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

from ..utils.generic import ModelOutput

class __snake_case		(      __lowercase     ):
    def __init__(  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      dataset
           lowercase							:     Any       	=      process
           lowercase							:     Any       	=      params
    def __len__(  self      ):



           '''simple docstring'''







           return len(self.dataset      )
    def __getitem__(  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     str       	=      self.dataset[i]
           lowercase							:     Tuple       	=      self.process(snake_case		,**self.params      )
           return processed






class __snake_case		(      __lowercase     ):
    def __init__(  self		,snake_case		,snake_case		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     int       	=      loader
           lowercase							:     Optional[Any]       	=      infer
           lowercase							:     List[str]       	=      params
           if loader_batch_size == 1:
                  # Let's spare some time by deactivating altogether
                  lowercase							:     str       	=      None
           lowercase							:     Tuple       	=      loader_batch_size

           # Internal bookkeeping
           lowercase							:     List[str]       	=      None
           lowercase							:     Optional[int]       	=      None
    def __len__(  self      ):



           '''simple docstring'''







           return len(self.loader      )
    def __iter__(  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      iter(self.loader      )
           return self
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if isinstance(self._loader_batch_data		,torch.Tensor      ):
                  # Batch data is simple tensor, just fetch the slice
                  lowercase							:     List[Any]       	=      self._loader_batch_data[self._loader_batch_index]
           else:
                  # Batch data is assumed to be BaseModelOutput (or dict)
                  lowercase							:     Optional[Any]       	=      {}
                  for k, element in self._loader_batch_data.items():
                         if isinstance(snake_case		,snake_case      ):
                                # Convert ModelOutput to tuple first
                                lowercase							:     Tuple       	=      element.to_tuple()
                                if isinstance(element[0]		,torch.Tensor      ):
                                       lowercase							:     List[str]       	=      tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
                                elif isinstance(element[0]		,np.ndarray      ):
                                       lowercase							:     str       	=      tuple(np.expand_dims(el[self._loader_batch_index]		,0      ) for el in element      )
                                continue
                         if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case		,snake_case      ):
                                # Those are stored as lists of tensors so need specific unbatching.
                                if isinstance(element[0]		,torch.Tensor      ):
                                       lowercase							:     Tuple       	=      tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
                                elif isinstance(element[0]		,np.ndarray      ):
                                       lowercase							:     List[str]       	=      tuple(np.expand_dims(el[self._loader_batch_index]		,0      ) for el in element      )
                                continue
                         if element is None:
                                # This can happen for optional data that get passed around
                                lowercase							:     str       	=      None
                         elif isinstance(element[self._loader_batch_index]		,torch.Tensor      ):
                                # Take correct batch data, but make it looked like batch_size=1
                                # For compatibility with other methods within transformers

                                lowercase							:     Tuple       	=      element[self._loader_batch_index].unsqueeze(0      )
                         elif isinstance(element[self._loader_batch_index]		,np.ndarray      ):
                                # Take correct batch data, but make it looked like batch_size=1
                                # For compatibility with other methods within transformers
                                lowercase							:     Union[str, Any]       	=      np.expand_dims(element[self._loader_batch_index]		,0      )
                         else:
                                # This is typically a list, so no need to `unsqueeze`.
                                lowercase							:     Union[str, Any]       	=      element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
                  lowercase							:     Optional[int]       	=      self._loader_batch_data.__class__(snake_case      )
           self._loader_batch_index += 1
           return result
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
                  # We are currently unrolling a batch so we just need to return
                  # the current item within a batch
                  return self.loader_batch_item()

           # We're out of items within a batch
           lowercase							:     Optional[int]       	=      next(self.iterator      )
           lowercase							:     Any       	=      self.infer(snake_case		,**self.params      )
           # We now have a batch of "inferred things".
           if self.loader_batch_size is not None:
                  # Try to infer the size of the batch
                  if isinstance(snake_case		,torch.Tensor      ):
                         lowercase							:     Optional[Any]       	=      processed
                  else:
                         lowercase							:     List[Any]       	=      list(processed.keys()      )[0]
                         lowercase							:     Optional[int]       	=      processed[key]
                  if isinstance(snake_case		,snake_case      ):
                         lowercase							:     Dict       	=      len(snake_case      )
                  else:
                         lowercase							:     List[Any]       	=      first_tensor.shape[0]
                  if 0 < observed_batch_size < self.loader_batch_size:
                         # could be last batch so we can't unroll as many
                         # elements.
                         lowercase							:     Dict       	=      observed_batch_size
                  # Setting internal index to unwrap the batch
                  lowercase							:     Union[str, Any]       	=      processed
                  lowercase							:     Optional[Any]       	=      0
                  return self.loader_batch_item()
           else:
                  # We're not unrolling batches
                  return processed






