
'''simple docstring'''







import argparse
import datetime







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	str:
	_snake_case   						=		{
	    '0': 'Sunday',
	    '1': 'Monday',
	    '2': 'Tuesday',
	    '3': 'Wednesday',
	    '4': 'Thursday',
	    '5': 'Friday',
	    '6': 'Saturday',
	}

	_snake_case   						=		{0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}

	# Validate
	if not 0 < len(__A	) < 11:
		raise ValueError('Must be 10 characters long'	)

	# Get month
	_snake_case   						=		int(date_input[0] + date_input[1]	)
	# Validate
	if not 0 < m < 13:
		raise ValueError('Month must be between 1 - 12'	)

	_snake_case   						=		date_input[2]
	# Validate
	if sep_a not in ["-", "/"]:
		raise ValueError('Date separator must be \'-\' or \'/\''	)

	# Get day
	_snake_case   						=		int(date_input[3] + date_input[4]	)
	# Validate
	if not 0 < d < 32:
		raise ValueError('Date must be between 1 - 31'	)

	# Get second separator
	_snake_case   						=		date_input[5]
	# Validate
	if sep_a not in ["-", "/"]:
		raise ValueError('Date separator must be \'-\' or \'/\''	)

	# Get year
	_snake_case   						=		int(date_input[6] + date_input[7] + date_input[8] + date_input[9]	)
	# Arbitrary year range
	if not 45 < y < 8_500:
		raise ValueError(
		    'Year out of range. There has to be some sort of limit...right?'	)

	# Get datetime obj for validation
	_snake_case   						=		datetime.date(int(__A	)			,				int(__A	)			,				int(__A	)	)

	# Start math
	if m <= 2:
		_snake_case   						=		y - 1
		_snake_case   						=		m + 12
	# maths var
	_snake_case   						=		int(str(__A	)[:2]	)
	_snake_case   						=		int(str(__A	)[2:]	)
	_snake_case   						=		int(2.6 * m - 5.3_9	)
	_snake_case   						=		int(c / 4	)
	_snake_case   						=		int(k / 4	)
	_snake_case   						=		int(d + k	)
	_snake_case   						=		int(t + u + v + x	)
	_snake_case   						=		int(z - (2 * c)	)
	_snake_case   						=		round(w % 7	)
	# End math

	# Validate math
	if f != convert_datetime_days[dt_ck.weekday()]:
		raise AssertionError('The date was evaluated incorrectly. Contact developer.'	)

	# Response
	_snake_case   						=		F'Your date {date_input}, is a {days[str(__A	)]}!'
	return response


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					lowercase    :							Union[str, Any]      =					argparse.ArgumentParser(
					    description=(
					        "Find out what day of the week nearly any date is or was. Enter "
					        "date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
					    )
					)
					parser.add_argument(
					    "date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
					)
					lowercase    :							Union[str, Any]      =					parser.parse_args()
					zeller(args.date_input)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase           =					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''MvpTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''MvpForCausalLM''',
           '''MvpForConditionalGeneration''',
           '''MvpForQuestionAnswering''',
           '''MvpForSequenceClassification''',
           '''MvpModel''',
           '''MvpPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
