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 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				List[Any]=13    ,  _UpperCamelCase     :				Dict=7    ,  _UpperCamelCase     :				Any=True    ,  _UpperCamelCase     :				Tuple=True    ,  _UpperCamelCase     :				str=True    ,  _UpperCamelCase     :				Union[str, Any]=True    ,  _UpperCamelCase     :				str=99    ,  _UpperCamelCase     :				Optional[Any]=32    ,  _UpperCamelCase     :				Optional[Any]=2    ,  _UpperCamelCase     :				Tuple=4    ,  _UpperCamelCase     :				Optional[Any]=37    ,  _UpperCamelCase     :				Tuple="gelu"    ,  _UpperCamelCase     :				Any=0.1    ,  _UpperCamelCase     :				str=0.1    ,  _UpperCamelCase     :				Dict=512    ,  _UpperCamelCase     :				int=16    ,  _UpperCamelCase     :				Tuple=2    ,  _UpperCamelCase     :				int=0.0_2    ,  _UpperCamelCase     :				Union[str, Any]=3    ,  _UpperCamelCase     :				str=4    ,  _UpperCamelCase     :				Dict=None    ,  _UpperCamelCase     :				Dict=1000    ,  )				->int:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						parent
          _lowerCamelCase     :		Optional[int] 						=						batch_size
          _lowerCamelCase     :		Any 						=						seq_length
          _lowerCamelCase     :		Dict 						=						is_training
          _lowerCamelCase     :		Optional[int] 						=						use_input_mask
          _lowerCamelCase     :		Tuple 						=						use_token_type_ids
          _lowerCamelCase     :		Any 						=						use_labels
          _lowerCamelCase     :		Optional[int] 						=						vocab_size
          _lowerCamelCase     :		List[str] 						=						hidden_size
          _lowerCamelCase     :		int 						=						num_hidden_layers
          _lowerCamelCase     :		Optional[Any] 						=						num_attention_heads
          _lowerCamelCase     :		str 						=						intermediate_size
          _lowerCamelCase     :		Optional[int] 						=						hidden_act
          _lowerCamelCase     :		Tuple 						=						hidden_dropout_prob
          _lowerCamelCase     :		List[Any] 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		Any 						=						max_position_embeddings
          _lowerCamelCase     :		List[Any] 						=						type_vocab_size
          _lowerCamelCase     :		Any 						=						type_sequence_label_size
          _lowerCamelCase     :		Tuple 						=						initializer_range
          _lowerCamelCase     :		Optional[Any] 						=						num_labels
          _lowerCamelCase     :		Union[str, Any] 						=						num_choices
          _lowerCamelCase     :		str 						=						scope
          _lowerCamelCase     :		str 						=						range_bbox



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->int:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size)

          # convert bbox to numpy since TF does not support item assignment
          _lowerCamelCase     :		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]:
                      _lowerCamelCase     :		List[Any] 						=						bbox[i, j, 3]
                      _lowerCamelCase     :		Tuple 						=						bbox[i, j, 1]
                      _lowerCamelCase     :		Optional[Any] 						=						t
                  if bbox[i, j, 2] < bbox[i, j, 0]:
                      _lowerCamelCase     :		int 						=						bbox[i, j, 2]
                      _lowerCamelCase     :		List[Any] 						=						bbox[i, j, 0]
                      _lowerCamelCase     :		List[Any] 						=						t
          _lowerCamelCase     :		List[Any] 						=						tf.convert_to_tensor(UpperCAmelCase__)

          _lowerCamelCase     :		List[Any] 						=						None
          if self.use_input_mask:
              _lowerCamelCase     :		Union[str, Any] 						=						random_attention_mask([self.batch_size, self.seq_length])

          _lowerCamelCase     :		Dict 						=						None
          if self.use_token_type_ids:
              _lowerCamelCase     :		Any 						=						ids_tensor([self.batch_size, self.seq_length]    ,  self.type_vocab_size)

