import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

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


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

require_version('tokenizers>=0.13.3')

if is_sentencepiece_available():
			from .tokenization_llama import LlamaTokenizer
else:
			a_					      = None

a_					      = logging.get_logger(__name__)
a_					      = {'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

a_      ,				a_					      = '[INST]', '[/INST]'
a_      ,				a_					      = '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
a_					      = 'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'





class 	_lowercase		(      snake_case_ ):
	lowercase         =     VOCAB_FILES_NAMES
	lowercase         =     LlamaTokenizer
	lowercase         =     'left'
	lowercase         =     ['input_ids', 'attention_mask']

	def __init__(				self			:							List[Any]     ,   snake_case			:							int=None     ,   snake_case			:							str=None     ,   snake_case			:							Tuple=False     ,   snake_case			:							List[str]="<unk>"     ,   snake_case			:							str="<s>"     ,   snake_case			:							Optional[int]="</s>"     ,   snake_case			:							Union[str, Any]=True     ,   snake_case			:							List[str]=False     ,   **snake_case			:							int     ,   )     ->		int:

		"""simple docstring"""





		super().__init__(
		    vocab_file=snake_case     ,   tokenizer_file=snake_case     ,   clean_up_tokenization_spaces=snake_case     ,   unk_token=snake_case     ,   bos_token=snake_case     ,   eos_token=snake_case     ,   **snake_case     ,   )
		UpperCamelCase_      :		List[str]				=      add_bos_token
		UpperCamelCase_      :		str				=      add_eos_token
		self.update_post_processor()

		UpperCamelCase_      :		List[str]				=      vocab_file
		UpperCamelCase_      :		str				=      False if not self.vocab_file else True

	def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		Dict:

		"""simple docstring"""





		UpperCamelCase_      :		Dict				=      self.bos_token
		UpperCamelCase_      :		int				=      self.bos_token_id

		UpperCamelCase_      :		List[str]				=      self.eos_token
		UpperCamelCase_      :		List[Any]				=      self.eos_token_id

		UpperCamelCase_      :		Optional[int]				=      f"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
		UpperCamelCase_      :		Optional[Any]				=      f"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"

		UpperCamelCase_      :		int				=      []
		if self.add_bos_token:
			special_tokens.append((bos, bos_token_id)    )
		if self.add_eos_token:
			special_tokens.append((eos, eos_token_id)    )
		UpperCamelCase_      :		Optional[int]				=      processors.TemplateProcessing(
		    single=snake_case     ,   pair=snake_case     ,   special_tokens=snake_case    )

	@property
	def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		int:

		"""simple docstring"""





		return self._add_eos_token

	@property
	def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		Union[str, Any]:

		"""simple docstring"""





		return self._add_bos_token

	@add_eos_token.setter
	def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							Optional[int]    )     ->		List[Any]:

		"""simple docstring"""





		UpperCamelCase_      :		Tuple				=      value
		self.update_post_processor()

	@add_bos_token.setter
	def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							Union[str, Any]    )     ->		List[Any]:

		"""simple docstring"""





		UpperCamelCase_      :		Tuple				=      value
		self.update_post_processor()

	def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							str     ,   snake_case			:							Optional[str] = None    )     ->		Tuple[str]:

		"""simple docstring"""





		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(snake_case    ):
			logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
			return
		UpperCamelCase_      :		Optional[int]				=      os.path.join(
		    snake_case     ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

		if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case    ):
			copyfile(self.vocab_file     ,   snake_case    )

		return (out_vocab_file,)


	def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]     ,   snake_case			:							"Conversation"    )     ->		Optional[int]:

		"""simple docstring"""





		UpperCamelCase_      :		Union[str, Any]				=      list(conversation.iter_texts()    )
		if not all([is_user for is_user, msg in dialogue[::2]]    ) or not all(
		    [not is_user for is_user, msg in dialogue[1::2]]    ):
			raise ValueError(
			    'The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'    )

