




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

from tokenizers import processors

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


if is_sentencepiece_available():
   from .tokenization_nllb import NllbTokenizer
else:
   lowerCamelCase__					:  int      							=				None


lowerCamelCase__					:  Any      							=				logging.get_logger(__name__)


lowerCamelCase__					:  str      							=				{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__					:  Union[str, Any]      							=				{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase__					:  Optional[Any]      							=				{
    'facebook/nllb-large-en-ro': 1_024,
    'facebook/nllb-200-distilled-600M': 1_024,
}

# fmt: off
lowerCamelCase__					:  List[Any]      							=				['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       VOCAB_FILES_NAMES
 lowercase_							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_							       =       PRETRAINED_VOCAB_FILES_MAP
 lowercase_							       =       ["input_ids", "attention_mask"]
 lowercase_							       =       NllbTokenizer

 lowercase_							       =       []
 lowercase_							       =       []



 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						Union[str, Any]=None							,							_lowerCAmelCase    :						Optional[Any]=None							,							_lowerCAmelCase    :						Union[str, Any]="<s>"							,							_lowerCAmelCase    :						Tuple="</s>"							,							_lowerCAmelCase    :						Any="</s>"							,							_lowerCAmelCase    :						Union[str, Any]="<s>"							,							_lowerCAmelCase    :						Dict="<unk>"							,							_lowerCAmelCase    :						int="<pad>"							,							_lowerCAmelCase    :						Optional[int]="<mask>"							,							_lowerCAmelCase    :						List[Any]=None							,							_lowerCAmelCase    :						Any=None							,							_lowerCAmelCase    :						str=None							,							_lowerCAmelCase    :						Union[str, Any]=False							,							**_lowerCAmelCase    :						List[str]							,							):
       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE_      		=     AddedToken(_lowerCAmelCase							,							lstrip=_lowerCAmelCase							,							rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		) else mask_token
       SCREAMING_SNAKE_CASE_      		=     legacy_behaviour
       super().__init__(
           vocab_file=_lowerCAmelCase							,							tokenizer_file=_lowerCAmelCase							,							bos_token=_lowerCAmelCase							,							eos_token=_lowerCAmelCase							,							sep_token=_lowerCAmelCase							,							cls_token=_lowerCAmelCase							,							unk_token=_lowerCAmelCase							,							pad_token=_lowerCAmelCase							,							mask_token=_lowerCAmelCase							,							src_lang=_lowerCAmelCase							,							tgt_lang=_lowerCAmelCase							,							additional_special_tokens=_lowerCAmelCase							,							legacy_behaviour=_lowerCAmelCase							,							**_lowerCAmelCase							,							)

       SCREAMING_SNAKE_CASE_      		=     vocab_file
       SCREAMING_SNAKE_CASE_      		=     False if not self.vocab_file else True

       SCREAMING_SNAKE_CASE_      		=     FAIRSEQ_LANGUAGE_CODES.copy()

       if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             _additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in _additional_special_tokens]		)

       self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}		)
       SCREAMING_SNAKE_CASE_      		=     {
           lang_code: self.convert_tokens_to_ids(_lowerCAmelCase		) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       SCREAMING_SNAKE_CASE_      		=     src_lang if src_lang is not None else 'eng_Latn'
       SCREAMING_SNAKE_CASE_      		=     self.convert_tokens_to_ids(self._src_lang		)
       SCREAMING_SNAKE_CASE_      		=     tgt_lang
       self.set_src_lang_special_tokens(self._src_lang		)



 @property
 def  lowerCAmelCase_    (				self    :						Tuple		):
       return self._src_lang



 @src_lang.setter
 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						str		):
       SCREAMING_SNAKE_CASE_      		=     new_src_lang
       self.set_src_lang_special_tokens(self._src_lang		)



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = None		):
       if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = None		):
       SCREAMING_SNAKE_CASE_      		=     [self.sep_token_id]
       SCREAMING_SNAKE_CASE_      		=     [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  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Optional[str]							,							_lowerCAmelCase    :						Optional[str]							,							**_lowerCAmelCase    :						Optional[Any]		):
       if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'		)
       SCREAMING_SNAKE_CASE_      		=     src_lang
       SCREAMING_SNAKE_CASE_      		=     self(_lowerCAmelCase							,							add_special_tokens=_lowerCAmelCase							,							return_tensors=_lowerCAmelCase							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.convert_tokens_to_ids(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     tgt_lang_id
       return inputs



 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						List[str]							,							_lowerCAmelCase    :						str = "eng_Latn"							,							_lowerCAmelCase    :						Optional[List[str]] = None							,							_lowerCAmelCase    :						str = "fra_Latn"							,							**_lowerCAmelCase    :						List[str]							,							):
       SCREAMING_SNAKE_CASE_      		=     src_lang
       SCREAMING_SNAKE_CASE_      		=     tgt_lang
       return super().prepare_seqaseq_batch(_lowerCAmelCase							,							_lowerCAmelCase							,							**_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						List[str]		):
       return self.set_src_lang_special_tokens(self.src_lang		)



 def  lowerCAmelCase_    (				self    :						Any		):
       return self.set_tgt_lang_special_tokens(self.tgt_lang		)



 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.convert_tokens_to_ids(_lowerCAmelCase		)

       if self.legacy_behaviour:
             SCREAMING_SNAKE_CASE_      		=     []
             SCREAMING_SNAKE_CASE_      		=     [self.eos_token_id, self.cur_lang_code]
       else:
             SCREAMING_SNAKE_CASE_      		=     [self.cur_lang_code]
             SCREAMING_SNAKE_CASE_      		=     [self.eos_token_id]

