"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
				import torch


__lowerCamelCase											=  logging.get_logger(__name__)
@dataclass
class     UpperCamelCase__:





						def __init__(       self		,__UpperCAmelCase=False		,__UpperCAmelCase=False		,__UpperCAmelCase=6.0		,__UpperCAmelCase=None		,__UpperCAmelCase=False		,__UpperCAmelCase=False		,__UpperCAmelCase=None		,__UpperCAmelCase="fp4"		,__UpperCAmelCase=False		,**__UpperCAmelCase		,)					->				Union[str, Any]:
													A__	     =							load_in_abit
													A__	     =							load_in_abit
													A__	     =							llm_inta_threshold
													A__	     =							llm_inta_skip_modules
													A__	     =							llm_inta_enable_fpaa_cpu_offload
													A__	     =							llm_inta_has_fpaa_weight
													A__	     =							bnb_abit_quant_type
													A__	     =							bnb_abit_use_double_quant

													if bnb_abit_compute_dtype is None:
																				A__	     =							torch.floataa
													elif isinstance(__UpperCAmelCase		,__UpperCAmelCase     ):
																				A__	     =							getattr(__UpperCAmelCase		,__UpperCAmelCase     )
													elif isinstance(__UpperCAmelCase		,torch.dtype     ):
																				A__	     =							bnb_abit_compute_dtype
													else:
																				raise ValueError('bnb_4bit_compute_dtype must be a string or a torch.dtype'     )

													self.post_init()





						def 				snake_case__	(       self     )					->				str:
													if not isinstance(self.llm_inta_threshold		,__UpperCAmelCase     ):
																				raise ValueError('llm_int8_threshold must be a float'     )

													if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules		,__UpperCAmelCase     ):
																				raise ValueError('llm_int8_skip_modules must be a list of strings'     )
													if not isinstance(self.llm_inta_enable_fpaa_cpu_offload		,__UpperCAmelCase     ):
																				raise ValueError('llm_int8_enable_fp32_cpu_offload must be a boolean'     )

													if not isinstance(self.llm_inta_has_fpaa_weight		,__UpperCAmelCase     ):
																				raise ValueError('llm_int8_has_fp16_weight must be a boolean'     )

													if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype		,torch.dtype     ):
																				raise ValueError('bnb_4bit_compute_dtype must be torch.dtype'     )

													if not isinstance(self.bnb_abit_quant_type		,__UpperCAmelCase     ):
																				raise ValueError('bnb_4bit_quant_type must be a string'     )

													if not isinstance(self.bnb_abit_use_double_quant		,__UpperCAmelCase     ):
																				raise ValueError('bnb_4bit_use_double_quant must be a boolean'     )

													if self.load_in_abit and not version.parse(importlib.metadata.version('bitsandbytes'     )     ) >= version.parse(
													    '0.39.0'     ):
																				raise ValueError(
																				    '4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version'     )





						def 				snake_case__	(       self     )					->				int:
													return self.load_in_abit or self.load_in_abit





						def 				snake_case__	(       self     )					->				Optional[int]:
													if self.load_in_abit:
																				return "llm_int8"
													elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
																				return "fp4"
													elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
																				return "nf4"
													else:
																				return None





						@classmethod
						def 				snake_case__	(       cls		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[Any]:
													A__	     =							cls(**__UpperCAmelCase     )

													A__	     =							[]
													for key, value in kwargs.items():
																				if hasattr(__UpperCAmelCase		,__UpperCAmelCase     ):
																											setattr(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )
																											to_remove.append(__UpperCAmelCase     )
													for key in to_remove:
																				kwargs.pop(__UpperCAmelCase		,__UpperCAmelCase     )

													if return_unused_kwargs:
																				return config, kwargs
													else:
																				return config





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				str:
													with open(__UpperCAmelCase		,'w'		,encoding='utf-8'     ) as writer:
																				A__	     =							self.to_dict()
																				A__	     =							json.dumps(__UpperCAmelCase		,indent=2		,sort_keys=__UpperCAmelCase     ) + '\n'

																				writer.write(__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Dict[str, Any]:
													A__	     =							copy.deepcopy(self.__dict__     )
													A__	     =							str(output['bnb_4bit_compute_dtype']     ).split('.'     )[1]

													return output





						def __repr__(       self     )					->				Union[str, Any]:
													return f'''{self.__class__.__name__} {self.to_json_string()}'''





						def 				snake_case__	(       self		,__UpperCAmelCase = True     )					->				str:
													if use_diff is True:
																				A__	     =							self.to_diff_dict()
													else:
																				A__	     =							self.to_dict()
													return json.dumps(__UpperCAmelCase		,indent=2		,sort_keys=__UpperCAmelCase     ) + "\n"





						def 				snake_case__	(       self     )					->				Dict[str, Any]:
													A__	     =							self.to_dict()