		UpperCamelCase_      :		Optional[Any]				=      []
		if len(conversation.past_user_inputs    ) > 0:
			if not conversation.past_user_inputs[0].startswith(snake_case    ) or E_SYS not in conversation.past_user_inputs[0]:
				UpperCamelCase_      :		Tuple				=      (
				    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
				)
		elif not dialogue[0][1].startswith(snake_case    ) or E_SYS not in dialogue[0][1]:
			UpperCamelCase_      :		int				=      (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

		dialog_tokens += sum(
		    [
		        [self.bos_token_id]
		        + self.encode(
		            f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "     ,   add_special_tokens=snake_case    )
		        + [self.eos_token_id]
		        for prompt, answer in zip(dialogue[::2]     ,   dialogue[1::2]    )
		    ]     ,   []     ,   )
		if not (dialogue[-1][0]):
			raise ValueError(f"Last message must be from user, got {dialogue[-1]['role']}"    )
		dialog_tokens += [self.bos_token_id] + self.encode(
		    f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"     ,   add_special_tokens=snake_case    )
		return dialog_tokens




from typing import Any

import numpy as np





class 	_lowercase		:

 def __init__(				self			:							Dict     ,   snake_case			:							np.ndarray     ,   snake_case			:							int    )     ->		None:

  """simple docstring"""





  if np.any(tableau[:, -1]     ,   where=tableau[:, -1] < 0    ):
   raise ValueError('RHS must be > 0'    )

  UpperCamelCase_      :		List[str]				=      tableau
  UpperCamelCase_,				UpperCamelCase_      :		Tuple				=      tableau.shape

  # Number of decision variables x1, x2, x3...
  UpperCamelCase_      :		Dict				=      n_vars

  # Number of artificial variables to be minimised
  UpperCamelCase_      :		str				=      len(np.where(tableau[self.n_vars : -1] == -1    )[0]    )

  # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
  UpperCamelCase_      :		int				=      (self.n_art_vars > 0) + 1

  # Number of slack variables added to make inequalities into equalities
  UpperCamelCase_      :		Union[str, Any]				=      self.n_rows - self.n_stages

  # Objectives for each stage
  UpperCamelCase_      :		Dict				=      ['max']

  # In two stage simplex, first minimise then maximise
  if self.n_art_vars:
   self.objectives.append('min'    )

  UpperCamelCase_      :		str				=      ['']

  # Index of current pivot row and column
  UpperCamelCase_      :		str				=      None
  UpperCamelCase_      :		Optional[int]				=      None

  # Does objective row only contain (non)-negative values?
  UpperCamelCase_      :		Any				=      False

 @staticmethod
 def 		SCREAMING_SNAKE_CASE__     (				*snake_case			:							int    )     ->		list[str]:

  """simple docstring"""





  if len(snake_case    ) != 3:
   raise ValueError('Must provide n_vars, n_slack, and n_art_vars'    )

  if not all(x >= 0 and isinstance(snake_case     ,   snake_case    ) for x in args    ):
   raise ValueError('All arguments must be non-negative integers'    )

  # decision | slack | artificial
  UpperCamelCase_      :		Tuple				=      ['x', 's', 'a']
  UpperCamelCase_      :		Optional[int]				=      []
  for i in range(3    ):
   for j in range(args[i]    ):
    titles.append(string_starts[i] + str(j + 1    )    )
  titles.append('RHS'    )
  return titles

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							np.ndarray    )     ->		tuple[Any, Any]:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      self.objectives[-1]

  # Find entries of highest magnitude in objective rows
  UpperCamelCase_      :		str				=      (objective == 'min') - (objective == 'max')
  UpperCamelCase_      :		str				=      np.argmax(sign * tableau[0, : self.n_vars]    )

  # Choice is only valid if below 0 for maximise, and above for minimise
  if sign * self.tableau[0, col_idx] <= 0:
   UpperCamelCase_      :		Tuple				=      True
   return 0, 0

  # Pivot row is chosen as having the lowest quotient when elements of
  # the pivot column divide the right-hand side

  # Slice excluding the objective rows
  UpperCamelCase_      :		Dict				=      slice(self.n_stages     ,   self.n_rows    )

  # RHS
  UpperCamelCase_      :		str				=      tableau[s, -1]

  # Elements of pivot column within slice
  UpperCamelCase_      :		List[Any]				=      tableau[s, col_idx]

  # Array filled with nans
  UpperCamelCase_      :		Optional[int]				=      np.full(self.n_rows - self.n_stages     ,   np.nan    )

