
"""simple docstring"""

import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin






class 	_lowercase       (    a__		):
	# to overwrite at feature extractactor specific tests
	lowercase_				=     None
	lowercase_				=     None




	@property
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			return self.feat_extract_tester.prepare_feat_extract_dict()




	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      int          =   self.feature_extraction_class(**self.feat_extract_dict )
			self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_		,			'feature_size' ) )
			self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_		,			'sampling_rate' ) )
			self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_		,			'padding_value' ) )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   self.feat_extract_tester.prepare_inputs_for_common()
			lowerCamelCase					:      str          =   self.feature_extraction_class(**self.feat_extract_dict )
			lowerCamelCase					:      Optional[int]          =   feat_extract.model_input_names[0]

			lowerCamelCase					:      List[Any]          =   BatchFeature({input_name: speech_inputs} )

			self.assertTrue(all(len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ ) for x, y in zip(SCREAMING_SNAKE_CASE_		,			processed_features[input_name] ) ) )

			lowerCamelCase					:      List[str]          =   self.feat_extract_tester.prepare_inputs_for_common(equal_length=SCREAMING_SNAKE_CASE_ )
			lowerCamelCase					:      int          =   BatchFeature({input_name: speech_inputs}		,			tensor_type='np' )

			lowerCamelCase					:      Optional[int]          =   processed_features[input_name]

			if len(batch_features_input.shape ) < 3:
					lowerCamelCase					:      List[Any]          =   batch_features_input[:, :, None]

			self.assertTrue(
			    batch_features_input.shape
			    == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )




	@require_torch
	def       _UpperCamelCase   (			self )		->							Tuple:
			lowerCamelCase					:      str          =   self.feat_extract_tester.prepare_inputs_for_common(equal_length=SCREAMING_SNAKE_CASE_ )
			lowerCamelCase					:      int          =   self.feature_extraction_class(**self.feat_extract_dict )
			lowerCamelCase					:      Dict          =   feat_extract.model_input_names[0]

			lowerCamelCase					:      Union[str, Any]          =   BatchFeature({input_name: speech_inputs}		,			tensor_type='pt' )

			lowerCamelCase					:      Dict          =   processed_features[input_name]

			if len(batch_features_input.shape ) < 3:
					lowerCamelCase					:      Union[str, Any]          =   batch_features_input[:, :, None]

			self.assertTrue(
			    batch_features_input.shape
			    == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )




	@require_tf
	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      Optional[Any]          =   self.feat_extract_tester.prepare_inputs_for_common(equal_length=SCREAMING_SNAKE_CASE_ )
			lowerCamelCase					:      Union[str, Any]          =   self.feature_extraction_class(**self.feat_extract_dict )
			lowerCamelCase					:      int          =   feat_extract.model_input_names[0]

			lowerCamelCase					:      str          =   BatchFeature({input_name: speech_inputs}		,			tensor_type='tf' )

			lowerCamelCase					:      Dict          =   processed_features[input_name]

			if len(batch_features_input.shape ) < 3:
					lowerCamelCase					:      Union[str, Any]          =   batch_features_input[:, :, None]

			self.assertTrue(
			    batch_features_input.shape
			    == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=False )		->							Any:
			def _inputs_have_equal_length(UpperCAmelCase_ ):
					lowerCamelCase					:      Tuple          =   len(input[0] )
					for input_slice in input[1:]:
							if len(SCREAMING_SNAKE_CASE_ ) != length:
									return False
					return True

			def _inputs_are_equal(UpperCAmelCase_		,			UpperCAmelCase_ ):
					if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
							return False

					for input_slice_a, input_slice_a in zip(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_ ):
							if not np.allclose(np.asarray(SCREAMING_SNAKE_CASE_ )		,			np.asarray(SCREAMING_SNAKE_CASE_ )		,			atol=1E-3 ):
									return False
					return True

			lowerCamelCase					:      Optional[int]          =   self.feature_extraction_class(**self.feat_extract_dict )
			lowerCamelCase					:      int          =   self.feat_extract_tester.prepare_inputs_for_common(numpify=SCREAMING_SNAKE_CASE_ )
			lowerCamelCase					:      str          =   feat_extract.model_input_names[0]

			lowerCamelCase					:      Tuple          =   BatchFeature({input_name: speech_inputs} )

			lowerCamelCase					:      List[Any]          =   self.feat_extract_tester.seq_length_diff
			lowerCamelCase					:      Any          =   self.feat_extract_tester.max_seq_length + pad_diff
			lowerCamelCase					:      Tuple          =   self.feat_extract_tester.min_seq_length
			lowerCamelCase					:      Dict          =   self.feat_extract_tester.batch_size
			lowerCamelCase					:      str          =   self.feat_extract_tester.feature_size

