


import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase      :Dict			    =       get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
      from transformers.models.mbart.modeling_mbart import shift_tokens_right

__UpperCAmelCase      :List[Any]			    =       2_5_0_0_0_4
__UpperCAmelCase      :Optional[int]			    =       2_5_0_0_2_0



@require_sentencepiece
@require_tokenizers
class      a    (		A_     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			str							       =       MBartTokenizer
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       MBartTokenizerFast
     SCREAMING_SNAKE_CASE	:			Dict							       =       True
     SCREAMING_SNAKE_CASE	:			Dict							       =       True

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          super().setUp()

          # We have a SentencePiece fixture for testing
          __UpperCAmelCase     :			List[Any]  							=      MBartTokenizer(_lowerCamelCase							,			keep_accents=_lowerCamelCase      )
          tokenizer.save_pretrained(self.tmpdirname      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Any:
          __UpperCAmelCase     :			List[Any]  							=      MBartTokenizer(_lowerCamelCase							,			keep_accents=_lowerCamelCase      )

          __UpperCAmelCase     :			str  							=      tokenizer.tokenize('''This is a test'''      )
          self.assertListEqual(_lowerCamelCase							,			['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(_lowerCamelCase      )							,			[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]							,			)

          __UpperCAmelCase     :			Any  							=      tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
          self.assertListEqual(
              _lowerCamelCase							,			[
                  SPIECE_UNDERLINE + '''I''',
                  SPIECE_UNDERLINE + '''was''',
                  SPIECE_UNDERLINE + '''b''',
                  '''or''',
                  '''n''',
                  SPIECE_UNDERLINE + '''in''',
                  SPIECE_UNDERLINE + '''''',
                  '''9''',
                  '''2''',
                  '''0''',
                  '''0''',
                  '''0''',
                  ''',''',
                  SPIECE_UNDERLINE + '''and''',
                  SPIECE_UNDERLINE + '''this''',
                  SPIECE_UNDERLINE + '''is''',
                  SPIECE_UNDERLINE + '''f''',
                  '''al''',
                  '''s''',
                  '''é''',
                  '''.''',
              ]							,			)
          __UpperCAmelCase     :			Tuple  							=      tokenizer.convert_tokens_to_ids(_lowerCamelCase      )
          self.assertListEqual(
              _lowerCamelCase							,			[
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                  #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
              ]							,			)

          __UpperCAmelCase     :			Union[str, Any]  							=      tokenizer.convert_ids_to_tokens(_lowerCamelCase      )
          self.assertListEqual(
              _lowerCamelCase							,			[
                  SPIECE_UNDERLINE + '''I''',
                  SPIECE_UNDERLINE + '''was''',
                  SPIECE_UNDERLINE + '''b''',
                  '''or''',
                  '''n''',
                  SPIECE_UNDERLINE + '''in''',
                  SPIECE_UNDERLINE + '''''',
                  '''<unk>''',
                  '''2''',
                  '''0''',
                  '''0''',
                  '''0''',
                  ''',''',
                  SPIECE_UNDERLINE + '''and''',
                  SPIECE_UNDERLINE + '''this''',
                  SPIECE_UNDERLINE + '''is''',
                  SPIECE_UNDERLINE + '''f''',
                  '''al''',
                  '''s''',
                  '''<unk>''',
                  '''.''',
              ]							,			)






     def 			lowerCamelCase__			(    self					:		Any      )						->							str:
          if not self.test_slow_tokenizer:
               # as we don't have a slow version, we can't compare the outputs between slow and fast versions
               return

          __UpperCAmelCase     :			Dict  							=      (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
                    __UpperCAmelCase     :			Tuple  							=      self.rust_tokenizer_class.from_pretrained(_lowerCamelCase							,			**_lowerCamelCase      )
                    __UpperCAmelCase     :			List[Any]  							=      self.tokenizer_class.from_pretrained(_lowerCamelCase							,			**_lowerCamelCase      )

                    __UpperCAmelCase     :			Any  							=      tempfile.mkdtemp()

                    __UpperCAmelCase     :			Optional[int]  							=      tokenizer_r.save_pretrained(_lowerCamelCase      )
                    __UpperCAmelCase     :			List[Any]  							=      tokenizer_p.save_pretrained(_lowerCamelCase      )

                    # Checks it save with the same files + the tokenizer.json file for the fast one
                    self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files      )      )
                    __UpperCAmelCase     :			Any  							=      tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f      )
                    self.assertSequenceEqual(_lowerCamelCase							,			_lowerCamelCase      )