													# get the default config dict
													A__	     =							BitsAndBytesConfig().to_dict()

													A__	     =							{}

													# only serialize values that differ from the default config
													for key, value in config_dict.items():
																				if value != default_config_dict[key]:
																											A__	     =							value

													return serializable_config_dict






"""simple docstring"""




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     UpperCamelCase__(			enum.Enum ):
						lowerCAmelCase__  :						Optional[Any]													=		0
						lowerCAmelCase__  :						Optional[int]													=		1
						lowerCAmelCase__  :						List[Any]													=		2






@add_end_docstrings(__A )
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Optional[Any]													=		'\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     )					->				Any:
													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.
																				A__	     =							None
																				if self.model.config.prefix is not None:
																											A__	     =							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.
																											A__	     =							self.XL_PREFIX
																				if prefix is not None:
																											# Recalculate some generate_kwargs linked to prefix.
																											A__     ,  A__     ,  A__	     =							self._sanitize_parameters(prefix=__UpperCAmelCase		,**self._forward_params     )
																											A__	     =							{**self._preprocess_params, **preprocess_params}
																											A__	     =							{**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		,)					->				Dict:
													A__	     =							{}
													if prefix is not None:
																				A__	     =							prefix
													if prefix:
																				A__	     =							self.tokenizer(
																				    __UpperCAmelCase		,padding=__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase		,return_tensors=self.framework     )
																				A__	     =							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\']'     )
																				A__	     =							handle_long_generation

													preprocess_params.update(__UpperCAmelCase     )
													A__	     =							generate_kwargs

													A__	     =							{}
													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`'     )
																				A__	     =							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`'     )
																				A__	     =							ReturnType.TENSORS
													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(__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.'     )
																				A__	     =							stop_sequence_ids[0]

													return preprocess_params, forward_params, postprocess_params





						def 				snake_case__	(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				Dict:
													# 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     )					->				Dict:
													return super().__call__(__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=""		,__UpperCAmelCase=None		,**__UpperCAmelCase     )					->				Dict:
													A__	     =							self.tokenizer(
													    prefix + prompt_text		,padding=__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase		,return_tensors=self.framework     )
													A__	     =							prompt_text

													if handle_long_generation == "hole":
																				A__	     =							inputs['input_ids'].shape[-1]
																				if "max_new_tokens" in generate_kwargs:
																											A__	     =							generate_kwargs['max_new_tokens']
																				else:
																											A__	     =							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:
																											A__	     =							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'     )

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

													return inputs





						def 				snake_case__	(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[Any]:
													A__	     =							model_inputs['input_ids']
													A__	     =							model_inputs.get('attention_mask'		,__UpperCAmelCase     )
													# Allow empty prompts
													if input_ids.shape[1] == 0:
																				A__	     =							None
																				A__	     =							None
																				A__	     =							1
													else:
																				A__	     =							input_ids.shape[0]
													A__	     =							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.
													A__	     =							generate_kwargs.pop('prefix_length'		,0     )
													if prefix_length > 0:
																				A__	     =							'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:
																											A__	     =							generate_kwargs.get('max_length'     ) or self.model.config.max_length
																											generate_kwargs["max_length"] += prefix_length
																				A__	     =							'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
													A__	     =							self.model.generate(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							generated_sequence.shape[0]
													if self.framework == "pt":
																				A__	     =							generated_sequence.reshape(__UpperCAmelCase		,out_b // in_b		,*generated_sequence.shape[1:]     )
													elif self.framework == "tf":
																				A__	     =							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     )					->				str:
													A__	     =							model_outputs['generated_sequence'][0]
													A__	     =							model_outputs['input_ids']
													A__	     =							model_outputs['prompt_text']
													A__	     =							generated_sequence.numpy().tolist()
													A__	     =							[]
													for sequence in generated_sequence:
																				if return_type == ReturnType.TENSORS:
																											A__	     =							{'generated_token_ids': sequence}
																				elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
																											# Decode text
																											A__	     =							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:
																																		A__	     =							0
																											else:
																																		A__	     =							len(
																																		    self.tokenizer.decode(
																																		        input_ids[0]		,skip_special_tokens=__UpperCAmelCase		,clean_up_tokenization_spaces=__UpperCAmelCase		,)     )

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

																											A__	     =							{'generated_text': all_text}
																				records.append(__UpperCAmelCase     )

													return records





