


"""simple docstring"""
import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


__SCREAMING_SNAKE_CASE							:    Optional[int]		=    get_logger(__name__)


__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    R'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'





class      __A     :





							'''simple docstring'''

							@add_start_docstrings(UpperCAmelCase_       )
							def __call__(				self							:      int		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      jnp.ndarray       )					->jnp.ndarray:



														"""simple docstring"""



														raise NotImplementedError(
														    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""       )





class      __A     :





							'''simple docstring'''

							@add_start_docstrings(UpperCAmelCase_       )
							def __call__(				self							:      Dict		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      jnp.ndarray       )					->jnp.ndarray:



														"""simple docstring"""



														raise NotImplementedError(
														    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""       )





class      __A     (snake_case__):





							'''simple docstring'''

							@add_start_docstrings(UpperCAmelCase_       )
							def __call__(				self							:      int		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      int		,       **UpperCAmelCase_							:      List[Any]       )					->jnp.ndarray:



														"""simple docstring"""



														for processor in self:
																					snake_case_           =		inspect.signature(processor.__call__       ).parameters
																					if len(UpperCAmelCase_       ) > 3:
																												if not all(arg in kwargs for arg in list(function_args.keys()       )[2:]       ):
																																			raise ValueError(
																																			    F"""Make sure that all the required parameters: {list(function_args.keys()       )} for """
																																			    F"""{processor.__class__} are passed to the logits processor."""       )
																												snake_case_           =		processor(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       **UpperCAmelCase_       )
																					else:
																												snake_case_           =		processor(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )
														return scores





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Optional[int]		,       UpperCAmelCase_							:      float       )					->Optional[int]:



														"""simple docstring"""



														if not isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ) or not (temperature > 0):
																					raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}"""       )

														snake_case_           =		temperature



							def __call__(				self							:      Dict		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      int       )					->jnp.ndarray:



														"""simple docstring"""



														snake_case_           =		scores / self.temperature
														return scores





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Optional[int]		,       UpperCAmelCase_							:      float		,       UpperCAmelCase_							:      float = -float("""Inf"""       )		,       UpperCAmelCase_							:      int = 1       )					->str:



														"""simple docstring"""



														if not isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ) or (top_p < 0 or top_p > 1.0):
																					raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}"""       )
														if not isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ) or (min_tokens_to_keep < 1):
																					raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"""       )

														snake_case_           =		top_p
														snake_case_           =		filter_value
														snake_case_           =		min_tokens_to_keep



							def __call__(				self							:      Union[str, Any]		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      int       )					->jnp.ndarray:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		lax.top_k(UpperCAmelCase_		,       scores.shape[-1]       )

														snake_case_           =		jnp.full_like(UpperCAmelCase_		,       self.filter_value       )
														snake_case_           =		jax.nn.softmax(UpperCAmelCase_		,       axis=-1       ).cumsum(axis=-1       )
														snake_case_           =		cumulative_probs < self.top_p

														# include the token that is higher than top_p as well
														snake_case_           =		jnp.roll(UpperCAmelCase_		,       1       )
														score_mask |= score_mask.at[:, 0].set(UpperCAmelCase_       )

														# min tokens to keep
														snake_case_           =		score_mask.at[:, : self.min_tokens_to_keep].set(UpperCAmelCase_       )

														snake_case_           =		jnp.where(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )
														snake_case_           =		jax.lax.sort_key_val(UpperCAmelCase_		,       UpperCAmelCase_       )[-1]

														return next_scores





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      int		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      float = -float("""Inf"""       )		,       UpperCAmelCase_							:      int = 1       )					->Dict:



														"""simple docstring"""



														if not isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ) or top_k <= 0:
																					raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}"""       )

														snake_case_           =		max(UpperCAmelCase_		,       UpperCAmelCase_       )
														snake_case_           =		filter_value



							def __call__(				self							:      Tuple		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      int       )					->jnp.ndarray:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		scores.shape
														snake_case_           =		jnp.full(batch_size * vocab_size		,       self.filter_value       )

														snake_case_           =		min(self.top_k		,       scores.shape[-1]       )  # Safety check
														snake_case_       ,						snake_case_           =		lax.top_k(UpperCAmelCase_		,       UpperCAmelCase_       )
														snake_case_           =		jnp.broadcast_to((jnp.arange(UpperCAmelCase_       ) * vocab_size)[:, None]		,       (batch_size, topk)       ).flatten()
														snake_case_           =		topk_scores.flatten()
														snake_case_           =		topk_indices.flatten() + shift

														snake_case_           =		next_scores_flat.at[topk_indices_flat].set(UpperCAmelCase_       )
														snake_case_           =		next_scores_flat.reshape(UpperCAmelCase_		,       UpperCAmelCase_       )
														return next_scores