                    # Checks everything loads correctly in the same way
                    __UpperCAmelCase     :			str  							=      tokenizer_r.from_pretrained(_lowerCamelCase      )
                    __UpperCAmelCase     :			List[Any]  							=      tokenizer_p.from_pretrained(_lowerCamelCase      )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_lowerCamelCase							,			_lowerCamelCase      )      )
                         # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                         # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                    shutil.rmtree(_lowerCamelCase      )

                    # Save tokenizer rust, legacy_format=True
                    __UpperCAmelCase     :			int  							=      tempfile.mkdtemp()

                    __UpperCAmelCase     :			List[Any]  							=      tokenizer_r.save_pretrained(_lowerCamelCase							,			legacy_format=_lowerCamelCase      )
                    __UpperCAmelCase     :			Any  							=      tokenizer_p.save_pretrained(_lowerCamelCase      )

                    # Checks it save with the same files
                    self.assertSequenceEqual(_lowerCamelCase							,			_lowerCamelCase      )

                    # Checks everything loads correctly in the same way
                    __UpperCAmelCase     :			Dict  							=      tokenizer_r.from_pretrained(_lowerCamelCase      )
                    __UpperCAmelCase     :			Optional[Any]  							=      tokenizer_p.from_pretrained(_lowerCamelCase      )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_lowerCamelCase							,			_lowerCamelCase      )      )

                    shutil.rmtree(_lowerCamelCase      )

                    # Save tokenizer rust, legacy_format=False
                    __UpperCAmelCase     :			Union[str, Any]  							=      tempfile.mkdtemp()

                    __UpperCAmelCase     :			Any  							=      tokenizer_r.save_pretrained(_lowerCamelCase							,			legacy_format=_lowerCamelCase      )
                    __UpperCAmelCase     :			Any  							=      tokenizer_p.save_pretrained(_lowerCamelCase      )

                    # Checks it saved the tokenizer.json file
                    self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files      )      )

                    # Checks everything loads correctly in the same way
                    __UpperCAmelCase     :			List[str]  							=      tokenizer_r.from_pretrained(_lowerCamelCase      )
                    __UpperCAmelCase     :			List[Any]  							=      tokenizer_p.from_pretrained(_lowerCamelCase      )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_lowerCamelCase							,			_lowerCamelCase      )      )

                    shutil.rmtree(_lowerCamelCase      )



@require_torch
@require_sentencepiece
@require_tokenizers
class      a    (		unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			List[Any]							       =       "facebook/mbart-large-en-ro"
     SCREAMING_SNAKE_CASE	:			str							       =       [
         " UN Chief Says There Is No Military Solution in Syria",
         " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
     ]
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       [
         "Şeful ONU declară că nu există o soluţie militară în Siria",
         "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
         " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
         " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
     ]
     SCREAMING_SNAKE_CASE	:			Dict							       =       [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]

     @classmethod
     def 			lowerCamelCase__			(    cls					:		Tuple      )						->							Tuple:
          __UpperCAmelCase     :			Optional[int]  							=      MBartTokenizer.from_pretrained(
              cls.checkpoint_name							,			src_lang='''en_XX'''							,			tgt_lang='''ro_RO'''      )
          __UpperCAmelCase     :			Tuple  							=      1
          return cls

     def 			lowerCamelCase__			(    self					:		str      )						->							List[Any]:
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']							,			25_0001      )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']							,			25_0004      )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']							,			25_0020      )

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			int  							=      self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens							,			_lowerCamelCase      )

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Optional[int]:
          self.assertIn(_lowerCamelCase							,			self.tokenizer.all_special_ids      )
          __UpperCAmelCase     :			Union[str, Any]  							=      [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
          __UpperCAmelCase     :			Tuple  							=      self.tokenizer.decode(_lowerCamelCase							,			skip_special_tokens=_lowerCamelCase      )
          __UpperCAmelCase     :			List[Any]  							=      self.tokenizer.decode(generated_ids[1:]							,			skip_special_tokens=_lowerCamelCase      )
          self.assertEqual(_lowerCamelCase							,			_lowerCamelCase      )
          self.assertNotIn(self.tokenizer.eos_token							,			_lowerCamelCase      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							int:
          __UpperCAmelCase     :			int  							=      ['''this is gunna be a long sentence ''' * 20]
          assert isinstance(src_text[0]							,			_lowerCamelCase      )
          __UpperCAmelCase     :			Optional[int]  							=      10
          __UpperCAmelCase     :			Dict  							=      self.tokenizer(_lowerCamelCase							,			max_length=_lowerCamelCase							,			truncation=_lowerCamelCase      ).input_ids[0]
          self.assertEqual(ids[-2]							,			2      )
          self.assertEqual(ids[-1]							,			_lowerCamelCase      )
          self.assertEqual(len(_lowerCamelCase      )							,			_lowerCamelCase      )

