
"""simple docstring"""







import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_ta import TaTokenizer
else:
       lowerCamelCase__           =	None


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__           =	{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase__           =	{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}




class 				A__ (			_lowerCamelCase):
  A_		: List[str]            =			VOCAB_FILES_NAMES
  A_		: int            =			PRETRAINED_VOCAB_FILES_MAP
  A_		: List[Any]            =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_		: List[Any]            =			['input_ids', 'attention_mask']
  A_		: Any            =			TaTokenizer

  A_		: List[int]            =			[]

  def __init__(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE="</s>" ,	_SCREAMING_SNAKE_CASE="<unk>" ,	_SCREAMING_SNAKE_CASE="<pad>" ,	_SCREAMING_SNAKE_CASE=1_00 ,	_SCREAMING_SNAKE_CASE=None ,	**_SCREAMING_SNAKE_CASE ,	):
   # Add extra_ids to the special token list
   if extra_ids > 0 and additional_special_tokens is None:
    __lowerCAmelCase    :		Optional[int]		    =   [f"<extra_id_{i}>" for i in range(_SCREAMING_SNAKE_CASE				)]
   elif extra_ids > 0 and additional_special_tokens is not None:
    # Check that we have the right number of extra special tokens
    __lowerCAmelCase    :		Union[str, Any]		    =   len(set(filter(lambda _SCREAMING_SNAKE_CASE				: bool('extra_id_' in str(_SCREAMING_SNAKE_CASE				)				) ,	_SCREAMING_SNAKE_CASE				)				)				)
    if extra_tokens != extra_ids:
     raise ValueError(
         f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
         ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
         ' tokens'				)

   super().__init__(
       _SCREAMING_SNAKE_CASE ,	tokenizer_file=_SCREAMING_SNAKE_CASE ,	eos_token=_SCREAMING_SNAKE_CASE ,	unk_token=_SCREAMING_SNAKE_CASE ,	pad_token=_SCREAMING_SNAKE_CASE ,	extra_ids=_SCREAMING_SNAKE_CASE ,	additional_special_tokens=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)

   __lowerCAmelCase    :		List[Any]		    =   vocab_file
   __lowerCAmelCase    :		int		    =   False if not self.vocab_file else True
   __lowerCAmelCase    :		Any		    =   extra_ids

  @staticmethod
  def 							__lowerCamelCase							(   _SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
   if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
    __lowerCAmelCase    :		Optional[Any]		    =   TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
    if init_max_model_length is not None and init_max_model_length != max_model_length:
     return init_max_model_length
    elif init_max_model_length is None:
     warnings.warn(
         'This tokenizer was incorrectly instantiated with a model max length of'
         f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
         ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
         ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
         f" {pretrained_model_name_or_path} automatically truncating your input to"
         f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
         f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
         ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
         ' instantiate this tokenizer with `model_max_length` set to your preferred value.' ,	_SCREAMING_SNAKE_CASE ,	)

   return max_model_length

  def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
   if not self.can_save_slow_tokenizer:
    raise ValueError(
        'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
        'tokenizer.'				)

   if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
    logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
    return
   __lowerCAmelCase    :		List[Any]		    =   os.path.join(
       _SCREAMING_SNAKE_CASE ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

   if os.path.abspath(self.vocab_file				) != os.path.abspath(_SCREAMING_SNAKE_CASE				):
    copyfile(self.vocab_file ,	_SCREAMING_SNAKE_CASE				)
    logger.info(f"Copy vocab file to {out_vocab_file}"				)

   return (out_vocab_file,)

  def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
   __lowerCAmelCase    :		Any		    =   token_ids_a + [self.eos_token_id]
   if token_ids_a is None:
    return self.prefix_tokens + token_ids_a
   else:
    __lowerCAmelCase    :		str		    =   token_ids_a + [self.eos_token_id]
    return self.prefix_tokens + token_ids_a + token_ids_a

  def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
   __lowerCAmelCase    :		Dict		    =   [self.eos_token_id]

   if token_ids_a is None:
    return len(token_ids_a + eos				) * [0]
   return len(token_ids_a + eos + token_ids_a + eos				) * [0]

  def 							__lowerCamelCase							(   self				):
   return list(
       set(filter(lambda _SCREAMING_SNAKE_CASE				: bool(re.search(R'<extra_id_\d+>' ,	_SCREAMING_SNAKE_CASE				)				) is not None ,	self.additional_special_tokens				)				)				)







  def 							__lowerCamelCase							(   self				):
   return [self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				) for token in self.get_sentinel_tokens()]

"""simple docstring"""







# Imports
import numpy as np




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			self.set_matricies(red=_SCREAMING_SNAKE_CASE ,	green=_SCREAMING_SNAKE_CASE ,	blue=_SCREAMING_SNAKE_CASE ,	red_edge=_SCREAMING_SNAKE_CASE ,	nir=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			if red is not None:
				__lowerCAmelCase    :		Optional[int]		    =   red
			if green is not None:
				__lowerCAmelCase    :		Any		    =   green
			if blue is not None:
				__lowerCAmelCase    :		str		    =   blue
			if red_edge is not None:
				__lowerCAmelCase    :		Dict		    =   red_edge
			if nir is not None:
				__lowerCAmelCase    :		Dict		    =   nir
			return True

