'''simple docstring'''





from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.layoutlm.modeling_tf_layoutlm import (
     TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFLayoutLMForMaskedLM,
     TFLayoutLMForQuestionAnswering,
     TFLayoutLMForSequenceClassification,
     TFLayoutLMForTokenClassification,
     TFLayoutLMModel,
 )






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=13    ,							lowercase__=7    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=99    ,							lowercase__=32    ,							lowercase__=2    ,							lowercase__=4    ,							lowercase__=37    ,							lowercase__="gelu"    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=512    ,							lowercase__=16    ,							lowercase__=2    ,							lowercase__=0.0_2    ,							lowercase__=3    ,							lowercase__=4    ,							lowercase__=None    ,							lowercase__=1_000    ,							)							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       parent
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       batch_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       seq_length
         SCREAMING_SNAKE_CASE					:			Dict    	=       is_training
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       use_input_mask
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       use_token_type_ids
         SCREAMING_SNAKE_CASE					:			List[str]    	=       use_labels
         SCREAMING_SNAKE_CASE					:			str    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Dict    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Dict    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			str    	=       intermediate_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			str    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Any    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			List[str]    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			List[str]    	=       type_vocab_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       type_sequence_label_size
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Tuple    	=       num_labels
         SCREAMING_SNAKE_CASE					:			int    	=       num_choices
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scope
         SCREAMING_SNAKE_CASE					:			Any    	=       range_bbox






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

         # convert bbox to numpy since TF does not support item assignment
         SCREAMING_SNAKE_CASE					:			int    	=       ids_tensor([self.batch_size, self.seq_length, 4]    ,							self.range_bbox ).numpy()
         # Ensure that bbox is legal
         for i in range(bbox.shape[0] ):
              for j in range(bbox.shape[1] ):
                   if bbox[i, j, 3] < bbox[i, j, 1]:
                        SCREAMING_SNAKE_CASE					:			Dict    	=       bbox[i, j, 3]
                        SCREAMING_SNAKE_CASE					:			Optional[int]    	=       bbox[i, j, 1]
                        SCREAMING_SNAKE_CASE					:			Dict    	=       t
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                        SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       bbox[i, j, 2]
                        SCREAMING_SNAKE_CASE					:			Any    	=       bbox[i, j, 0]
                        SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       t
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tf.convert_to_tensor(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Dict    	=       None
         if self.use_input_mask:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       random_attention_mask([self.batch_size, self.seq_length] )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       None
         if self.use_token_type_ids:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.type_vocab_size )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       None
         SCREAMING_SNAKE_CASE					:			List[str]    	=       None
         SCREAMING_SNAKE_CASE					:			Tuple    	=       None
         if self.use_labels:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       ids_tensor([self.batch_size]    ,							self.type_sequence_label_size )
              SCREAMING_SNAKE_CASE					:			List[str]    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.num_labels )
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       ids_tensor([self.batch_size]    ,							self.num_choices )

         SCREAMING_SNAKE_CASE					:			int    	=       LayoutLMConfig(
             vocab_size=self.vocab_size    ,							hidden_size=self.hidden_size    ,							num_hidden_layers=self.num_hidden_layers    ,							num_attention_heads=self.num_attention_heads    ,							intermediate_size=self.intermediate_size    ,							hidden_act=self.hidden_act    ,							hidden_dropout_prob=self.hidden_dropout_prob    ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							type_vocab_size=self.type_vocab_size    ,							initializer_range=self.initializer_range    ,							)

         return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       TFLayoutLMModel(config=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       model(lowercase__    ,							lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__    ,							lowercase__    ,							token_type_ids=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(lowercase__    ,							lowercase__ )
         self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size) )
         self.parent.assertEqual(result.pooler_output.shape    ,							(self.batch_size, self.hidden_size) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       TFLayoutLMForMaskedLM(config=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(lowercase__    ,							lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.vocab_size) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.num_labels
         SCREAMING_SNAKE_CASE					:			Tuple    	=       TFLayoutLMForSequenceClassification(config=lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       model(lowercase__    ,							lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> int:
         SCREAMING_SNAKE_CASE					:			int    	=       self.num_labels
         SCREAMING_SNAKE_CASE					:			int    	=       TFLayoutLMForTokenClassification(config=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__    ,							lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.num_labels) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> str:
         SCREAMING_SNAKE_CASE					:			str    	=       TFLayoutLMForQuestionAnswering(config=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       model(lowercase__    ,							lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__ )
         self.parent.assertEqual(result.start_logits.shape    ,							(self.batch_size, self.seq_length) )
         self.parent.assertEqual(result.end_logits.shape    ,							(self.batch_size, self.seq_length) )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			int    	=       self.prepare_config_and_inputs()
         (
             SCREAMING_SNAKE_CASE   
         )					:			List[str]    	=       config_and_inputs
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {
             'input_ids': input_ids,
             'bbox': bbox,
             'token_type_ids': token_type_ids,
             'attention_mask': input_mask,
         }
         return config, inputs_dict