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Optional[Any]:
          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']      )							,			[25_0026, 25_0001]      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							int:
          __UpperCAmelCase     :			int  							=      tempfile.mkdtemp()
          __UpperCAmelCase     :			int  							=      self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(_lowerCamelCase      )
          __UpperCAmelCase     :			Optional[int]  							=      MBartTokenizer.from_pretrained(_lowerCamelCase      )
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,			_lowerCamelCase      )

     @require_torch
     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							List[str]:
          __UpperCAmelCase     :			str  							=      self.tokenizer(self.src_text							,			text_target=self.tgt_text							,			padding=_lowerCamelCase							,			return_tensors='''pt'''      )
          __UpperCAmelCase     :			str  							=      shift_tokens_right(batch['''labels''']							,			self.tokenizer.pad_token_id      )

          # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
          assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
          assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
          assert batch.decoder_input_ids[1][-1] == 2
          assert batch.labels[1][-2:].tolist() == [2, RO_CODE]

     @require_torch
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Tuple:
          __UpperCAmelCase     :			Union[str, Any]  							=      self.tokenizer(
              self.src_text							,			text_target=self.tgt_text							,			padding=_lowerCamelCase							,			truncation=_lowerCamelCase							,			max_length=len(self.expected_src_tokens      )							,			return_tensors='''pt'''							,			)

          __UpperCAmelCase     :			Optional[Any]  							=      shift_tokens_right(batch['''labels''']							,			self.tokenizer.pad_token_id      )

          self.assertIsInstance(_lowerCamelCase							,			_lowerCamelCase      )

          self.assertEqual((2, 14)							,			batch.input_ids.shape      )
          self.assertEqual((2, 14)							,			batch.attention_mask.shape      )
          __UpperCAmelCase     :			Union[str, Any]  							=      batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens							,			_lowerCamelCase      )
          self.assertEqual(2							,			batch.decoder_input_ids[0, -1]      )  # EOS
          # Test that special tokens are reset
          self.assertEqual(self.tokenizer.prefix_tokens							,			[]      )
          self.assertEqual(self.tokenizer.suffix_tokens							,			[self.tokenizer.eos_token_id, EN_CODE]      )

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							List[str]:
          __UpperCAmelCase     :			Union[str, Any]  							=      self.tokenizer(self.src_text							,			padding=_lowerCamelCase							,			truncation=_lowerCamelCase							,			max_length=3							,			return_tensors='''pt'''      )
          __UpperCAmelCase     :			Tuple  							=      self.tokenizer(
              text_target=self.tgt_text							,			padding=_lowerCamelCase							,			truncation=_lowerCamelCase							,			max_length=10							,			return_tensors='''pt'''      )
          __UpperCAmelCase     :			Union[str, Any]  							=      targets['''input_ids''']
          __UpperCAmelCase     :			str  							=      shift_tokens_right(_lowerCamelCase							,			self.tokenizer.pad_token_id      )

          self.assertEqual(batch.input_ids.shape[1]							,			3      )
          self.assertEqual(batch.decoder_input_ids.shape[1]							,			10      )






     @require_torch
     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Any:
          __UpperCAmelCase     :			int  							=      self.tokenizer._build_translation_inputs(
              '''A test'''							,			return_tensors='''pt'''							,			src_lang='''en_XX'''							,			tgt_lang='''ar_AR'''      )

          self.assertEqual(
              nested_simplify(_lowerCamelCase      )							,			{
                  # A, test, EOS, en_XX
                  '''input_ids''': [[62, 3034, 2, 25_0004]],
                  '''attention_mask''': [[1, 1, 1, 1]],
                  # ar_AR
                  '''forced_bos_token_id''': 25_0001,
              }							,			)



'''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      a    :


