





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


lowercase    : Union[str, Any]    	=  logging.get_logger(__name__)

lowercase    : Union[str, Any]    	=  {
    """Visual-Attention-Network/van-base""": (
        """https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
    ),
}

class  __A(						__UpperCAmelCase ):
     __A	  =       "van"

     def __init__(      self,       A=224,       A=3,       A=[7, 3, 3, 3],       A=[4, 2, 2, 2],       A=[64, 128, 320, 512],       A=[3, 3, 12, 3],       A=[8, 8, 4, 4],       A="gelu",       A=0.02,       A=1E-6,       A=1E-2,       A=0.0,       A=0.0,       **A,       ):





            """simple docstring"""

            super().__init__(**A      )
            _UpperCamelCase              =						image_size
            _UpperCamelCase              =						num_channels
            _UpperCamelCase              =						patch_sizes
            _UpperCamelCase              =						strides
            _UpperCamelCase              =						hidden_sizes
            _UpperCamelCase              =						depths
            _UpperCamelCase              =						mlp_ratios
            _UpperCamelCase              =						hidden_act
            _UpperCamelCase              =						initializer_range
            _UpperCamelCase              =						layer_norm_eps
            _UpperCamelCase              =						layer_scale_init_value
            _UpperCamelCase              =						drop_path_rate
            _UpperCamelCase              =						dropout_rate













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  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						3
            _UpperCamelCase              =						250

            _UpperCamelCase              =						ids_tensor((batch_size, length),       A      )
            _UpperCamelCase              =						torch.ones((batch_size, length),       device=A,       dtype=torch.float      ) / length
            return input_ids, scores

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )

            _UpperCamelCase              =						StoppingCriteriaList(
                [
                    MaxLengthCriteria(max_length=10      ),
                    MaxTimeCriteria(max_time=0.1      ),
                ]      )

            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaxLengthCriteria(max_length=10      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaxNewTokensCriteria(start_length=5,       max_new_tokens=5      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

            _UpperCamelCase              =						StoppingCriteriaList([criteria]      )
            self.assertEqual(criteria_list.max_length,       10      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )

            _UpperCamelCase              =						MaxTimeCriteria(max_time=0.1      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase              =						MaxTimeCriteria(max_time=0.1,       initial_timestamp=time.time() - 0.2      )
            self.assertTrue(criteria(A,       A      )      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      ),       10      )

            with self.assertWarns(A      ):
                   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      ),       11      )

            _UpperCamelCase              =						validate_stopping_criteria(StoppingCriteriaList(),       11      )

            self.assertEqual(len(A      ),       1      )