@require_tf
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   Union[str, Any]							=						(
        (
            TFLayoutLMModel,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForTokenClassification,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForQuestionAnswering,
        )
        if is_tf_available()
        else ()
    )
    snake_case__							:   str							=						(
        {
            "feature-extraction": TFLayoutLMModel,
            "fill-mask": TFLayoutLMForMaskedLM,
            "text-classification": TFLayoutLMForSequenceClassification,
            "token-classification": TFLayoutLMForTokenClassification,
            "zero-shot": TFLayoutLMForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    snake_case__							:   Union[str, Any]							=						False
    snake_case__							:   List[Any]							=						True
    snake_case__							:   int							=						1_0






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       TFLayoutLMModelTester(self )
         SCREAMING_SNAKE_CASE					:			Any    	=       ConfigTester(self    ,							config_class=lowercase__    ,							hidden_size=37 )






    def       _UpperCamelCase    (       self )							-> str:
         self.config_tester.run_common_tests()






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowercase__ )






    @slow
    def       _UpperCamelCase    (       self )							-> int:
         for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       TFLayoutLMModel.from_pretrained(lowercase__ )
              self.assertIsNotNone(lowercase__ )






    @unittest.skip('Onnx compliancy broke with TF 2.10' )
    def       _UpperCamelCase    (       self )							-> Tuple:
         pass






def     __lowerCAmelCase						(       )      ->	List[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Tuple    	=       tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]		)  # noqa: E231
     SCREAMING_SNAKE_CASE					:			Any    	=       tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]		)  # noqa: E231
     SCREAMING_SNAKE_CASE					:			str    	=       tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]		)  # noqa: E231
     SCREAMING_SNAKE_CASE					:			Any    	=       tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]		)  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     SCREAMING_SNAKE_CASE					:			List[str]    	=       tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]		)  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @slow
    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased' )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       prepare_layoutlm_batch_inputs()

         # forward pass
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(input_ids=lowercase__    ,							bbox=lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__ )

         # test the sequence output on [0, :3, :3]
         SCREAMING_SNAKE_CASE					:			int    	=       tf.convert_to_tensor(
             [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]    ,							)

         self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]    ,							lowercase__    ,							atol=1E-3 ) )

         # test the pooled output on [1, :3]
         SCREAMING_SNAKE_CASE					:			int    	=       tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2] )

         self.assertTrue(np.allclose(outputs.pooler_output[1, :3]    ,							lowercase__    ,							atol=1E-3 ) )






    @slow
    def       _UpperCamelCase    (       self )							-> Any:
         # initialize model with randomly initialized sequence classification head
         SCREAMING_SNAKE_CASE					:			int    	=       TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'    ,							num_labels=2 )

         SCREAMING_SNAKE_CASE					:			str    	=       prepare_layoutlm_batch_inputs()

         # forward pass
         SCREAMING_SNAKE_CASE					:			List[str]    	=       model(
             input_ids=lowercase__    ,							bbox=lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							labels=tf.convert_to_tensor([1, 1] )    ,							)

         # test whether we get a loss as a scalar
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       outputs.loss
         SCREAMING_SNAKE_CASE					:			int    	=       (2,)
         self.assertEqual(loss.shape    ,							lowercase__ )

         # test the shape of the logits
         SCREAMING_SNAKE_CASE					:			Dict    	=       outputs.logits
         SCREAMING_SNAKE_CASE					:			Any    	=       (2, 2)
         self.assertEqual(logits.shape    ,							lowercase__ )






    @slow
    def       _UpperCamelCase    (       self )							-> str:
         # initialize model with randomly initialized token classification head
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'    ,							num_labels=13 )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       prepare_layoutlm_batch_inputs()

         # forward pass
         SCREAMING_SNAKE_CASE					:			int    	=       model(
             input_ids=lowercase__    ,							bbox=lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							labels=lowercase__ )

         # test the shape of the logits
         SCREAMING_SNAKE_CASE					:			int    	=       outputs.logits
         SCREAMING_SNAKE_CASE					:			Tuple    	=       tf.convert_to_tensor((2, 25, 13) )
         self.assertEqual(logits.shape    ,							lowercase__ )






