



from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


UpperCAmelCase__     = logging.get_logger(__name__)







class     __lowerCAmelCase					(    A		):



			def __init__(		self     :						str				,	A     :						int				,	A     :						int				,	A     :						float				,	**A     :						str)     ->					Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     feature_size
						_UpperCAmelCase	 =     sampling_rate
						_UpperCAmelCase	 =     padding_value

						_UpperCAmelCase	 =     kwargs.pop('padding_side'				,	'right')
						_UpperCAmelCase	 =     kwargs.pop('return_attention_mask'				,	A)

						super().__init__(**A)



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						Union[
			        BatchFeature,
			        List[BatchFeature],
			        Dict[str, BatchFeature],
			        Dict[str, List[BatchFeature]],
			        List[Dict[str, BatchFeature]],
			    ]				,	A     :						Union[bool, str, PaddingStrategy] = True				,	A     :						Optional[int] = None				,	A     :						bool = False				,	A     :						Optional[int] = None				,	A     :						Optional[bool] = None				,	A     :						Optional[Union[str, TensorType]] = None				,	)     ->					BatchFeature:



						"""simple docstring"""






						if isinstance(A				,	(list, tuple)) and isinstance(processed_features[0]				,	(dict, BatchFeature)):
									_UpperCAmelCase	 =     {
									    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
									}

						# The model's main input name, usually `input_values`, has be passed for padding
						if self.model_input_names[0] not in processed_features:
									raise ValueError(
									    'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
									    F" to this method that includes {self.model_input_names[0]}, but you provided"
									    F" {list(processed_features.keys())}")

						_UpperCAmelCase	 =     processed_features[self.model_input_names[0]]
						_UpperCAmelCase	 =     (
						    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
						)

						if len(A) == 0:
									if return_attention_mask:
												_UpperCAmelCase	 =     []
									return processed_features

						# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
						# and rebuild them afterwards if no return_tensors is specified
						# Note that we lose the specific device the tensor may be on for PyTorch

						_UpperCAmelCase	 =     required_input[0]
						if isinstance(A				,	(list, tuple)):
									# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
									_UpperCAmelCase	 =     0
									while len(required_input[index]) == 0:
												index += 1
									if index < len(A):
												_UpperCAmelCase	 =     required_input[index][0]

						if return_tensors is None:
									if is_tf_tensor(A):
												_UpperCAmelCase	 =     'tf'
									elif is_torch_tensor(A):
												_UpperCAmelCase	 =     'pt'
									elif isinstance(A				,	(int, float, list, tuple, np.ndarray)):
												_UpperCAmelCase	 =     'np'
									else:
												raise ValueError(
												    F"type of {first_element} unknown: {type(A)}. "
												    'Should be one of a python, numpy, pytorch or tensorflow object.')

						for key, value in processed_features.items():
									if isinstance(value[0]				,	(int, float)):
												_UpperCAmelCase	 =     to_numpy(A)
									else:
												_UpperCAmelCase	 =     [to_numpy(A) for v in value]

        # Convert padding_strategy in PaddingStrategy
						_UpperCAmelCase	 =     self._get_padding_strategies(padding=A				,	max_length=A)

						_UpperCAmelCase	 =     processed_features[self.model_input_names[0]]

						_UpperCAmelCase	 =     len(A)
						if not all(len(A) == batch_size for v in processed_features.values()):
									raise ValueError('Some items in the output dictionary have a different batch size than others.')

						_UpperCAmelCase	 =     []
						for i in range(A):
									_UpperCAmelCase	 =     {k: v[i] for k, v in processed_features.items()}
									# truncation
									_UpperCAmelCase	 =     self._truncate(
									    A				,	max_length=A				,	pad_to_multiple_of=A				,	truncation=A				,	)
									truncated_inputs.append(A)

						if padding_strategy == PaddingStrategy.LONGEST:
									# make sure that `max_length` cannot be longer than the longest truncated length
									_UpperCAmelCase	 =     max(len(input_slice[self.model_input_names[0]]) for input_slice in truncated_inputs)
									_UpperCAmelCase	 =     PaddingStrategy.MAX_LENGTH

						_UpperCAmelCase	 =     {}
						for i in range(A):
									# padding
									_UpperCAmelCase	 =     self._pad(
									    truncated_inputs[i]				,	max_length=A				,	padding_strategy=A				,	pad_to_multiple_of=A				,	return_attention_mask=A				,	)

									for key, value in outputs.items():
												if key not in batch_outputs:
															_UpperCAmelCase	 =     []
												if value.dtype is np.dtype(np.floataa):
															_UpperCAmelCase	 =     value.astype(np.floataa)
												batch_outputs[key].append(A)

						return BatchFeature(A				,	tensor_type=A)



			def 					_lowerCamelCase (		self     :						Union[str, Any]				,	A     :						Union[Dict[str, np.ndarray], BatchFeature]				,	A     :						Optional[int] = None				,	A     :						PaddingStrategy = PaddingStrategy.DO_NOT_PAD				,	A     :						Optional[int] = None				,	A     :						Optional[bool] = None				,	)     ->					dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
									_UpperCAmelCase	 =     len(A)

