






'''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 		UpperCamelCase__     (unittest.TestCase      ):




		'''simple docstring'''





		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								lowerCamelCase__								=	3
								lowerCamelCase__								=	2_50

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






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	self._get_tensors(5							)

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

								self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase							)							)

								lowerCamelCase__							,      lowerCamelCase__								=	self._get_tensors(9							)
								self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase							)							)

								lowerCamelCase__							,      lowerCamelCase__								=	self._get_tensors(10							)
								self.assertTrue(criteria(_lowerCAmelCase ,_lowerCAmelCase							)							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	MaxLengthCriteria(max_length=10							)

								lowerCamelCase__							,      lowerCamelCase__								=	self._get_tensors(5							)
								self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase							)							)

								lowerCamelCase__							,      lowerCamelCase__								=	self._get_tensors(9							)
								self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase							)							)

								lowerCamelCase__							,      lowerCamelCase__								=	self._get_tensors(10							)
								self.assertTrue(criteria(_lowerCAmelCase ,_lowerCAmelCase							)							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	MaxNewTokensCriteria(start_length=5 ,max_new_tokens=5							)

								lowerCamelCase__							,      lowerCamelCase__								=	self._get_tensors(5							)
								self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase							)							)

								lowerCamelCase__							,      lowerCamelCase__								=	self._get_tensors(9							)
								self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase							)							)

								lowerCamelCase__							,      lowerCamelCase__								=	self._get_tensors(10							)
								self.assertTrue(criteria(_lowerCAmelCase ,_lowerCAmelCase							)							)

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






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	self._get_tensors(5							)

								lowerCamelCase__								=	MaxTimeCriteria(max_time=0.1							)
								self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase							)							)

								lowerCamelCase__								=	MaxTimeCriteria(max_time=0.1 ,initial_timestamp=time.time() - 0.2							)
								self.assertTrue(criteria(_lowerCAmelCase ,_lowerCAmelCase							)							)






		def       UpperCamelCase_       (   self							):
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10							)]							) ,10							)

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

								lowerCamelCase__								=	validate_stopping_criteria(StoppingCriteriaList() ,11							)

								self.assertEqual(len(_lowerCAmelCase							) ,1							)








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


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'donut-swin'

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

 def __init__(  self,		A_=224,		A_=4,		A_=3,		A_=96,		A_=[2, 2, 6, 2],		A_=[3, 6, 12, 24],		A_=7,		A_=4.0,		A_=True,		A_=0.0,		A_=0.0,		A_=0.1,		A_="gelu",		A_=False,		A_=0.02,		A_=1E-5,		**A_,		)	->       List[str]:
    super().__init__(**A_		)

    UpperCAmelCase__						=image_size
    UpperCAmelCase__						=patch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=embed_dim
    UpperCAmelCase__						=depths
    UpperCAmelCase__						=len(A_		)
    UpperCAmelCase__						=num_heads
    UpperCAmelCase__						=window_size
    UpperCAmelCase__						=mlp_ratio
    UpperCAmelCase__						=qkv_bias
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=drop_path_rate
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=use_absolute_embeddings
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=initializer_range
    # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
    # this indicates the channel dimension after the last stage of the model
    UpperCAmelCase__						=int(embed_dim * 2 ** (len(A_		) - 1)		)



