
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

__a         =   logging.get_logger(__name__)
class 					lowercase__(							enum.Enum				):



	"""simple docstring"""




	a     :Optional[int]       	=      0
	a     :List[str]       	=      1

@add_end_docstrings(UpperCAmelCase				)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[Any]       	=      'generated'
	def __init__(			self    :     Dict			,				*SCREAMING_SNAKE_CASE_    :     List[Any]			,				**SCREAMING_SNAKE_CASE_    :     str			)			->	Optional[int]:
					super().__init__(*SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)

					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 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     str=None			,				SCREAMING_SNAKE_CASE_    :     List[Any]=None			,				SCREAMING_SNAKE_CASE_    :     str=None			,				SCREAMING_SNAKE_CASE_    :     List[str]=None			,				SCREAMING_SNAKE_CASE_    :     Tuple=None			,				SCREAMING_SNAKE_CASE_    :     List[Any]=None			,				**SCREAMING_SNAKE_CASE_    :     List[str]			,				)			->	int:
					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(SCREAMING_SNAKE_CASE_			,				add_special_tokens=SCREAMING_SNAKE_CASE_			)
									if len(SCREAMING_SNAKE_CASE_			) > 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 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			)			->	Dict:
					return True
	def 		_lowercase    (			self    :     Dict			,				*SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	Union[str, Any]:
					lowercase_	    =   self.model.config.prefix if self.model.config.prefix is not None else ''''''
					if isinstance(args[0]			,				SCREAMING_SNAKE_CASE_			):
									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]			,				SCREAMING_SNAKE_CASE_			):
									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(*SCREAMING_SNAKE_CASE_			,				padding=SCREAMING_SNAKE_CASE_			,				truncation=SCREAMING_SNAKE_CASE_			,				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    :     Dict			,				*SCREAMING_SNAKE_CASE_    :     str			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	int:
					lowercase_	    =   super().__call__(*SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					if (
					    isinstance(args[0]			,				SCREAMING_SNAKE_CASE_			)
					    and all(isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) for el in args[0]			)
					    and all(len(SCREAMING_SNAKE_CASE_			) == 1 for res in result			)
					):
									return [res[0] for res in result]
					return result
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=TruncationStrategy.DO_NOT_TRUNCATE			,				**SCREAMING_SNAKE_CASE_    :     int			)			->	int:
					lowercase_	    =   self._parse_and_tokenize(SCREAMING_SNAKE_CASE_			,				truncation=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					return inputs
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				**SCREAMING_SNAKE_CASE_    :     List[str]			)			->	Any:
					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(SCREAMING_SNAKE_CASE_			,				generate_kwargs['''min_length''']			,				generate_kwargs['''max_length''']			)
					lowercase_	    =   self.model.generate(**SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   output_ids.shape[0]
					if self.framework == "pt":
									lowercase_	    =   output_ids.reshape(SCREAMING_SNAKE_CASE_			,				out_b // in_b			,				*output_ids.shape[1:]			)
					elif self.framework == "tf":
									lowercase_	    =   tf.reshape(SCREAMING_SNAKE_CASE_			,				(in_b, out_b // in_b, *output_ids.shape[1:])			)
					return {"output_ids": output_ids}




	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=ReturnType.TEXT			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=False			)			->	int:
					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(
													        SCREAMING_SNAKE_CASE_			,				skip_special_tokens=SCREAMING_SNAKE_CASE_			,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_			,				)
													}
									records.append(SCREAMING_SNAKE_CASE_			)
					return records

@add_end_docstrings(UpperCAmelCase				)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[Any]       	=      'summary'
	def __call__(			self    :     Dict			,				*SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				**SCREAMING_SNAKE_CASE_    :     List[str]			)			->	List[Any]:
					return super().__call__(*SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     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(UpperCAmelCase				)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[Any]       	=      'translation'
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			)			->	Optional[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 		_lowercase    (			self    :     Union[str, Any]			,				*SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]=TruncationStrategy.DO_NOT_TRUNCATE			,				SCREAMING_SNAKE_CASE_    :     Tuple=None			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=None			)			->	int:
					if getattr(self.tokenizer			,				'''_build_translation_inputs'''			,				SCREAMING_SNAKE_CASE_			):
									return self.tokenizer._build_translation_inputs(
									    *SCREAMING_SNAKE_CASE_			,				return_tensors=self.framework			,				truncation=SCREAMING_SNAKE_CASE_			,				src_lang=SCREAMING_SNAKE_CASE_			,				tgt_lang=SCREAMING_SNAKE_CASE_			)
					else:
									return super()._parse_and_tokenize(*SCREAMING_SNAKE_CASE_			,				truncation=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     str=None			,				SCREAMING_SNAKE_CASE_    :     str=None			,				**SCREAMING_SNAKE_CASE_    :     Tuple			)			->	Tuple:
					lowercase_    ,      lowercase_    ,      lowercase_	    =   super()._sanitize_parameters(**SCREAMING_SNAKE_CASE_			)
					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(SCREAMING_SNAKE_CASE_			) == 4:
													# translation, XX, to YY
													lowercase_	    =   items[1]
													lowercase_	    =   items[3]
					return preprocess_params, forward_params, postprocess_params




	def __call__(			self    :     List[str]			,				*SCREAMING_SNAKE_CASE_    :     int			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	Optional[int]:
					return super().__call__(*SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)






from __future__ import annotations




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Optional[int]		,  snake_case__:   Any		,  snake_case__:   Optional[int]   ):  # noqa: E741


				'''simple docstring'''
				while r - l > 1:
								lowercase_	    =   (l + r) // 2
								if v[m] >= key:
												lowercase_	    =   m
								else:
												lowercase_	    =   m  # noqa: E741
				return r




def 							a		(				snake_case__:   list[int]   ):


				'''simple docstring'''
				if len(snake_case__   ) == 0:
								return 0

				lowercase_	    =   [0] * len(snake_case__   )
				lowercase_	    =   1

				lowercase_	    =   v[0]

				for i in range(1		,  len(snake_case__   )   ):
								if v[i] < tail[0]:
												lowercase_	    =   v[i]
								elif v[i] > tail[length - 1]:
												lowercase_	    =   v[i]
												length += 1
								else:
												lowercase_	    =   v[i]

				return length


if __name__ == "__main__":
						import doctest

						doctest.testmod()




