



'''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 		lowerCamelCase__      :




					'''simple docstring'''



					def __init__(  self				:	Optional[int]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Tuple=13     ,  UpperCamelCase_				:	int=7     ,  UpperCamelCase_				:	Optional[int]=True     ,  UpperCamelCase_				:	List[Any]=True     ,  UpperCamelCase_				:	int=True     ,  UpperCamelCase_				:	int=True     ,  UpperCamelCase_				:	int=99     ,  UpperCamelCase_				:	Optional[Any]=32     ,  UpperCamelCase_				:	int=2     ,  UpperCamelCase_				:	List[Any]=4     ,  UpperCamelCase_				:	int=37     ,  UpperCamelCase_				:	Union[str, Any]="gelu"     ,  UpperCamelCase_				:	int=0.1     ,  UpperCamelCase_				:	Tuple=0.1     ,  UpperCamelCase_				:	Tuple=512     ,  UpperCamelCase_				:	List[Any]=16     ,  UpperCamelCase_				:	Union[str, Any]=2     ,  UpperCamelCase_				:	Dict=0.02     ,  UpperCamelCase_				:	List[Any]=3     ,  UpperCamelCase_				:	Tuple=4     ,  UpperCamelCase_				:	int=None     ,  UpperCamelCase_				:	Tuple=1000     ,  )  ->       str:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		parent
						_lowercase					:       List[Any]       =		batch_size
						_lowercase					:       Optional[int]       =		seq_length
						_lowercase					:       Any       =		is_training
						_lowercase					:       Union[str, Any]       =		use_input_mask
						_lowercase					:       Dict       =		use_token_type_ids
						_lowercase					:       Optional[Any]       =		use_labels
						_lowercase					:       str       =		vocab_size
						_lowercase					:       Optional[Any]       =		hidden_size
						_lowercase					:       Dict       =		num_hidden_layers
						_lowercase					:       int       =		num_attention_heads
						_lowercase					:       Optional[Any]       =		intermediate_size
						_lowercase					:       int       =		hidden_act
						_lowercase					:       str       =		hidden_dropout_prob
						_lowercase					:       int       =		attention_probs_dropout_prob
						_lowercase					:       int       =		max_position_embeddings
						_lowercase					:       int       =		type_vocab_size
						_lowercase					:       int       =		type_sequence_label_size
						_lowercase					:       str       =		initializer_range
						_lowercase					:       List[Any]       =		num_labels
						_lowercase					:       Optional[int]       =		num_choices
						_lowercase					:       Union[str, Any]       =		scope
						_lowercase					:       List[Any]       =		range_bbox




					def 						__UpperCAmelCase     (  self				:	str					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

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

						_lowercase					:       List[str]       =		None
						if self.use_input_mask:
							_lowercase					:       Optional[Any]       =		random_attention_mask([self.batch_size, self.seq_length]					)

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

						_lowercase					:       Any       =		None
						_lowercase					:       Any       =		None
						_lowercase					:       Dict       =		None
						if self.use_labels:
							_lowercase					:       int       =		ids_tensor([self.batch_size]     ,  self.type_sequence_label_size					)
							_lowercase					:       str       =		ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels					)
							_lowercase					:       Tuple       =		ids_tensor([self.batch_size]     ,  self.num_choices					)

						_lowercase					:       Dict       =		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				:	List[str]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Optional[int]     ,  UpperCamelCase_				:	Optional[int]     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	List[Any]					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       Any       =		TFLayoutLMModel(config=UpperCamelCase_					)

						_lowercase					:       Dict       =		model(UpperCamelCase_     ,  UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_					)
						_lowercase					:       Any       =		model(UpperCamelCase_     ,  UpperCamelCase_     ,  token_type_ids=UpperCamelCase_					)
						_lowercase					:       List[Any]       =		model(UpperCamelCase_     ,  UpperCamelCase_					)
						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				:	List[Any]     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Optional[int]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Optional[int]     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	List[str]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		TFLayoutLMForMaskedLM(config=UpperCamelCase_					)