    @slow
    def       _UpperCamelCase    (       self )							-> Optional[int]:
         # initialize model with randomly initialized token classification head
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased' )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       prepare_layoutlm_batch_inputs()

         # forward pass
         SCREAMING_SNAKE_CASE					:			List[str]    	=       model(input_ids=lowercase__    ,							bbox=lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__ )

         # test the shape of the logits
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tf.convert_to_tensor((2, 25) )
         self.assertEqual(outputs.start_logits.shape    ,							lowercase__ )
         self.assertEqual(outputs.end_logits.shape    ,							lowercase__ )



'''simple docstring'''





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_lowerCAmelCase  :int  				=    logging.get_logger(__name__)


_lowerCAmelCase  :Union[str, Any]  				=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase  :int  				=    {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

_lowerCAmelCase  :str  				=    {
    """allenai/led-base-16384""": 16_384,
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[Any]							=						VOCAB_FILES_NAMES
    snake_case__							:   Union[str, Any]							=						PRETRAINED_VOCAB_FILES_MAP
    snake_case__							:   Tuple							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__							:   int							=						LEDTokenizer
    snake_case__							:   Optional[int]							=						["input_ids", "attention_mask"]






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

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

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         SCREAMING_SNAKE_CASE					:			Dict    	=       'post_processor'
         SCREAMING_SNAKE_CASE					:			Dict    	=       getattr(self.backend_tokenizer    ,							lowercase__    ,							lowercase__ )
         if tokenizer_component_instance:
              SCREAMING_SNAKE_CASE					:			List[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:
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       tuple(state['sep'] )
              if "cls" in state:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       tuple(state['cls'] )

              SCREAMING_SNAKE_CASE					:			str    	=       False

              if state.get('add_prefix_space'    ,							lowercase__ ) != add_prefix_space:
                   SCREAMING_SNAKE_CASE					:			Dict    	=       add_prefix_space
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       True

              if state.get('trim_offsets'    ,							lowercase__ ) != trim_offsets:
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       trim_offsets
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       True

              if changes_to_apply:
                   SCREAMING_SNAKE_CASE					:			str    	=       getattr(lowercase__    ,							state.pop('type' ) )
                   SCREAMING_SNAKE_CASE					:			Dict    	=       component_class(**lowercase__ )
                   setattr(self.backend_tokenizer    ,							lowercase__    ,							lowercase__ )






    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    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    ,							lowercase__ )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       AddedToken(lowercase__    ,							lstrip=lowercase__    ,							rstrip=lowercase__ ) if isinstance(lowercase__    ,							lowercase__ ) else value
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       value






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> BatchEncoding:
         SCREAMING_SNAKE_CASE					:			int    	=       kwargs.get('is_split_into_words'    ,							lowercase__ )

         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(*lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> BatchEncoding:
         SCREAMING_SNAKE_CASE					:			Dict    	=       kwargs.get('is_split_into_words'    ,							lowercase__ )

         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(*lowercase__    ,							**lowercase__ )






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






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=None )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       [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    ,							lowercase__    ,							lowercase__ = None )							-> List[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       [self.sep_token_id]
         SCREAMING_SNAKE_CASE					:			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]






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = PaddingStrategy.DO_NOT_PAD    ,							lowercase__ = None    ,							lowercase__ = None    ,							)							-> dict:
         SCREAMING_SNAKE_CASE					:			int    	=       super()._pad(
             encoded_inputs=lowercase__    ,							max_length=lowercase__    ,							padding_strategy=lowercase__    ,							pad_to_multiple_of=lowercase__    ,							return_attention_mask=lowercase__    ,							)

         # Load from model defaults
         if return_attention_mask is None:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       'attention_mask' in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       encoded_inputs[self.model_input_names[0]]
              # `global_attention_mask` need to have the same length as other (sequential) inputs.
              SCREAMING_SNAKE_CASE					:			str    	=       len(encoded_inputs['global_attention_mask'] ) != len(lowercase__ )

              if needs_to_be_padded:
                   SCREAMING_SNAKE_CASE					:			Dict    	=       len(lowercase__ ) - len(encoded_inputs['global_attention_mask'] )

                   if self.padding_side == "right":
                        # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                        SCREAMING_SNAKE_CASE					:			str    	=       (
                            encoded_inputs['global_attention_mask'] + [-1] * difference
                        )
                   elif self.padding_side == "left":
                        SCREAMING_SNAKE_CASE					:			Tuple    	=       [-1] * difference + encoded_inputs[
                            'global_attention_mask'
                        ]
                   else:
                        raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )

         return encoded_inputs



