
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
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 (
           LiltForQuestionAnswering,
           LiltForSequenceClassification,
           LiltForTokenClassification,
           LiltModel,
       )
       from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST







class        lowercase	:




       def __init__(       self     ,A__     ,A__=1_3     ,A__=7     ,A__=True     ,A__=True     ,A__=True     ,A__=True     ,A__=9_9     ,A__=2_4     ,A__=2     ,A__=6     ,A__=3_7     ,A__="gelu"     ,A__=0.1     ,A__=0.1     ,A__=5_1_2     ,A__=1_6     ,A__=2     ,A__=0.02     ,A__=3     ,A__=None     ,A__=1_0_0_0     ,):
            lowercase					=		parent
            lowercase					=		batch_size
            lowercase					=		seq_length
            lowercase					=		is_training
            lowercase					=		use_input_mask
            lowercase					=		use_token_type_ids
            lowercase					=		use_labels
            lowercase					=		vocab_size
            lowercase					=		hidden_size
            lowercase					=		num_hidden_layers
            lowercase					=		num_attention_heads
            lowercase					=		intermediate_size
            lowercase					=		hidden_act
            lowercase					=		hidden_dropout_prob
            lowercase					=		attention_probs_dropout_prob
            lowercase					=		max_position_embeddings
            lowercase					=		type_vocab_size
            lowercase					=		type_sequence_label_size
            lowercase					=		initializer_range
            lowercase					=		num_labels
            lowercase					=		scope
            lowercase					=		range_bbox




       def 				A__   (       self):
            lowercase					=		ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

            lowercase					=		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]:
                           lowercase					=		bbox[i, j, 3]
                           lowercase					=		bbox[i, j, 1]
                           lowercase					=		t
                      if bbox[i, j, 2] < bbox[i, j, 0]:
                           lowercase					=		bbox[i, j, 2]
                           lowercase					=		bbox[i, j, 0]
                           lowercase					=		t

            lowercase					=		None
            if self.use_input_mask:
                 lowercase					=		ids_tensor([self.batch_size, self.seq_length]     ,vocab_size=2)

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

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

            lowercase					=		self.get_config()

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




       def 				A__   (       self):
            return LiltConfig(
                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 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,):
            lowercase					=		LiltModel(config=__lowercase)
            model.to(__lowercase)
            model.eval()
            lowercase					=		model(__lowercase     ,bbox=__lowercase     ,attention_mask=__lowercase     ,token_type_ids=__lowercase)
            lowercase					=		model(__lowercase     ,bbox=__lowercase     ,token_type_ids=__lowercase)
            lowercase					=		model(__lowercase     ,bbox=__lowercase)

            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 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,):
            lowercase					=		self.num_labels
            lowercase					=		LiltForTokenClassification(config=__lowercase)
            model.to(__lowercase)
            model.eval()
            lowercase					=		model(
                __lowercase     ,bbox=__lowercase     ,attention_mask=__lowercase     ,token_type_ids=__lowercase     ,labels=__lowercase)
            self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels))




       def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,A__     ,):
            lowercase					=		LiltForQuestionAnswering(config=__lowercase)
            model.to(__lowercase)
            model.eval()
            lowercase					=		model(
                __lowercase     ,bbox=__lowercase     ,attention_mask=__lowercase     ,token_type_ids=__lowercase     ,start_positions=__lowercase     ,end_positions=__lowercase     ,)
            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 				A__   (       self):
            lowercase					=		self.prepare_config_and_inputs()
            (
                (
                lowercase       
            )			,		(
                lowercase       
            )			,		(
                lowercase       
            )			,		(
                lowercase       
            )			,		(
                lowercase       
            )			,		(
                lowercase       
            )			,		(
                lowercase       
            )			,		       
            )					=		config_and_inputs
            lowercase					=		{
                '''input_ids''': input_ids,
                '''bbox''': bbox,
                '''token_type_ids''': token_type_ids,
                '''attention_mask''': input_mask,
            }
            return config, inputs_dict







@require_torch
class        lowercase	(      lowercase__ ,						lowercase__ ,						lowercase__ ,						unittest.TestCase	):
       lowercase_ : str       =(
           (
               LiltModel,
               LiltForSequenceClassification,
               LiltForTokenClassification,
               LiltForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       lowercase_ : Optional[int]       =(
           {
               """feature-extraction""": LiltModel,
               """question-answering""": LiltForQuestionAnswering,
               """text-classification""": LiltForSequenceClassification,
               """token-classification""": LiltForTokenClassification,
               """zero-shot""": LiltForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       lowercase_ : Any       =False
       lowercase_ : Dict       =False




       def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__):
            return True




       def 				A__   (       self):
            lowercase					=		LiltModelTester(self)
            lowercase					=		ConfigTester(self     ,config_class=__lowercase     ,hidden_size=3_7)




       def 				A__   (       self):
            self.config_tester.run_common_tests()




       def 				A__   (       self):
            lowercase					=		self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowercase)




       def 				A__   (       self):
            lowercase					=		self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                 lowercase					=		type
                 self.model_tester.create_and_check_model(*__lowercase)




       def 				A__   (       self):
            lowercase					=		self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__lowercase)




       def 				A__   (       self):
            lowercase					=		self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__lowercase)





