






import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


UpperCamelCase        =				os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
							from run_translation import main  # noqa


set_seed(42)
UpperCamelCase        =				"sshleifer/student_marian_en_ro_6_1"
UpperCamelCase        =				"sshleifer/tiny-mbart"

@require_torch
class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				def 				__a   ( self     :str  ,     lowerCamelCase__     :List[str]=False  ,     lowerCamelCase__     :Optional[int]=None  ,     lowerCamelCase__     :int=True  ,     lowerCamelCase__     :List[Any]=True  ,     lowerCamelCase__     :str=True  ,     lowerCamelCase__     :Optional[Any]=True  ,     ):
					UpperCamelCase__ :Tuple						=      self.run_trainer(
					    eval_steps=1  ,     max_len=12  ,     model_name=lowerCamelCase__  ,     num_train_epochs=1  ,     distributed=lowerCamelCase__  ,     extra_args_str=lowerCamelCase__  ,     predict_with_generate=lowerCamelCase__  ,     do_train=lowerCamelCase__  ,     do_eval=lowerCamelCase__  ,     do_predict=lowerCamelCase__  ,     )
					UpperCamelCase__ :str						=      TrainerState.load_from_json(os.path.join(lowerCamelCase__  ,     """trainer_state.json"""     )     ).log_history

					if not do_eval:
						return

					UpperCamelCase__ :int						=      [log for log in logs if """eval_loss""" in log.keys()]

					UpperCamelCase__ :List[str]						=      eval_metrics[0]
					if predict_with_generate:
						assert "eval_bleu" in first_step_stats

						UpperCamelCase__ :List[Any]						=      eval_metrics[-1]
						assert isinstance(last_step_stats["""eval_bleu"""]  ,     lowerCamelCase__     )
						assert not math.isnan(float(last_step_stats["""eval_loss"""]     )     ), "eval_loss must not be `nan`"







				@require_torch_non_multi_gpu
				def 				__a   ( self     :Optional[Any]     ):
					self.run_seqaseq_quick()







				@require_torch_multi_gpu
				def 				__a   ( self     :Any     ):
					self.run_seqaseq_quick(distributed=lowerCamelCase__     )







				@require_torch_multi_gpu
				def 				__a   ( self     :int     ):
					self.run_seqaseq_quick(distributed=lowerCamelCase__     )







				@unittest.skip("""Requires an update of the env running those tests"""     )
				@require_torch_multi_gpu
				@require_fairscale
				def 				__a   ( self     :Dict     ):
					self.run_seqaseq_quick(distributed=lowerCamelCase__  ,     extra_args_str="""--sharded_ddp simple"""     )







				@unittest.skip("""Requires an update of the env running those tests"""     )
				@require_torch_multi_gpu
				@require_fairscale
				def 				__a   ( self     :Union[str, Any]     ):
					self.run_seqaseq_quick(distributed=lowerCamelCase__  ,     extra_args_str="""--sharded_ddp simple --fp16"""     )







				@unittest.skip("""Requires an update of the env running those tests"""     )
				@require_torch_multi_gpu
				@require_fairscale
				def 				__a   ( self     :List[str]     ):
					self.run_seqaseq_quick(distributed=lowerCamelCase__  ,     extra_args_str="""--sharded_ddp zero_dp_2"""  ,     predict_with_generate=lowerCamelCase__     )







				@unittest.skip("""Requires an update of the env running those tests"""     )
				@require_torch_multi_gpu
				@require_fairscale
				def 				__a   ( self     :str     ):
					self.run_seqaseq_quick(
					    distributed=lowerCamelCase__  ,     extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""  ,     predict_with_generate=lowerCamelCase__     )







				@require_apex
				@require_torch_gpu
				def 				__a   ( self     :str     ):
					# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
					# program and it breaks other tests that run from the same pytest worker, therefore until this is
					# sorted out it must be run only in an external program, that is distributed=True in this
					# test and only under one or more gpus - if we want cpu will need to make a special test
					#
					# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
					# 2nd main() call it botches the future eval.
					#
					self.run_seqaseq_quick(distributed=lowerCamelCase__  ,     extra_args_str="""--fp16 --fp16_backend=apex"""     )
					# test 2nd time - was getting eval_loss': nan'
					# to reproduce the problem set distributed=False
					self.run_seqaseq_quick(distributed=lowerCamelCase__  ,     extra_args_str="""--fp16 --fp16_backend=apex"""     )







