



'''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			):







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			3
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			2_50

													SCREAMING_SNAKE_CASE					:      Dict     					=			ids_tensor((batch_size, length)				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.ones((batch_size, length)				,    device=lowerCAmelCase__				,    dtype=torch.float				) / length
													return input_ids, scores







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			self._get_tensors(5				)

													SCREAMING_SNAKE_CASE					:      Any     					=			StoppingCriteriaList(
													    [
													        MaxLengthCriteria(max_length=10				),
													        MaxTimeCriteria(max_time=0.1				),
													    ]				)

													self.assertFalse(criteria(lowerCAmelCase__				,    lowerCAmelCase__				)				)

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			self._get_tensors(9				)
													self.assertFalse(criteria(lowerCAmelCase__				,    lowerCAmelCase__				)				)

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Tuple     					=			self._get_tensors(10				)
													self.assertTrue(criteria(lowerCAmelCase__				,    lowerCAmelCase__				)				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			MaxLengthCriteria(max_length=10				)

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			self._get_tensors(5				)
													self.assertFalse(criteria(lowerCAmelCase__				,    lowerCAmelCase__				)				)

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

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Any     					=			self._get_tensors(10				)
													self.assertTrue(criteria(lowerCAmelCase__				,    lowerCAmelCase__				)				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			MaxNewTokensCriteria(start_length=5				,    max_new_tokens=5				)

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			self._get_tensors(5				)
													self.assertFalse(criteria(lowerCAmelCase__				,    lowerCAmelCase__				)				)

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Tuple     					=			self._get_tensors(9				)
													self.assertFalse(criteria(lowerCAmelCase__				,    lowerCAmelCase__				)				)

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Any     					=			self._get_tensors(10				)
													self.assertTrue(criteria(lowerCAmelCase__				,    lowerCAmelCase__				)				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			StoppingCriteriaList([criteria]				)
													self.assertEqual(criteria_list.max_length				,    10				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[Any]     					=			self._get_tensors(5				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			MaxTimeCriteria(max_time=0.1				)
													self.assertFalse(criteria(lowerCAmelCase__				,    lowerCAmelCase__				)				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			MaxTimeCriteria(max_time=0.1				,    initial_timestamp=time.time() - 0.2				)
													self.assertTrue(criteria(lowerCAmelCase__				,    lowerCAmelCase__				)				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)				,    10				)

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

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			validate_stopping_criteria(StoppingCriteriaList()				,    11				)

													self.assertEqual(len(lowerCAmelCase__				)				,    1				)









'''simple docstring'''



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


lowerCAmelCase_     :       Optional[int]								=						logging.get_logger(__name__)

lowerCAmelCase_     :       Optional[Any]								=						{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   str	     =				'swinv2'

						_lowerCAmelCase     :   Optional[int]	     =				{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}







						def __init__(  self   :      Optional[int]				,    lowerCAmelCase__   :      Any=2_24				,    lowerCAmelCase__   :      Optional[Any]=4				,    lowerCAmelCase__   :      Tuple=3				,    lowerCAmelCase__   :      Tuple=96				,    lowerCAmelCase__   :      Optional[int]=[2, 2, 6, 2]				,    lowerCAmelCase__   :      Tuple=[3, 6, 12, 24]				,    lowerCAmelCase__   :      List[str]=7				,    lowerCAmelCase__   :      Tuple=4.0				,    lowerCAmelCase__   :      Dict=True				,    lowerCAmelCase__   :      Any=0.0				,    lowerCAmelCase__   :      Optional[int]=0.0				,    lowerCAmelCase__   :      Optional[Any]=0.1				,    lowerCAmelCase__   :      int="gelu"				,    lowerCAmelCase__   :      Tuple=False				,    lowerCAmelCase__   :      Tuple=0.02				,    lowerCAmelCase__   :      Dict=1e-5				,    lowerCAmelCase__   :      List[str]=32				,    **lowerCAmelCase__   :      Union[str, Any]				,    ):

													"""simple docstring"""
													super().__init__(**lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			image_size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			patch_size
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			num_channels
													SCREAMING_SNAKE_CASE					:      int     					=			embed_dim
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			depths
													SCREAMING_SNAKE_CASE					:      str     					=			len(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			num_heads
													SCREAMING_SNAKE_CASE					:      Tuple     					=			window_size
													SCREAMING_SNAKE_CASE					:      Any     					=			mlp_ratio
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			qkv_bias
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      int     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      List[str]     					=			drop_path_rate
													SCREAMING_SNAKE_CASE					:      Any     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			use_absolute_embeddings
													SCREAMING_SNAKE_CASE					:      List[str]     					=			layer_norm_eps
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      Dict     					=			encoder_stride
													# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
													# this indicates the channel dimension after the last stage of the model
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			int(embed_dim * 2 ** (len(lowerCAmelCase__				) - 1)				)
													SCREAMING_SNAKE_CASE					:      int     					=			(0, 0, 0, 0)