						_lowercase					:       Dict       =		model(UpperCamelCase_     ,  UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_     ,  labels=UpperCamelCase_					)
						self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.vocab_size)					)




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	Optional[int]     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		self.num_labels
						_lowercase					:       Optional[int]       =		TFLayoutLMForSequenceClassification(config=UpperCamelCase_					)

						_lowercase					:       str       =		model(UpperCamelCase_     ,  UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_					)
						self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)					)




					def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	List[Any]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		self.num_labels
						_lowercase					:       Any       =		TFLayoutLMForTokenClassification(config=UpperCamelCase_					)

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




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Optional[int]     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	str					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		TFLayoutLMForQuestionAnswering(config=UpperCamelCase_					)

						_lowercase					:       int       =		model(UpperCamelCase_     ,  UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_					)
						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]					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Any       =		self.prepare_config_and_inputs()
						(
						    (
						    _lowercase
						)  ,    (
						    _lowercase
						)  ,    (
						    _lowercase
						)  ,    (
						    _lowercase
						)  ,    (
						    _lowercase
						)  ,    (
						    _lowercase
						)  ,    (
						    _lowercase
						)  ,    (
						    _lowercase
						)  ,    
						)					:       Union[str, Any]       =		config_and_inputs
						_lowercase					:       Union[str, Any]       =		{
						    'input_ids': input_ids,
						    'bbox': bbox,
						    'token_type_ids': token_type_ids,
						    'attention_mask': input_mask,
						}
						return config, inputs_dict






@require_tf
class 		lowerCamelCase__      (						A	,   A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						(
					    (
					        TFLayoutLMModel,
					        TFLayoutLMForMaskedLM,
					        TFLayoutLMForTokenClassification,
					        TFLayoutLMForSequenceClassification,
					        TFLayoutLMForQuestionAnswering,
					    )
					    if is_tf_available()
					    else ()
					)
					A_						    =						(
					    {
					        """feature-extraction""": TFLayoutLMModel,
					        """fill-mask""": TFLayoutLMForMaskedLM,
					        """text-classification""": TFLayoutLMForSequenceClassification,
					        """token-classification""": TFLayoutLMForTokenClassification,
					        """zero-shot""": TFLayoutLMForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					A_						    =						False
					A_						    =						True
					A_						    =						10




					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Any       =		TFLayoutLMModelTester(self					)
						_lowercase					:       Union[str, Any]       =		ConfigTester(self     ,  config_class=UpperCamelCase_     ,  hidden_size=37					)




					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       int:



						'''simple docstring'''





						self.config_tester.run_common_tests()




					def 						__UpperCAmelCase     (  self				:	Any					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Any       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Dict       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	int					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_					)




					@slow
					def 						__UpperCAmelCase     (  self				:	str					)  ->       Dict:



						'''simple docstring'''





						for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							_lowercase					:       Dict       =		TFLayoutLMModel.from_pretrained(UpperCamelCase_					)
							self.assertIsNotNone(UpperCamelCase_					)




					@unittest.skip('Onnx compliancy broke with TF 2.10'					)
					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Optional[Any]:



						'''simple docstring'''





						pass







def 						__UpperCamelCase				(							) ->			str:
	# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
	# fmt: off
	_lowercase					:       Dict       =		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
	_lowercase					:       Union[str, 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
	_lowercase					:       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
	_lowercase					:       Any       =		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)
	_lowercase					:       Optional[Any]       =		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 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					@slow
					def 						__UpperCAmelCase     (  self				:	Any					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'					)

						_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       str       =		prepare_layoutlm_batch_inputs()

						# forward pass
						_lowercase					:       Optional[int]       =		model(input_ids=UpperCamelCase_     ,  bbox=UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_					)