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

          _lowerCamelCase     :		str 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						TFLayoutLMModel(config=UpperCAmelCase__)

          _lowerCamelCase     :		Union[str, Any] 						=						model(UpperCAmelCase__    ,  UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__)
          _lowerCamelCase     :		Tuple 						=						model(UpperCAmelCase__    ,  UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__)
          _lowerCamelCase     :		Dict 						=						model(UpperCAmelCase__    ,  UpperCAmelCase__)
          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 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						TFLayoutLMForMaskedLM(config=UpperCAmelCase__)

          _lowerCamelCase     :		Optional[int] 						=						model(UpperCAmelCase__    ,  UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__    ,  labels=UpperCAmelCase__)
          self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Optional[Any])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						self.num_labels
          _lowerCamelCase     :		List[Any] 						=						TFLayoutLMForSequenceClassification(config=UpperCAmelCase__)

          _lowerCamelCase     :		Any 						=						model(UpperCAmelCase__    ,  UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__)
          self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				List[str])				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.num_labels
          _lowerCamelCase     :		int 						=						TFLayoutLMForTokenClassification(config=UpperCAmelCase__)

          _lowerCamelCase     :		Optional[int] 						=						model(UpperCAmelCase__    ,  UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__    ,  labels=UpperCAmelCase__)
          self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.num_labels))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Optional[Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						TFLayoutLMForQuestionAnswering(config=UpperCAmelCase__)

          _lowerCamelCase     :		Dict 						=						model(UpperCAmelCase__    ,  UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__)
          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 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.prepare_config_and_inputs()
          (
              _lowerCamelCase	
          )     :		Any 						=						config_and_inputs
          _lowerCamelCase     :		Union[str, Any] 						=						{
              '''input_ids''': input_ids,
              '''bbox''': bbox,
              '''token_type_ids''': token_type_ids,
              '''attention_mask''': input_mask,
          }
          return config, inputs_dict





@require_tf
class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       (
          (
              TFLayoutLMModel,
              TFLayoutLMForMaskedLM,
              TFLayoutLMForTokenClassification,
              TFLayoutLMForSequenceClassification,
              TFLayoutLMForQuestionAnswering,
          )
          if is_tf_available()
          else ()
      )
      _snake_case    		=       (
          {
              'feature-extraction': TFLayoutLMModel,
              'fill-mask': TFLayoutLMForMaskedLM,
              'text-classification': TFLayoutLMForSequenceClassification,
              'token-classification': TFLayoutLMForTokenClassification,
              'zero-shot': TFLayoutLMForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      _snake_case    		=       False
      _snake_case    		=       True
      _snake_case    		=       10



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						TFLayoutLMModelTester(self)
          _lowerCamelCase     :		List[Any] 						=						ConfigTester(self    ,  config_class=UpperCAmelCase__    ,  hidden_size=37)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Any:


          """simple docstring"""


          self.config_tester.run_common_tests()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCAmelCase__)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__)



      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Any:


          """simple docstring"""


          for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowerCamelCase     :		Any 						=						TFLayoutLMModel.from_pretrained(UpperCAmelCase__)
              self.assertIsNotNone(UpperCAmelCase__)




      @unittest.skip("""Onnx compliancy broke with TF 2.10""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Tuple:


          """simple docstring"""


          pass







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Dict 						=						tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]	)  # noqa: E231
    _lowerCamelCase     :		Optional[int] 						=						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
    _lowerCamelCase     :		int 						=						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],[1_000,1_000,1_000,1_000]],[[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],[1_000,1_000,1_000,1_000]]]	)  # noqa: E231
    _lowerCamelCase     :		str 						=						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)
    _lowerCamelCase     :		Union[str, Any] 						=						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 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased""")

          _lowerCamelCase     :		int 						=						prepare_layoutlm_batch_inputs()

          # forward pass
          _lowerCamelCase     :		Dict 						=						model(input_ids=UpperCAmelCase__    ,  bbox=UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__)

