




"""simple docstring"""






from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


lowercase__  		=		logging.get_logger(__name__)






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			def       A_					(						self						,   lowercase						):
							if isinstance(lowercase						,   lowercase						):
											_lowerCamelCase  :    Any					=					[label.strip() for label in labels.split(','						) if label.strip()]
							return labels






			def __call__(						self						,   lowercase						,   lowercase						,   lowercase						):
							if len(lowercase						) == 0 or len(lowercase						) == 0:
											raise ValueError('You must include at least one label and at least one sequence.'						)
							if hypothesis_template.format(labels[0]						) == hypothesis_template:
											raise ValueError(
											    (
											        'The provided hypothesis_template "{}" was not able to be formatted with the target labels. '
											        'Make sure the passed template includes formatting syntax such as {{}} where the label should go.'
											    ).format(lowercase						)						)

							if isinstance(lowercase						,   lowercase						):
											_lowerCamelCase  :    List[Any]					=					[sequences]

							_lowerCamelCase  :    List[Any]					=					[]
							for sequence in sequences:
											sequence_pairs.extend([[sequence, hypothesis_template.format(lowercase						)] for label in labels]						)

							return sequence_pairs, sequences






@add_end_docstrings(lowercase		)
class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			def __init__(						self						,   lowercase=ZeroShotClassificationArgumentHandler()						,   *lowercase						,   **lowercase						):
							_lowerCamelCase  :    Dict					=					args_parser
							super().__init__(*lowercase						,   **lowercase						)
							if self.entailment_id == -1:
											logger.warning(
											    'Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '
											    '-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.'						)
			@property
			def       A_					(						self						):
							for label, ind in self.model.config.labelaid.items():
											if label.lower().startswith('entail'						):
															return ind
							return -1
			def       A_					(						self						,   lowercase						,   lowercase=True						,   lowercase=True						,   lowercase=TruncationStrategy.ONLY_FIRST						,   **lowercase						):
							_lowerCamelCase  :    str					=					self.framework
							if self.tokenizer.pad_token is None:
											# Override for tokenizers not supporting padding
											logger.error(
											    'Tokenizer was not supporting padding necessary for zero-shot, attempting to use '
											    ' `pad_token=eos_token`'						)
											_lowerCamelCase  :    int					=					self.tokenizer.eos_token
							try:
											_lowerCamelCase  :    str					=					self.tokenizer(
											    lowercase						,   add_special_tokens=lowercase						,   return_tensors=lowercase						,   padding=lowercase						,   truncation=lowercase						,   )
							except Exception as e:
											if "too short" in str(lowercase						):
															# tokenizers might yell that we want to truncate
															# to a value that is not even reached by the input.
															# In that case we don't want to truncate.
															# It seems there's not a really better way to catch that
															# exception.

															_lowerCamelCase  :    Tuple					=					self.tokenizer(
															    lowercase						,   add_special_tokens=lowercase						,   return_tensors=lowercase						,   padding=lowercase						,   truncation=TruncationStrategy.DO_NOT_TRUNCATE						,   )
											else:
															raise e

							return inputs
			def       A_					(						self						,   **lowercase						):
							if kwargs.get('multi_class'						,   lowercase						) is not None:
											_lowerCamelCase  :    Union[str, Any]					=					kwargs['multi_class']
											logger.warning(
											    'The `multi_class` argument has been deprecated and renamed to `multi_label`. '
											    '`multi_class` will be removed in a future version of Transformers.'						)
							_lowerCamelCase  :    int					=					{}
							if "candidate_labels" in kwargs:
											_lowerCamelCase  :    Any					=					self._args_parser._parse_labels(kwargs['candidate_labels']						)
							if "hypothesis_template" in kwargs:
											_lowerCamelCase  :    Tuple					=					kwargs['hypothesis_template']