			# test padding for List[int] + numpy
			lowerCamelCase					:      Dict          =   feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding=SCREAMING_SNAKE_CASE_ )
			lowerCamelCase					:      str          =   input_a[input_name]

			lowerCamelCase					:      str          =   feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='longest' )
			lowerCamelCase					:      Union[str, Any]          =   input_a[input_name]

			lowerCamelCase					:      str          =   feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			max_length=len(speech_inputs[-1] ) )
			lowerCamelCase					:      Any          =   input_a[input_name]

			lowerCamelCase					:      Tuple          =   feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='longest'		,			return_tensors='np' )
			lowerCamelCase					:      Any          =   input_a[input_name]

			# max_length parameter has to be provided when setting `padding="max_length"`
			with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
					feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='max_length' )[input_name]

			lowerCamelCase					:      int          =   feat_extract.pad(
			    SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			max_length=SCREAMING_SNAKE_CASE_		,			return_tensors='np' )
			lowerCamelCase					:      Optional[int]          =   input_a[input_name]

			self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
			self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
			self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
			self.assertTrue(_inputs_are_equal(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_ ) )
			self.assertTrue(len(input_a[0] ) == pad_min_length )
			self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
			self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
			self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )

			if feature_size > 1:
					self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )

			# test padding for `pad_to_multiple_of` for List[int] + numpy
			lowerCamelCase					:      List[Any]          =   feat_extract.pad(SCREAMING_SNAKE_CASE_		,			pad_to_multiple_of=10 )
			lowerCamelCase					:      List[str]          =   input_a[input_name]

			lowerCamelCase					:      Dict          =   feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='longest'		,			pad_to_multiple_of=10 )
			lowerCamelCase					:      Dict          =   input_a[input_name]

			lowerCamelCase					:      List[Any]          =   feat_extract.pad(
			    SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			pad_to_multiple_of=10		,			max_length=SCREAMING_SNAKE_CASE_ )
			lowerCamelCase					:      List[str]          =   input_a[input_name]

			lowerCamelCase					:      Optional[int]          =   feat_extract.pad(
			    SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			pad_to_multiple_of=10		,			max_length=SCREAMING_SNAKE_CASE_		,			return_tensors='np'		,			)
			lowerCamelCase					:      Dict          =   input_a[input_name]

			self.assertTrue(all(len(SCREAMING_SNAKE_CASE_ ) % 10 == 0 for x in input_a ) )
			self.assertTrue(_inputs_are_equal(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_ ) )

			lowerCamelCase					:      int          =   pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
			self.assertTrue(all(len(SCREAMING_SNAKE_CASE_ ) == expected_mult_pad_length for x in input_a ) )
			self.assertEqual(input_a.shape[:2]		,			(batch_size, expected_mult_pad_length) )

			if feature_size > 1:
					self.assertTrue(input_a.shape[2] == feature_size )

			# Check padding value is correct
			lowerCamelCase					:      List[Any]          =   (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
			self.assertTrue(
			    abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
			    < 1E-3 )
			self.assertTrue(
			    abs(
			        np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
			        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
			    < 1E-3 )
			self.assertTrue(
			    abs(
			        np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
			        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
			    < 1E-3 )
			self.assertTrue(
			    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
			self.assertTrue(
			    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
			    < 1E-3 )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=False )		->							int:
			def _inputs_have_equal_length(UpperCAmelCase_ ):
					lowerCamelCase					:      Optional[int]          =   len(input[0] )
					for input_slice in input[1:]:
							if len(SCREAMING_SNAKE_CASE_ ) != length:
									return False
					return True

			def _inputs_are_equal(UpperCAmelCase_		,			UpperCAmelCase_ ):
					if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
							return False

					for input_slice_a, input_slice_a in zip(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_ ):
							if not np.allclose(np.asarray(SCREAMING_SNAKE_CASE_ )		,			np.asarray(SCREAMING_SNAKE_CASE_ )		,			atol=1E-3 ):
									return False
					return True

			lowerCamelCase					:      int          =   self.feature_extraction_class(**self.feat_extract_dict )
			lowerCamelCase					:      List[str]          =   self.feat_extract_tester.prepare_inputs_for_common(numpify=SCREAMING_SNAKE_CASE_ )
			lowerCamelCase					:      List[Any]          =   feat_extract.model_input_names[0]

			lowerCamelCase					:      Any          =   BatchFeature({input_name: speech_inputs} )