          # test the sequence output on [0, :3, :3]
          _lowerCamelCase     :		Tuple 						=						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]    ,  UpperCAmelCase__    ,  atol=1E-3))

          # test the pooled output on [1, :3]
          _lowerCamelCase     :		Dict 						=						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]    ,  UpperCAmelCase__    ,  atol=1E-3))



      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""    ,  num_labels=2)

          _lowerCamelCase     :		int 						=						prepare_layoutlm_batch_inputs()

          # forward pass
          _lowerCamelCase     :		Tuple 						=						model(
              input_ids=UpperCAmelCase__    ,  bbox=UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__    ,  labels=tf.convert_to_tensor([1, 1])    ,  )

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

          # test the shape of the logits
          _lowerCamelCase     :		Union[str, Any] 						=						outputs.logits
          _lowerCamelCase     :		Optional[int] 						=						(2, 2)
          self.assertEqual(logits.shape    ,  UpperCAmelCase__)



      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""    ,  num_labels=13)

          _lowerCamelCase     :		List[Any] 						=						prepare_layoutlm_batch_inputs()

          # forward pass
          _lowerCamelCase     :		Tuple 						=						model(
              input_ids=UpperCAmelCase__    ,  bbox=UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__    ,  labels=UpperCAmelCase__)

          # test the shape of the logits
          _lowerCamelCase     :		str 						=						outputs.logits
          _lowerCamelCase     :		List[str] 						=						tf.convert_to_tensor((2, 25, 13))
          self.assertEqual(logits.shape    ,  UpperCAmelCase__)




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased""")

          _lowerCamelCase     :		str 						=						prepare_layoutlm_batch_inputs()

          # forward pass
          _lowerCamelCase     :		Union[str, Any] 						=						model(input_ids=UpperCAmelCase__    ,  bbox=UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__)

          # test the shape of the logits
          _lowerCamelCase     :		Optional[int] 						=						tf.convert_to_tensor((2, 25))
          self.assertEqual(outputs.start_logits.shape    ,  UpperCAmelCase__)
          self.assertEqual(outputs.end_logits.shape    ,  UpperCAmelCase__)





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase			:							Dict     =logging.get_logger(__name__)

lowerCAmelCase			:							Dict     ={"vocab_file": "vocab.json"}

lowerCAmelCase			:							List[str]     ={
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCAmelCase			:							int     ={"mgp-str": 27}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int="[GO]"    ,  _UpperCamelCase     :				Any="[GO]"    ,  _UpperCamelCase     :				Optional[Any]="[s]"    ,  _UpperCamelCase     :				List[str]="[GO]"    ,  **_UpperCamelCase     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              unk_token=_UpperCamelCase    ,  bos_token=_UpperCamelCase    ,  eos_token=_UpperCamelCase    ,  pad_token=_UpperCamelCase    ,  **_UpperCamelCase    ,  )

          with open(_UpperCamelCase    ,  encoding="""utf-8""") as vocab_handle:
              _lowerCamelCase     :		Optional[Any] 						=						json.load(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						{v: k for k, v in self.vocab.items()}



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


          """simple docstring"""


          return len(self.vocab)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          return dict(self.vocab    ,  **self.added_tokens_encoder)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						[]
          for s in text:
              char_tokens.extend(_UpperCamelCase)
          return char_tokens



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


          """simple docstring"""


          return self.vocab.get(_UpperCamelCase    ,  self.vocab.get(self.unk_token))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[Any])				->Dict:


          """simple docstring"""


          return self.decoder.get(_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCamelCase))
              return
          _lowerCamelCase     :		Tuple 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

          with open(_UpperCamelCase    ,  """w"""    ,  encoding="""utf-8""") as f:
              f.write(json.dumps(self.vocab    ,  indent=2    ,  sort_keys=_UpperCamelCase    ,  ensure_ascii=_UpperCamelCase) + """\n""")

          return (vocab_file,)