     """simple docstring"""






     def __init__(    self					:		Union[str, Any]							,			snake_case					:		str							,			snake_case					:		Dict=13							,			snake_case					:		Optional[Any]=7							,			snake_case					:		Tuple=True							,			snake_case					:		Optional[int]=True							,			snake_case					:		str=True							,			snake_case					:		int=True							,			snake_case					:		List[str]=99							,			snake_case					:		Any=32							,			snake_case					:		List[str]=2							,			snake_case					:		Tuple=4							,			snake_case					:		Union[str, Any]=37							,			snake_case					:		Dict="gelu"							,			snake_case					:		str=0.1							,			snake_case					:		List[Any]=0.1							,			snake_case					:		Any=512							,			snake_case					:		Optional[Any]=16							,			snake_case					:		Optional[int]=2							,			snake_case					:		Union[str, Any]=0.02							,			snake_case					:		List[Any]=3							,			snake_case					:		str=4							,			snake_case					:		int=None							,			snake_case					:		Union[str, Any]=1000							,			)						->							Tuple:
          __UpperCAmelCase     :			int  							=      parent
          __UpperCAmelCase     :			Optional[int]  							=      batch_size
          __UpperCAmelCase     :			Dict  							=      seq_length
          __UpperCAmelCase     :			List[Any]  							=      is_training
          __UpperCAmelCase     :			Optional[Any]  							=      use_input_mask
          __UpperCAmelCase     :			List[Any]  							=      use_token_type_ids
          __UpperCAmelCase     :			str  							=      use_labels
          __UpperCAmelCase     :			Any  							=      vocab_size
          __UpperCAmelCase     :			List[str]  							=      hidden_size
          __UpperCAmelCase     :			Dict  							=      num_hidden_layers
          __UpperCAmelCase     :			Any  							=      num_attention_heads
          __UpperCAmelCase     :			List[str]  							=      intermediate_size
          __UpperCAmelCase     :			str  							=      hidden_act
          __UpperCAmelCase     :			int  							=      hidden_dropout_prob
          __UpperCAmelCase     :			List[Any]  							=      attention_probs_dropout_prob
          __UpperCAmelCase     :			List[str]  							=      max_position_embeddings
          __UpperCAmelCase     :			str  							=      type_vocab_size
          __UpperCAmelCase     :			Dict  							=      type_sequence_label_size
          __UpperCAmelCase     :			Any  							=      initializer_range
          __UpperCAmelCase     :			Optional[int]  							=      num_labels
          __UpperCAmelCase     :			Optional[Any]  							=      num_choices
          __UpperCAmelCase     :			List[Any]  							=      scope
          __UpperCAmelCase     :			str  							=      range_bbox

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Union[str, Any]:
          __UpperCAmelCase     :			List[str]  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size      )

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

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

          __UpperCAmelCase     :			int  							=      None
          if self.use_token_type_ids:
               __UpperCAmelCase     :			Optional[Any]  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.type_vocab_size      )

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

          __UpperCAmelCase     :			Optional[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 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		int							,			snake_case					:		str							,			snake_case					:		Tuple							,			snake_case					:		List[str]							,			snake_case					:		Any							,			snake_case					:		Any							,			snake_case					:		List[Any]							,			snake_case					:		Any      )						->							Optional[Any]:
          __UpperCAmelCase     :			Tuple  							=      TFLayoutLMModel(config=snake_case      )

          __UpperCAmelCase     :			Optional[Any]  							=      model(snake_case							,			snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case      )
          __UpperCAmelCase     :			Tuple  							=      model(snake_case							,			snake_case							,			token_type_ids=snake_case      )
          __UpperCAmelCase     :			List[Any]  							=      model(snake_case							,			snake_case      )
          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 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		Union[str, Any]							,			snake_case					:		List[str]							,			snake_case					:		Dict							,			snake_case					:		str							,			snake_case					:		Optional[int]							,			snake_case					:		Optional[Any]							,			snake_case					:		Optional[Any]							,			snake_case					:		str      )						->							int:
          __UpperCAmelCase     :			Any  							=      TFLayoutLMForMaskedLM(config=snake_case      )

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

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		Any							,			snake_case					:		Dict							,			snake_case					:		str							,			snake_case					:		Tuple							,			snake_case					:		str							,			snake_case					:		Optional[Any]							,			snake_case					:		str							,			snake_case					:		str      )						->							Any:
          __UpperCAmelCase     :			List[str]  							=      self.num_labels
          __UpperCAmelCase     :			Optional[int]  							=      TFLayoutLMForSequenceClassification(config=snake_case      )

