
"""simple docstring"""







from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
		import tensorflow as tf

		from ..tf_utils import stable_softmax


if is_torch_available():
		import torch


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)







@add_end_docstrings(
    lowercase_      ,		R'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '      ,		)
class 							__magic_name__     (		lowercase_   ):





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							GenericTensor    ):



												if self.framework == "tf":
																			__snake_case     			=       tf.where(input_ids == self.tokenizer.mask_token_id    ).numpy()
												elif self.framework == "pt":
																			__snake_case     			=       torch.nonzero(input_ids == self.tokenizer.mask_token_id							,						as_tuple=lowerCamelCase_    )
												else:
																			raise ValueError("Unsupported framework"    )
												return masked_index





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							GenericTensor    ):
												__snake_case     			=       self.get_masked_index(lowerCamelCase_    )
												__snake_case     			=       np.prod(masked_index.shape    )
												if numel < 1:
																			raise PipelineException(
																			    "fill-mask"							,						self.model.base_model_prefix							,						F'''No mask_token ({self.tokenizer.mask_token}) found on the input'''							,						)





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							GenericTensor    ):



												if isinstance(lowerCamelCase_							,						lowerCamelCase_    ):
																			for model_input in model_inputs:
																										self._ensure_exactly_one_mask_token(model_input["input_ids"][0]    )
												else:
																			for input_ids in model_inputs["input_ids"]:
																										self._ensure_exactly_one_mask_token(lowerCamelCase_    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							Tuple							,						snake_case_   :							Tuple=None							,						**snake_case_   :							List[str]    ):



												if return_tensors is None:
																			__snake_case     			=       self.framework
												__snake_case     			=       self.tokenizer(lowerCamelCase_							,						return_tensors=lowerCamelCase_    )
												self.ensure_exactly_one_mask_token(lowerCamelCase_    )
												return model_inputs





					def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							Any    ):
												__snake_case     			=       self.model(**lowerCamelCase_    )
												__snake_case     			=       model_inputs["""input_ids"""]
												return model_outputs





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Dict							,						snake_case_   :							Optional[int]=5							,						snake_case_   :							Dict=None    ):



												if target_ids is not None and target_ids.shape[0] < top_k:
																			__snake_case     			=       target_ids.shape[0]
												__snake_case     			=       model_outputs["""input_ids"""][0]
												__snake_case     			=       model_outputs["""logits"""]

												if self.framework == "tf":
																			__snake_case     			=       tf.where(input_ids == self.tokenizer.mask_token_id    ).numpy()[:, 0]

																			__snake_case     			=       outputs.numpy()

																			__snake_case     			=       outputs[0, masked_index, :]
																			__snake_case     			=       stable_softmax(lowerCamelCase_							,						axis=-1    )
																			if target_ids is not None:
																										__snake_case     			=       tf.gather_nd(tf.squeeze(lowerCamelCase_							,						0    )							,						target_ids.reshape(-1							,						1    )    )
																										__snake_case     			=       tf.expand_dims(lowerCamelCase_							,						0    )

																			__snake_case     			=       tf.math.top_k(lowerCamelCase_							,						k=lowerCamelCase_    )
																			__snake_case     			=       topk.values.numpy(), topk.indices.numpy()
												else:
																			__snake_case     			=       torch.nonzero(input_ids == self.tokenizer.mask_token_id							,						as_tuple=lowerCamelCase_    ).squeeze(-1    )
																			# Fill mask pipeline supports only one ${mask_token} per sample

																			__snake_case     			=       outputs[0, masked_index, :]
																			__snake_case     			=       logits.softmax(dim=-1    )
																			if target_ids is not None:
																										__snake_case     			=       probs[..., target_ids]

																			__snake_case     			=       probs.topk(lowerCamelCase_    )

												__snake_case     			=       []
												__snake_case     			=       values.shape[0] == 1
												for i, (_values, _predictions) in enumerate(zip(values.tolist()							,						predictions.tolist()    )    ):
																			__snake_case     			=       []
																			for v, p in zip(_values							,						_predictions    ):
																										# Copy is important since we're going to modify this array in place
																										__snake_case     			=       input_ids.numpy().copy()
																										if target_ids is not None:
																																	__snake_case     			=       target_ids[p].tolist()

