




import logging

from transformers.configuration_utils import PretrainedConfig


__UpperCamelCase   :			Optional[int]          = logging.getLogger(__name__)

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''masked_bert'''
							def __init__(		self		:Tuple						, __magic_name__		:List[str]=3_0522						, __magic_name__		:Dict=768						, __magic_name__		:Union[str, Any]=12						, __magic_name__		:int=12						, __magic_name__		:List[str]=3072						, __magic_name__		:Tuple="gelu"						, __magic_name__		:Any=0.1						, __magic_name__		:Optional[int]=0.1						, __magic_name__		:str=512						, __magic_name__		:Tuple=2						, __magic_name__		:Tuple=0.02						, __magic_name__		:str=1E-1_2						, __magic_name__		:Tuple=0						, __magic_name__		:Any="topK"						, __magic_name__		:Dict="constant"						, __magic_name__		:List[str]=0.0						, **__magic_name__		:Tuple						, ):







								'''simple docstring'''







								super().__init__(pad_token_id=__magic_name__						, **__magic_name__							)

								a									=				vocab_size
								a									=				hidden_size
								a									=				num_hidden_layers
								a									=				num_attention_heads
								a									=				hidden_act
								a									=				intermediate_size
								a									=				hidden_dropout_prob
								a									=				attention_probs_dropout_prob
								a									=				max_position_embeddings
								a									=				type_vocab_size
								a									=				initializer_range
								a									=				layer_norm_eps
								a									=				pruning_method
								a									=				mask_init
								a									=				mask_scale







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

__UpperCamelCase   :			Dict          = logging.get_logger(__name__)

class 						__lowerCAmelCase      (      enum.Enum					):
							UpperCamelCase__   	=    0
							UpperCamelCase__   	=    1







@add_end_docstrings(__magic_name__					)
class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''generated'''
							def __init__(		self		:Any						, *__magic_name__		:Tuple						, **__magic_name__		:Tuple							):







								'''simple docstring'''







								super().__init__(*__magic_name__						, **__magic_name__							)

								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		:Optional[int]						, __magic_name__		:Any=None						, __magic_name__		:Optional[Any]=None						, __magic_name__		:Any=None						, __magic_name__		:List[str]=None						, __magic_name__		:Tuple=None						, __magic_name__		:str=None						, **__magic_name__		:List[Any]						, ):







								'''simple docstring'''







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

								a									=				generate_kwargs

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

								if clean_up_tokenization_spaces is not None:
									a									=				clean_up_tokenization_spaces

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

								return preprocess_params, forward_params, postprocess_params
							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:int						, __magic_name__		:int						, __magic_name__		:int							):







								'''simple docstring'''







								return True
							def  lowerCamelCase__				(		self		:Dict						, *__magic_name__		:Optional[int]						, __magic_name__		:List[str]							):







								'''simple docstring'''







								a									=				self.model.config.prefix if self.model.config.prefix is not None else """"""
								if isinstance(args[0]						, __magic_name__							):
									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"""							)
									a									=				([prefix + arg for arg in args[0]],)
									a									=				True

								elif isinstance(args[0]						, __magic_name__							):
									a									=				(prefix + args[0],)
									a									=				False
								else:
									raise ValueError(
									    F' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'							)
								a									=				self.tokenizer(*__magic_name__						, padding=__magic_name__						, truncation=__magic_name__						, 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		:Tuple						, *__magic_name__		:Any						, **__magic_name__		:str							):







								'''simple docstring'''







								a									=				super().__call__(*__magic_name__						, **__magic_name__							)
								if (
								    isinstance(args[0]						, __magic_name__							)
								    and all(isinstance(__magic_name__						, __magic_name__							) for el in args[0]							)
								    and all(len(__magic_name__							) == 1 for res in result							)
								):
									return [res[0] for res in result]
								return result
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:Optional[Any]						, __magic_name__		:List[str]=TruncationStrategy.DO_NOT_TRUNCATE						, **__magic_name__		:Any							):







								'''simple docstring'''







								a									=				self._parse_and_tokenize(__magic_name__						, truncation=__magic_name__						, **__magic_name__							)
								return inputs
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int						, **__magic_name__		:int							):







								'''simple docstring'''







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

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





							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:Dict						, __magic_name__		:Any=ReturnType.TEXT						, __magic_name__		:int=False							):







								'''simple docstring'''







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







@add_end_docstrings(__magic_name__					)
class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''summary'''
							def __call__(		self		:Any						, *__magic_name__		:List[str]						, **__magic_name__		:Optional[int]							):







								'''simple docstring'''







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





							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int						, __magic_name__		:int						, __magic_name__		:int							):







								'''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(__magic_name__					)
class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''translation'''
							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:int						, __magic_name__		:int						, __magic_name__		:int							):







								'''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		:str						, *__magic_name__		:Union[str, Any]						, __magic_name__		:Any=TruncationStrategy.DO_NOT_TRUNCATE						, __magic_name__		:Optional[Any]=None						, __magic_name__		:List[str]=None							):







								'''simple docstring'''







								if getattr(self.tokenizer						, """_build_translation_inputs"""						, __magic_name__							):
									return self.tokenizer._build_translation_inputs(
									    *__magic_name__						, return_tensors=self.framework						, truncation=__magic_name__						, src_lang=__magic_name__						, tgt_lang=__magic_name__							)
								else:
									return super()._parse_and_tokenize(*__magic_name__						, truncation=__magic_name__							)
							def  lowerCamelCase__				(		self		:int						, __magic_name__		:List[str]=None						, __magic_name__		:Union[str, Any]=None						, **__magic_name__		:Optional[int]							):







								'''simple docstring'''







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





							def __call__(		self		:Optional[Any]						, *__magic_name__		:Any						, **__magic_name__		:str							):







								'''simple docstring'''







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