						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									_UpperCAmelCase	 =     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						_UpperCAmelCase	 =     padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A) < max_length

						if return_attention_mask and "attention_mask" not in processed_features:
									_UpperCAmelCase	 =     np.ones(len(A)				,	dtype=np.intaa)

						if needs_to_be_padded:
									_UpperCAmelCase	 =     max_length - len(A)
									if self.padding_side == "right":
												if return_attention_mask:
															_UpperCAmelCase	 =     np.pad(
															    processed_features['attention_mask']				,	(0, difference))
												_UpperCAmelCase	 =     ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
												_UpperCAmelCase	 =     np.pad(
												    A				,	A				,	'constant'				,	constant_values=self.padding_value)
									elif self.padding_side == "left":
												if return_attention_mask:
															_UpperCAmelCase	 =     np.pad(
															    processed_features['attention_mask']				,	(difference, 0))
												_UpperCAmelCase	 =     ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
												_UpperCAmelCase	 =     np.pad(
												    A				,	A				,	'constant'				,	constant_values=self.padding_value)
									else:
												raise ValueError('Invalid padding strategy:' + str(self.padding_side))

						return processed_features



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						Union[Dict[str, np.ndarray], BatchFeature]				,	A     :						Optional[int] = None				,	A     :						Optional[int] = None				,	A     :						Optional[bool] = None				,	)     ->					Union[str, Any]:



						"""simple docstring"""






						if not truncation:
									return processed_features
						elif truncation and max_length is None:
									raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.')

						_UpperCAmelCase	 =     processed_features[self.model_input_names[0]]

						# find `max_length` that fits `pad_to_multiple_of`
						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									_UpperCAmelCase	 =     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						_UpperCAmelCase	 =     len(A) > max_length

						if needs_to_be_truncated:
									_UpperCAmelCase	 =     processed_features[self.model_input_names[0]][:max_length]
									if "attention_mask" in processed_features:
												_UpperCAmelCase	 =     processed_features['attention_mask'][:max_length]

						return processed_features



			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						Tuple=False				,	A     :						Tuple=None)     ->					Optional[int]:



						"""simple docstring"""






						if padding is not False:
									if padding is True:
												_UpperCAmelCase	 =     PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
									elif not isinstance(A				,	A):
												_UpperCAmelCase	 =     PaddingStrategy(A)
									elif isinstance(A				,	A):
												_UpperCAmelCase	 =     padding
						else:
									_UpperCAmelCase	 =     PaddingStrategy.DO_NOT_PAD

						# Set max length if needed
						if max_length is None:
									if padding_strategy == PaddingStrategy.MAX_LENGTH:
												raise ValueError(
												    F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined")

        # Test if we have a padding value
						if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
									raise ValueError(
									    'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
									    ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.')

						return padding_strategy





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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xlnet import XLNetTokenizer
else:
							UpperCAmelCase__     = None


UpperCAmelCase__     = logging.get_logger(__name__)

UpperCAmelCase__     = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__     = {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase__     = {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

UpperCAmelCase__     = "▁"

# Segments (not really needed)
UpperCAmelCase__     = 0
UpperCAmelCase__     = 1
UpperCAmelCase__     = 2
UpperCAmelCase__     = 3
UpperCAmelCase__     = 4







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  VOCAB_FILES_NAMES
			UpperCamelCase           =  PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase           =  '''left'''
			UpperCamelCase           =  XLNetTokenizer



			def __init__(		self     :						Any				,	A     :						Union[str, Any]=None				,	A     :						str=None				,	A     :						Tuple=False				,	A     :						Tuple=True				,	A     :						Any=False				,	A     :						List[str]="<s>"				,	A     :						List[str]="</s>"				,	A     :						Optional[int]="<unk>"				,	A     :						Tuple="<sep>"				,	A     :						str="<pad>"				,	A     :						Dict="<cls>"				,	A     :						Dict="<mask>"				,	A     :						Optional[Any]=["<eop>", "<eod>"]				,	**A     :						Optional[Any]				,	)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     AddedToken(A				,	lstrip=A				,	rstrip=A) if isinstance(A				,	A) else mask_token

						super().__init__(
						    vocab_file=A				,	tokenizer_file=A				,	do_lower_case=A				,	remove_space=A				,	keep_accents=A				,	bos_token=A				,	eos_token=A				,	unk_token=A				,	sep_token=A				,	pad_token=A				,	cls_token=A				,	mask_token=A				,	additional_special_tokens=A				,	**A				,	)

						_UpperCAmelCase	 =     3
						_UpperCAmelCase	 =     do_lower_case
						_UpperCAmelCase	 =     remove_space
						_UpperCAmelCase	 =     keep_accents
						_UpperCAmelCase	 =     vocab_file
						_UpperCAmelCase	 =     False if not self.vocab_file else True



			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						List[int]				,	A     :						Optional[List[int]] = None)     ->					List[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     [self.sep_token_id]
						_UpperCAmelCase	 =     [self.cls_token_id]
						if token_ids_a is None:
									return token_ids_a + sep + cls
						return token_ids_a + sep + token_ids_a + sep + cls



			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						List[int]				,	A     :						Optional[List[int]] = None)     ->					List[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     [self.sep_token_id]
						_UpperCAmelCase	 =     [2]

						if token_ids_a is None:
									return len(token_ids_a + sep) * [0] + cls_segment_id
						return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						str				,	A     :						Optional[str] = None)     ->					Tuple[str]:



						"""simple docstring"""






						if not self.can_save_slow_tokenizer:
									raise ValueError(
									    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
									    'tokenizer.')

						if not os.path.isdir(A):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory")
									return
						_UpperCAmelCase	 =     os.path.join(
						    A				,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

						if os.path.abspath(self.vocab_file) != os.path.abspath(A):
									copyfile(self.vocab_file				,	A)

						return (out_vocab_file,)
