
"""simple docstring"""

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


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Any:
			lowerCamelCase					:      Dict          =   feature_size
			lowerCamelCase					:      int          =   sampling_rate
			lowerCamelCase					:      Any          =   padding_value

			lowerCamelCase					:      Optional[int]          =   kwargs.pop('padding_side'		,			'right' )
			lowerCamelCase					:      Any          =   kwargs.pop('return_attention_mask'		,			UpperCAmelCase_ )

			super().__init__(**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			)		->							BatchFeature:
			# If we have a list of dicts, let's convert it in a dict of lists
			# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
			if isinstance(UpperCAmelCase_		,			(list, tuple) ) and isinstance(processed_features[0]		,			(dict, BatchFeature) ):
					lowerCamelCase					:      Any          =   {
					    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() )}""" )

			lowerCamelCase					:      Optional[int]          =   processed_features[self.model_input_names[0]]
			lowerCamelCase					:      int          =   (
			    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
			)

			if len(UpperCAmelCase_ ) == 0:
					if return_attention_mask:
							lowerCamelCase					:      List[Any]          =   []
					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

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

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

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

        # Convert padding_strategy in PaddingStrategy
			lowerCamelCase					:      List[str]          =   self._get_padding_strategies(padding=UpperCAmelCase_		,			max_length=UpperCAmelCase_ )

			lowerCamelCase					:      str          =   processed_features[self.model_input_names[0]]

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

			lowerCamelCase					:      Optional[Any]          =   []
			for i in range(UpperCAmelCase_ ):
					lowerCamelCase					:      Tuple          =   {k: v[i] for k, v in processed_features.items()}
					# truncation
					lowerCamelCase					:      str          =   self._truncate(
					    UpperCAmelCase_		,			max_length=UpperCAmelCase_		,			pad_to_multiple_of=UpperCAmelCase_		,			truncation=UpperCAmelCase_		,			)
					truncated_inputs.append(UpperCAmelCase_ )

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

			lowerCamelCase					:      int          =   {}
			for i in range(UpperCAmelCase_ ):
					# padding
					lowerCamelCase					:      Any          =   self._pad(
					    truncated_inputs[i]		,			max_length=UpperCAmelCase_		,			padding_strategy=UpperCAmelCase_		,			pad_to_multiple_of=UpperCAmelCase_		,			return_attention_mask=UpperCAmelCase_		,			)

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

			return BatchFeature(UpperCAmelCase_		,			tensor_type=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = PaddingStrategy.DO_NOT_PAD		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			)		->							dict:
			lowerCamelCase					:      Tuple          =   processed_features[self.model_input_names[0]]

			if padding_strategy == PaddingStrategy.LONGEST:
					lowerCamelCase					:      Dict          =   len(UpperCAmelCase_ )

			if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
					lowerCamelCase					:      Union[str, Any]          =   ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

			lowerCamelCase					:      Optional[int]          =   padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCAmelCase_ ) < max_length

			if return_attention_mask and "attention_mask" not in processed_features:
					lowerCamelCase					:      Union[str, Any]          =   np.ones(len(UpperCAmelCase_ )		,			dtype=np.intaa )

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

			return processed_features




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			)		->							Any:
			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.' )

			lowerCamelCase					:      Any          =   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):
					lowerCamelCase					:      List[str]          =   ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

			lowerCamelCase					:      Any          =   len(UpperCAmelCase_ ) > max_length

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

			return processed_features




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=False		,			UpperCAmelCase_=None )		->							Union[str, Any]:

			# Get padding strategy
			if padding is not False:
					if padding is True:
							lowerCamelCase					:      List[str]          =   PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
					elif not isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
							lowerCamelCase					:      int          =   PaddingStrategy(UpperCAmelCase_ )
					elif isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
							lowerCamelCase					:      Dict          =   padding
			else:
					lowerCamelCase					:      Union[str, Any]          =   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








"""simple docstring"""

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_A						=			logging.get_logger(__name__)


_A						=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
_A						=			{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