				@parameterized.expand(["""base""", """low""", """high""", """mixed"""]     )
				@require_torch_multi_gpu
				def 				__a   ( self     :List[str]  ,     lowerCamelCase__     :Union[str, Any]     ):
					# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
					UpperCamelCase__ :Optional[int]						=      {
					    # test with the default log_level - should be info and thus log info once
					    """base""": {"""extra_args_str""": """""", """n_matches""": 1},
					    # test with low log_level and log_level_replica - should be noisy on all processes
					    # now the info string should appear twice on 2 processes
					    """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
					    # test with high log_level and low log_level_replica
					    # now the info string should appear once only on the replica
					    """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
					    # test with high log_level and log_level_replica - should be quiet on all processes
					    """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
					}

					UpperCamelCase__ :Union[str, Any]						=      experiments[experiment_id]
					UpperCamelCase__ :int						=      {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
					UpperCamelCase__ :Any						=      """Running training"""
					with CaptureStderr() as cl:
						self.run_seqaseq_quick(**lowerCamelCase__  ,     extra_args_str=data["""extra_args_str"""]     )
					UpperCamelCase__ :Any						=      len(re.findall(lowerCamelCase__  ,     cl.err     )     )
					self.assertEqual(lowerCamelCase__  ,     data["""n_matches"""]     )







				@slow
				def 				__a   ( self     :List[Any]     ):
					UpperCamelCase__ :List[str]						=      self.run_trainer(
					    eval_steps=2  ,     max_len=1_28  ,     model_name=lowerCamelCase__  ,     learning_rate=3e-4  ,     num_train_epochs=10  ,     distributed=lowerCamelCase__  ,     )

					# Check metrics
					UpperCamelCase__ :Tuple						=      TrainerState.load_from_json(os.path.join(lowerCamelCase__  ,     """trainer_state.json"""     )     ).log_history
					UpperCamelCase__ :int						=      [log for log in logs if """eval_loss""" in log.keys()]
					UpperCamelCase__ :Tuple						=      eval_metrics[0]
					UpperCamelCase__ :List[Any]						=      eval_metrics[-1]

					assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
					assert isinstance(last_step_stats["""eval_bleu"""]  ,     lowerCamelCase__     )

					# test if do_predict saves generations and metrics
					UpperCamelCase__ :int						=      os.listdir(lowerCamelCase__     )
					UpperCamelCase__ :Dict						=      {os.path.basename(lowerCamelCase__     ) for p in contents}
					assert "generated_predictions.txt" in contents
					assert "predict_results.json" in contents







				@slow
				@require_bitsandbytes
				def 				__a   ( self     :Union[str, Any]     ):
					from transformers.training_args import OptimizerNames

					def train_and_return_metrics(lowerCamelCase__     :str     ) -> Tuple[int, float]:
						UpperCamelCase__ :List[str]						=      """--skip_memory_metrics 0"""

						UpperCamelCase__ :Tuple						=      self.run_trainer(
						    max_len=1_28  ,     model_name=lowerCamelCase__  ,     learning_rate=3e-4  ,     num_train_epochs=1  ,     optim=lowerCamelCase__  ,     distributed=lowerCamelCase__  ,     extra_args_str=lowerCamelCase__  ,     do_eval=lowerCamelCase__  ,     do_predict=lowerCamelCase__  ,     n_gpus_to_use=1  ,     )

						# Check metrics
						UpperCamelCase__ :List[Any]						=      TrainerState.load_from_json(Path(lowerCamelCase__  ,     """trainer_state.json"""     )     ).log_history
						UpperCamelCase__ :List[Any]						=      int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20     )
						UpperCamelCase__ :Tuple						=      int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20     )

						UpperCamelCase__ :Tuple						=      logs[0]["""train_loss"""]
						return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

					UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :Tuple						=      train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value     )
					UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :List[Any]						=      train_and_return_metrics(OptimizerNames.ADAMW_BNB.value     )

					UpperCamelCase__ :Optional[Any]						=      gpu_alloc_mem_orig - gpu_alloc_mem_bnb