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

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		List[str]							,			snake_case					:		Dict							,			snake_case					:		Union[str, Any]							,			snake_case					:		List[Any]							,			snake_case					:		Union[str, Any]							,			snake_case					:		Any							,			snake_case					:		Tuple							,			snake_case					:		List[str]      )						->							List[str]:
          __UpperCAmelCase     :			Tuple  							=      self.num_labels
          __UpperCAmelCase     :			Optional[int]  							=      TFLayoutLMForTokenClassification(config=snake_case      )

          __UpperCAmelCase     :			Any  							=      model(snake_case							,			snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.num_labels)      )

     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Dict							,			snake_case					:		int							,			snake_case					:		Union[str, Any]							,			snake_case					:		List[str]							,			snake_case					:		Tuple							,			snake_case					:		Union[str, Any]							,			snake_case					:		Dict							,			snake_case					:		Optional[int]      )						->							Dict:
          __UpperCAmelCase     :			int  							=      TFLayoutLMForQuestionAnswering(config=snake_case      )

          __UpperCAmelCase     :			Union[str, Any]  							=      model(snake_case							,			snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case      )
          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 			lowerCamelCase__			(    self					:		Dict      )						->							List[str]:
          __UpperCAmelCase     :			str  							=      self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       
          )     :			Tuple  							=      config_and_inputs
          __UpperCAmelCase     :			Any  							=      {
              '''input_ids''': input_ids,
              '''bbox''': bbox,
              '''token_type_ids''': token_type_ids,
              '''attention_mask''': input_mask,
          }
          return config, inputs_dict



@require_tf
class      a    (		_a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






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

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Any:
          __UpperCAmelCase     :			Optional[int]  							=      TFLayoutLMModelTester(self      )
          __UpperCAmelCase     :			Dict  							=      ConfigTester(self							,			config_class=snake_case							,			hidden_size=37      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Dict:
          self.config_tester.run_common_tests()

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[str]:
          __UpperCAmelCase     :			Dict  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Optional[Any]  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case      )

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							Any:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*snake_case      )

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			Tuple  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[Any]:
          __UpperCAmelCase     :			Dict  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*snake_case      )

     @slow
     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Union[str, Any]:
          for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase     :			int  							=      TFLayoutLMModel.from_pretrained(snake_case      )
               self.assertIsNotNone(snake_case      )






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




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			str  							=      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     :			Optional[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
     __UpperCAmelCase     :			Optional[Any]  							=      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     :			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)
     __UpperCAmelCase     :			Optional[int]  							=      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      a    (		unittest.TestCase	):


     """simple docstring"""






     @slow
     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Optional[int]:
          __UpperCAmelCase     :			int  							=      TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''      )

          __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			str  							=      prepare_layoutlm_batch_inputs()

          # forward pass
          __UpperCAmelCase     :			Dict  							=      model(input_ids=snake_case							,			bbox=snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case      )

          # test the sequence output on [0, :3, :3]
          __UpperCAmelCase     :			Union[str, Any]  							=      tf.convert_to_tensor(
              [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]							,			)

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

          # test the pooled output on [1, :3]
          __UpperCAmelCase     :			str  							=      tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]      )

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

     @slow
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							int:
          # initialize model with randomly initialized sequence classification head
          __UpperCAmelCase     :			str  							=      TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''							,			num_labels=2      )

          __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			int  							=      prepare_layoutlm_batch_inputs()

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

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

          # test the shape of the logits
          __UpperCAmelCase     :			List[str]  							=      outputs.logits
          __UpperCAmelCase     :			List[Any]  							=      (2, 2)
          self.assertEqual(logits.shape							,			snake_case      )

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

          __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Tuple  							=      prepare_layoutlm_batch_inputs()

          # forward pass
          __UpperCAmelCase     :			Tuple  							=      model(
              input_ids=snake_case							,			bbox=snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )

          # test the shape of the logits
          __UpperCAmelCase     :			List[Any]  							=      outputs.logits
          __UpperCAmelCase     :			Optional[int]  							=      tf.convert_to_tensor((2, 25, 13)      )
          self.assertEqual(logits.shape							,			snake_case      )






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

          __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Tuple  							=      prepare_layoutlm_batch_inputs()

          # forward pass
          __UpperCAmelCase     :			Optional[Any]  							=      model(input_ids=snake_case							,			bbox=snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case      )

          # test the shape of the logits
          __UpperCAmelCase     :			Union[str, Any]  							=      tf.convert_to_tensor((2, 25)      )
          self.assertEqual(outputs.start_logits.shape							,			snake_case      )
          self.assertEqual(outputs.end_logits.shape							,			snake_case      )