  # If element in pivot column is greater than zeron_stages, return
  # quotient or nan otherwise
  UpperCamelCase_      :		int				=      np.divide(snake_case     ,   snake_case     ,   out=snake_case     ,   where=divisor > 0    )

  # Arg of minimum quotient excluding the nan values. n_stages is added
  # to compensate for earlier exclusion of objective columns
  UpperCamelCase_      :		int				=      np.nanargmin(snake_case    ) + self.n_stages
  return row_idx, col_idx

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							np.ndarray     ,   snake_case			:							int     ,   snake_case			:							int    )     ->		np.ndarray:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      tableau[row_idx].copy()

  UpperCamelCase_      :		List[Any]				=      piv_row[col_idx]

  # Entry becomes 1
  piv_row *= 1 / piv_val

  # Variable in pivot column becomes basic, ie the only non-zero entry
  for idx, coeff in enumerate(tableau[:, col_idx]    ):
   tableau[idx] += -coeff * piv_row
  UpperCamelCase_      :		Any				=      piv_row
  return tableau

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							np.ndarray    )     ->		np.ndarray:

  """simple docstring"""





  self.objectives.pop()

  if not self.objectives:
   return tableau

  # Slice containing ids for artificial columns
  UpperCamelCase_      :		int				=      slice(-self.n_art_vars - 1     ,   -1    )

  # Delete the artificial variable columns
  UpperCamelCase_      :		Any				=      np.delete(snake_case     ,   snake_case     ,   axis=1    )

  # Delete the objective row of the first stage
  UpperCamelCase_      :		Dict				=      np.delete(snake_case     ,   0     ,   axis=0    )

  UpperCamelCase_      :		List[Any]				=      1
  self.n_rows -= 1
  UpperCamelCase_      :		int				=      0
  UpperCamelCase_      :		Optional[int]				=      False
  return tableau

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		dict[Any, Any]:

  """simple docstring"""





  for _ in range(1_0_0    ):
   # Completion of each stage removes an objective. If both stages
   # are complete, then no objectives are left
   if not self.objectives:
    UpperCamelCase_      :		Any				=      self.generate_col_titles(
        self.n_vars     ,   self.n_slack     ,   self.n_art_vars    )

    # Find the values of each variable at optimal solution
    return self.interpret_tableau(self.tableau     ,   self.col_titles    )

   UpperCamelCase_,				UpperCamelCase_      :		Tuple				=      self.find_pivot(self.tableau    )

   # If there are no more negative values in objective row
   if self.stop_iter:
    # Delete artificial variable columns and rows. Update attributes
    UpperCamelCase_      :		Union[str, Any]				=      self.change_stage(self.tableau    )
   else:
    UpperCamelCase_      :		Union[str, Any]				=      self.pivot(self.tableau     ,   snake_case     ,   snake_case    )
  return {}


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							np.ndarray     ,   snake_case			:							list[str]    )     ->		dict[str, float]:

  """simple docstring"""





  UpperCamelCase_      :		str				=      {'P': abs(tableau[0, -1]    )}

  for i in range(self.n_vars    ):
   # Gives ids of nonzero entries in the ith column
   UpperCamelCase_      :		Dict				=      np.nonzero(tableau[:, i]    )
   UpperCamelCase_      :		Union[str, Any]				=      len(nonzero[0]    )

   # First entry in the nonzero ids
   UpperCamelCase_      :		Optional[int]				=      nonzero[0][0]
   UpperCamelCase_      :		Union[str, Any]				=      tableau[nonzero_rowidx, i]

   # If there is only one nonzero value in column, which is one
   if n_nonzero == nonzero_val == 1:
    UpperCamelCase_      :		Optional[int]				=      tableau[nonzero_rowidx, -1]
    UpperCamelCase_      :		Union[str, Any]				=      rhs_val

        # Check for basic variables
  for title in col_titles:
   # Don't add RHS or slack variables to output dict
   if title[0] not in "R-s-a":
    output_dict.setdefault(snake_case     ,   0    )
  return output_dict


if __name__ == "__main__":
   import doctest

   doctest.testmod()