							_lowerCamelCase  :    Any					=					{}
							if "multi_label" in kwargs:
											_lowerCamelCase  :    int					=					kwargs['multi_label']
							return preprocess_params, {}, postprocess_params
			def __call__(						self						,   lowercase						,   *lowercase						,   **lowercase						,   ):
							if len(lowercase						) == 0:
											pass
							elif len(lowercase						) == 1 and "candidate_labels" not in kwargs:
											_lowerCamelCase  :    Any					=					args[0]
							else:
											raise ValueError(F'''Unable to understand extra arguments {args}'''						)

							return super().__call__(lowercase						,   **lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase=None						,   lowercase="This example is {}."						):
							_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					self._args_parser(lowercase						,   lowercase						,   lowercase						)

							for i, (candidate_label, sequence_pair) in enumerate(zip(lowercase						,   lowercase						)						):
											_lowerCamelCase  :    Union[str, Any]					=					self._parse_and_tokenize([sequence_pair]						)

											yield {
											    "candidate_label": candidate_label,
											    "sequence": sequences[0],
											    "is_last": i == len(lowercase						) - 1,
											    **model_input,
											}
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    str					=					inputs['candidate_label']
							_lowerCamelCase  :    Optional[Any]					=					inputs['sequence']
							_lowerCamelCase  :    str					=					{k: inputs[k] for k in self.tokenizer.model_input_names}
							_lowerCamelCase  :    Optional[int]					=					self.model(**lowercase						)

							_lowerCamelCase  :    Tuple					=					{
							    'candidate_label': candidate_label,
							    'sequence': sequence,
							    'is_last': inputs['is_last'],
							    **outputs,
							}
							return model_outputs






			def       A_					(						self						,   lowercase						,   lowercase=False						):
							_lowerCamelCase  :    int					=					[outputs['candidate_label'] for outputs in model_outputs]
							_lowerCamelCase  :    Optional[int]					=					[outputs['sequence'] for outputs in model_outputs]
							_lowerCamelCase  :    Union[str, Any]					=					np.concatenate([output['logits'].numpy() for output in model_outputs]						)
							_lowerCamelCase  :    Union[str, Any]					=					logits.shape[0]
							_lowerCamelCase  :    Dict					=					len(lowercase						)
							_lowerCamelCase  :    Tuple					=					N // n
							_lowerCamelCase  :    int					=					logits.reshape((num_sequences, n, -1)						)

							if multi_label or len(lowercase						) == 1:
											# softmax over the entailment vs. contradiction dim for each label independently
											_lowerCamelCase  :    List[Any]					=					self.entailment_id
											_lowerCamelCase  :    int					=					-1 if entailment_id == 0 else 0
											_lowerCamelCase  :    int					=					reshaped_outputs[..., [contradiction_id, entailment_id]]
											_lowerCamelCase  :    Dict					=					np.exp(lowercase						) / np.exp(lowercase						).sum(-1						,   keepdims=lowercase						)
											_lowerCamelCase  :    int					=					scores[..., 1]
							else:
											# softmax the "entailment" logits over all candidate labels
											_lowerCamelCase  :    Any					=					reshaped_outputs[..., self.entailment_id]
											_lowerCamelCase  :    Union[str, Any]					=					np.exp(lowercase						) / np.exp(lowercase						).sum(-1						,   keepdims=lowercase						)

							_lowerCamelCase  :    List[Any]					=					list(reversed(scores[0].argsort()						)						)
							return {
							    "sequence": sequences[0],
							    "labels": [candidate_labels[i] for i in top_inds],
							    "scores": scores[0, top_inds].tolist(),
							}





"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		"""▁"""

lowercase__  		=		{"""vocab_file""": """sentencepiece.bpe.model"""}

lowercase__  		=		{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowercase__  		=		{
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    VOCAB_FILES_NAMES
			lowerCamelCase__   =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   =    ["""input_ids""", """attention_mask"""]
			def __init__(						self						,   lowercase						,   lowercase="<s>"						,   lowercase="</s>"						,   lowercase="</s>"						,   lowercase="<s>"						,   lowercase="<unk>"						,   lowercase="<pad>"						,   lowercase="<mask>"						,   lowercase = None						,   **lowercase						,   ):
							# Mask token behave like a normal word, i.e. include the space before it
							_lowerCamelCase  :    str					=					AddedToken(lowercase						,   lstrip=lowercase						,   rstrip=lowercase						) if isinstance(lowercase						,   lowercase						) else mask_token