					UpperCamelCase__ :List[str]						=      gpu_peak_mem_orig + gpu_alloc_mem_orig
					UpperCamelCase__ :str						=      gpu_peak_mem_bnb + gpu_alloc_mem_bnb
					UpperCamelCase__ :Optional[Any]						=      gpu_total_mem_orig - gpu_total_mem_bnb

					# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
					# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
					# in 2 bytes and the diff in optim memory usage is derived as so:
					#
					# - normal 25*8=~200MB (8 bytes per param)
					# - bnb    25*2= ~50MB (2 bytes per param)
					#
					# Thus we should expect ~150MB total memory saved.
					#
					# Peak memory should be the same - the total should be different by about that same margin
					#
					# After leaving a small margin to accommodate for differences between gpus let's check
					# that we have at least 120MB in savings
					UpperCamelCase__ :Union[str, Any]						=      1_20

					# uncomment the following if this test starts failing - requires py38 for a new print feature
					# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
					# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
					# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
					# print(f"{gpu_alloc_mem_diff=}MB")
					# print(f"{gpu_peak_mem_diff=}MB")
					# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
					# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

					self.assertGreater(
					    lowerCamelCase__  ,     lowerCamelCase__  ,     """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
					    f""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
					    f""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"""  ,     )

					self.assertGreater(
					    lowerCamelCase__  ,     lowerCamelCase__  ,     """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
					    f""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
					    f""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"""  ,     )

					self.assertEqual(
					    lowerCamelCase__  ,     lowerCamelCase__  ,     f"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""     )







				def 				__a   ( self     :Dict  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :float = 3e-3  ,     lowerCamelCase__     :str = "adafactor"  ,     lowerCamelCase__     :bool = False  ,     lowerCamelCase__     :str = None  ,     lowerCamelCase__     :int = 0  ,     lowerCamelCase__     :bool = True  ,     lowerCamelCase__     :bool = True  ,     lowerCamelCase__     :bool = True  ,     lowerCamelCase__     :bool = True  ,     lowerCamelCase__     :int = None  ,     ):
					UpperCamelCase__ :List[Any]						=      self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
					UpperCamelCase__ :int						=      self.get_auto_remove_tmp_dir()
					UpperCamelCase__ :List[Any]						=      f"""
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(lowerCamelCase__     )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(lowerCamelCase__     )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

					UpperCamelCase__ :Optional[Any]						=      f"""
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(lowerCamelCase__     )}
        """.split()

					UpperCamelCase__ :Optional[Any]						=      """
            --do_predict
        """.split()

					UpperCamelCase__ :Union[str, Any]						=      []
					if do_train:
						args += args_train

					if do_eval:
						args += args_eval

					if do_predict:
						args += args_predict

					if predict_with_generate:
						args += "--predict_with_generate".split()

					if do_train:
						if optim == "adafactor":
							args += "--adafactor".split()
						else:
							args += f"""--optim {optim}""".split()

					if extra_args_str is not None:
						args += extra_args_str.split()

					if distributed:
						if n_gpus_to_use is None:
							UpperCamelCase__ :List[Any]						=      get_gpu_count()
						UpperCamelCase__ :int						=      get_torch_dist_unique_port()
						UpperCamelCase__ :Tuple						=      f"""
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            """.split()
						UpperCamelCase__ :Dict						=      [sys.executable] + distributed_args + args
						# keep for quick debug
						# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
						execute_subprocess_async(lowerCamelCase__  ,     env=self.get_env()     )
					else:
						UpperCamelCase__ :Dict						=      ["""run_translation.py"""] + args
						with patch.object(lowerCamelCase__  ,     """argv"""  ,     lowerCamelCase__     ):
							main()

					return output_dir


'''simple docstring'''



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 		lowerCAmelCase__  :



