



'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase							:List[str]       = logging.get_logger(__name__)

lowerCamelCase							:List[Any]       = {
    '''xlm-mlm-en-2048''': '''https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json''',
    '''xlm-mlm-ende-1024''': '''https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-enfr-1024''': '''https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json''',
    '''xlm-mlm-enro-1024''': '''https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json''',
    '''xlm-mlm-tlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-mlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-clm-enfr-1024''': '''https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json''',
    '''xlm-clm-ende-1024''': '''https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-17-1280''': '''https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json''',
    '''xlm-mlm-100-1280''': '''https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json''',
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = 'xlm'
	__SCREAMING_SNAKE_CASE       :		int		      = {
	    'hidden_size': 'emb_dim',
	    'num_attention_heads': 'n_heads',
	    'num_hidden_layers': 'n_layers',
	    'n_words': 'vocab_size',  # For backward compatibility
	}


	def __init__(self	,				lowercase=30145	,				lowercase=2048	,				lowercase=12	,				lowercase=16	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=True	,				lowercase=False	,				lowercase=False	,				lowercase=False	,				lowercase=1	,				lowercase=True	,				lowercase=512	,				lowercase=2048**-0.5	,				lowercase=1E-12	,				lowercase=0.02	,				lowercase=0	,				lowercase=1	,				lowercase=2	,				lowercase=3	,				lowercase=5	,				lowercase=True	,				lowercase="first"	,				lowercase=True	,				lowercase=None	,				lowercase=True	,				lowercase=0.1	,				lowercase=5	,				lowercase=5	,				lowercase=0	,				lowercase=0	,				lowercase=2	,				lowercase=0	,				**lowercase	,				):
				A_     :						List[str]     =					vocab_size
				A_     :						str     =					emb_dim
				A_     :						Optional[int]     =					n_layers
				A_     :						Tuple     =					n_heads
				A_     :						Dict     =					dropout
				A_     :						List[str]     =					attention_dropout
				A_     :						List[Any]     =					gelu_activation
				A_     :						Tuple     =					sinusoidal_embeddings
				A_     :						List[Any]     =					causal
				A_     :						str     =					asm
				A_     :						Optional[int]     =					n_langs
				A_     :						Optional[int]     =					use_lang_emb
				A_     :						Optional[int]     =					layer_norm_eps
				A_     :						int     =					bos_index
				A_     :						List[str]     =					eos_index
				A_     :						Any     =					pad_index
				A_     :						Any     =					unk_index
				A_     :						List[Any]     =					mask_index
				A_     :						str     =					is_encoder
				A_     :						Tuple     =					max_position_embeddings
				A_     :						Union[str, Any]     =					embed_init_std
				A_     :						str     =					init_std
				A_     :						Optional[int]     =					summary_type
				A_     :						Any     =					summary_use_proj
				A_     :						str     =					summary_activation
				A_     :						List[str]     =					summary_proj_to_labels
				A_     :						List[str]     =					summary_first_dropout
				A_     :						List[Any]     =					start_n_top
				A_     :						Optional[int]     =					end_n_top
				A_     :						Any     =					mask_token_id
				A_     :						List[Any]     =					lang_id

				if "n_words" in kwargs:
							A_     :						List[Any]     =					kwargs["""n_words"""]

				super().__init__(pad_token_id=lowercase	,				bos_token_id=lowercase	,				**lowercase							)







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	@property
	def    _a     (self							):
				if self.task == "multiple-choice":
							A_     :						Optional[Any]     =					{0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
							A_     :						Dict     =					{0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				        ("""token_type_ids""", dynamic_axis),
				    ]							)




'''simple docstring'''




def      a      (			):
			'''simple docstring'''





			A_     :						Optional[int]     =					[31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

			A_     :						Dict     =					6
			A_     :						List[Any]     =					1
			A_     :						Optional[Any]     =					19_01

			A_     :						Tuple     =					0

			while year < 20_01:
						day += 7

						if (year % 4 == 0 and year % 1_00 != 0) or (year % 4_00 == 0):
									if day > days_per_month[month - 1] and month != 2:
												month += 1
												A_     :						Optional[Any]     =					day - days_per_month[month - 2]
									elif day > 29 and month == 2:
												month += 1
												A_     :						str     =					day - 29
						else:
									if day > days_per_month[month - 1]:
												month += 1
												A_     :						List[str]     =					day - days_per_month[month - 2]

						if month > 12:
									year += 1
									A_     :						Tuple     =					1

						if year < 20_01 and day == 1:
									sundays += 1
			return sundays


if __name__ == "__main__":
						print(solution())