
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


snake_case__      :		int				=       get_logger(__name__)


snake_case__      :		str				=       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(_snake_case	)
       def __call__(					self       ,					_snake_case       ,					_snake_case	):
        raise NotImplementedError(
            F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."	)







class 						_a   :

       """simple docstring"""





       @add_start_docstrings(_snake_case	)
       def __call__(					self       ,					_snake_case       ,					_snake_case	):
        raise NotImplementedError(
            F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."	)







class 						_a   (						_UpperCamelCase       ):

       """simple docstring"""





       @add_start_docstrings(_snake_case	)
       def __call__(					self       ,					_snake_case       ,					_snake_case       ,					_snake_case       ,					**_snake_case	):
        for processor in self:
         _UpperCAmelCase 				=inspect.signature(processor.__call__	).parameters
         if len(_snake_case	) > 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."	)
          _UpperCAmelCase 				=processor(_snake_case       ,					_snake_case       ,					_snake_case       ,					**_snake_case	)
         else:
          _UpperCAmelCase 				=processor(_snake_case       ,					_snake_case       ,					_snake_case	)
        return scores







class 						_a   (						_UpperCamelCase       ):

       """simple docstring"""





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

        _UpperCAmelCase 				=temperature

       def __call__(					self       ,					_snake_case       ,					_snake_case       ,					_snake_case	):
        _UpperCAmelCase 				=scores / self.temperature
        return scores







class 						_a   (						_UpperCamelCase       ):

       """simple docstring"""





       def __init__(					self       ,					_snake_case       ,					_snake_case = -float("Inf"	)       ,					_snake_case = 1	):
        if not isinstance(_snake_case       ,					_snake_case	) 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(_snake_case       ,					_snake_case	) 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}"	)

        _UpperCAmelCase 				=top_p
        _UpperCAmelCase 				=filter_value
        _UpperCAmelCase 				=min_tokens_to_keep

       def __call__(					self       ,					_snake_case       ,					_snake_case       ,					_snake_case	):
        _UpperCAmelCase 				=lax.top_k(_snake_case       ,					scores.shape[-1]	)

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

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

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

        _UpperCAmelCase 				=jnp.where(_snake_case       ,					_snake_case       ,					_snake_case	)
        _UpperCAmelCase 				=jax.lax.sort_key_val(_snake_case       ,					_snake_case	)[-1]

        return next_scores







class 						_a   (						_UpperCamelCase       ):

       """simple docstring"""





       def __init__(					self       ,					_snake_case       ,					_snake_case = -float("Inf"	)       ,					_snake_case = 1	):
        if not isinstance(_snake_case       ,					_snake_case	) or top_k <= 0:
         raise ValueError(F"`top_k` has to be a strictly positive integer, but is {top_k}"	)

        _UpperCAmelCase 				=max(_snake_case       ,					_snake_case	)
        _UpperCAmelCase 				=filter_value

       def __call__(					self       ,					_snake_case       ,					_snake_case       ,					_snake_case	):
        _UpperCAmelCase 				=scores.shape
        _UpperCAmelCase 				=jnp.full(batch_size * vocab_size       ,					self.filter_value	)

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

        _UpperCAmelCase 				=next_scores_flat.at[topk_indices_flat].set(_snake_case	)
        _UpperCAmelCase 				=next_scores_flat.reshape(_snake_case       ,					_snake_case	)
        return next_scores







class 						_a   (						_UpperCamelCase       ):

       """simple docstring"""





       def __init__(					self       ,					_snake_case	):
        _UpperCAmelCase 				=bos_token_id

       def __call__(					self       ,					_snake_case       ,					_snake_case       ,					_snake_case	):
        _UpperCAmelCase 				=jnp.full(scores.shape       ,					-float("inf"	)	)

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

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

        return scores







class 						_a   (						_UpperCamelCase       ):

       """simple docstring"""





       def __init__(					self       ,					_snake_case       ,					_snake_case	):
        _UpperCAmelCase 				=max_length
        _UpperCAmelCase 				=eos_token_id

       def __call__(					self       ,					_snake_case       ,					_snake_case       ,					_snake_case	):
        _UpperCAmelCase 				=jnp.full(scores.shape       ,					-float("inf"	)	)

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

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

        return scores







class 						_a   (						_UpperCamelCase       ):

       """simple docstring"""





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

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

        _UpperCAmelCase 				=min_length
        _UpperCAmelCase 				=eos_token_id

       def __call__(					self       ,					_snake_case       ,					_snake_case       ,					_snake_case	):
        # create boolean flag to decide if min length penalty should be applied
        _UpperCAmelCase 				=1 - jnp.clip(cur_len - self.min_length       ,					0       ,					1	)

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

        return scores







class 						_a   (						_UpperCamelCase       ):