_A						=			{
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     VOCAB_FILES_NAMES
	lowercase_				=     PRETRAINED_VOCAB_FILES_MAP
	lowercase_				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase_				=     ['input_ids', 'attention_mask']
	lowercase_				=     BartTokenizer




	def __init__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_="replace"		,			UpperCAmelCase_="<s>"		,			UpperCAmelCase_="</s>"		,			UpperCAmelCase_="</s>"		,			UpperCAmelCase_="<s>"		,			UpperCAmelCase_="<unk>"		,			UpperCAmelCase_="<pad>"		,			UpperCAmelCase_="<mask>"		,			UpperCAmelCase_=False		,			UpperCAmelCase_=True		,			**UpperCAmelCase_		,			)		->							Union[str, Any]:
			super().__init__(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			tokenizer_file=UpperCAmelCase_		,			errors=UpperCAmelCase_		,			bos_token=UpperCAmelCase_		,			eos_token=UpperCAmelCase_		,			sep_token=UpperCAmelCase_		,			cls_token=UpperCAmelCase_		,			unk_token=UpperCAmelCase_		,			pad_token=UpperCAmelCase_		,			mask_token=UpperCAmelCase_		,			add_prefix_space=UpperCAmelCase_		,			trim_offsets=UpperCAmelCase_		,			**UpperCAmelCase_		,			)

			lowerCamelCase					:      List[Any]          =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
			if pre_tok_state.get('add_prefix_space'		,			UpperCAmelCase_ ) != add_prefix_space:
					lowerCamelCase					:      Tuple          =   getattr(UpperCAmelCase_		,			pre_tok_state.pop('type' ) )
					lowerCamelCase					:      Optional[Any]          =   add_prefix_space
					lowerCamelCase					:      str          =   pre_tok_class(**UpperCAmelCase_ )

			lowerCamelCase					:      Optional[Any]          =   add_prefix_space

			# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
			lowerCamelCase					:      Dict          =   'post_processor'
			lowerCamelCase					:      str          =   getattr(self.backend_tokenizer		,			UpperCAmelCase_		,			UpperCAmelCase_ )
			if tokenizer_component_instance:
					lowerCamelCase					:      Optional[Any]          =   json.loads(tokenizer_component_instance.__getstate__() )

					# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
					if "sep" in state:
							lowerCamelCase					:      int          =   tuple(state['sep'] )
					if "cls" in state:
							lowerCamelCase					:      str          =   tuple(state['cls'] )

					lowerCamelCase					:      Optional[Any]          =   False

					if state.get('add_prefix_space'		,			UpperCAmelCase_ ) != add_prefix_space:
							lowerCamelCase					:      Dict          =   add_prefix_space
							lowerCamelCase					:      Tuple          =   True

					if state.get('trim_offsets'		,			UpperCAmelCase_ ) != trim_offsets:
							lowerCamelCase					:      Tuple          =   trim_offsets
							lowerCamelCase					:      Dict          =   True

					if changes_to_apply:
							lowerCamelCase					:      Optional[int]          =   getattr(UpperCAmelCase_		,			state.pop('type' ) )
							lowerCamelCase					:      Any          =   component_class(**UpperCAmelCase_ )
							setattr(self.backend_tokenizer		,			UpperCAmelCase_		,			UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							str:
			if self._mask_token is None:
					if self.verbose:
							logger.error('Using mask_token, but it is not set yet.' )
					return None
			return str(self._mask_token )




	@mask_token.setter
	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      Optional[int]          =   AddedToken(UpperCAmelCase_		,			lstrip=UpperCAmelCase_		,			rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ) else value
			lowerCamelCase					:      int          =   value




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							BatchEncoding:
			lowerCamelCase					:      str          =   kwargs.get('is_split_into_words'		,			UpperCAmelCase_ )

			if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
					    'to use it with pretokenized inputs.' )

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




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							BatchEncoding:
			lowerCamelCase					:      Optional[Any]          =   kwargs.get('is_split_into_words'		,			UpperCAmelCase_ )

			if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
					    'to use it with pretokenized inputs.' )

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




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							Tuple[str]:
			lowerCamelCase					:      Any          =   self._tokenizer.model.save(UpperCAmelCase_		,			name=UpperCAmelCase_ )
			return tuple(UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							List[Any]:
			lowerCamelCase					:      Union[str, Any]          =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
			if token_ids_a is None:
					return output

			return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							List[int]:
			lowerCamelCase					:      List[Any]          =   [self.sep_token_id]
			lowerCamelCase					:      str          =   [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]