 """simple docstring"""



 def __init__(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			str=13   ,				__SCREAMING_SNAKE_CASE   :			int=7   ,				__SCREAMING_SNAKE_CASE   :			List[str]=True   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			str=True   ,				__SCREAMING_SNAKE_CASE   :			str=True   ,				__SCREAMING_SNAKE_CASE   :			Dict=99   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=24   ,				__SCREAMING_SNAKE_CASE   :			Any=2   ,				__SCREAMING_SNAKE_CASE   :			Any=6   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=37   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			int=512   ,				__SCREAMING_SNAKE_CASE   :			str=16   ,				__SCREAMING_SNAKE_CASE   :			Any=2   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.02   ,				__SCREAMING_SNAKE_CASE   :			int=3   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=1_000   ,				)    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = parent
      __SCREAMING_SNAKE_CASE      = batch_size
      __SCREAMING_SNAKE_CASE      = seq_length
      __SCREAMING_SNAKE_CASE      = is_training
      __SCREAMING_SNAKE_CASE      = use_input_mask
      __SCREAMING_SNAKE_CASE      = use_token_type_ids
      __SCREAMING_SNAKE_CASE      = use_labels
      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = type_sequence_label_size
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = num_labels
      __SCREAMING_SNAKE_CASE      = scope
      __SCREAMING_SNAKE_CASE      = range_bbox
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Optional[int]:


      """simple docstring"""




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

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

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

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

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

      __SCREAMING_SNAKE_CASE      = self.get_config()

      return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
 def  UpperCAmelCase__						(						self   :			str     )    ->					int:


      """simple docstring"""




      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  UpperCAmelCase__						(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			int   ,				)    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = LiltModel(config=__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE   ,				bbox=__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE   ,				token_type_ids=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE   ,				bbox=__SCREAMING_SNAKE_CASE   ,				token_type_ids=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE   ,				bbox=__SCREAMING_SNAKE_CASE     )

      self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )
      self.parent.assertEqual(result.pooler_output.shape   ,				(self.batch_size, self.hidden_size)     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				)    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.num_labels
      __SCREAMING_SNAKE_CASE      = LiltForTokenClassification(config=__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(
          __SCREAMING_SNAKE_CASE   ,				bbox=__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE   ,				token_type_ids=__SCREAMING_SNAKE_CASE   ,				labels=__SCREAMING_SNAKE_CASE     )
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.num_labels)     )
 def  UpperCAmelCase__						(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				)    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = LiltForQuestionAnswering(config=__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(
          __SCREAMING_SNAKE_CASE   ,				bbox=__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE   ,				token_type_ids=__SCREAMING_SNAKE_CASE   ,				start_positions=__SCREAMING_SNAKE_CASE   ,				end_positions=__SCREAMING_SNAKE_CASE   ,				)
      self.parent.assertEqual(result.start_logits.shape   ,				(self.batch_size, self.seq_length)     )
      self.parent.assertEqual(result.end_logits.shape   ,				(self.batch_size, self.seq_length)     )


 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
      (
          (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     
      )      = config_and_inputs
      __SCREAMING_SNAKE_CASE      = {
          """input_ids""": input_ids,
          """bbox""": bbox,
          """token_type_ids""": token_type_ids,
          """attention_mask""": input_mask,
      }
      return config, inputs_dict







@require_torch
class 		lowerCAmelCase__  ( a					,   a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = (
     (
         LiltModel,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 lowerCAmelCase__       = (
     {
         "feature-extraction": LiltModel,
         "question-answering": LiltForQuestionAnswering,
         "text-classification": LiltForSequenceClassification,
         "token-classification": LiltForTokenClassification,
         "zero-shot": LiltForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 def  UpperCAmelCase__						(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			int     )    ->					str:


      """simple docstring"""




      return True
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = LiltModelTester(self     )
      __SCREAMING_SNAKE_CASE      = ConfigTester(self   ,				config_class=__SCREAMING_SNAKE_CASE   ,				hidden_size=37     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					Any:


      """simple docstring"""




      self.config_tester.run_common_tests()
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           __SCREAMING_SNAKE_CASE      = type
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE     )


 @slow
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					int:


      """simple docstring"""




      for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __SCREAMING_SNAKE_CASE      = LiltModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE     )







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



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""     ).to(__SCREAMING_SNAKE_CASE     )

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

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

      __SCREAMING_SNAKE_CASE      = torch.Size([1, 2, 768]     )
      __SCREAMING_SNAKE_CASE      = torch.tensor(
          [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]   ,				device=__SCREAMING_SNAKE_CASE   ,				)

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