						# test the sequence output on [0, :3, :3]
						_lowercase					:       Dict       =		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]     ,  UpperCamelCase_     ,  atol=1E-3					)					)

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

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




					@slow
					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       int:



						'''simple docstring'''





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

						_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Union[str, Any]       =		prepare_layoutlm_batch_inputs()

						# forward pass
						_lowercase					:       List[Any]       =		model(
						    input_ids=UpperCamelCase_     ,  bbox=UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_     ,  labels=tf.convert_to_tensor([1, 1]					)     ,  )

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

						# test the shape of the logits
						_lowercase					:       List[str]       =		outputs.logits
						_lowercase					:       Optional[int]       =		(2, 2)
						self.assertEqual(logits.shape     ,  UpperCamelCase_					)




					@slow
					def 						__UpperCAmelCase     (  self				:	Any					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       List[str]       =		TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'     ,  num_labels=13					)

						_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       List[str]       =		prepare_layoutlm_batch_inputs()

						# forward pass
						_lowercase					:       Any       =		model(
						    input_ids=UpperCamelCase_     ,  bbox=UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_     ,  labels=UpperCamelCase_					)

						# test the shape of the logits
						_lowercase					:       Union[str, Any]       =		outputs.logits
						_lowercase					:       Any       =		tf.convert_to_tensor((2, 25, 13)					)
						self.assertEqual(logits.shape     ,  UpperCamelCase_					)




					@slow
					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       str       =		TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'					)

						_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Optional[int]       =		prepare_layoutlm_batch_inputs()

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

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





'''simple docstring'''

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)







def 						__UpperCamelCase				(							_lowercase					) ->			Tuple:
	_lowercase					:       int       =		torch.exp(_lowercase					)
	_lowercase					:       List[str]       =		torch.sum(_lowercase,				dim=1					)  # sum of exp(x_i)
	_lowercase					:       str       =		torch.sum(x * exp_x,				dim=1					)  # sum of x_i * exp(x_i)
	return torch.log(_lowercase					) - B / A



class 		lowerCamelCase__      (						nn.Module     ):




					'''simple docstring'''



					def __init__(  self				:	Optional[Any]     ,  UpperCamelCase_				:	List[str]					)  ->       Optional[Any]:



						'''simple docstring'''





						super().__init__()
						_lowercase					:       int       =		config.output_attentions
						_lowercase					:       int       =		config.output_hidden_states
						_lowercase					:       Union[str, Any]       =		nn.ModuleList([BertLayer(UpperCamelCase_					) for _ in range(config.num_hidden_layers					)]					)
						_lowercase					:       List[Any]       =		nn.ModuleList([BertHighway(UpperCamelCase_					) for _ in range(config.num_hidden_layers					)]					)

						_lowercase					:       Tuple       =		[-1 for _ in range(config.num_hidden_layers					)]




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	str					)  ->       int:



						'''simple docstring'''





						if (type(UpperCamelCase_					) is float) or (type(UpperCamelCase_					) is int):
							for i in range(len(self.early_exit_entropy					)					):
								_lowercase					:       Optional[Any]       =		x
						else:
							_lowercase					:       Optional[int]       =		x




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	List[Any]					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		pooler.state_dict()
						for highway in self.highway:
							for name, param in highway.pooler.state_dict().items():
								param.copy_(loaded_model[name]					)




					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	List[Any]=None     ,  UpperCamelCase_				:	Optional[int]=None     ,  UpperCamelCase_				:	Union[str, Any]=None     ,  UpperCamelCase_				:	Optional[Any]=None     ,  )  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       int       =		()
						_lowercase					:       List[Any]       =		()
						_lowercase					:       Tuple       =		()
						for i, layer_module in enumerate(self.layer					):
							if self.output_hidden_states:
								_lowercase					:       Optional[int]       =		all_hidden_states + (hidden_states,)