																										__snake_case     			=       p
																										# Filter padding out:
																										__snake_case     			=       tokens[np.where(tokens != self.tokenizer.pad_token_id    )]
																										# Originally we skip special tokens to give readable output.
																										# For multi masks though, the other [MASK] would be removed otherwise
																										# making the output look odd, so we add them back
																										__snake_case     			=       self.tokenizer.decode(lowerCamelCase_							,						skip_special_tokens=lowerCamelCase_    )
																										__snake_case     			=       {"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p]    ), """sequence""": sequence}
																										row.append(lowerCamelCase_    )
																			result.append(lowerCamelCase_    )
												if single_mask:
																			return result[0]
												return result





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							Optional[int]							,						snake_case_   :							Union[str, Any]=None    ):



												if isinstance(lowerCamelCase_							,						lowerCamelCase_    ):
																			__snake_case     			=       [targets]
												try:
																			__snake_case     			=       self.tokenizer.get_vocab()
												except Exception:
																			__snake_case     			=       {}
												__snake_case     			=       []
												for target in targets:
																			__snake_case     			=       vocab.get(lowerCamelCase_							,						lowerCamelCase_    )
																			if id_ is None:
																										__snake_case     			=       self.tokenizer(
																										    lowerCamelCase_							,						add_special_tokens=lowerCamelCase_							,						return_attention_mask=lowerCamelCase_							,						return_token_type_ids=lowerCamelCase_							,						max_length=1							,						truncation=lowerCamelCase_							,						)["""input_ids"""]
																										if len(lowerCamelCase_    ) == 0:
																																	logger.warning(
																																	    F'''The specified target token `{target}` does not exist in the model vocabulary. '''
																																	    "We cannot replace it with anything meaningful, ignoring it"    )
																																	continue
																										__snake_case     			=       input_ids[0]
																										# XXX: If users encounter this pass
																										# it becomes pretty slow, so let's make sure
																										# The warning enables them to fix the input to
																										# get faster performance.
																										logger.warning(
																										    F'''The specified target token `{target}` does not exist in the model vocabulary. '''
																										    F'''Replacing with `{self.tokenizer.convert_ids_to_tokens(id_    )}`.'''    )
																			target_ids.append(id_    )
												__snake_case     			=       list(set(lowerCamelCase_    )    )
												if len(lowerCamelCase_    ) == 0:
																			raise ValueError("At least one target must be provided when passed."    )
												__snake_case     			=       np.array(lowerCamelCase_    )
												return target_ids





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							int=None							,						snake_case_   :							Optional[int]=None    ):
												__snake_case     			=       {}

												if targets is not None:
																			__snake_case     			=       self.get_target_ids(lowerCamelCase_							,						lowerCamelCase_    )
																			__snake_case     			=       target_ids

												if top_k is not None:
																			__snake_case     			=       top_k

												if self.tokenizer.mask_token_id is None:
																			raise PipelineException(
																			    "fill-mask"							,						self.model.base_model_prefix							,						"The tokenizer does not define a `mask_token`."    )
												return {}, {}, postprocess_params





					def __call__(				self   :							Optional[int]							,						snake_case_   :							Tuple							,						*snake_case_   :							Optional[Any]							,						**snake_case_   :							Any    ):
												__snake_case     			=       super().__call__(lowerCamelCase_							,						**lowerCamelCase_    )
												if isinstance(lowerCamelCase_							,						lowerCamelCase_    ) and len(lowerCamelCase_    ) == 1:
																			return outputs[0]
												return outputs


"""simple docstring"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     list:







							"""simple docstring"""


							__snake_case     			=       len(SCREAMING_SNAKE_CASE )
							for i in range(1							,    SCREAMING_SNAKE_CASE ):
														__snake_case     			=       collection[i]
														__snake_case     			=       0
														__snake_case     			=       i - 1

														while low <= high:
																					__snake_case     			=       (low + high) // 2
																					if val < collection[mid]:
																												__snake_case     			=       mid - 1
																					else:
																												__snake_case     			=       mid + 1
														for j in range(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    -1 ):
																					__snake_case     			=       collection[j - 1]
														__snake_case     			=       val
							return collection


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