       @slow
       def 				A__   (       self):
            for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowercase					=		LiltModel.from_pretrained(__lowercase)
                 self.assertIsNotNone(__lowercase)







@require_torch
@slow
class        lowercase	(      unittest.TestCase	):




       def 				A__   (       self):
            lowercase					=		LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base''').to(__lowercase)

            lowercase					=		torch.tensor([[1, 2]]     ,device=__lowercase)
            lowercase					=		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]     ,device=__lowercase)

            # forward pass
            with torch.no_grad():
                 lowercase					=		model(input_ids=__lowercase     ,bbox=__lowercase)

            lowercase					=		torch.Size([1, 2, 7_6_8])
            lowercase					=		torch.tensor(
                [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]     ,device=__lowercase     ,)

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






import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm
							import torch_xla.debug.metrics as met







class        lowercase	(      SCREAMING_SNAKE_CASE__	):




							def __init__(       self     ,*A__     ,A__=None     ,A__=None     ,**A__):
												super().__init__(*A__     ,**A__)
												lowercase					=		eval_examples
												lowercase					=		post_process_function




							def 				A__   (       self     ,A__ = None     ,A__=None     ,A__ = None     ,A__ = "eval"     ,**A__     ,):
												lowercase					=		gen_kwargs.copy()
												lowercase					=		(
												    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''') is not None else self.args.generation_max_length
												)
												lowercase					=		(
												    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''') is not None else self.args.generation_num_beams
												)
												lowercase					=		gen_kwargs

												lowercase					=		self.eval_dataset if eval_dataset is None else eval_dataset
												lowercase					=		self.get_eval_dataloader(A__)
												lowercase					=		self.eval_examples if eval_examples is None else eval_examples

												# Temporarily disable metric computation, we will do it in the loop here.
												lowercase					=		self.compute_metrics
												lowercase					=		None
												lowercase					=		time.time()
												lowercase					=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
												try:
																	lowercase					=		eval_loop(
																	    A__     ,description='''Evaluation'''     ,prediction_loss_only=True if compute_metrics is None else None     ,ignore_keys=A__     ,metric_key_prefix=A__     ,)
												finally:
																	lowercase					=		compute_metrics
												lowercase					=		self.args.eval_batch_size * self.args.world_size
												if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
																	start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
												output.metrics.update(
												    speed_metrics(
												        A__     ,A__     ,num_samples=output.num_samples     ,num_steps=math.ceil(output.num_samples / total_batch_size)     ,))

												if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
																	# Only the main node write the results by default
																	lowercase					=		self.post_process_function(A__     ,A__     ,A__)
																	lowercase					=		self.compute_metrics(A__)

																	# Prefix all keys with metric_key_prefix + '_'
																	for key in list(metrics.keys()):
																						if not key.startswith(f'{metric_key_prefix}_'):
																											lowercase					=		metrics.pop(A__)

																	metrics.update(output.metrics)
												else:
																	lowercase					=		output.metrics

												if self.args.should_log:
																	# Only the main node log the results by default
																	self.log(A__)

												if self.args.tpu_metrics_debug or self.args.debug:
																	# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																	xm.master_print(met.metrics_report())

												lowercase					=		self.callback_handler.on_evaluate(self.args     ,self.state     ,self.control     ,A__)
												return metrics





							def 				A__   (       self     ,A__     ,A__     ,A__=None     ,A__ = "test"     ,**A__):
												lowercase					=		gen_kwargs.copy()

												lowercase					=		self.get_test_dataloader(A__)

												# Temporarily disable metric computation, we will do it in the loop here.
												lowercase					=		self.compute_metrics
												lowercase					=		None
												lowercase					=		time.time()
												lowercase					=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
												try:
																	lowercase					=		eval_loop(
																	    A__     ,description='''Prediction'''     ,prediction_loss_only=True if compute_metrics is None else None     ,ignore_keys=A__     ,metric_key_prefix=A__     ,)
												finally:
																	lowercase					=		compute_metrics

												lowercase					=		self.args.eval_batch_size * self.args.world_size
												if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
																	start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
												output.metrics.update(
												    speed_metrics(
												        A__     ,A__     ,num_samples=output.num_samples     ,num_steps=math.ceil(output.num_samples / total_batch_size)     ,))
												if self.post_process_function is None or self.compute_metrics is None:
																	return output

												lowercase					=		self.post_process_function(A__     ,A__     ,A__     ,'''predict''')
												lowercase					=		self.compute_metrics(A__)

												# Prefix all keys with metric_key_prefix + '_'
												for key in list(metrics.keys()):
																	if not key.startswith(f'{metric_key_prefix}_'):
																						lowercase					=		metrics.pop(A__)
												metrics.update(output.metrics)
												return PredictionOutput(predictions=predictions.predictions     ,label_ids=predictions.label_ids     ,metrics=A__)




