





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

__lowercase       :Tuple       =  logging.get_logger(__name__)



class    _a     (				enum.Enum							):






							"""simple docstring"""

							snake_case_							 =						0
							snake_case_							 =						1







@add_end_docstrings(lowercase__							)
class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						"generated"

							def __init__(      self				:     List[str]							,	*a				:     Any							,	**a				:     int    )	->Tuple:
													super().__init__(*a							,	**a    )

													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      A_  (      self				:     Dict							,	a				:     Any=None							,	a				:     List[Any]=None							,	a				:     Union[str, Any]=None							,	a				:     str=None							,	a				:     Optional[int]=None							,	a				:     List[Any]=None							,	**a				:     Union[str, Any]							,	)	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					{}
													if truncation is not None:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					truncation

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					generate_kwargs

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

													if clean_up_tokenization_spaces is not None:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					clean_up_tokenization_spaces

													if stop_sequence is not None:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.tokenizer.encode(a							,	add_special_tokens=a    )
																			if len(a    ) > 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."    )
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					stop_sequence_ids[0]

													return preprocess_params, forward_params, postprocess_params

							def      A_  (      self				:     int							,	a				:     int							,	a				:     int							,	a				:     int    )	->Any:
													return True

							def      A_  (      self				:     int							,	*a				:     Dict							,	a				:     List[str]    )	->Any:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.model.config.prefix if self.model.config.prefix is not None else ""
													if isinstance(args[0]							,	a    ):
																			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"    )
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					([prefix + arg for arg in args[0]],)
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					True

													elif isinstance(args[0]							,	a    ):
																			SCREAMING_SNAKE_CASE__     :				Any   =					(prefix + args[0],)
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					False
													else:
																			raise ValueError(
																			    f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.tokenizer(*a							,	padding=a							,	truncation=a							,	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				:     Optional[int]							,	*a				:     Optional[Any]							,	**a				:     Optional[Any]    )	->List[str]:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					super().__call__(*a							,	**a    )
													if (
													    isinstance(args[0]							,	a    )
													    and all(isinstance(a							,	a    ) for el in args[0]    )
													    and all(len(a    ) == 1 for res in result    )
													):
																			return [res[0] for res in result]
													return result

							def      A_  (      self				:     Any							,	a				:     Optional[Any]							,	a				:     Tuple=TruncationStrategy.DO_NOT_TRUNCATE							,	**a				:     int    )	->Any:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self._parse_and_tokenize(a							,	truncation=a							,	**a    )
													return inputs

							def      A_  (      self				:     Optional[Any]							,	a				:     Dict							,	**a				:     List[Any]    )	->Optional[int]:
													if self.framework == "pt":
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					model_inputs["input_ids"].shape
													elif self.framework == "tf":
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					tf.shape(model_inputs["input_ids"]    ).numpy()

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					generate_kwargs.get("min_length"							,	self.model.config.min_length    )
													SCREAMING_SNAKE_CASE__     :				Any   =					generate_kwargs.get("max_length"							,	self.model.config.max_length    )
													self.check_inputs(a							,	generate_kwargs["min_length"]							,	generate_kwargs["max_length"]    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.model.generate(**a							,	**a    )
													SCREAMING_SNAKE_CASE__     :				str   =					output_ids.shape[0]
													if self.framework == "pt":
																			SCREAMING_SNAKE_CASE__     :				str   =					output_ids.reshape(a							,	out_b // in_b							,	*output_ids.shape[1:]    )
													elif self.framework == "tf":
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					tf.reshape(a							,	(in_b, out_b // in_b, *output_ids.shape[1:])    )
													return {"output_ids": output_ids}






							def      A_  (      self				:     int							,	a				:     str							,	a				:     Union[str, Any]=ReturnType.TEXT							,	a				:     Tuple=False    )	->str:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					[]
													for output_ids in model_outputs["output_ids"][0]:
																			if return_type == ReturnType.TENSORS:
																									SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					{f"""{self.return_name}_token_ids""": output_ids}
																			elif return_type == ReturnType.TEXT:
																									SCREAMING_SNAKE_CASE__     :				List[str]   =					{
																									    f"""{self.return_name}_text""": self.tokenizer.decode(
																									        a							,	skip_special_tokens=a							,	clean_up_tokenization_spaces=a							,	)
																									}
																			records.append(a    )
													return records







@add_end_docstrings(lowercase__							)
class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						"summary"

							def __call__(      self				:     Union[str, Any]							,	*a				:     Dict							,	**a				:     Union[str, Any]    )	->int:
													return super().__call__(*a							,	**a    )






							def      A_  (      self				:     Optional[Any]							,	a				:     int							,	a				:     int							,	a				:     int    )	->bool:
													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(lowercase__							)
class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						"translation"

							def      A_  (      self				:     Tuple							,	a				:     int							,	a				:     int							,	a				:     int    )	->List[Any]:
													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      A_  (      self				:     Optional[Any]							,	*a				:     List[Any]							,	a				:     str=TruncationStrategy.DO_NOT_TRUNCATE							,	a				:     List[str]=None							,	a				:     Optional[Any]=None    )	->Optional[int]:
													if getattr(self.tokenizer							,	"_build_translation_inputs"							,	a    ):
																			return self.tokenizer._build_translation_inputs(
																			    *a							,	return_tensors=self.framework							,	truncation=a							,	src_lang=a							,	tgt_lang=a    )
													else:
																			return super()._parse_and_tokenize(*a							,	truncation=a    )

							def      A_  (      self				:     Union[str, Any]							,	a				:     int=None							,	a				:     Tuple=None							,	**a				:     int    )	->Any:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					super()._sanitize_parameters(**a    )
													if src_lang is not None:
																			SCREAMING_SNAKE_CASE__     :				int   =					src_lang
													if tgt_lang is not None:
																			SCREAMING_SNAKE_CASE__     :				str   =					tgt_lang
													if src_lang is None and tgt_lang is None:
																			# Backward compatibility, direct arguments use is preferred.
																			SCREAMING_SNAKE_CASE__     :				int   =					kwargs.get("task"							,	self.task    )
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					task.split("_"    )
																			if task and len(a    ) == 4:
																									# translation, XX, to YY
																									SCREAMING_SNAKE_CASE__     :				str   =					items[1]
																									SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					items[3]
													return preprocess_params, forward_params, postprocess_params






							def __call__(      self				:     str							,	*a				:     int							,	**a				:     int    )	->int:
													return super().__call__(*a							,	**a    )






def       UpperCAmelCase							(     _lowerCamelCase      :  int = 4_000_000     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Any   =					[0, 1]
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					0
						while fib[i] <= n:
												fib.append(fib[i] + fib[i + 1]     )
												if fib[i + 2] > n:
																		break
												i += 1
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					0
						for j in range(len(_lowerCamelCase     ) - 1     ):
												if fib[j] % 2 == 0:
																		total += fib[j]

						return total


if __name__ == "__main__":
				print(f"{solution() = }")