							_lowerCamelCase  :    Union[str, Any]					=					{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=lowercase						,   eos_token=lowercase						,   unk_token=lowercase						,   sep_token=lowercase						,   cls_token=lowercase						,   pad_token=lowercase						,   mask_token=lowercase						,   sp_model_kwargs=self.sp_model_kwargs						,   **lowercase						,   )

							_lowerCamelCase  :    Any					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(str(lowercase						)						)
							_lowerCamelCase  :    str					=					vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							_lowerCamelCase  :    List[str]					=					{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							_lowerCamelCase  :    List[Any]					=					1

							_lowerCamelCase  :    List[Any]					=					len(self.sp_model						) + self.fairseq_offset
							_lowerCamelCase  :    int					=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
			def __getstate__(						self						):
							_lowerCamelCase  :    Tuple					=					self.__dict__.copy()
							_lowerCamelCase  :    Tuple					=					None
							_lowerCamelCase  :    Union[str, Any]					=					self.sp_model.serialized_model_proto()
							return state
			def __setstate__(						self						,   lowercase						):
							_lowerCamelCase  :    List[Any]					=					d

							# for backward compatibility
							if not hasattr(self						,   'sp_model_kwargs'						):
											_lowerCamelCase  :    str					=					{}

							_lowerCamelCase  :    List[str]					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)
			def       A_					(						self						,   lowercase						,   lowercase = None						):

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_lowerCamelCase  :    Any					=					[self.cls_token_id]
							_lowerCamelCase  :    List[Any]					=					[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep
			def       A_					(						self						,   lowercase						,   lowercase = None						,   lowercase = False						):

							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowercase						,   token_ids_a=lowercase						,   already_has_special_tokens=lowercase						)

							if token_ids_a is None:
											return [1] + ([0] * len(lowercase						)) + [1]
							return [1] + ([0] * len(lowercase						)) + [1, 1] + ([0] * len(lowercase						)) + [1]
			def       A_					(						self						,   lowercase						,   lowercase = None						):
							_lowerCamelCase  :    Tuple					=					[self.sep_token_id]
							_lowerCamelCase  :    Optional[int]					=					[self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]
			@property
			def       A_					(						self						):
							return len(self.sp_model						) + self.fairseq_offset + 1  # Add the <mask> token
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					{self.convert_ids_to_tokens(lowercase						): i for i in range(self.vocab_size						)}
							vocab.update(self.added_tokens_encoder						)
							return vocab
			def       A_					(						self						,   lowercase						):
							return self.sp_model.encode(lowercase						,   out_type=lowercase						)
			def       A_					(						self						,   lowercase						):
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							_lowerCamelCase  :    Union[str, Any]					=					self.sp_model.PieceToId(lowercase						)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
			def       A_					(						self						,   lowercase						):
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset						)
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    List[Any]					=					''.join(lowercase						).replace(lowercase						,   ' '						).strip()
							return out_string






			def       A_					(						self						,   lowercase						,   lowercase = None						):
							if not os.path.isdir(lowercase						):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
											return
							_lowerCamelCase  :    int					=					os.path.join(
							    lowercase						,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase						) and os.path.isfile(self.vocab_file						):
											copyfile(self.vocab_file						,   lowercase						)
							elif not os.path.isfile(self.vocab_file						):
											with open(lowercase						,   'wb'						) as fi:
															_lowerCamelCase  :    str					=					self.sp_model.serialized_model_proto()
															fi.write(lowercase						)

							return (out_vocab_file,)