       """simple docstring"""





       def __init__(					self       ,					_snake_case       ,					_snake_case	):
        _UpperCAmelCase 				=list(_snake_case	)
        _UpperCAmelCase 				=begin_index

       def __call__(					self       ,					_snake_case       ,					_snake_case       ,					_snake_case	):
        _UpperCAmelCase 				=1 - jnp.bool_(cur_len - self.begin_index	)

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

        return scores







class 						_a   (						_UpperCamelCase       ):

       """simple docstring"""





       def __init__(					self       ,					_snake_case	):
        _UpperCAmelCase 				=list(_snake_case	)

       def __call__(					self       ,					_snake_case       ,					_snake_case       ,					_snake_case	):
        _UpperCAmelCase 				=scores.at[..., self.suppress_tokens].set(-float("inf"	)	)

        return scores







class 						_a   (						_UpperCamelCase       ):

       """simple docstring"""





       def __init__(					self       ,					_snake_case	):
        _UpperCAmelCase 				=dict(_snake_case	)
        # 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.
        _UpperCAmelCase 				=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:
          _UpperCAmelCase 				=force_token_array.at[index].set(_snake_case	)
        _UpperCAmelCase 				=jnp.intaa(_snake_case	)

       def __call__(					self       ,					_snake_case       ,					_snake_case       ,					_snake_case	):
        def _force_token(_snake_case	):
         _UpperCAmelCase 				=scores.shape[0]
         _UpperCAmelCase 				=self.force_token_array[generation_idx]

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

        _UpperCAmelCase 				=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(_snake_case	)       ,					lambda: scores       ,					)       ,					)
        return scores







class 						_a   (						_UpperCamelCase       ):

       """simple docstring"""





       def __init__(					self       ,					_snake_case       ,					_snake_case       ,					_snake_case	):
        _UpperCAmelCase 				=generate_config.eos_token_id
        _UpperCAmelCase 				=generate_config.no_timestamps_token_id
        _UpperCAmelCase 				=generate_config.no_timestamps_token_id + 1

        _UpperCAmelCase 				=decoder_input_length + 1

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

       def __call__(					self       ,					_snake_case       ,					_snake_case       ,					_snake_case	):
        # suppress <|notimestamps|> which is handled by without_timestamps
        _UpperCAmelCase 				=scores.at[:, self.no_timestamps_token_id].set(-float("inf"	)	)

        def handle_pairs(_snake_case       ,					_snake_case	):
         _UpperCAmelCase 				=jnp.where((cur_len - self.begin_index) >= 1       ,					_snake_case       ,					_snake_case	)
         _UpperCAmelCase 				=jnp.where(
             input_ids_k[cur_len - 1] >= self.timestamp_begin       ,					True and last_was_timestamp       ,					_snake_case       ,					)

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

         return jnp.where(
             _snake_case       ,					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"	)	)       ,					)       ,					_snake_case       ,					)

        _UpperCAmelCase 				=jax.vmap(_snake_case	)(_snake_case       ,					_snake_case	)

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

        _UpperCAmelCase 				=self.timestamp_begin + self.max_initial_timestamp_index

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

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

        def handle_cumulative_probs(_snake_case       ,					_snake_case	):
         _UpperCAmelCase 				=jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]       ,					axis=-1	)
         _UpperCAmelCase 				=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"	)	)       ,					_snake_case       ,					)

        _UpperCAmelCase 				=jax.vmap(_snake_case	)(_snake_case       ,					_snake_case	)

        return scores










"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class       UpperCAmelCase_    (							_UpperCamelCase		):



      def __lt__(	self	:      int	,      A	:      Dict		):
           return self[-1] < other[-1]



      def __eq__(	self	:      Union[str, Any]	,      A	:      Union[str, Any]		):
           return self[-1] == other[-1]







def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list							)    ->  list:





     '''simple docstring'''

     _UpperCAmelCase     :      list[Stack]														=       []
     # sort into stacks
     for element in collection:
          _UpperCAmelCase     :      Any														=       Stack([element]							)
          _UpperCAmelCase     :      str														=       bisect_left(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          if i != len(SCREAMING_SNAKE_CASE__							):
               stacks[i].append(SCREAMING_SNAKE_CASE__							)
          else:
               stacks.append(SCREAMING_SNAKE_CASE__							)

    # use a heap-based merge to merge stack efficiently
     _UpperCAmelCase     :      Optional[Any]														=       merge(*(reversed(SCREAMING_SNAKE_CASE__							) for stack in stacks)							)
     return collection


if __name__ == "__main__":
 _lowerCAmelCase							:   Any				=				input("Enter numbers separated by a comma:\n").strip()
 _lowerCAmelCase							:   int				=				[int(item) for item in user_input.split(",")]
 print(patience_sort(unsorted))