       SCREAMING_SNAKE_CASE_      		=     self.convert_ids_to_tokens(self.prefix_tokens		)
       SCREAMING_SNAKE_CASE_      		=     self.convert_ids_to_tokens(self.suffix_tokens		)

       SCREAMING_SNAKE_CASE_      		=     processors.TemplateProcessing(
           single=prefix_tokens_str + ['$A'] + suffix_tokens_str							,							pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str							,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,							self.prefix_tokens + self.suffix_tokens		)		)							,							)



 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						str		):
       SCREAMING_SNAKE_CASE_      		=     self.convert_tokens_to_ids(_lowerCAmelCase		)
       if self.legacy_behaviour:
             SCREAMING_SNAKE_CASE_      		=     []
             SCREAMING_SNAKE_CASE_      		=     [self.eos_token_id, self.cur_lang_code]
       else:
             SCREAMING_SNAKE_CASE_      		=     [self.cur_lang_code]
             SCREAMING_SNAKE_CASE_      		=     [self.eos_token_id]

       SCREAMING_SNAKE_CASE_      		=     self.convert_ids_to_tokens(self.prefix_tokens		)
       SCREAMING_SNAKE_CASE_      		=     self.convert_ids_to_tokens(self.suffix_tokens		)

       SCREAMING_SNAKE_CASE_      		=     processors.TemplateProcessing(
           single=prefix_tokens_str + ['$A'] + suffix_tokens_str							,							pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str							,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,							self.prefix_tokens + self.suffix_tokens		)		)							,							)




 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Optional[str] = None		):
       if not self.can_save_slow_tokenizer:
             raise ValueError(
                 'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                 'tokenizer.'		)

       if not os.path.isdir(_lowerCAmelCase		):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory."		)
             return
       SCREAMING_SNAKE_CASE_      		=     os.path.join(
           _lowerCAmelCase							,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

       return (out_vocab_file,)






"""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							:



 def __init__(     self				,       _snake_case				,       _snake_case=13				,       _snake_case=7				,       _snake_case=True				,       _snake_case=True				,       _snake_case=True				,       _snake_case=True				,       _snake_case=99				,       _snake_case=32				,       _snake_case=2				,       _snake_case=4				,       _snake_case=37				,       _snake_case="gelu"				,       _snake_case=0.1				,       _snake_case=0.1				,       _snake_case=5_12				,       _snake_case=16				,       _snake_case=2				,       _snake_case=0.02				,       _snake_case=3				,       _snake_case=4				,       _snake_case=None				,       _snake_case=10_00				,       ):







  """simple docstring"""






  lowerCAmelCase    				= parent
  lowerCAmelCase    				= batch_size
  lowerCAmelCase    				= seq_length
  lowerCAmelCase    				= is_training
  lowerCAmelCase    				= use_input_mask
  lowerCAmelCase    				= use_token_type_ids
  lowerCAmelCase    				= use_labels
  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= max_position_embeddings
  lowerCAmelCase    				= type_vocab_size
  lowerCAmelCase    				= type_sequence_label_size
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= num_labels
  lowerCAmelCase    				= num_choices
  lowerCAmelCase    				= scope
  lowerCAmelCase    				= range_bbox



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size     )

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

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

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

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

  lowerCAmelCase    				= 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				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMModel(config=_snake_case     )

  lowerCAmelCase    				= model(_snake_case				,       _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case     )
  lowerCAmelCase    				= model(_snake_case				,       _snake_case				,       token_type_ids=_snake_case     )
  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForMaskedLM(config=_snake_case     )

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.num_labels
  lowerCAmelCase    				= TFLayoutLMForSequenceClassification(config=_snake_case     )

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.num_labels
  lowerCAmelCase    				= TFLayoutLMForTokenClassification(config=_snake_case     )

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForQuestionAnswering(config=_snake_case     )

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.prepare_config_and_inputs()
  (
      (
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,
  )    				= config_and_inputs
  lowerCAmelCase    				= {
      '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  ):
 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__             =       1_0



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMModelTester(self     )
  lowerCAmelCase    				= ConfigTester(self				,       config_class=_snake_case				,       hidden_size=37     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_sequence_classification(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_token_classification(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_question_answering(*_snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   lowerCAmelCase    				= TFLayoutLMModel.from_pretrained(_snake_case     )
   self.assertIsNotNone(_snake_case     )



 @unittest.skip('Onnx compliancy broke with TF 2.10'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  pass


def 		_SCREAMING_SNAKE_CASE			():
 # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
 # fmt: off
 lowerCAmelCase    				= 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
 lowerCAmelCase    				= 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    				= 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
 lowerCAmelCase    				= 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    				= 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  ):



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= 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]
  lowerCAmelCase    				= 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]
  lowerCAmelCase    				= 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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,       num_labels=2     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= 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
  lowerCAmelCase    				= outputs.loss
  lowerCAmelCase    				= (2,)
  self.assertEqual(loss.shape				,       _snake_case     )

  # test the shape of the logits
  lowerCAmelCase    				= outputs.logits
  lowerCAmelCase    				= (2, 2)
  self.assertEqual(logits.shape				,       _snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,       num_labels=13     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= 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
  lowerCAmelCase    				= outputs.logits
  lowerCAmelCase    				= tf.convert_to_tensor((2, 25, 13)     )
  self.assertEqual(logits.shape				,       _snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= model(input_ids=_snake_case				,       bbox=_snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case     )

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