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Any		,       UpperCAmelCase_							:      int       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		bos_token_id



							def __call__(				self							:      Tuple		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      int       )					->jnp.ndarray:



														"""simple docstring"""



														snake_case_           =		jnp.full(scores.shape		,       -float("""inf"""       )       )

														snake_case_           =		1 - jnp.bool_(cur_len - 1       )

														snake_case_           =		jnp.where(UpperCAmelCase_		,       new_scores.at[:, self.bos_token_id].set(0       )		,       UpperCAmelCase_       )

														return scores





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      int       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		max_length
														snake_case_           =		eos_token_id



							def __call__(				self							:      List[Any]		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      int       )					->jnp.ndarray:



														"""simple docstring"""



														snake_case_           =		jnp.full(scores.shape		,       -float("""inf"""       )       )

														snake_case_           =		1 - jnp.bool_(cur_len - self.max_length + 1       )

														snake_case_           =		jnp.where(UpperCAmelCase_		,       new_scores.at[:, self.eos_token_id].set(0       )		,       UpperCAmelCase_       )

														return scores





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Dict		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      int       )					->List[str]:



														"""simple docstring"""



														if not isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ) or min_length < 0:
																					raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}"""       )

														if not isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ) or eos_token_id < 0:
																					raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}"""       )

														snake_case_           =		min_length
														snake_case_           =		eos_token_id



							def __call__(				self							:      Union[str, Any]		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      int       )					->jnp.ndarray:



														"""simple docstring"""



														snake_case_           =		1 - jnp.clip(cur_len - self.min_length		,       0		,       1       )

														snake_case_           =		jnp.where(UpperCAmelCase_		,       scores.at[:, self.eos_token_id].set(-float("""inf"""       )       )		,       UpperCAmelCase_       )

														return scores





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Tuple		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      Optional[int]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		list(UpperCAmelCase_       )
														snake_case_           =		begin_index



							def __call__(				self							:      Any		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      int       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		1 - jnp.bool_(cur_len - self.begin_index       )

														snake_case_           =		jnp.where(UpperCAmelCase_		,       scores.at[:, self.begin_suppress_tokens].set(-float("""inf"""       )       )		,       UpperCAmelCase_       )

														return scores





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Any		,       UpperCAmelCase_							:      list       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		list(UpperCAmelCase_       )



							def __call__(				self							:      int		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      int       )					->jnp.ndarray:



														"""simple docstring"""



														snake_case_           =		scores.at[..., self.suppress_tokens].set(-float("""inf"""       )       )

														return scores





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      str		,       UpperCAmelCase_							:      Tuple       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		dict(UpperCAmelCase_       )
														# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
														# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
														# Indexes without forced tokens will have a negative value.
														snake_case_           =		jnp.ones((max(force_token_map.keys()       ) + 1)		,       dtype=jnp.intaa       ) * -1
														for index, token in force_token_map.items():
																					if token is not None:
																												snake_case_           =		force_token_array.at[index].set(UpperCAmelCase_       )
														snake_case_           =		jnp.intaa(UpperCAmelCase_       )



							def __call__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      jnp.ndarray		,       UpperCAmelCase_							:      int       )					->jnp.ndarray:



														"""simple docstring"""



														def _force_token(UpperCAmelCase_							:      List[Any]       ):
																					snake_case_           =		scores.shape[0]
																					snake_case_           =		self.force_token_array[generation_idx]

																					snake_case_           =		jnp.ones_like(UpperCAmelCase_		,       dtype=scores.dtype       ) * -float("""inf"""       )
																					snake_case_           =		jnp.zeros((batch_size, 1)		,       dtype=scores.dtype       )
																					snake_case_           =		lax.dynamic_update_slice(UpperCAmelCase_		,       UpperCAmelCase_		,       (0, current_token)       )
																					return new_scores

														snake_case_           =		lax.cond(
														    cur_len >= self.force_token_array.shape[0]		,       lambda: scores		,       lambda: lax.cond(
														        self.force_token_array[cur_len] >= 0		,       lambda: _force_token(UpperCAmelCase_       )		,       lambda: scores		,       )		,       )
														return scores





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Optional[int]		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      str       )					->int:



														"""simple docstring"""



														snake_case_           =		generate_config.eos_token_id
														snake_case_           =		generate_config.no_timestamps_token_id
														snake_case_           =		generate_config.no_timestamps_token_id + 1

														snake_case_           =		decoder_input_length + 1

														if generate_config.is_multilingual:
																					# room for language token and task token
																					self.begin_index += 2
														if hasattr(UpperCAmelCase_		,       """max_initial_timestamp_index"""       ):
																					snake_case_           =		generate_config.max_initial_timestamp_index
														else:
																					snake_case_           =		model_config.vocab_size
														if self.max_initial_timestamp_index is None:
																					snake_case_           =		model_config.vocab_size



							def __call__(				self							:      List[Any]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Union[str, Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		scores.at[:, self.no_timestamps_token_id].set(-float("""inf"""       )       )

														def handle_pairs(UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      List[str]       ):
																					snake_case_           =		jnp.where((cur_len - self.begin_index) >= 1		,       UpperCAmelCase_		,       UpperCAmelCase_       )
																					snake_case_           =		jnp.where(
																					    input_ids_k[cur_len - 1] >= self.timestamp_begin		,       True and last_was_timestamp		,       UpperCAmelCase_		,       )

																					snake_case_           =		jnp.where((cur_len - self.begin_index) < 2		,       UpperCAmelCase_		,       UpperCAmelCase_       )
																					snake_case_           =		jnp.where(
																					    input_ids_k[cur_len - 2] >= self.timestamp_begin		,       UpperCAmelCase_		,       UpperCAmelCase_		,       )

																					return jnp.where(
																					    UpperCAmelCase_		,       jnp.where(
																					        penultimate_was_timestamp > 0		,       scores_k.at[self.timestamp_begin :].set(-float("""inf"""       )       )		,       scores_k.at[: self.eos_token_id].set(-float("""inf"""       )       )		,       )		,       UpperCAmelCase_		,       )

														snake_case_           =		jax.vmap(UpperCAmelCase_       )(UpperCAmelCase_		,       UpperCAmelCase_       )

														snake_case_           =		jnp.where(cur_len == self.begin_index		,       UpperCAmelCase_		,       UpperCAmelCase_       )
														snake_case_           =		jnp.where(
														    self.max_initial_timestamp_index is not None		,       True and apply_max_initial_timestamp		,       UpperCAmelCase_		,       )

														snake_case_           =		self.timestamp_begin + self.max_initial_timestamp_index

														snake_case_           =		jnp.where(
														    UpperCAmelCase_		,       scores.at[:, last_allowed + 1 :].set(-float("""inf"""       )       )		,       UpperCAmelCase_		,       )

														# if sum of probability over timestamps is above any other token, sample timestamp
														snake_case_           =		jax.nn.log_softmax(UpperCAmelCase_		,       axis=-1       )

														def handle_cumulative_probs(UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Union[str, Any]       ):
																					snake_case_           =		jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]		,       axis=-1       )
																					snake_case_           =		jnp.max(logprobs_k[: self.timestamp_begin]       )
																					return jnp.where(
																					    timestamp_logprob > max_text_token_logprob		,       scores_k.at[: self.timestamp_begin].set(-float("""inf"""       )       )		,       UpperCAmelCase_		,       )

														snake_case_           =		jax.vmap(UpperCAmelCase_       )(UpperCAmelCase_		,       UpperCAmelCase_       )

														return scores







"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation







def 						_a				(      _SCREAMING_SNAKE_CASE = 8				)    ->				str:
							snake_case_           =		ascii_letters + digits + punctuation
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							# Password Generator = full boot with random_number, random_letters, and
							# random_character FUNCTIONS
							# Put your code here...
							i -= len(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		i // 3
							snake_case_           =		i % 3
							# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
							#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
							snake_case_           =		(
							    chars_incl
							    + random(_SCREAMING_SNAKE_CASE				, quotient + remainder				)
							    + random(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							    + random(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							)
							snake_case_           =		list(_SCREAMING_SNAKE_CASE				)
							shuffle(_SCREAMING_SNAKE_CASE				)
							return "".join(_SCREAMING_SNAKE_CASE				)

							# random is a generalised function for letters, characters and numbers







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List[str]:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = 8				)    ->				bool:

							if len(_SCREAMING_SNAKE_CASE				) < min_length:
														# Your Password must be at least 8 characters long
														return False

							snake_case_           =		any(char in ascii_uppercase for char in password				)
							snake_case_           =		any(char in ascii_lowercase for char in password				)
							snake_case_           =		any(char in digits for char in password				)
							snake_case_           =		any(char in punctuation for char in password				)

							return upper and lower and num and spec_char
							# Passwords should contain UPPERCASE, lowerase
							# numbers, and special characters







def 						_a				(      )    ->				str:
							snake_case_           =		int(input("""Please indicate the max length of your password: """				).strip()				)
							snake_case_           =		input(
							    """Please indicate the characters that must be in your password: """				).strip()
							print("""Password generated:"""				, password_generator(_SCREAMING_SNAKE_CASE				)				)
							print(
							    """Alternative Password generated:"""				, alternative_password_generator(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)				, )
							print("""[If you are thinking of using this passsword, You better save it.]"""				)


if __name__ == "__main__":
				main()