			# truncate to smallest
			lowerCamelCase					:      Union[str, Any]          =   feat_extract.pad(
			    SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			max_length=len(speech_inputs[0] )		,			truncation=SCREAMING_SNAKE_CASE_ )
			lowerCamelCase					:      List[Any]          =   input_a[input_name]

			lowerCamelCase					:      Optional[int]          =   feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			max_length=len(speech_inputs[0] ) )
			lowerCamelCase					:      Any          =   input_a[input_name]

			self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
			self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )

			# truncate to smallest with np
			lowerCamelCase					:      Optional[int]          =   feat_extract.pad(
			    SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			max_length=len(speech_inputs[0] )		,			return_tensors='np'		,			truncation=SCREAMING_SNAKE_CASE_		,			)
			lowerCamelCase					:      Tuple          =   input_a[input_name]

			lowerCamelCase					:      List[str]          =   feat_extract.pad(
			    SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			max_length=len(speech_inputs[0] )		,			return_tensors='np' )
			lowerCamelCase					:      Optional[Any]          =   input_a[input_name]

			self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
			self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )

			# since truncation forces padding to be smaller than longest input
			# function can't return `np.ndarray`, but has to return list
			self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )

			# truncate to middle
			lowerCamelCase					:      Optional[int]          =   feat_extract.pad(
			    SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			max_length=len(speech_inputs[1] )		,			truncation=SCREAMING_SNAKE_CASE_		,			return_tensors='np'		,			)
			lowerCamelCase					:      Any          =   input_a[input_name]

			lowerCamelCase					:      Union[str, Any]          =   feat_extract.pad(
			    SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			max_length=len(speech_inputs[1] )		,			truncation=SCREAMING_SNAKE_CASE_ )
			lowerCamelCase					:      Any          =   input_a[input_name]

			lowerCamelCase					:      str          =   feat_extract.pad(
			    SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			max_length=len(speech_inputs[1] )		,			return_tensors='np' )
			lowerCamelCase					:      List[str]          =   input_a[input_name]

			self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
			self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
			self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
			self.assertTrue(_inputs_are_equal(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_ ) )

			# since truncation forces padding to be smaller than longest input
			# function can't return `np.ndarray`, but has to return list
			self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
			self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )

			# padding has to be max_length when setting `truncation=True`
			with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
					feat_extract.pad(SCREAMING_SNAKE_CASE_		,			truncation=SCREAMING_SNAKE_CASE_ )[input_name]

			# padding has to be max_length when setting `truncation=True`
			with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
					feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='longest'		,			truncation=SCREAMING_SNAKE_CASE_ )[input_name]

			# padding has to be max_length when setting `truncation=True`
			with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
					feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='longest'		,			truncation=SCREAMING_SNAKE_CASE_ )[input_name]

			# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
			with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
					feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			truncation=SCREAMING_SNAKE_CASE_ )[input_name]

			# test truncation for `pad_to_multiple_of` for List[int] + numpy
			lowerCamelCase					:      Optional[Any]          =   12
			lowerCamelCase					:      Optional[Any]          =   feat_extract.pad(
			    SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			max_length=len(speech_inputs[0] )		,			pad_to_multiple_of=SCREAMING_SNAKE_CASE_		,			truncation=SCREAMING_SNAKE_CASE_		,			)
			lowerCamelCase					:      Tuple          =   input_a[input_name]

			lowerCamelCase					:      List[Any]          =   feat_extract.pad(
			    SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			max_length=len(speech_inputs[0] )		,			pad_to_multiple_of=SCREAMING_SNAKE_CASE_		,			)
			lowerCamelCase					:      Union[str, Any]          =   input_a[input_name]

