





"""simple docstring"""
import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin







class       a							(   a__   , a__  ):



 @register_to_config
 def __init__(     self				,       _snake_case = 1_28				,       _snake_case = 2_56				,       _snake_case = 2_000.0				,       _snake_case = 7_68				,       _snake_case = 12				,       _snake_case = 12				,       _snake_case = 64				,       _snake_case = 20_48				,       _snake_case = 0.1				,       ):







  """simple docstring"""






  super().__init__()

  lowerCAmelCase    				= nn.Sequential(
      nn.Linear(_snake_case				,       d_model * 4				,       bias=_snake_case     )				,       nn.SiLU()				,       nn.Linear(d_model * 4				,       d_model * 4				,       bias=_snake_case     )				,       nn.SiLU()				,       )

  lowerCAmelCase    				= nn.Embedding(_snake_case				,       _snake_case     )
  lowerCAmelCase    				= False

  lowerCAmelCase    				= nn.Linear(_snake_case				,       _snake_case				,       bias=_snake_case     )

  lowerCAmelCase    				= nn.Dropout(p=_snake_case     )

  lowerCAmelCase    				= nn.ModuleList()
  for lyr_num in range(_snake_case     ):
   # FiLM conditional T5 decoder
   lowerCAmelCase    				= DecoderLayer(d_model=_snake_case				,       d_kv=_snake_case				,       num_heads=_snake_case				,       d_ff=_snake_case				,       dropout_rate=_snake_case     )
   self.decoders.append(_snake_case     )

  lowerCAmelCase    				= TaLayerNorm(_snake_case     )

  lowerCAmelCase    				= nn.Dropout(p=_snake_case     )
  lowerCAmelCase    				= nn.Linear(_snake_case				,       _snake_case				,       bias=_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= torch.mul(query_input.unsqueeze(-1     )				,       key_input.unsqueeze(-2     )     )
  return mask.unsqueeze(-3     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= decoder_input_tokens.shape
  assert decoder_noise_time.shape == (batch,)

  # decoder_noise_time is in [0, 1), so rescale to expected timing range.
  lowerCAmelCase    				= get_timestep_embedding(
      decoder_noise_time * self.config.max_decoder_noise_time				,       embedding_dim=self.config.d_model				,       max_period=self.config.max_decoder_noise_time				,       ).to(dtype=self.dtype     )

  lowerCAmelCase    				= self.conditioning_emb(_snake_case     ).unsqueeze(1     )

  assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

  lowerCAmelCase    				= decoder_input_tokens.shape[1]

  # If we want to use relative positions for audio context, we can just offset
  # this sequence by the length of encodings_and_masks.
  lowerCAmelCase    				= torch.broadcast_to(
      torch.arange(_snake_case				,       device=decoder_input_tokens.device     )				,       (batch, seq_length)				,       )

  lowerCAmelCase    				= self.position_encoding(_snake_case     )

  lowerCAmelCase    				= self.continuous_inputs_projection(_snake_case     )
  inputs += position_encodings
  lowerCAmelCase    				= self.dropout(_snake_case     )

  # decoder: No padding present.
  lowerCAmelCase    				= torch.ones(
      decoder_input_tokens.shape[:2]				,       device=decoder_input_tokens.device				,       dtype=inputs.dtype     )

  # Translate encoding masks to encoder-decoder masks.
  lowerCAmelCase    				= [(x, self.encoder_decoder_mask(_snake_case				,       _snake_case     )) for x, y in encodings_and_masks]

  # cross attend style: concat encodings
  lowerCAmelCase    				= torch.cat([x[0] for x in encodings_and_encdec_masks]				,       dim=1     )
  lowerCAmelCase    				= torch.cat([x[1] for x in encodings_and_encdec_masks]				,       dim=-1     )

  for lyr in self.decoders:
   lowerCAmelCase    				= lyr(
       _snake_case				,       conditioning_emb=_snake_case				,       encoder_hidden_states=_snake_case				,       encoder_attention_mask=_snake_case				,       )[0]

  lowerCAmelCase    				= self.decoder_norm(_snake_case     )
  lowerCAmelCase    				= self.post_dropout(_snake_case     )

  lowerCAmelCase    				= self.spec_out(_snake_case     )
  return spec_out







class       a							(   nn.Module  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case=1E-6     ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase    				= nn.ModuleList()

  # cond self attention: layer 0
  self.layer.append(
      TaLayerSelfAttentionCond(d_model=_snake_case				,       d_kv=_snake_case				,       num_heads=_snake_case				,       dropout_rate=_snake_case     )     )

  # cross attention: layer 1
  self.layer.append(
      TaLayerCrossAttention(
          d_model=_snake_case				,       d_kv=_snake_case				,       num_heads=_snake_case				,       dropout_rate=_snake_case				,       layer_norm_epsilon=_snake_case				,       )     )

