



'''simple docstring'''





import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    LayoutLMForMaskedLM,
				    LayoutLMForQuestionAnswering,
				    LayoutLMForSequenceClassification,
				    LayoutLMForTokenClassification,
				    LayoutLMModel,
				)



class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_=13	, lowerCamelCase_=7	, lowerCamelCase_=True	, lowerCamelCase_=True	, lowerCamelCase_=True	, lowerCamelCase_=True	, lowerCamelCase_=99	, lowerCamelCase_=32	, lowerCamelCase_=5	, lowerCamelCase_=4	, lowerCamelCase_=37	, lowerCamelCase_="gelu"	, lowerCamelCase_=0.1	, lowerCamelCase_=0.1	, lowerCamelCase_=512	, lowerCamelCase_=16	, lowerCamelCase_=2	, lowerCamelCase_=0.02	, lowerCamelCase_=3	, lowerCamelCase_=4	, lowerCamelCase_=None	, lowerCamelCase_=1000	, ):
						"""simple docstring"""

						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	):
						"""simple docstring"""

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

						a        =     ids_tensor([self.batch_size, self.seq_length, 4]	, self.range_bbox	)
						# 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        =     None
						if self.use_input_mask:
											a        =     ids_tensor([self.batch_size, self.seq_length]	, vocab_size=2	)

						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        =     self.get_config()

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return 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	, )




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     LayoutLMModel(config=lowerCamelCase_	)
						model.to(lowerCamelCase_	)
						model.eval()
						a        =     model(lowerCamelCase_	, lowerCamelCase_	, attention_mask=lowerCamelCase_	, token_type_ids=lowerCamelCase_	)
						a        =     model(lowerCamelCase_	, lowerCamelCase_	, token_type_ids=lowerCamelCase_	)
						a        =     model(lowerCamelCase_	, lowerCamelCase_	)
						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	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     LayoutLMForMaskedLM(config=lowerCamelCase_	)
						model.to(lowerCamelCase_	)
						model.eval()
						a        =     model(lowerCamelCase_	, lowerCamelCase_	, attention_mask=lowerCamelCase_	, token_type_ids=lowerCamelCase_	, labels=lowerCamelCase_	)
						self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.vocab_size)	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     self.num_labels
						a        =     LayoutLMForSequenceClassification(lowerCamelCase_	)
						model.to(lowerCamelCase_	)
						model.eval()
						a        =     model(
						    lowerCamelCase_	, lowerCamelCase_	, attention_mask=lowerCamelCase_	, token_type_ids=lowerCamelCase_	, labels=lowerCamelCase_	)
						self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.num_labels)	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     self.num_labels
						a        =     LayoutLMForTokenClassification(config=lowerCamelCase_	)
						model.to(lowerCamelCase_	)
						model.eval()
						a        =     model(lowerCamelCase_	, lowerCamelCase_	, attention_mask=lowerCamelCase_	, token_type_ids=lowerCamelCase_	, labels=lowerCamelCase_	)
						self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.num_labels)	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     LayoutLMForQuestionAnswering(config=lowerCamelCase_	)
						model.to(lowerCamelCase_	)
						model.eval()
						a        =     model(
						    lowerCamelCase_	, bbox=lowerCamelCase_	, attention_mask=lowerCamelCase_	, token_type_ids=lowerCamelCase_	, start_positions=lowerCamelCase_	, end_positions=lowerCamelCase_	, )
						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"""

						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_torch
class 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase,   unittest.TestCase			):





	"""simple docstring"""
	__A					=							(
	    (
	        LayoutLMModel,
	        LayoutLMForMaskedLM,
	        LayoutLMForSequenceClassification,
	        LayoutLMForTokenClassification,
	        LayoutLMForQuestionAnswering,
	    )
	    if is_torch_available()
	    else None
	)
	__A					=							(
	    {
	        "document-question-answering": LayoutLMForQuestionAnswering,
	        "feature-extraction": LayoutLMModel,
	        "fill-mask": LayoutLMForMaskedLM,
	        "text-classification": LayoutLMForSequenceClassification,
	        "token-classification": LayoutLMForTokenClassification,
	        "zero-shot": LayoutLMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	__A					=							True




