



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		,__lowerCamelCase		,__lowerCamelCase=13		,__lowerCamelCase=7		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=99		,__lowerCamelCase=32		,__lowerCamelCase=2		,__lowerCamelCase=4		,__lowerCamelCase=37		,__lowerCamelCase="gelu"		,__lowerCamelCase=0.1		,__lowerCamelCase=0.1		,__lowerCamelCase=5_12		,__lowerCamelCase=16		,__lowerCamelCase=2		,__lowerCamelCase=0.02		,__lowerCamelCase=3		,__lowerCamelCase=4		,__lowerCamelCase=None		,__lowerCamelCase=10_00		,)					->      List[str]:

						"""simple docstring"""






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



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

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size			)

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

						lowerCAmelCase__					:    Optional[Any]												=					None
						if self.use_input_mask:
										lowerCAmelCase__					:    int												=					random_attention_mask([self.batch_size, self.seq_length]			)

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

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

						lowerCAmelCase__					:    Optional[Any]												=					LayoutLMConfig(
						    vocab_size=self.vocab_size		,hidden_size=self.hidden_size		,num_hidden_layers=self.num_hidden_layers		,num_attention_heads=self.num_attention_heads		,intermediate_size=self.intermediate_size		,hidden_act=self.hidden_act		,hidden_dropout_prob=self.hidden_dropout_prob		,attention_probs_dropout_prob=self.attention_probs_dropout_prob		,max_position_embeddings=self.max_position_embeddings		,type_vocab_size=self.type_vocab_size		,initializer_range=self.initializer_range		,)

						return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					TFLayoutLMModel(config=_snake_case			)

						lowerCAmelCase__					:    Optional[Any]												=					model(_snake_case		,_snake_case		,attention_mask=_snake_case		,token_type_ids=_snake_case			)
						lowerCAmelCase__					:    Any												=					model(_snake_case		,_snake_case		,token_type_ids=_snake_case			)
						lowerCAmelCase__					:    Optional[int]												=					model(_snake_case		,_snake_case			)
						self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)			)
						self.parent.assertEqual(result.pooler_output.shape		,(self.batch_size, self.hidden_size)			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					TFLayoutLMForMaskedLM(config=_snake_case			)

						lowerCAmelCase__					:    int												=					model(_snake_case		,_snake_case		,attention_mask=_snake_case		,token_type_ids=_snake_case		,labels=_snake_case			)
						self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.vocab_size)			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					self.num_labels
						lowerCAmelCase__					:    Optional[Any]												=					TFLayoutLMForSequenceClassification(config=_snake_case			)

						lowerCAmelCase__					:    Dict												=					model(_snake_case		,_snake_case		,attention_mask=_snake_case		,token_type_ids=_snake_case			)
						self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					self.num_labels
						lowerCAmelCase__					:    List[str]												=					TFLayoutLMForTokenClassification(config=_snake_case			)

						lowerCAmelCase__					:    Optional[int]												=					model(_snake_case		,_snake_case		,attention_mask=_snake_case		,token_type_ids=_snake_case		,labels=_snake_case			)
						self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.num_labels)			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					TFLayoutLMForQuestionAnswering(config=_snake_case			)

						lowerCAmelCase__					:    Any												=					model(_snake_case		,_snake_case		,attention_mask=_snake_case		,token_type_ids=_snake_case			)
						self.parent.assertEqual(result.start_logits.shape		,(self.batch_size, self.seq_length)			)
						self.parent.assertEqual(result.end_logits.shape		,(self.batch_size, self.seq_length)			)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






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



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




		'''simple docstring'''



		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_     =10



		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					TFLayoutLMModelTester(self			)
						lowerCAmelCase__					:    Optional[Any]												=					ConfigTester(self		,config_class=_snake_case		,hidden_size=37			)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						self.config_tester.run_common_tests()



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_snake_case			)



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

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*_snake_case			)



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

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*_snake_case			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*_snake_case			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*_snake_case			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






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



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

						"""simple docstring"""






						pass






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]])  # noqa: E231
				lowerCAmelCase__					:    List[str]												=					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__					:    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
				lowerCAmelCase__					:    str												=					tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]])  # noqa: E231
				# these are sequence labels (i.e. at the token level)
				lowerCAmelCase__					:    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 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






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

						lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    List[Any]												=					prepare_layoutlm_batch_inputs()

						# forward pass
						lowerCAmelCase__					:    Any												=					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__					:    int												=					tf.convert_to_tensor(
						    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]		,)

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

						# test the pooled output on [1, :3]
						lowerCAmelCase__					:    int												=					tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]			)

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



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

						"""simple docstring"""






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

						lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    Tuple												=					prepare_layoutlm_batch_inputs()

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

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

						# test the shape of the logits
						lowerCAmelCase__					:    int												=					outputs.logits
						lowerCAmelCase__					:    Optional[int]												=					(2, 2)
						self.assertEqual(logits.shape		,_snake_case			)



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

						"""simple docstring"""






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

						lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    Any												=					prepare_layoutlm_batch_inputs()

						# forward pass
						lowerCAmelCase__					:    int												=					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__					:    Optional[Any]												=					outputs.logits
						lowerCAmelCase__					:    Optional[Any]												=					tf.convert_to_tensor((2, 25, 13)			)
						self.assertEqual(logits.shape		,_snake_case			)



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

						"""simple docstring"""






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

						lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    Any												=					prepare_layoutlm_batch_inputs()

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

						# test the shape of the logits
						lowerCAmelCase__					:    List[str]												=					tf.convert_to_tensor((2, 25)			)
						self.assertEqual(outputs.start_logits.shape		,_snake_case			)
						self.assertEqual(outputs.end_logits.shape		,_snake_case			)






import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		List[Any]=1024	,lowerCamelCase_		:		int=1024	,lowerCamelCase_		:		Dict=False	,**lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					AutoTokenizer.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Optional[Any]												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''train'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    int												=					tok.pad_token_id

				def get_lens(lowerCamelCase_		:		Tuple):
								lowerCAmelCase__					:    Tuple												=					tqdm(
								    DataLoader(lowerCamelCase_	,batch_size=512	,num_workers=8	,shuffle=lowerCamelCase_	,collate_fn=ds.collate_fn)	,desc=str(ds.len_file)	,)
								lowerCAmelCase__					:    Tuple												=					[]
								for batch in dl:
												lowerCAmelCase__					:    Dict												=					batch['''input_ids'''].ne(lowerCamelCase_).sum(1).tolist()
												lowerCAmelCase__					:    Dict												=					batch['''labels'''].ne(lowerCamelCase_).sum(1).tolist()
												if consider_target:
																for src, tgt in zip(lowerCamelCase_	,lowerCamelCase_):
																				max_lens.append(max(lowerCamelCase_	,lowerCamelCase_))
												else:
																max_lens.extend(lowerCamelCase_)
								return max_lens

				lowerCAmelCase__					:    str												=					get_lens(lowerCamelCase_)
				lowerCAmelCase__					:    Tuple												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''val'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    Optional[int]												=					get_lens(lowerCamelCase_)
				pickle_save(lowerCamelCase_	,train_ds.len_file)
				pickle_save(lowerCamelCase_	,val_ds.len_file)


if __name__ == "__main__":
							fire.Fire(save_len_file)

