






import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
							import tensorflow as tf

class       __A     (						enum.Enum    ):
				__A      =				0
				__A      =				1
				__A      =				2




@add_end_docstrings(lowercase__    )
class       __A     (						lowercase__    ):
				__A      =				'\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    '


				def __init__(		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								super().__init__(*UpperCAmelCase_   , **UpperCAmelCase_							)
								self.check_model_type(
								    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == """tf""" else MODEL_FOR_CAUSAL_LM_MAPPING							)
								if "prefix" not in self._preprocess_params:
												# This is very specific. The logic is quite complex and needs to be done
												# as a "default".
												# It also defines both some preprocess_kwargs and generate_kwargs
												# which is why we cannot put them in their respective methods.
												lowerCamelCase	 =None
												if self.model.config.prefix is not None:
																lowerCamelCase	 =self.model.config.prefix
												if prefix is None and self.model.__class__.__name__ in [
												    "XLNetLMHeadModel",
												    "TransfoXLLMHeadModel",
												    "TFXLNetLMHeadModel",
												    "TFTransfoXLLMHeadModel",
												]:
																# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
																lowerCamelCase	 =self.XL_PREFIX
												if prefix is not None:
																# Recalculate some generate_kwargs linked to prefix.
																lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self._sanitize_parameters(prefix=UpperCAmelCase_   , **self._forward_params							)
																lowerCamelCase	 ={**self._preprocess_params, **preprocess_params}
																lowerCamelCase	 ={**self._forward_params, **forward_params}


				def _snake_case       (		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , **UpperCAmelCase_   , ):
								lowerCamelCase	 ={}
								if prefix is not None:
												lowerCamelCase	 =prefix
								if prefix:
												lowerCamelCase	 =self.tokenizer(
												    UpperCAmelCase_   , padding=UpperCAmelCase_   , add_special_tokens=UpperCAmelCase_   , return_tensors=self.framework							)
												lowerCamelCase	 =prefix_inputs["""input_ids"""].shape[-1]

								if handle_long_generation is not None:
												if handle_long_generation not in {"hole"}:
																raise ValueError(
																    f"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
																    """ [None, 'hole']"""							)
												lowerCamelCase	 =handle_long_generation

								preprocess_params.update(UpperCAmelCase_							)
								lowerCamelCase	 =generate_kwargs

								lowerCamelCase	 ={}
								if return_full_text is not None and return_type is None:
												if return_text is not None:
																raise ValueError("""`return_text` is mutually exclusive with `return_full_text`"""							)
												if return_tensors is not None:
																raise ValueError("""`return_full_text` is mutually exclusive with `return_tensors`"""							)
												lowerCamelCase	 =ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
								if return_tensors is not None and return_type is None:
												if return_text is not None:
																raise ValueError("""`return_text` is mutually exclusive with `return_tensors`"""							)
												lowerCamelCase	 =ReturnType.TENSORS
								if return_type is not None:
												lowerCamelCase	 =return_type
								if clean_up_tokenization_spaces is not None:
												lowerCamelCase	 =clean_up_tokenization_spaces

								if stop_sequence is not None:
												lowerCamelCase	 =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."""							)
												lowerCamelCase	 =stop_sequence_ids[0]

								return preprocess_params, forward_params, postprocess_params


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								# Parse arguments
								if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
												kwargs.update({"""add_space_before_punct_symbol""": True}							)

								return super()._parse_and_tokenize(*UpperCAmelCase_   , **UpperCAmelCase_							)


				def __call__(		self   , UpperCAmelCase_   , **UpperCAmelCase_							):
								return super().__call__(UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=""   , UpperCAmelCase_=None   , **UpperCAmelCase_							):
								lowerCamelCase	 =self.tokenizer(
								    prefix + prompt_text   , padding=UpperCAmelCase_   , add_special_tokens=UpperCAmelCase_   , return_tensors=self.framework							)
								lowerCamelCase	 =prompt_text

								if handle_long_generation == "hole":
												lowerCamelCase	 =inputs["""input_ids"""].shape[-1]
												if "max_new_tokens" in generate_kwargs:
																lowerCamelCase	 =generate_kwargs["""max_new_tokens"""]
												else:
																lowerCamelCase	 =generate_kwargs.get("""max_length"""   , self.model.config.max_length							) - cur_len
																if new_tokens < 0:
																				raise ValueError("""We cannot infer how many new tokens are expected"""							)
												if cur_len + new_tokens > self.tokenizer.model_max_length:
																lowerCamelCase	 =self.tokenizer.model_max_length - new_tokens
																if keep_length <= 0:
																				raise ValueError(
																				    """We cannot use `hole` to handle this generation the number of desired tokens exceeds the"""
																				    """ models max length"""							)

																lowerCamelCase	 =inputs["""input_ids"""][:, -keep_length:]
																if "attention_mask" in inputs:
																				lowerCamelCase	 =inputs["""attention_mask"""][:, -keep_length:]

								return inputs


				def _snake_case       (		self   , UpperCAmelCase_   , **UpperCAmelCase_							):
								lowerCamelCase	 =model_inputs["""input_ids"""]
								lowerCamelCase	 =model_inputs.get("""attention_mask"""   , UpperCAmelCase_							)
								# Allow empty prompts
								if input_ids.shape[1] == 0:
												lowerCamelCase	 =None
												lowerCamelCase	 =None
												lowerCamelCase	 =1
								else:
												lowerCamelCase	 =input_ids.shape[0]
								lowerCamelCase	 =model_inputs.pop("""prompt_text"""							)

								# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
								# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
								lowerCamelCase	 =generate_kwargs.pop("""prefix_length"""   , 0							)
								if prefix_length > 0:
												lowerCamelCase	 ="""max_new_tokens""" in generate_kwargs or (
												    """generation_config""" in generate_kwargs
												    and generate_kwargs["""generation_config"""].max_new_tokens is not None
												)
												if not has_max_new_tokens:
																lowerCamelCase	 =generate_kwargs.get("""max_length"""							) or self.model.config.max_length
																generate_kwargs["max_length"] += prefix_length
												lowerCamelCase	 ="""min_new_tokens""" in generate_kwargs or (
												    """generation_config""" in generate_kwargs
												    and generate_kwargs["""generation_config"""].min_new_tokens is not None
												)
												if not has_min_new_tokens and "min_length" in generate_kwargs:
																generate_kwargs["min_length"] += prefix_length

        # BS x SL
								lowerCamelCase	 =self.model.generate(input_ids=UpperCAmelCase_   , attention_mask=UpperCAmelCase_   , **UpperCAmelCase_							)
								lowerCamelCase	 =generated_sequence.shape[0]
								if self.framework == "pt":
												lowerCamelCase	 =generated_sequence.reshape(UpperCAmelCase_   , out_b // in_b   , *generated_sequence.shape[1:]							)
								elif self.framework == "tf":
												lowerCamelCase	 =tf.reshape(UpperCAmelCase_   , (in_b, out_b // in_b, *generated_sequence.shape[1:])							)
								return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=ReturnType.FULL_TEXT   , UpperCAmelCase_=True							):
								lowerCamelCase	 =model_outputs["""generated_sequence"""][0]
								lowerCamelCase	 =model_outputs["""input_ids"""]
								lowerCamelCase	 =model_outputs["""prompt_text"""]
								lowerCamelCase	 =generated_sequence.numpy().tolist()
								lowerCamelCase	 =[]
								for sequence in generated_sequence:
												if return_type == ReturnType.TENSORS:
																lowerCamelCase	 ={"""generated_token_ids""": sequence}
												elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
																# Decode text
																lowerCamelCase	 =self.tokenizer.decode(
																    UpperCAmelCase_   , skip_special_tokens=UpperCAmelCase_   , clean_up_tokenization_spaces=UpperCAmelCase_   , )

																# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
																if input_ids is None:
																				lowerCamelCase	 =0
																else:
																				lowerCamelCase	 =len(
																				    self.tokenizer.decode(
																				        input_ids[0]   , skip_special_tokens=UpperCAmelCase_   , clean_up_tokenization_spaces=UpperCAmelCase_   , )							)

																if return_type == ReturnType.FULL_TEXT:
																				lowerCamelCase	 =prompt_text + text[prompt_length:]
																else:
																				lowerCamelCase	 =text[prompt_length:]

																lowerCamelCase	 ={"""generated_text""": all_text}
												records.append(UpperCAmelCase_							)

								return records












from typing import List

from .keymap import KEYMAP, get_character





def      _lowercase					(						_UpperCAmelCase				)						->  Tuple:

				def decorator(_UpperCAmelCase				):
								lowerCamelCase	 =getattr(_UpperCAmelCase			,      """handle_key"""			,      []				)
								handle += [key]
								setattr(_UpperCAmelCase			,      """handle_key"""			,      _UpperCAmelCase				)
								return func

				return decorator





def      _lowercase					(						*_UpperCAmelCase				)						->  Tuple:

				def decorator(_UpperCAmelCase				):
								lowerCamelCase	 =getattr(_UpperCAmelCase			,      """handle_key"""			,      []				)
								handle += keys
								setattr(_UpperCAmelCase			,      """handle_key"""			,      _UpperCAmelCase				)
								return func

				return decorator

class       __A     (						a    ):


				def __new__(		cls   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =super().__new__(cls   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)
								if not hasattr(UpperCAmelCase_   , """key_handler"""							):
												setattr(UpperCAmelCase_   , """key_handler"""   , {}							)
								setattr(UpperCAmelCase_   , """handle_input"""   , KeyHandler.handle_input							)

								for value in attrs.values():
												lowerCamelCase	 =getattr(UpperCAmelCase_   , """handle_key"""   , []							)
												for key in handled_keys:
																lowerCamelCase	 =value
								return new_cls


				@staticmethod
				def _snake_case       (		cls							):
								lowerCamelCase	 =get_character()
								if char != KEYMAP["undefined"]:
												lowerCamelCase	 =ord(UpperCAmelCase_							)
								lowerCamelCase	 =cls.key_handler.get(UpperCAmelCase_							)
								if handler:
												lowerCamelCase	 =char
												return handler(cls							)
								else:
												return None





def      _lowercase					(						cls				)						->  List[str]:
				return KeyHandler(cls.__name__			,      cls.__bases__			,      cls.__dict__.copy()				)