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     LayoutLMModelTester(self	)
						a        =     ConfigTester(self	, config_class=lowerCamelCase_	, hidden_size=37	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						self.config_tester.run_common_tests()




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
											a        =     type
											self.model_tester.create_and_check_model(*lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_	)




	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						pass






def 					a(   )	-> Optional[int]:
					"""simple docstring"""





					a        =     torch.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]]				,  device=A )  # noqa: E231
					a        =     torch.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],]				,  device=A )  # noqa: E231
					a        =     torch.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]]]				,  device=A )  # noqa: E231
					a        =     torch.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]]				,  device=A )  # noqa: E231
					# these are sequence labels (i.e. at the token level)
					a        =     torch.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]]				,  device=A )  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 			_lowercase							(     unittest.TestCase			):





	"""simple docstring"""




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"	).to(lowerCamelCase_	)

						a  ,       a  ,       a  ,       a  ,       a        =     prepare_layoutlm_batch_inputs()

						# forward pass
						a        =     model(input_ids=lowerCamelCase_	, bbox=lowerCamelCase_	, attention_mask=lowerCamelCase_	, token_type_ids=lowerCamelCase_	)

						# test the sequence output on [0, :3, :3]
						a        =     torch.tensor(
						    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]	, device=lowerCamelCase_	, )

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

						# test the pooled output on [1, :3]
						a        =     torch.tensor([-0.6580, -0.0214, 0.8552]	, device=lowerCamelCase_	)

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




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"	, num_labels=2	).to(
						    lowerCamelCase_	)

						a  ,       a  ,       a  ,       a  ,       a        =     prepare_layoutlm_batch_inputs()

						# forward pass
						a        =     model(
						    input_ids=lowerCamelCase_	, bbox=lowerCamelCase_	, attention_mask=lowerCamelCase_	, token_type_ids=lowerCamelCase_	, labels=torch.tensor([1, 1]	, device=lowerCamelCase_	)	, )

						# test whether we get a loss as a scalar
						a        =     outputs.loss
						a        =     torch.Size([]	)
						self.assertEqual(loss.shape	, lowerCamelCase_	)

						# test the shape of the logits
						a        =     outputs.logits
						a        =     torch.Size((2, 2)	)
						self.assertEqual(logits.shape	, lowerCamelCase_	)




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"	, num_labels=13	).to(
						    lowerCamelCase_	)

						a  ,       a  ,       a  ,       a  ,       a        =     prepare_layoutlm_batch_inputs()

						# forward pass
						a        =     model(
						    input_ids=lowerCamelCase_	, bbox=lowerCamelCase_	, attention_mask=lowerCamelCase_	, token_type_ids=lowerCamelCase_	, labels=lowerCamelCase_	)

						# test the loss calculation to be around 2.65
						# expected_loss = torch.tensor(2.65, device=torch_device)

						# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
						# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

						# test the shape of the logits
						a        =     outputs.logits
						a        =     torch.Size((2, 25, 13)	)
						self.assertEqual(logits.shape	, lowerCamelCase_	)




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"	).to(lowerCamelCase_	)

						a  ,       a  ,       a  ,       a  ,       a        =     prepare_layoutlm_batch_inputs()

						# forward pass
						a        =     model(input_ids=lowerCamelCase_	, bbox=lowerCamelCase_	, attention_mask=lowerCamelCase_	, token_type_ids=lowerCamelCase_	)

						# test the shape of the logits
						a        =     torch.Size((2, 25)	)
						self.assertEqual(outputs.start_logits.shape	, lowerCamelCase_	)
						self.assertEqual(outputs.end_logits.shape	, lowerCamelCase_	)





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls




	@slow
	@require_torch_gpu
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.get_auto_remove_tmp_dir()
						a        =     F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						a        =     get_gpu_count()
						a        =     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
						a        =     [sys.executable] + distributed_args + args
						execute_subprocess_async(lowerCamelCase_	, env=self.get_env()	)

						a        =     os.path.join(lowerCamelCase_	, "test_bleu.json"	)
						a        =     load_json(lowerCamelCase_	)
						# print(metrics)
						self.assertGreaterEqual(metrics["bleu"]	, 25	)
