"""simple docstring"""







import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_a	:	str=					logging.get_logger(__name__)

_a	:	str=					{
    "source_spm": "source.spm",
    "target_spm": "target.spm",
    "vocab": "vocab.json",
    "target_vocab_file": "target_vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

_a	:	Any=					{
    "source_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"
    },
    "target_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"
    },
    "vocab": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"
    },
    "tokenizer_config_file": {
        "Helsinki-NLP/opus-mt-en-de": (
            "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"
        )
    },
}

_a	:	List[Any]=					{"Helsinki-NLP/opus-mt-en-de": 512}
_a	:	List[str]=					{}

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: Tuple       =						VOCAB_FILES_NAMES
				UpperCAmelCase							: Optional[Any]       =						PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase							: Optional[Any]       =						PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase							: Dict       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase							: int       =						["""input_ids""", """attention_mask"""]
				UpperCAmelCase							: Optional[int]       =						re.compile(""">>.+<<"""		)  # type: re.Pattern



				def __init__(self							: Tuple      ,    _A							: List[Any]      ,    _A							: List[str]      ,    _A							: str      ,    _A							: List[Any]=None      ,    _A							: Dict=None      ,    _A							: Dict=None      ,    _A							: List[str]="<unk>"      ,    _A							: Union[str, Any]="</s>"      ,    _A							: List[str]="<pad>"      ,    _A							: Any=5_12      ,    _A							: Optional[Dict[str, Any]] = None      ,    _A							: Any=False      ,    **_A							: str      ,    )       ->      None:
					__snake_case     :				List[Any] 						=					{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
					    source_lang=_A      ,    target_lang=_A      ,    unk_token=_A      ,    eos_token=_A      ,    pad_token=_A      ,    model_max_length=_A      ,    sp_model_kwargs=self.sp_model_kwargs      ,    target_vocab_file=_A      ,    separate_vocabs=_A      ,    **_A      ,    )
					assert Path(_A).exists(), f"cannot find spm source {source_spm}"

					__snake_case     :				Optional[int] 						=					separate_vocabs
					__snake_case     :				List[Any] 						=					load_json(_A)
					if self.unk_token not in self.encoder:
						raise KeyError('<unk> token must be in vocab')
					assert self.pad_token in self.encoder

					if separate_vocabs:
						__snake_case     :				str 						=					load_json(_A)
						__snake_case     :				Union[str, Any] 						=					{v: k for k, v in self.target_encoder.items()}
						__snake_case     :				Dict 						=					[]
					else:
						__snake_case     :				int 						=					{v: k for k, v in self.encoder.items()}
						__snake_case     :				list 						=					[k for k in self.encoder if k.startswith('>>') and k.endswith('<<')]

					__snake_case     :				Tuple 						=					source_lang
					__snake_case     :				Tuple 						=					target_lang
					__snake_case     :				Union[str, Any] 						=					[source_spm, target_spm]

					# load SentencePiece model for pre-processing
					__snake_case     :				Optional[Any] 						=					load_spm(_A      ,    self.sp_model_kwargs)
					__snake_case     :				List[Any] 						=					load_spm(_A      ,    self.sp_model_kwargs)
					__snake_case     :				Dict 						=					self.spm_source
					__snake_case     :				int 						=					self.encoder

					# Multilingual target side: default to using first supported language code.

					self._setup_normalizer()



				def    _lowercase	(self							: Tuple)       ->      Optional[Any]:
					try:
						from sacremoses import MosesPunctNormalizer

						__snake_case     :				str 						=					MosesPunctNormalizer(self.source_lang).normalize
					except (ImportError, FileNotFoundError):
						warnings.warn('Recommended: pip install sacremoses.')
						__snake_case     :				Tuple 						=					lambda _A: x



				def    _lowercase	(self							: Dict      ,    _A							: str)       ->      str:
					return self.punc_normalizer(_A) if x else ""



				def    _lowercase	(self							: str      ,    _A							: Tuple)       ->      Optional[int]:
					return self.current_encoder.get(_A      ,    self.current_encoder[self.unk_token])



