


'''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:
		__UpperCAmelCase       =							None


__UpperCAmelCase       =							logging.get_logger(__name__)

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

__UpperCAmelCase       =							{
    "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
__UpperCAmelCase       =							{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}




class 		a__    (							UpperCamelCase_  ):





							'''simple docstring'''






							lowercase__      :       Tuple     						=				VOCAB_FILES_NAMES
							lowercase__      :       int     						=				PRETRAINED_VOCAB_FILES_MAP
							lowercase__      :       Union[str, Any]     						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__      :       Optional[int]     						=				["""input_ids""", """attention_mask"""]
							lowercase__      :       List[str]     						=				TaTokenizer

							lowercase__      :       List[int]     						=				[]







							def __init__(		self ,   lowerCamelCase_=None ,   lowerCamelCase_=None ,   lowerCamelCase_="</s>" ,   lowerCamelCase_="<unk>" ,   lowerCamelCase_="<pad>" ,   lowerCamelCase_=1_00 ,   lowerCamelCase_=None ,   **lowerCamelCase_ ,   )	->							Any:


														if extra_ids > 0 and additional_special_tokens is None:
																					lowerCAmelCase__	       =     [F"""<extra_id_{i}>""" for i in range(_a       )]
														elif extra_ids > 0 and additional_special_tokens is not None:
																					# Check that we have the right number of extra special tokens
																					lowerCAmelCase__	       =     len(set(filter(lambda lowerCamelCase_       : bool('''extra_id_''' in str(_a       )       ) ,   _a       )       )       )
																					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__(
														    _a ,   tokenizer_file=_a ,   eos_token=_a ,   unk_token=_a ,   pad_token=_a ,   extra_ids=_a ,   additional_special_tokens=_a ,   **_a ,   )

														lowerCAmelCase__	       =     vocab_file
														lowerCAmelCase__	       =     False if not self.vocab_file else True
														lowerCAmelCase__	       =     extra_ids







							@staticmethod
							def   __SCREAMING_SNAKE_CASE							(		lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Tuple:


														if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																					lowerCAmelCase__	       =     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.''' ,   _a ,   )

														return max_model_length







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							Tuple[str]:


														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(_a       ):
																					logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
																					return
														lowerCAmelCase__	       =     os.path.join(
														    _a ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

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

														return (out_vocab_file,)







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							List[int]:
														lowerCAmelCase__	       =     token_ids_a + [self.eos_token_id]
														if token_ids_a is None:
																					return self.prefix_tokens + token_ids_a
														else:
																					lowerCAmelCase__	       =     token_ids_a + [self.eos_token_id]
																					return self.prefix_tokens + token_ids_a + token_ids_a







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							List[int]:
														lowerCAmelCase__	       =     [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   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														return list(
														    set(filter(lambda lowerCamelCase_       : bool(re.search(r'''<extra_id_\d+>''' ,   _a       )       ) is not None ,   self.additional_special_tokens       )       )       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														return [self.convert_tokens_to_ids(_a       ) for token in self.get_sentinel_tokens()]



'''simple docstring'''






from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray


def  _snake_case       (					A			,   A			,   A			,   A			,   )						->   list[float]:
							lowerCAmelCase__       , lowerCAmelCase__	       =     coefficient_matrix.shape
							lowerCAmelCase__       , lowerCAmelCase__	       =     constant_matrix.shape

							if rowsa != colsa:
														lowerCAmelCase__	       =     F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
														raise ValueError(A	)

							if colsa != 1:
														lowerCAmelCase__	       =     F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
														raise ValueError(A	)

							if rowsa != rowsa:
														lowerCAmelCase__	       =     (
														    '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
														    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
														)
														raise ValueError(A	)

							if len(A	) != rowsa:
														lowerCAmelCase__	       =     (
														    '''Number of initial values must be equal to number of rows in coefficient '''
														    F"""matrix but received {len(A	)} and {rowsa}"""
														)
														raise ValueError(A	)

							if iterations <= 0:
														raise ValueError('''Iterations must be at least 1'''	)

							lowerCAmelCase__	       =     np.concatenate(
							    (coefficient_matrix, constant_matrix)			,   axis=1	)

							lowerCAmelCase__       , lowerCAmelCase__	       =     table.shape

							strictly_diagonally_dominant(A	)

							# Iterates the whole matrix for given number of times
							for _ in range(A	):
														lowerCAmelCase__	       =     []
														for row in range(A	):
																					lowerCAmelCase__	       =     0
																					for col in range(A	):
																												if col == row:
																																			lowerCAmelCase__	       =     table[row][col]
																												elif col == cols - 1:
																																			lowerCAmelCase__	       =     table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					lowerCAmelCase__	       =     (temp + val) / denom
																					new_val.append(A	)
														lowerCAmelCase__	       =     new_val

							return [float(A	) for i in new_val]


def  _snake_case       (					A	)						->   bool:
							lowerCAmelCase__       , lowerCAmelCase__	       =     table.shape

							lowerCAmelCase__	       =     True

							for i in range(0			,   A	):
														lowerCAmelCase__	       =     0
														for j in range(0			,   cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('''Coefficient matrix is not strictly diagonally dominant'''	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()