							_lowercase					:       str       =		layer_module(
							    UpperCamelCase_     ,  UpperCamelCase_     ,  head_mask[i]     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							_lowercase					:       List[str]       =		layer_outputs[0]

							if self.output_attentions:
								_lowercase					:       Tuple       =		all_attentions + (layer_outputs[1],)

							_lowercase					:       Optional[int]       =		(hidden_states,)
							if self.output_hidden_states:
								_lowercase					:       str       =		current_outputs + (all_hidden_states,)
							if self.output_attentions:
								_lowercase					:       Optional[int]       =		current_outputs + (all_attentions,)

							_lowercase					:       List[Any]       =		self.highway[i](UpperCamelCase_					)
							# logits, pooled_output

							if not self.training:
								_lowercase					:       Dict       =		highway_exit[0]
								_lowercase					:       Tuple       =		entropy(UpperCamelCase_					)
								_lowercase					:       Dict       =		highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
								_lowercase					:       str       =		all_highway_exits + (highway_exit,)

								if highway_entropy < self.early_exit_entropy[i]:
									_lowercase					:       Tuple       =		(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
									raise HighwayException(UpperCamelCase_     ,  i + 1					)
							else:
								_lowercase					:       Optional[int]       =		all_highway_exits + (highway_exit,)

        # Add last layer
						if self.output_hidden_states:
							_lowercase					:       str       =		all_hidden_states + (hidden_states,)

						_lowercase					:       Optional[Any]       =		(hidden_states,)
						if self.output_hidden_states:
							_lowercase					:       Dict       =		outputs + (all_hidden_states,)
						if self.output_attentions:
							_lowercase					:       Optional[Any]       =		outputs + (all_attentions,)

						_lowercase					:       Optional[int]       =		outputs + (all_highway_exits,)
						return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    """The Bert Model transformer with early exiting (DeeBERT). """	,   A	,   )
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	int     ,  UpperCamelCase_				:	Optional[int]					)  ->       Optional[Any]:



						'''simple docstring'''





						super().__init__(UpperCamelCase_					)
						_lowercase					:       int       =		config

						_lowercase					:       int       =		BertEmbeddings(UpperCamelCase_					)
						_lowercase					:       List[Any]       =		DeeBertEncoder(UpperCamelCase_					)
						_lowercase					:       Any       =		BertPooler(UpperCamelCase_					)

						self.init_weights()




					def 						__UpperCAmelCase     (  self				:	int					)  ->       Union[str, Any]:



						'''simple docstring'''





						self.encoder.init_highway_pooler(self.pooler					)




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       List[str]:



						'''simple docstring'''





						return self.embeddings.word_embeddings




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	Dict					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		value




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	int					)  ->       Union[str, Any]:



						'''simple docstring'''





						for layer, heads in heads_to_prune.items():
							self.encoder.layer[layer].attention.prune_heads(UpperCamelCase_					)




					@add_start_docstrings_to_model_forward(UpperCamelCase_					)
					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Optional[Any]=None     ,  UpperCamelCase_				:	str=None     ,  UpperCamelCase_				:	Optional[Any]=None     ,  UpperCamelCase_				:	Tuple=None     ,  UpperCamelCase_				:	Any=None     ,  UpperCamelCase_				:	Optional[int]=None     ,  UpperCamelCase_				:	int=None     ,  UpperCamelCase_				:	Tuple=None     ,  )  ->       Union[str, Any]:



						'''simple docstring'''





						if input_ids is not None and inputs_embeds is not None:
							raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'					)
						elif input_ids is not None:
							_lowercase					:       Any       =		input_ids.size()
						elif inputs_embeds is not None:
							_lowercase					:       Any       =		inputs_embeds.size()[:-1]
						else:
							raise ValueError('You have to specify either input_ids or inputs_embeds'					)