				def    _lowercase	(self							: int      ,    _A							: str)       ->      Optional[Any]:
					__snake_case     :				Union[str, Any] 						=					self.language_code_re.match(_A)
					__snake_case     :				list 						=					[match.group(0)] if match else []
					return code, self.language_code_re.sub(''      ,    _A)



				def    _lowercase	(self							: Dict      ,    _A							: str)       ->      List[str]:
					__snake_case						,							__snake_case     :				List[Any] 						=					self.remove_language_code(_A)
					__snake_case     :				str 						=					self.current_spm.encode(_A      ,    out_type=_A)
					return code + pieces



				def    _lowercase	(self							: int      ,    _A							: int)       ->      str:
					return self.decoder.get(_A      ,    self.unk_token)



				def    _lowercase	(self							: str      ,    _A							: Dict      ,    **_A							: str)       ->      Dict:
					return super().batch_decode(_A      ,    **_A)



				def    _lowercase	(self							: List[Any]      ,    _A							: Dict      ,    **_A							: Dict)       ->      Dict:
					return super().decode(_A      ,    **_A)



				def    _lowercase	(self							: List[Any]      ,    _A							: List[str])       ->      str:
					__snake_case     :				List[Any] 						=					self.spm_source if self._decode_use_source_tokenizer else self.spm_target
					__snake_case     :				int 						=					[]
					__snake_case     :				List[str] 						=					''
					for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
							out_string += sp_model.decode_pieces(_A) + token + " "
							__snake_case     :				Tuple 						=					[]
						else:
							current_sub_tokens.append(_A)
					out_string += sp_model.decode_pieces(_A)
					return out_string.strip()



				def    _lowercase	(self							: Dict      ,    _A							: List[str]      ,    _A							: Tuple=None)       ->      List[int]:
					if token_ids_a is None:
						return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]



				def    _lowercase	(self							: str)       ->      Optional[int]:
					__snake_case     :				List[str] 						=					self.spm_source
					__snake_case     :				List[str] 						=					self.encoder



				def    _lowercase	(self							: Dict)       ->      Optional[int]:
					__snake_case     :				Union[str, Any] 						=					self.spm_target
					if self.separate_vocabs:
						__snake_case     :				Tuple 						=					self.target_encoder



				@property
				def    _lowercase	(self							: Dict)       ->      int:
					return len(self.encoder)



				def    _lowercase	(self							: Dict      ,    _A							: str      ,    _A							: Optional[str] = None)       ->      Tuple[str]:
					if not os.path.isdir(_A):
						logger.error(f"Vocabulary path ({save_directory}) should be a directory")
						return
					__snake_case     :				List[Any] 						=					[]

					if self.separate_vocabs:
						__snake_case     :				Dict 						=					os.path.join(
						    _A      ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']      ,    )
						__snake_case     :				Union[str, Any] 						=					os.path.join(
						    _A      ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']      ,    )
						save_json(self.encoder      ,    _A)
						save_json(self.target_encoder      ,    _A)
						saved_files.append(_A)
						saved_files.append(_A)
					else:
						__snake_case     :				Optional[int] 						=					os.path.join(
						    _A      ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab'])
						save_json(self.encoder      ,    _A)
						saved_files.append(_A)

					for spm_save_filename, spm_orig_path, spm_model in zip(
					    [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']]      ,    self.spm_files      ,    [self.spm_source, self.spm_target]      ,    ):
						__snake_case     :				Dict 						=					os.path.join(
						    _A      ,    (filename_prefix + '-' if filename_prefix else '') + spm_save_filename)
						if os.path.abspath(_A) != os.path.abspath(_A) and os.path.isfile(_A):
							copyfile(_A      ,    _A)
							saved_files.append(_A)
						elif not os.path.isfile(_A):
							with open(_A      ,    'wb') as fi:
								__snake_case     :				Tuple 						=					spm_model.serialized_model_proto()
								fi.write(_A)
							saved_files.append(_A)

					return tuple(_A)



				def    _lowercase	(self							: Dict)       ->      Dict:
					return self.get_src_vocab()



				def    _lowercase	(self							: Dict)       ->      Any:
					return dict(self.encoder      ,    **self.added_tokens_encoder)



				def    _lowercase	(self							: Tuple)       ->      List[str]:
					return dict(self.target_encoder      ,    **self.added_tokens_decoder)



				def __getstate__(self							: int)       ->      Dict:
					__snake_case     :				int 						=					self.__dict__.copy()
					state.update(
					    {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']})
					return state



				def __setstate__(self							: int      ,    _A							: Dict)       ->      None:
					__snake_case     :				str 						=					d

