


'''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 					lowercase_     :


    def __init__(     self  ,					a  ,					a=13  ,					a=7  ,					a=True  ,					a=True  ,					a=True  ,					a=True  ,					a=99  ,					a=32  ,					a=2  ,					a=4  ,					a=37  ,					a="gelu"  ,					a=0.1  ,					a=0.1  ,					a=5_12  ,					a=16  ,					a=2  ,					a=0.02  ,					a=3  ,					a=4  ,					a=None  ,					a=10_00  ,					):
          UpperCamelCase__										=				parent
          UpperCamelCase__										=				batch_size
          UpperCamelCase__										=				seq_length
          UpperCamelCase__										=				is_training
          UpperCamelCase__										=				use_input_mask
          UpperCamelCase__										=				use_token_type_ids
          UpperCamelCase__										=				use_labels
          UpperCamelCase__										=				vocab_size
          UpperCamelCase__										=				hidden_size
          UpperCamelCase__										=				num_hidden_layers
          UpperCamelCase__										=				num_attention_heads
          UpperCamelCase__										=				intermediate_size
          UpperCamelCase__										=				hidden_act
          UpperCamelCase__										=				hidden_dropout_prob
          UpperCamelCase__										=				attention_probs_dropout_prob
          UpperCamelCase__										=				max_position_embeddings
          UpperCamelCase__										=				type_vocab_size
          UpperCamelCase__										=				type_sequence_label_size
          UpperCamelCase__										=				initializer_range
          UpperCamelCase__										=				num_labels
          UpperCamelCase__										=				num_choices
          UpperCamelCase__										=				scope
          UpperCamelCase__										=				range_bbox


    def    __a							(     self    ):
          UpperCamelCase__										=				ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size    )

          # convert bbox to numpy since TF does not support item assignment
          UpperCamelCase__										=				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]:
                            UpperCamelCase__										=				bbox[i, j, 3]
                            UpperCamelCase__										=				bbox[i, j, 1]
                            UpperCamelCase__										=				t
                      if bbox[i, j, 2] < bbox[i, j, 0]:
                            UpperCamelCase__										=				bbox[i, j, 2]
                            UpperCamelCase__										=				bbox[i, j, 0]
                            UpperCamelCase__										=				t
          UpperCamelCase__										=				tf.convert_to_tensor(a    )

          UpperCamelCase__										=				None
          if self.use_input_mask:
                UpperCamelCase__										=				random_attention_mask([self.batch_size, self.seq_length]    )

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

          UpperCamelCase__										=				None
          UpperCamelCase__										=				None
          UpperCamelCase__										=				None
          if self.use_labels:
                UpperCamelCase__										=				ids_tensor([self.batch_size]  ,					self.type_sequence_label_size    )
                UpperCamelCase__										=				ids_tensor([self.batch_size, self.seq_length]  ,					self.num_labels    )
                UpperCamelCase__										=				ids_tensor([self.batch_size]  ,					self.num_choices    )

          UpperCamelCase__										=				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    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				TFLayoutLMModel(config=a    )

          UpperCamelCase__										=				model(a  ,					a  ,					attention_mask=a  ,					token_type_ids=a    )
          UpperCamelCase__										=				model(a  ,					a  ,					token_type_ids=a    )
          UpperCamelCase__										=				model(a  ,					a    )
          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    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				TFLayoutLMForMaskedLM(config=a    )

          UpperCamelCase__										=				model(a  ,					a  ,					attention_mask=a  ,					token_type_ids=a  ,					labels=a    )
          self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.vocab_size)    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				self.num_labels
          UpperCamelCase__										=				TFLayoutLMForSequenceClassification(config=a    )

          UpperCamelCase__										=				model(a  ,					a  ,					attention_mask=a  ,					token_type_ids=a    )
          self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels)    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				self.num_labels
          UpperCamelCase__										=				TFLayoutLMForTokenClassification(config=a    )

          UpperCamelCase__										=				model(a  ,					a  ,					attention_mask=a  ,					token_type_ids=a  ,					labels=a    )
          self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.num_labels)    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				TFLayoutLMForQuestionAnswering(config=a    )

          UpperCamelCase__										=				model(a  ,					a  ,					attention_mask=a  ,					token_type_ids=a    )
          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    __a							(     self    ):
          UpperCamelCase__										=				self.prepare_config_and_inputs()
          (
              (
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	(
              UpperCamelCase__
          )					,	
          )										=				config_and_inputs
          UpperCamelCase__										=				{
              "input_ids": input_ids,
              "bbox": bbox,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict

@require_tf
class 					lowercase_     (    a__  ,						a__  ,						unittest.TestCase   ):
    __UpperCAmelCase   			=	(
        (
            TFLayoutLMModel,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForTokenClassification,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForQuestionAnswering,
        )
        if is_tf_available()
        else ()
    )
    __UpperCAmelCase   			=	(
        {
            'feature-extraction': TFLayoutLMModel,
            'fill-mask': TFLayoutLMForMaskedLM,
            'text-classification': TFLayoutLMForSequenceClassification,
            'token-classification': TFLayoutLMForTokenClassification,
            'zero-shot': TFLayoutLMForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    __UpperCAmelCase   			=	False
    __UpperCAmelCase   			=	True
    __UpperCAmelCase   			=	10


    def    __a							(     self    ):
          UpperCamelCase__										=				TFLayoutLMModelTester(self    )
          UpperCamelCase__										=				ConfigTester(self  ,					config_class=a  ,					hidden_size=37    )


    def    __a							(     self    ):
          self.config_tester.run_common_tests()


