




'''simple docstring'''


import unittest

from transformers import DonutProcessor


_snake_case				:				Optional[Any]       							=		'naver-clova-ix/donut-base'

class       A   (		unittest.TestCase   ):







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Any:


														"""simple docstring"""


														_a     					=    DonutProcessor.from_pretrained(lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							int:


														"""simple docstring"""


														_a     					=    {
														    '''name''': '''John Doe''',
														    '''age''': '''99''',
														    '''city''': '''Atlanta''',
														    '''state''': '''GA''',
														    '''zip''': '''30301''',
														    '''phone''': '''123-4567''',
														    '''nicknames''': [{'''nickname''': '''Johnny'''}, {'''nickname''': '''JD'''}],
														}

														_a     					=    (
														    '''<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'''
														    '''<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'''
														    '''<s_nicknames><s_nickname>Johnny</s_nickname>'''
														    '''<sep/><s_nickname>JD</s_nickname></s_nicknames>'''
														)
														_a     					=    self.processor.tokenajson(lowerCAmelCase_ )

														self.assertDictEqual(lowerCAmelCase_							,      lowerCAmelCase_ )







'''simple docstring'''


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


_snake_case				:				Dict       							=		logging.get_logger(__name__)

_snake_case				:				Optional[Any]       							=		{
    '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       A   (		_a					,_a   ):
							lowercase_          =    'nat'

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







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  str=4							,      lowerCAmelCase_	:  Any=3							,      lowerCAmelCase_	:  List[Any]=64							,      lowerCAmelCase_	:  Dict=[3, 4, 6, 5]							,      lowerCAmelCase_	:  Dict=[2, 4, 8, 16]							,      lowerCAmelCase_	:  str=7							,      lowerCAmelCase_	:  Dict=3.0							,      lowerCAmelCase_	:  int=True							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=0.0							,      lowerCAmelCase_	:  List[str]=0.1							,      lowerCAmelCase_	:  int="gelu"							,      lowerCAmelCase_	:  List[str]=0.0_2							,      lowerCAmelCase_	:  str=1e-5							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=None							,      lowerCAmelCase_	:  int=None							,      **lowerCAmelCase_	:  List[Any]							,      )      ->							Any:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_ )

														_a     					=    patch_size
														_a     					=    num_channels
														_a     					=    embed_dim
														_a     					=    depths
														_a     					=    len(lowerCAmelCase_ )
														_a     					=    num_heads
														_a     					=    kernel_size
														_a     					=    mlp_ratio
														_a     					=    qkv_bias
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    drop_path_rate
														_a     					=    hidden_act
														_a     					=    layer_norm_eps
														_a     					=    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(embed_dim * 2 ** (len(lowerCAmelCase_ ) - 1) )
														_a     					=    layer_scale_init_value
														_a     					=    ['''stem'''] + [F'stage{idx}' for idx in range(1							,      len(lowerCAmelCase_ ) + 1 )]
														_a					,							_a     					=    get_aligned_output_features_output_indices(
														    out_features=lowerCAmelCase_							,      out_indices=lowerCAmelCase_							,      stage_names=self.stage_names )