					# for backward compatibility
					if not hasattr(self      ,    'sp_model_kwargs'):
						__snake_case     :				Union[str, Any] 						=					{}

					__snake_case						,							__snake_case     :				Tuple 						=					(load_spm(_A      ,    self.sp_model_kwargs) for f in self.spm_files)
					__snake_case     :				Tuple 						=					self.spm_source
					self._setup_normalizer()



				def    _lowercase	(self							: Tuple      ,    *_A							: Optional[int]      ,    **_A							: List[Any])       ->      List[str]:
					return 1



				def    _lowercase	(self							: List[str]      ,    _A							: str)       ->      Optional[int]:
					__snake_case     :				Tuple 						=					set(self.all_special_ids)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special
					return [1 if x in all_special_ids else 0 for x in seq]






				def    _lowercase	(self							: List[Any]      ,    _A							: List      ,    _A							: Optional[List] = None      ,    _A							: bool = False)       ->      List[int]:
					if already_has_special_tokens:
						return self._special_token_mask(_A)
					elif token_ids_a is None:
						return self._special_token_mask(_A) + [1]
					else:
						return self._special_token_mask(token_ids_a + token_ids_a) + [1]







def     __UpperCAmelCase							(       UpperCAmelCase_       :							str     ,				UpperCAmelCase_       :							Dict[str, Any]    )				->     sentencepiece.SentencePieceProcessor:

	'''simple docstring'''





	__snake_case     :				Optional[Any] 						=					sentencepiece.SentencePieceProcessor(**UpperCAmelCase_    )
	spm.Load(UpperCAmelCase_    )
	return spm
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Optional[int]     ,				UpperCAmelCase_       :							str    )				->     None:

	'''simple docstring'''





	with open(UpperCAmelCase_     ,				'w'    ) as f:
		json.dump(UpperCAmelCase_     ,				UpperCAmelCase_     ,				indent=2    )

def     __UpperCAmelCase							(       UpperCAmelCase_       :							str    )				->     Union[Dict, List]:

	'''simple docstring'''





	with open(UpperCAmelCase_     ,				'r'    ) as f:
		return json.load(UpperCAmelCase_    )






"""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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


_a	:	Union[str, Any]=					"Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: str       =						"""google/flan-t5-base"""
				UpperCAmelCase							: int       =						(
				    """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
				    """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
				)
				UpperCAmelCase							: Dict       =						"""text_qa"""
				UpperCAmelCase							: Dict       =						AutoTokenizer
				UpperCAmelCase							: Optional[Any]       =						AutoModelForSeqaSeqLM

				UpperCAmelCase							: List[str]       =						["""text""", """text"""]
				UpperCAmelCase							: Any       =						["""text"""]



				def    _lowercase	(self							: Tuple      ,    _A							: str      ,    _A							: str)       ->      Tuple:
					__snake_case     :				List[Any] 						=					QA_PROMPT.format(text=_A      ,    question=_A)
					return self.pre_processor(_A      ,    return_tensors='pt')



				def    _lowercase	(self							: Tuple      ,    _A							: Optional[Any])       ->      List[Any]:
					__snake_case     :				List[Any] 						=					self.model.generate(**_A)

					__snake_case						,							__snake_case     :				Any 						=					inputs['input_ids'].shape
					__snake_case     :				Any 						=					output_ids.shape[0]

					return output_ids.reshape(_A      ,    out_b // in_b      ,    *output_ids.shape[1:])[0][0]






				def    _lowercase	(self							: Optional[int]      ,    _A							: int)       ->      str:
					return self.pre_processor.decode(_A      ,    skip_special_tokens=_A      ,    clean_up_tokenization_spaces=_A)





