        self.assertTrue(criteria(lowerCAmelCase__							,    lowerCAmelCase__   )   )

        snake_case_   : Any						=			StoppingCriteriaList([criteria]   )
        self.assertEqual(criteria_list.max_length							,    1_0   )





  def    UpperCAmelCase__							(					self   ) ->						Any:







        '''simple docstring'''

        snake_case_			,		snake_case_   : str						=			self._get_tensors(5   )

        snake_case_   : List[str]						=			MaxTimeCriteria(max_time=0.1   )
        self.assertFalse(criteria(lowerCAmelCase__							,    lowerCAmelCase__   )   )

        snake_case_   : Optional[int]						=			MaxTimeCriteria(max_time=0.1							,    initial_timestamp=time.time() - 0.2   )
        self.assertTrue(criteria(lowerCAmelCase__							,    lowerCAmelCase__   )   )





  def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







        '''simple docstring'''

        validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0   )]   )							,    1_0   )

        with self.assertWarns(lowerCAmelCase__   ):
              validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0   )]   )							,    1_1   )

        snake_case_   : str						=			validate_stopping_criteria(StoppingCriteriaList()							,    1_1   )

        self.assertEqual(len(lowerCAmelCase__   )							,    1   )






"""simple docstring"""





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


__lowerCAmelCase       :					str      					=					logging.get_logger(__name__)

__lowerCAmelCase       :					Tuple      					=					{
    '''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    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       '''nat'''

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





		def __init__(					self							,    _lowercase=4							,    _lowercase=3							,    _lowercase=6_4							,    _lowercase=[3, 4, 6, 5]							,    _lowercase=[2, 4, 8, 1_6]							,    _lowercase=7							,    _lowercase=3.0							,    _lowercase=True							,    _lowercase=0.0							,    _lowercase=0.0							,    _lowercase=0.1							,    _lowercase="gelu"							,    _lowercase=0.02							,    _lowercase=1E-5							,    _lowercase=0.0							,    _lowercase=None							,    _lowercase=None							,    **_lowercase							,    ) ->						Optional[Any]:







								'''simple docstring'''

								super().__init__(**_lowercase   )

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