class __snake_case		(      __lowercase     ):
    def __init__(  self		,snake_case		,snake_case		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           super().__init__(snake_case		,snake_case		,snake_case      )
    def __iter__(  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      iter(self.loader      )
           lowercase							:     int       	=      None
           return self
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.subiterator is None:
                  lowercase							:     List[Any]       	=      self.infer(next(self.iterator      )		,**self.params      )
           try:
                  # Try to return next item
                  lowercase							:     List[str]       	=      next(self.subiterator      )
           except StopIteration:
                  # When a preprocess iterator ends, we can start lookig at the next item
                  # ChunkIterator will keep feeding until ALL elements of iterator
                  # all have created their subiterator and have been iterating against.
                  #
                  # Another way to look at it, is we're basically flattening lists of lists
                  # into a single list, but with generators
                  lowercase							:     Union[str, Any]       	=      self.infer(next(self.iterator      )		,**self.params      )
                  lowercase							:     Any       	=      next(self.subiterator      )
           return processed






class __snake_case		(      __lowercase     ):
    def __iter__(  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      iter(self.loader      )
           return self
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      False
           lowercase							:     Any       	=      []
           if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
                  while self._loader_batch_index < self.loader_batch_size:
                         lowercase							:     List[str]       	=      self.loader_batch_item()
                         lowercase							:     Any       	=      item.pop("""is_last"""      )
                         accumulator.append(snake_case      )
                         if is_last:
                                return accumulator

           while not is_last:
                  lowercase							:     Tuple       	=      self.infer(next(self.iterator      )		,**self.params      )
                  if self.loader_batch_size is not None:
                         if isinstance(snake_case		,torch.Tensor      ):
                                lowercase							:     Dict       	=      processed
                         else:
                                lowercase							:     List[str]       	=      list(processed.keys()      )[0]
                                lowercase							:     Tuple       	=      processed[key]
                         if isinstance(snake_case		,snake_case      ):
                                lowercase							:     Union[str, Any]       	=      len(snake_case      )
                         else:
                                lowercase							:     Optional[Any]       	=      first_tensor.shape[0]
                         if 0 < observed_batch_size < self.loader_batch_size:
                                # could be last batch so we can't unroll as many
                                # elements.
                                lowercase							:     Optional[int]       	=      observed_batch_size
                         lowercase							:     Any       	=      processed
                         lowercase							:     Tuple       	=      0
                         while self._loader_batch_index < self.loader_batch_size:
                                lowercase							:     int       	=      self.loader_batch_item()
                                lowercase							:     Union[str, Any]       	=      item.pop("""is_last"""      )
                                accumulator.append(snake_case      )
                                if is_last:
                                       return accumulator
                  else:
                         lowercase							:     Dict       	=      processed
                         lowercase							:     Optional[Any]       	=      item.pop("""is_last"""      )
                         accumulator.append(snake_case      )
           return accumulator






class __snake_case		(      __lowercase     ):
    def __init__(  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      dataset
           lowercase							:     Optional[Any]       	=      key
    def __len__(  self      ):



           '''simple docstring'''







           return len(self.dataset      )
    def __getitem__(  self		,snake_case      ):



           '''simple docstring'''







           return self.dataset[i][self.key]






class __snake_case		(      __lowercase     ):
    def __init__(  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      dataset
           lowercase							:     Optional[int]       	=      keya
           lowercase							:     Tuple       	=      keya
    def __len__(  self      ):



           '''simple docstring'''







           return len(self.dataset      )
    def __getitem__(  self		,snake_case      ):



           '''simple docstring'''







           return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}


import argparse
import datetime




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					{
							    "0": "Sunday",
							    "1": "Monday",
							    "2": "Tuesday",
							    "3": "Wednesday",
							    "4": "Thursday",
							    "5": "Friday",
							    "6": "Saturday",
							}

							__UpperCamelCase	:   List[str]			   =					{0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}

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

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

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

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

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

							# Get year
							__UpperCamelCase	:   int			   =					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
							__UpperCamelCase	:   Union[str, Any]			   =					datetime.date(int(_lowerCamelCase)						,  int(_lowerCamelCase)						,  int(_lowerCamelCase))

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		lowercase  :					Optional[int]													=       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  :					Tuple													=       parser.parse_args()
		zeller(args.date_input)