'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger



class      UpperCAmelCase    (		UpperCamelCase__   ):





       '''simple docstring'''



       def __init__(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase					)    ->    str:
        lowercase__							:       List[str]										=   params

        lowercase__							:       List[Any]										=   np.array(__A					)
        lowercase__							:       Optional[Any]										=   np.array([len(__A					) for t in data]					)

        self.check()
        self.remove_long_sequences()
        self.remove_empty_sequences()
        self.remove_unknown_sequences()
        self.check()
        self.print_statistics()

       def __getitem__(		self      ,  __lowerCAmelCase					)    ->    Optional[Any]:
        return (self.token_ids[index], self.lengths[index])

       def __len__(		self					)    ->    List[Any]:
        return len(self.lengths					)

       def _lowerCAmelCase(		self					)    ->    str:
        assert len(self.token_ids					) == len(self.lengths					)
        assert all(self.lengths[i] == len(self.token_ids[i]					) for i in range(len(self.lengths					)					)					)

       def _lowerCAmelCase(		self					)    ->    Tuple:
        lowercase__							:       List[Any]										=   self.params.max_model_input_size
        lowercase__							:       Tuple										=   self.lengths > max_len
        logger.info(F"""Splitting {sum(__A					)} too long sequences."""					)

        def divide_chunks(__lowerCAmelCase      ,  __lowerCAmelCase					):
         return [l[i : i + n] for i in range(0      ,  len(__A					)      ,  __A					)]

        lowercase__							:       List[str]										=   []
        lowercase__							:       Union[str, Any]										=   []
        if self.params.mlm:
         lowercase__					,				lowercase__							:       Union[str, Any]										=   self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
        else:
         lowercase__					,				lowercase__							:       int										=   self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

        for seq_, len_ in zip(self.token_ids      ,  self.lengths					):
         assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
         if len_ <= max_len:
          new_tok_ids.append(seq_					)
          new_lengths.append(len_					)
         else:
          lowercase__							:       Tuple										=   []
          for sub_s in divide_chunks(seq_      ,  max_len - 2					):
           if sub_s[0] != cls_id:
            lowercase__							:       Dict										=   np.insert(__A      ,  0      ,  __A					)
           if sub_s[-1] != sep_id:
            lowercase__							:       List[str]										=   np.insert(__A      ,  len(__A					)      ,  __A					)
           assert len(__A					) <= max_len
           assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
           sub_seqs.append(__A					)

          new_tok_ids.extend(__A					)
          new_lengths.extend([len(__A					) for l in sub_seqs]					)

        lowercase__							:       Tuple										=   np.array(__A					)
        lowercase__							:       Any										=   np.array(__A					)

       def _lowerCAmelCase(		self					)    ->    Optional[Any]:
        lowercase__							:       List[Any]										=   len(self					)
        lowercase__							:       Optional[int]										=   self.lengths > 11
        lowercase__							:       List[Any]										=   self.token_ids[indices]
        lowercase__							:       Optional[int]										=   self.lengths[indices]
        lowercase__							:       Optional[int]										=   len(self					)
        logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""					)

       def _lowerCAmelCase(		self					)    ->    Any:



        if "unk_token" not in self.params.special_tok_ids:
         return
        else:
         lowercase__							:       Union[str, Any]										=   self.params.special_tok_ids['''unk_token''']
        lowercase__							:       Tuple										=   len(self					)
        lowercase__							:       Tuple										=   np.array([np.count_nonzero(a == unk_token_id					) for a in self.token_ids]					)
        lowercase__							:       List[str]										=   (unk_occs / self.lengths) < 0.5
        lowercase__							:       List[Any]										=   self.token_ids[indices]
        lowercase__							:       Any										=   self.lengths[indices]
        lowercase__							:       int										=   len(self					)
        logger.info(F"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""					)

       def _lowerCAmelCase(		self					)    ->    List[str]:



        if not self.params.is_master:
         return
        logger.info(F"""{len(self					)} sequences"""					)
        # data_len = sum(self.lengths)
        # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
        # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

        # unk_idx = self.params.special_tok_ids['unk_token']
        # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
        # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')


       def _lowerCAmelCase(		self      ,  __lowerCAmelCase					)    ->    Union[str, Any]:
        lowercase__							:       str										=   [t[0] for t in batch]
        lowercase__							:       Union[str, Any]										=   [t[1] for t in batch]
        assert len(__A					) == len(__A					)

        # Max for paddings
        lowercase__							:       Dict										=   max(__A					)

        # Pad token ids
        if self.params.mlm:
         lowercase__							:       List[Any]										=   self.params.special_tok_ids['''pad_token''']
        else:
         lowercase__							:       Optional[int]										=   self.params.special_tok_ids['''unk_token''']
        lowercase__							:       List[Any]										=   [list(t.astype(__A					)					) + [pad_idx] * (max_seq_len_ - len(__A					)) for t in token_ids]
        assert len(tk_					) == len(__A					)
        assert all(len(__A					) == max_seq_len_ for t in tk_					)

        lowercase__							:       Tuple										=   torch.tensor(tk_					)  # (bs, max_seq_len_)
        lowercase__							:       Optional[Any]										=   torch.tensor(__A					)  # (bs)
        return tk_t, lg_t








import argparse
import datetime






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   {
		    """0""": """Sunday""",
		    """1""": """Monday""",
		    """2""": """Tuesday""",
		    """3""": """Wednesday""",
		    """4""": """Thursday""",
		    """5""": """Friday""",
		    """6""": """Saturday""",
		}

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

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

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

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

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

		# Get year
		SCREAMING_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
		SCREAMING_SNAKE_CASE__								=   datetime.date(int(UpperCamelCase__     )       , int(UpperCamelCase__     )       , int(UpperCamelCase__     )     )

		# Start math
		if m <= 2:
				SCREAMING_SNAKE_CASE__								=   y - 1
				SCREAMING_SNAKE_CASE__								=   m + 12
		# maths var
		SCREAMING_SNAKE_CASE__								=   int(str(UpperCamelCase__     )[:2]     )
		SCREAMING_SNAKE_CASE__								=   int(str(UpperCamelCase__     )[2:]     )
		SCREAMING_SNAKE_CASE__								=   int(2.6 * m - 5.3_9     )
		SCREAMING_SNAKE_CASE__								=   int(c / 4     )
		SCREAMING_SNAKE_CASE__								=   int(k / 4     )
		SCREAMING_SNAKE_CASE__								=   int(d + k     )
		SCREAMING_SNAKE_CASE__								=   int(t + u + v + x     )
		SCREAMING_SNAKE_CASE__								=   int(z - (2 * c)     )
		SCREAMING_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
		SCREAMING_SNAKE_CASE__								=   f'''Your date {date_input}, is a {days[str(UpperCamelCase__     )]}!'''
		return response


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	_lowerCamelCase 					=      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)'
	)
	_lowerCamelCase 					=      parser.parse_args()
	zeller(args.date_input)