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE="" ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			self.set_matricies(red=_SCREAMING_SNAKE_CASE ,	green=_SCREAMING_SNAKE_CASE ,	blue=_SCREAMING_SNAKE_CASE ,	red_edge=_SCREAMING_SNAKE_CASE ,	nir=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[int]		    =   {
			    'ARVI2': self.arvaa,
			    'CCCI': self.ccci,
			    'CVI': self.cvi,
			    'GLI': self.gli,
			    'NDVI': self.ndvi,
			    'BNDVI': self.bndvi,
			    'redEdgeNDVI': self.red_edge_ndvi,
			    'GNDVI': self.gndvi,
			    'GBNDVI': self.gbndvi,
			    'GRNDVI': self.grndvi,
			    'RBNDVI': self.rbndvi,
			    'PNDVI': self.pndvi,
			    'ATSAVI': self.atsavi,
			    'BWDRVI': self.bwdrvi,
			    'CIgreen': self.ci_green,
			    'CIrededge': self.ci_rededge,
			    'CI': self.ci,
			    'CTVI': self.ctvi,
			    'GDVI': self.gdvi,
			    'EVI': self.evi,
			    'GEMI': self.gemi,
			    'GOSAVI': self.gosavi,
			    'GSAVI': self.gsavi,
			    'Hue': self.hue,
			    'IVI': self.ivi,
			    'IPVI': self.ipvi,
			    'I': self.i,
			    'RVI': self.rvi,
			    'MRVI': self.mrvi,
			    'MSAVI': self.m_savi,
			    'NormG': self.norm_g,
			    'NormNIR': self.norm_nir,
			    'NormR': self.norm_r,
			    'NGRDI': self.ngrdi,
			    'RI': self.ri,
			    'S': self.s,
			    'IF': self._if,
			    'DVI': self.dvi,
			    'TVI': self.tvi,
			    'NDRE': self.ndre,
			}

			try:
				return funcs[index]()
			except KeyError:
				print('Index not in the list!'				)
				return False

		def 							__lowerCamelCase							(   self				):
			return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))

		def 							__lowerCamelCase							(   self				):
			return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
			    (self.nir - self.red) / (self.nir + self.red)
			)

		def 							__lowerCamelCase							(   self				):
			return self.nir * (self.red / (self.green**2))

		def 							__lowerCamelCase							(   self				):
			return (2 * self.green - self.red - self.blue) / (
			    2 * self.green + self.red + self.blue
			)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - self.red) / (self.nir + self.red)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - self.blue) / (self.nir + self.blue)

		def 							__lowerCamelCase							(   self				):
			return (self.redEdge - self.red) / (self.redEdge + self.red)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - self.green) / (self.nir + self.green)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - (self.green + self.blue)) / (
			    self.nir + (self.green + self.blue)
			)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - (self.green + self.red)) / (
			    self.nir + (self.green + self.red)
			)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))

		def 							__lowerCamelCase							(   self				):
			return (self.nir - (self.green + self.red + self.blue)) / (
			    self.nir + (self.green + self.red + self.blue)
			)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0.08 ,	_SCREAMING_SNAKE_CASE=1.22 ,	_SCREAMING_SNAKE_CASE=0.03				):
			return a * (
			    (self.nir - a * self.red - b)
			    / (a * self.nir + self.red - a * b + x * (1 + a**2))
			)

		def 							__lowerCamelCase							(   self				):
			return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)

		def 							__lowerCamelCase							(   self				):
			return (self.nir / self.green) - 1

		def 							__lowerCamelCase							(   self				):
			return (self.nir / self.redEdge) - 1

		def 							__lowerCamelCase							(   self				):
			return (self.red - self.blue) / self.red

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.ndvi()
			return ((ndvi + 0.5) / (abs(ndvi + 0.5				))) * (abs(ndvi + 0.5				) ** (1 / 2))

		def 							__lowerCamelCase							(   self				):
			return self.nir - self.green

		def 							__lowerCamelCase							(   self				):
			return 2.5 * (
			    (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
			)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
			    self.nir + self.red + 0.5
			)
			return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0.16				):
			return (self.nir - self.green) / (self.nir + self.green + y)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0.5				):
			return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)

		def 							__lowerCamelCase							(   self				):
			return np.arctan(
			    ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			return (self.nir - b) / (a * self.red)

		def 							__lowerCamelCase							(   self				):
			return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)

		def 							__lowerCamelCase							(   self				):
			return (self.red + self.green + self.blue) / 30.5

		def 							__lowerCamelCase							(   self				):
			return self.nir / self.red

		def 							__lowerCamelCase							(   self				):
			return (self.rvi() - 1) / (self.rvi() + 1)

		def 							__lowerCamelCase							(   self				):
			return (
			    (2 * self.nir + 1)
			    - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
			) / 2

		def 							__lowerCamelCase							(   self				):
			return self.green / (self.nir + self.red + self.green)

		def 							__lowerCamelCase							(   self				):
			return self.nir / (self.nir + self.red + self.green)

		def 							__lowerCamelCase							(   self				):
			return self.red / (self.nir + self.red + self.green)

		def 							__lowerCamelCase							(   self				):
			return (self.green - self.red) / (self.green + self.red)

		def 							__lowerCamelCase							(   self				):
			return (self.red - self.green) / (self.red + self.green)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   np.max([np.max(self.red				), np.max(self.green				), np.max(self.blue				)]				)
			__lowerCAmelCase    :		Dict		    =   np.min([np.min(self.red				), np.min(self.green				), np.min(self.blue				)]				)
			return (max_value - min_value) / max_value

		def 							__lowerCamelCase							(   self				):
			return (2 * self.red - self.green - self.blue) / (self.green - self.blue)

		def 							__lowerCamelCase							(   self				):
			return self.nir / self.red

		def 							__lowerCamelCase							(   self				):
			return (self.ndvi() + 0.5) ** (1 / 2)







		def 							__lowerCamelCase							(   self				):
			return (self.nir - self.redEdge) / (self.nir + self.redEdge)