			# retrieve expected_length as multiple of pad_to_multiple_of
			lowerCamelCase					:      Optional[int]          =   len(speech_inputs[0] )
			if expected_length % pad_to_multiple_of != 0:
					lowerCamelCase					:      Tuple          =   ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

			self.assertTrue(len(input_a[0] ) == expected_length )
			self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
			self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			self._check_padding(numpify=SCREAMING_SNAKE_CASE_ )




	def       _UpperCamelCase   (			self )		->							List[Any]:
			self._check_padding(numpify=SCREAMING_SNAKE_CASE_ )




	def       _UpperCamelCase   (			self )		->							Any:
			self._check_truncation(numpify=SCREAMING_SNAKE_CASE_ )




	def       _UpperCamelCase   (			self )		->							Any:
			self._check_truncation(numpify=SCREAMING_SNAKE_CASE_ )




	@require_torch
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Optional[Any]          =   self.feature_extraction_class(**self.feat_extract_dict )
			lowerCamelCase					:      Optional[int]          =   self.feat_extract_tester.prepare_inputs_for_common()
			lowerCamelCase					:      Tuple          =   feat_extract.model_input_names[0]

			lowerCamelCase					:      str          =   BatchFeature({input_name: speech_inputs} )

			lowerCamelCase					:      Optional[int]          =   feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='longest'		,			return_tensors='np' )[input_name]
			lowerCamelCase					:      Union[str, Any]          =   feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='longest'		,			return_tensors='pt' )[input_name]

			self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )




	@require_tf
	def       _UpperCamelCase   (			self )		->							Tuple:
			lowerCamelCase					:      Optional[Any]          =   self.feature_extraction_class(**self.feat_extract_dict )
			lowerCamelCase					:      Dict          =   self.feat_extract_tester.prepare_inputs_for_common()
			lowerCamelCase					:      str          =   feat_extract.model_input_names[0]

			lowerCamelCase					:      Optional[Any]          =   BatchFeature({input_name: speech_inputs} )

			lowerCamelCase					:      int          =   feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='longest'		,			return_tensors='np' )[input_name]
			lowerCamelCase					:      Union[str, Any]          =   feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='longest'		,			return_tensors='tf' )[input_name]

			self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )




	def       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase					:      List[Any]          =   self.feat_extract_dict
			lowerCamelCase					:      Optional[Any]          =   True
			lowerCamelCase					:      List[str]          =   self.feature_extraction_class(**SCREAMING_SNAKE_CASE_ )
			lowerCamelCase					:      Optional[int]          =   self.feat_extract_tester.prepare_inputs_for_common()
			lowerCamelCase					:      Optional[int]          =   [len(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
			lowerCamelCase					:      Union[str, Any]          =   feat_extract.model_input_names[0]

			lowerCamelCase					:      Optional[Any]          =   BatchFeature({input_name: speech_inputs} )

			lowerCamelCase					:      List[Any]          =   feat_extract.pad(SCREAMING_SNAKE_CASE_		,			padding='longest'		,			return_tensors='np' )
			self.assertIn('attention_mask'		,			SCREAMING_SNAKE_CASE_ )
			self.assertListEqual(list(processed.attention_mask.shape )		,			list(processed[input_name].shape[:2] ) )
			self.assertListEqual(processed.attention_mask.sum(-1 ).tolist()		,			SCREAMING_SNAKE_CASE_ )




	def       _UpperCamelCase   (			self )		->							Dict:
			lowerCamelCase					:      List[str]          =   self.feat_extract_dict
			lowerCamelCase					:      Optional[int]          =   True
			lowerCamelCase					:      int          =   self.feature_extraction_class(**SCREAMING_SNAKE_CASE_ )
			lowerCamelCase					:      Optional[Any]          =   self.feat_extract_tester.prepare_inputs_for_common()
			lowerCamelCase					:      Union[str, Any]          =   [len(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
			lowerCamelCase					:      int          =   feat_extract.model_input_names[0]

			lowerCamelCase					:      str          =   BatchFeature({input_name: speech_inputs} )
			lowerCamelCase					:      Dict          =   min(SCREAMING_SNAKE_CASE_ )

			lowerCamelCase					:      Dict          =   feat_extract.pad(
			    SCREAMING_SNAKE_CASE_		,			padding='max_length'		,			max_length=SCREAMING_SNAKE_CASE_		,			truncation=SCREAMING_SNAKE_CASE_		,			return_tensors='np' )
			self.assertIn('attention_mask'		,			SCREAMING_SNAKE_CASE_ )
			self.assertListEqual(
			    list(processed_pad.attention_mask.shape )		,			[processed_pad[input_name].shape[0], max_length] )
			self.assertListEqual(
			    processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist()		,			[max_length for x in speech_inputs] )








"""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]