  # Film Cond MLP + dropout: last layer
  self.layer.append(
      TaLayerFFCond(d_model=_snake_case				,       d_ff=_snake_case				,       dropout_rate=_snake_case				,       layer_norm_epsilon=_snake_case     )     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= self.layer[0](
      _snake_case				,       conditioning_emb=_snake_case				,       attention_mask=_snake_case				,       )

  if encoder_hidden_states is not None:
   lowerCAmelCase    				= torch.where(encoder_attention_mask > 0				,       0				,       -1E10     ).to(
       encoder_hidden_states.dtype     )

   lowerCAmelCase    				= self.layer[1](
       _snake_case				,       key_value_states=_snake_case				,       attention_mask=_snake_case				,       )

  # Apply Film Conditional Feed Forward layer
  lowerCAmelCase    				= self.layer[-1](_snake_case				,       _snake_case     )

  return (hidden_states,)







class       a							(   nn.Module  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase    				= TaLayerNorm(_snake_case     )
  lowerCAmelCase    				= TaFiLMLayer(in_features=d_model * 4				,       out_features=_snake_case     )
  lowerCAmelCase    				= Attention(query_dim=_snake_case				,       heads=_snake_case				,       dim_head=_snake_case				,       out_bias=_snake_case				,       scale_qk=_snake_case     )
  lowerCAmelCase    				= nn.Dropout(_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=None				,       _snake_case=None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= self.layer_norm(_snake_case     )

  if conditioning_emb is not None:
   lowerCAmelCase    				= self.FiLMLayer(_snake_case				,       _snake_case     )

  # Self-attention block
  lowerCAmelCase    				= self.attention(_snake_case     )

  lowerCAmelCase    				= hidden_states + self.dropout(_snake_case     )

  return hidden_states







class       a							(   nn.Module  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase    				= Attention(query_dim=_snake_case				,       heads=_snake_case				,       dim_head=_snake_case				,       out_bias=_snake_case				,       scale_qk=_snake_case     )
  lowerCAmelCase    				= TaLayerNorm(_snake_case				,       eps=_snake_case     )
  lowerCAmelCase    				= nn.Dropout(_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=None				,       _snake_case=None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= self.layer_norm(_snake_case     )
  lowerCAmelCase    				= self.attention(
      _snake_case				,       encoder_hidden_states=_snake_case				,       attention_mask=attention_mask.squeeze(1     )				,       )
  lowerCAmelCase    				= hidden_states + self.dropout(_snake_case     )
  return layer_output







class       a							(   nn.Module  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase    				= TaDenseGatedActDense(d_model=_snake_case				,       d_ff=_snake_case				,       dropout_rate=_snake_case     )
  lowerCAmelCase    				= TaFiLMLayer(in_features=d_model * 4				,       out_features=_snake_case     )
  lowerCAmelCase    				= TaLayerNorm(_snake_case				,       eps=_snake_case     )
  lowerCAmelCase    				= nn.Dropout(_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=None     ):







  """simple docstring"""






  lowerCAmelCase    				= self.layer_norm(_snake_case     )
  if conditioning_emb is not None:
   lowerCAmelCase    				= self.film(_snake_case				,       _snake_case     )

  lowerCAmelCase    				= self.DenseReluDense(_snake_case     )
  lowerCAmelCase    				= hidden_states + self.dropout(_snake_case     )
  return hidden_states







class       a							(   nn.Module  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase    				= nn.Linear(_snake_case				,       _snake_case				,       bias=_snake_case     )
  lowerCAmelCase    				= nn.Linear(_snake_case				,       _snake_case				,       bias=_snake_case     )
  lowerCAmelCase    				= nn.Linear(_snake_case				,       _snake_case				,       bias=_snake_case     )
  lowerCAmelCase    				= nn.Dropout(_snake_case     )
  lowerCAmelCase    				= NewGELUActivation()



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.act(self.wi_a(_snake_case     )     )
  lowerCAmelCase    				= self.wi_a(_snake_case     )
  lowerCAmelCase    				= hidden_gelu * hidden_linear
  lowerCAmelCase    				= self.dropout(_snake_case     )

  lowerCAmelCase    				= self.wo(_snake_case     )
  return hidden_states







class       a							(   nn.Module  ):



 def __init__(     self				,       _snake_case				,       _snake_case=1E-6     ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase    				= nn.Parameter(torch.ones(_snake_case     )     )
  lowerCAmelCase    				= eps



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= hidden_states.to(torch.floataa     ).pow(2     ).mean(-1				,       keepdim=_snake_case     )
  lowerCAmelCase    				= hidden_states * torch.rsqrt(variance + self.variance_epsilon     )

  # convert into half-precision if necessary
  if self.weight.dtype in [torch.floataa, torch.bfloataa]:
   lowerCAmelCase    				= hidden_states.to(self.weight.dtype     )

  return self.weight * hidden_states







class       a							(   nn.Module  ):



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi     ) * (input + 0.044_715 * torch.pow(_snake_case				,       3.0     ))     ))







class       a							(   nn.Module  ):



 def __init__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase    				= nn.Linear(_snake_case				,       out_features * 2				,       bias=_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.scale_bias(_snake_case     )
  lowerCAmelCase				,lowerCAmelCase    				= torch.chunk(_snake_case				,       2				,       -1     )
  lowerCAmelCase    				= x * (1 + scale) + shift
  return x












"""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     )