						_lowercase					:       str       =		input_ids.device if input_ids is not None else inputs_embeds.device

						if attention_mask is None:
							_lowercase					:       Tuple       =		torch.ones(UpperCamelCase_     ,  device=UpperCamelCase_					)
						if encoder_attention_mask is None:
							_lowercase					:       Dict       =		torch.ones(UpperCamelCase_     ,  device=UpperCamelCase_					)
						if token_type_ids is None:
							_lowercase					:       int       =		torch.zeros(UpperCamelCase_     ,  dtype=torch.long     ,  device=UpperCamelCase_					)

						# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
						# ourselves in which case we just need to make it broadcastable to all heads.
						_lowercase					:       torch.Tensor       =		self.get_extended_attention_mask(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)

						# If a 2D ou 3D attention mask is provided for the cross-attention
						# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
						if encoder_attention_mask.dim() == 3:
							_lowercase					:       int       =		encoder_attention_mask[:, None, :, :]
						if encoder_attention_mask.dim() == 2:
							_lowercase					:       int       =		encoder_attention_mask[:, None, None, :]

						_lowercase					:       str       =		encoder_extended_attention_mask.to(
						    dtype=next(self.parameters()					).dtype					)  # fp16 compatibility
						_lowercase					:       Optional[int]       =		(1.0 - encoder_extended_attention_mask) * -1_00_00.0

						# Prepare head mask if needed
						# 1.0 in head_mask indicate we keep the head
						# attention_probs has shape bsz x n_heads x N x N
						# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
						# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
						_lowercase					:       Optional[int]       =		self.get_head_mask(UpperCamelCase_     ,  self.config.num_hidden_layers					)

						_lowercase					:       Dict       =		self.embeddings(
						    input_ids=UpperCamelCase_     ,  position_ids=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_     ,  inputs_embeds=UpperCamelCase_					)
						_lowercase					:       List[Any]       =		self.encoder(
						    UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  head_mask=UpperCamelCase_     ,  encoder_hidden_states=UpperCamelCase_     ,  encoder_attention_mask=UpperCamelCase_     ,  )
						_lowercase					:       int       =		encoder_outputs[0]
						_lowercase					:       str       =		self.pooler(UpperCamelCase_					)

						_lowercase					:       List[Any]       =		(
						    sequence_output,
						    pooled_output,
						) + encoder_outputs[
						    1:
						]  # add hidden_states and attentions if they are here
						return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	Dict     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	Dict					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Any       =		message
						_lowercase					:       Dict       =		exit_layer  # start from 1!






class 		lowerCamelCase__      (						nn.Module     ):




					'''simple docstring'''



					def __init__(  self				:	int     ,  UpperCamelCase_				:	List[str]					)  ->       Dict:



						'''simple docstring'''





						super().__init__()
						_lowercase					:       Optional[Any]       =		BertPooler(UpperCamelCase_					)
						_lowercase					:       List[Any]       =		nn.Dropout(config.hidden_dropout_prob					)
						_lowercase					:       int       =		nn.Linear(config.hidden_size     ,  config.num_labels					)




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	Optional[int]					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       str       =		encoder_outputs[0]
						_lowercase					:       int       =		self.pooler(UpperCamelCase_					)
						# "return" pooler_output

						# BertModel
						_lowercase					:       Optional[int]       =		(pooler_input, pooler_output) + encoder_outputs[1:]
						# "return" bmodel_output

						# Dropout and classification
						_lowercase					:       Dict       =		bmodel_output[1]

						_lowercase					:       Union[str, Any]       =		self.dropout(UpperCamelCase_					)
						_lowercase					:       str       =		self.classifier(UpperCamelCase_					)

						return logits, pooled_output






@add_start_docstrings(
    """Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. """	,   A	,   )
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	int     ,  UpperCamelCase_				:	List[Any]					)  ->       List[str]:



						'''simple docstring'''





						super().__init__(UpperCamelCase_					)
						_lowercase					:       Dict       =		config.num_labels
						_lowercase					:       Any       =		config.num_hidden_layers

