


import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowerCAmelCase			=				logging.get_logger(__name__)


class 			_a				(     enum.Enum     ):
							_lowercase      :     Union[str, Any]     =					0
							_lowercase      :     List[str]     =					1


@add_end_docstrings(UpperCamelCase__     )
class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     Any     =					'''generated'''

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





									"""simple docstring"""







									super().__init__(*UpperCamelCase_       ,		**UpperCamelCase_				)

									self.check_model_type(
									    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
									    if self.framework == '''tf'''
									    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING				)

							def  lowerCamelCase_					(				self:      Union[str, Any]       ,		UpperCamelCase_:      int=None       ,		UpperCamelCase_:      Optional[int]=None       ,		UpperCamelCase_:      List[str]=None       ,		UpperCamelCase_:      int=None       ,		UpperCamelCase_:      Tuple=None       ,		UpperCamelCase_:      Any=None       ,		**UpperCamelCase_:      Optional[int]       ,		)     ->  Union[str, Any]:





									"""simple docstring"""







									lowercase__				= {}
									if truncation is not None:
											lowercase__				= truncation

									lowercase__				= generate_kwargs

									lowercase__				= {}
									if return_tensors is not None and return_type is None:
											lowercase__				= ReturnType.TENSORS if return_tensors else ReturnType.TEXT
									if return_type is not None:
											lowercase__				= return_type

									if clean_up_tokenization_spaces is not None:
											lowercase__				= clean_up_tokenization_spaces

									if stop_sequence is not None:
											lowercase__				= self.tokenizer.encode(UpperCamelCase_       ,		add_special_tokens=UpperCamelCase_				)
											if len(UpperCamelCase_				) > 1:
													warnings.warn(
													    '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
													    ''' the stop sequence will be used as the stop sequence string in the interim.'''				)
											lowercase__				= stop_sequence_ids[0]

									return preprocess_params, forward_params, postprocess_params

							def  lowerCamelCase_					(				self:      Dict       ,		UpperCamelCase_:      int       ,		UpperCamelCase_:      int       ,		UpperCamelCase_:      int				)     ->  Optional[int]:





									"""simple docstring"""







									return True

							def  lowerCamelCase_					(				self:      List[Any]       ,		*UpperCamelCase_:      Optional[Any]       ,		UpperCamelCase_:      Any				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= self.model.config.prefix if self.model.config.prefix is not None else ''''''
									if isinstance(args[0]       ,		UpperCamelCase_				):
											if self.tokenizer.pad_token_id is None:
													raise ValueError('''Please make sure that the tokenizer has a pad_token_id when using a batch input'''				)
											lowercase__				= ([prefix + arg for arg in args[0]],)
											lowercase__				= True

									elif isinstance(args[0]       ,		UpperCamelCase_				):
											lowercase__				= (prefix + args[0],)
											lowercase__				= False
									else:
											raise ValueError(
											    f' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'				)
									lowercase__				= self.tokenizer(*UpperCamelCase_       ,		padding=UpperCamelCase_       ,		truncation=UpperCamelCase_       ,		return_tensors=self.framework				)
									# This is produced by tokenizers but is an invalid generate kwargs
									if "token_type_ids" in inputs:
											del inputs["token_type_ids"]
									return inputs

							def __call__(				self:      int       ,		*UpperCamelCase_:      List[str]       ,		**UpperCamelCase_:      Optional[Any]				)     ->  Optional[Any]:





									"""simple docstring"""







									lowercase__				= super().__call__(*UpperCamelCase_       ,		**UpperCamelCase_				)
									if (
									    isinstance(args[0]       ,		UpperCamelCase_				)
									    and all(isinstance(UpperCamelCase_       ,		UpperCamelCase_				) for el in args[0]				)
									    and all(len(UpperCamelCase_				) == 1 for res in result				)
									):
											return [res[0] for res in result]
									return result

							def  lowerCamelCase_					(				self:      Optional[Any]       ,		UpperCamelCase_:      Dict       ,		UpperCamelCase_:      Any=TruncationStrategy.DO_NOT_TRUNCATE       ,		**UpperCamelCase_:      Dict				)     ->  Union[str, Any]:





									"""simple docstring"""







									lowercase__				= self._parse_and_tokenize(UpperCamelCase_       ,		truncation=UpperCamelCase_       ,		**UpperCamelCase_				)
									return inputs

							def  lowerCamelCase_					(				self:      Union[str, Any]       ,		UpperCamelCase_:      Optional[int]       ,		**UpperCamelCase_:      int				)     ->  List[Any]:





									"""simple docstring"""







									if self.framework == "pt":
											lowercase__	,						lowercase__				= model_inputs['''input_ids'''].shape
									elif self.framework == "tf":
											lowercase__	,						lowercase__				= tf.shape(model_inputs['''input_ids''']				).numpy()

									lowercase__				= generate_kwargs.get('''min_length'''       ,		self.model.config.min_length				)
									lowercase__				= generate_kwargs.get('''max_length'''       ,		self.model.config.max_length				)
									self.check_inputs(UpperCamelCase_       ,		generate_kwargs['''min_length''']       ,		generate_kwargs['''max_length''']				)
									lowercase__				= self.model.generate(**UpperCamelCase_       ,		**UpperCamelCase_				)
									lowercase__				= output_ids.shape[0]
									if self.framework == "pt":
											lowercase__				= output_ids.reshape(UpperCamelCase_       ,		out_b // in_b       ,		*output_ids.shape[1:]				)
									elif self.framework == "tf":
											lowercase__				= tf.reshape(UpperCamelCase_       ,		(in_b, out_b // in_b, *output_ids.shape[1:])				)
									return {"output_ids": output_ids}


							def  lowerCamelCase_					(				self:      str       ,		UpperCamelCase_:      Union[str, Any]       ,		UpperCamelCase_:      int=ReturnType.TEXT       ,		UpperCamelCase_:      Union[str, Any]=False				)     ->  Union[str, Any]:





									"""simple docstring"""







									lowercase__				= []
									for output_ids in model_outputs["output_ids"][0]:
											if return_type == ReturnType.TENSORS:
													lowercase__				= {f'{self.return_name}_token_ids': output_ids}
											elif return_type == ReturnType.TEXT:
													lowercase__				= {
													    f'{self.return_name}_text': self.tokenizer.decode(
													        UpperCamelCase_       ,		skip_special_tokens=UpperCamelCase_       ,		clean_up_tokenization_spaces=UpperCamelCase_       ,		)
													}
											records.append(UpperCamelCase_				)
									return records


@add_end_docstrings(UpperCamelCase__     )
class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     int     =					'''summary'''

							def __call__(				self:      Any       ,		*UpperCamelCase_:      Tuple       ,		**UpperCamelCase_:      str				)     ->  Tuple:





									"""simple docstring"""







									return super().__call__(*UpperCamelCase_       ,		**UpperCamelCase_				)


							def  lowerCamelCase_					(				self:      Optional[Any]       ,		UpperCamelCase_:      int       ,		UpperCamelCase_:      int       ,		UpperCamelCase_:      int				)     ->  bool:





									"""simple docstring"""







									if max_length < min_length:
											logger.warning(f'Your min_length={min_length} must be inferior than your max_length={max_length}.'				)

									if input_length < max_length:
											logger.warning(
											    f'Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '
											    '''a summarization task, where outputs shorter than the input are typically wanted, you might '''
											    f'consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'				)


@add_end_docstrings(UpperCamelCase__     )
class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     Union[str, Any]     =					'''translation'''

							def  lowerCamelCase_					(				self:      Optional[Any]       ,		UpperCamelCase_:      int       ,		UpperCamelCase_:      int       ,		UpperCamelCase_:      int				)     ->  Dict:





									"""simple docstring"""







									if input_length > 0.9 * max_length:
											logger.warning(
											    f'Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '
											    '''increasing your max_length manually, e.g. translator(\'...\', max_length=400)'''				)
									return True

							def  lowerCamelCase_					(				self:      List[Any]       ,		*UpperCamelCase_:      Optional[int]       ,		UpperCamelCase_:      int=TruncationStrategy.DO_NOT_TRUNCATE       ,		UpperCamelCase_:      Any=None       ,		UpperCamelCase_:      List[str]=None				)     ->  Any:





									"""simple docstring"""







									if getattr(self.tokenizer       ,		'''_build_translation_inputs'''       ,		UpperCamelCase_				):
											return self.tokenizer._build_translation_inputs(
											    *UpperCamelCase_       ,		return_tensors=self.framework       ,		truncation=UpperCamelCase_       ,		src_lang=UpperCamelCase_       ,		tgt_lang=UpperCamelCase_				)
									else:
											return super()._parse_and_tokenize(*UpperCamelCase_       ,		truncation=UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      str       ,		UpperCamelCase_:      Union[str, Any]=None       ,		UpperCamelCase_:      str=None       ,		**UpperCamelCase_:      Dict				)     ->  Dict:





									"""simple docstring"""







									lowercase__	,						lowercase__	,						lowercase__				= super()._sanitize_parameters(**UpperCamelCase_				)
									if src_lang is not None:
											lowercase__				= src_lang
									if tgt_lang is not None:
											lowercase__				= tgt_lang
									if src_lang is None and tgt_lang is None:
											# Backward compatibility, direct arguments use is preferred.
											lowercase__				= kwargs.get('''task'''       ,		self.task				)
											lowercase__				= task.split('''_'''				)
											if task and len(UpperCamelCase_				) == 4:
													# translation, XX, to YY
													lowercase__				= items[1]
													lowercase__				= items[3]
									return preprocess_params, forward_params, postprocess_params


							def __call__(				self:      Optional[Any]       ,		*UpperCamelCase_:      Tuple       ,		**UpperCamelCase_:      Dict				)     ->  Tuple:





									"""simple docstring"""







									return super().__call__(*UpperCamelCase_       ,		**UpperCamelCase_				)





'''simple docstring'''

SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			0  # The first color of the flag.
SCREAMING_SNAKE_CASE							:				List[str]     	=			1  # The second color of the flag.
SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			2  # The third color of the flag.
SCREAMING_SNAKE_CASE							:				Tuple     	=			(red, white, blue)




def 							_UpperCamelCase				(   lowerCAmelCase__:					list   )      -> list:
							if not sequence:
														return []
							if len(lowerCAmelCase__   ) == 1:
														return list(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				0
							SCREAMING_SNAKE_CASE_				=				len(lowerCAmelCase__   ) - 1
							SCREAMING_SNAKE_CASE_				=				0
							while mid <= high:
														if sequence[mid] == colors[0]:
																					SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				sequence[mid], sequence[low]
																					low += 1
																					mid += 1
														elif sequence[mid] == colors[1]:
																					mid += 1
														elif sequence[mid] == colors[2]:
																					SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				sequence[high], sequence[mid]
																					high -= 1
														else:
																					SCREAMING_SNAKE_CASE_				=				F"""The elements inside the sequence must contains only {colors} values"""
																					raise ValueError(lowerCAmelCase__   )
							return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							SCREAMING_SNAKE_CASE							:				str     	=			input("Enter numbers separated by commas:\n").strip()
							SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(item.strip()) for item in user_input.split(",")]
							print(f"{dutch_national_flag_sort(unsorted)}")