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*a    )


    @slow
    def    __a							(     self    ):
          for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCamelCase__										=				TFLayoutLMModel.from_pretrained(a    )
                self.assertIsNotNone(a    )


    @unittest.skip("Onnx compliancy broke with TF 2.10"    )
    def    __a							(     self    ):
          pass







def _UpperCamelCase       (     )							->		int:


      '''simple docstring'''



      UpperCamelCase__										=				tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]    )  # noqa: E231
      UpperCamelCase__										=				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
      UpperCamelCase__										=				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
      UpperCamelCase__										=				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)
      UpperCamelCase__										=				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 					lowercase_     (    unittest.TestCase   ):


    @slow
    def    __a							(     self    ):
          UpperCamelCase__										=				TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"    )

          UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				prepare_layoutlm_batch_inputs()

          # forward pass
          UpperCamelCase__										=				model(input_ids=a  ,					bbox=a  ,					attention_mask=a  ,					token_type_ids=a    )

          # test the sequence output on [0, :3, :3]
          UpperCamelCase__										=				tf.convert_to_tensor(
              [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]  ,					)

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

          # test the pooled output on [1, :3]
          UpperCamelCase__										=				tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]    )

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


    @slow
    def    __a							(     self    ):
          # initialize model with randomly initialized sequence classification head
          UpperCamelCase__										=				TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"  ,					num_labels=2    )

          UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				prepare_layoutlm_batch_inputs()

          # forward pass
          UpperCamelCase__										=				model(
              input_ids=a  ,					bbox=a  ,					attention_mask=a  ,					token_type_ids=a  ,					labels=tf.convert_to_tensor([1, 1]    )  ,					)

          # test whether we get a loss as a scalar
          UpperCamelCase__										=				outputs.loss
          UpperCamelCase__										=				(2,)
          self.assertEqual(loss.shape  ,					a    )

          # test the shape of the logits
          UpperCamelCase__										=				outputs.logits
          UpperCamelCase__										=				(2, 2)
          self.assertEqual(logits.shape  ,					a    )


    @slow
    def    __a							(     self    ):
          # initialize model with randomly initialized token classification head
          UpperCamelCase__										=				TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"  ,					num_labels=13    )

          UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				prepare_layoutlm_batch_inputs()

          # forward pass
          UpperCamelCase__										=				model(
              input_ids=a  ,					bbox=a  ,					attention_mask=a  ,					token_type_ids=a  ,					labels=a    )

          # test the shape of the logits
          UpperCamelCase__										=				outputs.logits
          UpperCamelCase__										=				tf.convert_to_tensor((2, 25, 13)    )
          self.assertEqual(logits.shape  ,					a    )


    @slow
    def    __a							(     self    ):
          # initialize model with randomly initialized token classification head
          UpperCamelCase__										=				TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"    )

          UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				prepare_layoutlm_batch_inputs()

          # forward pass
          UpperCamelCase__										=				model(input_ids=a  ,					bbox=a  ,					attention_mask=a  ,					token_type_ids=a    )

          # test the shape of the logits
          UpperCamelCase__										=				tf.convert_to_tensor((2, 25)    )
          self.assertEqual(outputs.start_logits.shape  ,					a    )
          self.assertEqual(outputs.end_logits.shape  ,					a    )







'''simple docstring'''




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

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


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      lowercase			:				Optional[Any]    		=			None

lowercase			:				Tuple    		=			logging.get_logger(__name__)
lowercase			:				Dict    		=			{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase			:				Tuple    		=			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

lowercase			:				List[str]    		=			{
    'google/rembert': 2_56,
}

lowercase			:				Dict    		=			'▁'





class  A				(							__snake_case						):
     __magic_name__						=		VOCAB_FILES_NAMES
     __magic_name__						=		PRETRAINED_VOCAB_FILES_MAP
     __magic_name__						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__						=		RemBertTokenizer





     def __init__(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="<unk>"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="<pad>"						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[MASK]"						,			**SCREAMING_SNAKE_CASE						,			) ->				List[str]:






         """simple docstring"""






         A       :   Optional[Any]					=						AddedToken(SCREAMING_SNAKE_CASE						,			lstrip=SCREAMING_SNAKE_CASE						,			rstrip=SCREAMING_SNAKE_CASE	) if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) else mask_token

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

         A       :   List[Any]					=						do_lower_case
         A       :   str					=						remove_space
         A       :   int					=						keep_accents
         A       :   Union[str, Any]					=						vocab_file
         A       :   List[Any]					=						False if not self.vocab_file else True





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   List[Any]					=						[self.sep_token_id]
         A       :   Tuple					=						[self.cls_token_id]
         if token_ids_a is None:
             return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False	) ->				List[int]:






         """simple docstring"""






         if already_has_special_tokens:
             if token_ids_a is not None:
                 raise ValueError(
                     '''You should not supply a second sequence if the provided sequence of '''
                     '''ids is already formatted with special tokens for the model.'''	)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1]
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1]





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   Tuple					=						[self.sep_token_id]
         A       :   Dict					=						[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	) * [0] + len(token_ids_a + sep	) * [1]





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				Tuple[str]:






         """simple docstring"""






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

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

         return (out_vocab_file,)