						_lowercase					:       Optional[int]       =		DeeBertModel(UpperCamelCase_					)
						_lowercase					:       Any       =		nn.Dropout(config.hidden_dropout_prob					)
						_lowercase					:       Optional[Any]       =		nn.Linear(config.hidden_size     ,  self.config.num_labels					)

						self.init_weights()




					@add_start_docstrings_to_model_forward(UpperCamelCase_					)
					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	Dict=None     ,  UpperCamelCase_				:	Union[str, Any]=None     ,  UpperCamelCase_				:	List[Any]=None     ,  UpperCamelCase_				:	Tuple=None     ,  UpperCamelCase_				:	Any=None     ,  UpperCamelCase_				:	Optional[int]=None     ,  UpperCamelCase_				:	Optional[int]=None     ,  UpperCamelCase_				:	str=-1     ,  UpperCamelCase_				:	Union[str, Any]=False     ,  )  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		self.num_layers
						try:
							_lowercase					:       Tuple       =		self.bert(
							    UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  token_type_ids=UpperCamelCase_     ,  position_ids=UpperCamelCase_     ,  head_mask=UpperCamelCase_     ,  inputs_embeds=UpperCamelCase_     ,  )
							# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

							_lowercase					:       List[Any]       =		outputs[1]

							_lowercase					:       int       =		self.dropout(UpperCamelCase_					)
							_lowercase					:       Optional[int]       =		self.classifier(UpperCamelCase_					)
							_lowercase					:       Union[str, Any]       =		(logits,) + outputs[2:]  # add hidden states and attention if they are here
						except HighwayException as e:
							_lowercase					:       Union[str, Any]       =		e.message
							_lowercase					:       Any       =		e.exit_layer
							_lowercase					:       Optional[int]       =		outputs[0]

						if not self.training:
							_lowercase					:       Union[str, Any]       =		entropy(UpperCamelCase_					)
							_lowercase					:       Tuple       =		[]
							_lowercase					:       Tuple       =		[]
						if labels is not None:
							if self.num_labels == 1:
								#  We are doing regression
								_lowercase					:       Tuple       =		MSELoss()
								_lowercase					:       Tuple       =		loss_fct(logits.view(-1					)     ,  labels.view(-1					)					)
							else:
								_lowercase					:       Union[str, Any]       =		CrossEntropyLoss()
								_lowercase					:       Tuple       =		loss_fct(logits.view(-1     ,  self.num_labels					)     ,  labels.view(-1					)					)

							# work with highway exits
							_lowercase					:       Optional[Any]       =		[]
							for highway_exit in outputs[-1]:
								_lowercase					:       Optional[Any]       =		highway_exit[0]
								if not self.training:
									highway_logits_all.append(UpperCamelCase_					)
									highway_entropy.append(highway_exit[2]					)
								if self.num_labels == 1:
									#  We are doing regression
									_lowercase					:       Union[str, Any]       =		MSELoss()
									_lowercase					:       Any       =		loss_fct(highway_logits.view(-1					)     ,  labels.view(-1					)					)
								else:
									_lowercase					:       Dict       =		CrossEntropyLoss()
									_lowercase					:       Optional[int]       =		loss_fct(highway_logits.view(-1     ,  self.num_labels					)     ,  labels.view(-1					)					)
								highway_losses.append(UpperCamelCase_					)

							if train_highway:
								_lowercase					:       List[str]       =		(sum(highway_losses[:-1]					),) + outputs
								# exclude the final highway, of course
							else:
								_lowercase					:       Optional[Any]       =		(loss,) + outputs
						if not self.training:
							_lowercase					:       List[Any]       =		outputs + ((original_entropy, highway_entropy), exit_layer)
							if output_layer >= 0:
								_lowercase					:       Dict       =		(
								    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
								)  # use the highway of the last layer

						return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)
