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			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Tuple=13  ,				__SCREAMING_SNAKE_CASE			:   Any=7  ,				__SCREAMING_SNAKE_CASE			:   Any=True  ,				__SCREAMING_SNAKE_CASE			:   str=True  ,				__SCREAMING_SNAKE_CASE			:   str=True  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Tuple=99  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=32  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=2  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=37  ,				__SCREAMING_SNAKE_CASE			:   Any="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Tuple=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=0.1  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=5_12  ,				__SCREAMING_SNAKE_CASE			:   Tuple=16  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=2  ,				__SCREAMING_SNAKE_CASE			:   Any=0.02  ,				__SCREAMING_SNAKE_CASE			:   Tuple=3  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=4  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=None  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=10_00  ,				):
   __a							=	parent
   __a							=	batch_size
   __a							=	seq_length
   __a							=	is_training
   __a							=	use_input_mask
   __a							=	use_token_type_ids
   __a							=	use_labels
   __a							=	vocab_size
   __a							=	hidden_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	intermediate_size
   __a							=	hidden_act
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	max_position_embeddings
   __a							=	type_vocab_size
   __a							=	type_sequence_label_size
   __a							=	initializer_range
   __a							=	num_labels
   __a							=	num_choices
   __a							=	scope
   __a							=	range_bbox

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

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

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

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

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

   __a							=	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			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   int							):
   __a							=	TFLayoutLMModel(config=__SCREAMING_SNAKE_CASE							)

   __a							=	model(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE							)
   __a							=	model(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE							)
   __a							=	model(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_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			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	TFLayoutLMForMaskedLM(config=__SCREAMING_SNAKE_CASE							)

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

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   __a							=	self.num_labels
   __a							=	TFLayoutLMForSequenceClassification(config=__SCREAMING_SNAKE_CASE							)

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

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]							):
   __a							=	self.num_labels
   __a							=	TFLayoutLMForTokenClassification(config=__SCREAMING_SNAKE_CASE							)

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

  def 		_UpperCAmelCase (		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Any							):
   __a							=	TFLayoutLMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE							)

   __a							=	model(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_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			:   List[str]							):
   __a							=	self.prepare_config_and_inputs()
   (
       (
       __a
   )							,  (
       __a
   )							,  (
       __a
   )							,  (
       __a
   )							,  (
       __a
   )							,  (
       __a
   )							,  (
       __a
   )							,  (
       __a
   )							,  
   )							=	config_and_inputs
   __a							=	{
       "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			):
  _SCREAMING_SNAKE_CASE     					=      (
      (
          TFLayoutLMModel,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForTokenClassification,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForQuestionAnswering,
      )
      if is_tf_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE     					=      (
      {
          """feature-extraction""": TFLayoutLMModel,
          """fill-mask""": TFLayoutLMForMaskedLM,
          """text-classification""": TFLayoutLMForSequenceClassification,
          """token-classification""": TFLayoutLMForTokenClassification,
          """zero-shot""": TFLayoutLMForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  _SCREAMING_SNAKE_CASE     					=      False
  _SCREAMING_SNAKE_CASE     					=      True
  _SCREAMING_SNAKE_CASE     					=      10

  def 		_UpperCAmelCase (		self			:   List[str]							):
   __a							=	TFLayoutLMModelTester(self							)
   __a							=	ConfigTester(self  ,				config_class=__SCREAMING_SNAKE_CASE  ,				hidden_size=37							)

  def 		_UpperCAmelCase (		self			:   Any							):
   self.config_tester.run_common_tests()

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE							)

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

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



def 	__A		(      ):



 """simple docstring"""


 __a							=	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
 __a							=	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
 __a							=	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
 __a							=	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)
 __a							=	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			:   Dict							):
   __a							=	TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"							)

   __a							,  __a							,  __a							,  __a							,  __a							=	prepare_layoutlm_batch_inputs()

   # forward pass
   __a							=	model(input_ids=__SCREAMING_SNAKE_CASE  ,				bbox=__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE							)

   # test the sequence output on [0, :3, :3]
   __a							=	tf.convert_to_tensor(
       [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]  ,				)

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

   # test the pooled output on [1, :3]
   __a							=	tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]							)

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

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

   __a							,  __a							,  __a							,  __a							,  __a							=	prepare_layoutlm_batch_inputs()

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

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

   # test the shape of the logits
   __a							=	outputs.logits
   __a							=	(2, 2)
   self.assertEqual(logits.shape  ,				__SCREAMING_SNAKE_CASE							)

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

   __a							,  __a							,  __a							,  __a							,  __a							=	prepare_layoutlm_batch_inputs()

   # forward pass
   __a							=	model(
       input_ids=__SCREAMING_SNAKE_CASE  ,				bbox=__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE  ,				labels=__SCREAMING_SNAKE_CASE							)

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

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

   __a							,  __a							,  __a							,  __a							,  __a							=	prepare_layoutlm_batch_inputs()

   # forward pass
   __a							=	model(input_ids=__SCREAMING_SNAKE_CASE  ,				bbox=__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE							)

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



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		[
      """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MCTCTForCTC""",
      """MCTCTModel""",
      """MCTCTPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
  from .feature_extraction_mctct import MCTCTFeatureExtractor
  from .processing_mctct import MCTCTProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
  import sys

  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


