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


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


set_seed(42)
_lowerCAmelCase       =		'sshleifer/student_marian_en_ro_6_1'
_lowerCAmelCase       =		'sshleifer/tiny-mbart'
@require_torch
class     lowerCAmelCase_     (       __lowercase ):







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     List[str]=False ,			_A      :     Optional[Any]=None ,			_A      :     List[str]=True ,			_A      :     Optional[Any]=True ,			_A      :     List[str]=True ,			_A      :     Optional[int]=True ,			):
												_UpperCamelCase			     =     self.run_trainer(
												    eval_steps=1 ,			max_len=12 ,			model_name=__SCREAMING_SNAKE_CASE ,			num_train_epochs=1 ,			distributed=__SCREAMING_SNAKE_CASE ,			extra_args_str=__SCREAMING_SNAKE_CASE ,			predict_with_generate=__SCREAMING_SNAKE_CASE ,			do_train=__SCREAMING_SNAKE_CASE ,			do_eval=__SCREAMING_SNAKE_CASE ,			do_predict=__SCREAMING_SNAKE_CASE ,			)
												_UpperCamelCase			     =     TrainerState.load_from_json(os.path.join(__SCREAMING_SNAKE_CASE ,			'''trainer_state.json'''		)		).log_history

												if not do_eval:
																	return

												_UpperCamelCase			     =     [log for log in logs if '''eval_loss''' in log.keys()]

												_UpperCamelCase			     =     eval_metrics[0]
												if predict_with_generate:
																	assert "eval_bleu" in first_step_stats

																	_UpperCamelCase			     =     eval_metrics[-1]
																	assert isinstance(last_step_stats['''eval_bleu'''] ,			__SCREAMING_SNAKE_CASE		)
																	assert not math.isnan(float(last_step_stats['''eval_loss''']		)		), "eval_loss must not be `nan`"







							@require_torch_non_multi_gpu
							def  UpperCamelCase_						(   self      :     List[Any]		):
												self.run_seqaseq_quick()







							@require_torch_multi_gpu
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												self.run_seqaseq_quick(distributed=__SCREAMING_SNAKE_CASE		)







							@require_torch_multi_gpu
							def  UpperCamelCase_						(   self      :     Any		):
												self.run_seqaseq_quick(distributed=__SCREAMING_SNAKE_CASE		)







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







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







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







							@require_apex
							@require_torch_gpu
							def  UpperCamelCase_						(   self      :     str		):
												self.run_seqaseq_quick(distributed=__SCREAMING_SNAKE_CASE ,			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=__SCREAMING_SNAKE_CASE ,			extra_args_str='''--fp16 --fp16_backend=apex'''		)







							@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']		)
							@require_torch_multi_gpu
							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     Optional[Any]		):
												_UpperCamelCase			     =     {
												    # 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			     =     experiments[experiment_id]
												_UpperCamelCase			     =     {'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
												_UpperCamelCase			     =     '''Running training'''
												with CaptureStderr() as cl:
																	self.run_seqaseq_quick(**__SCREAMING_SNAKE_CASE ,			extra_args_str=data['''extra_args_str''']		)
												_UpperCamelCase			     =     len(re.findall(__SCREAMING_SNAKE_CASE ,			cl.err		)		)
												self.assertEqual(__SCREAMING_SNAKE_CASE ,			data['''n_matches''']		)







							@slow
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												_UpperCamelCase			     =     self.run_trainer(
												    eval_steps=2 ,			max_len=128 ,			model_name=__SCREAMING_SNAKE_CASE ,			learning_rate=3e-4 ,			num_train_epochs=10 ,			distributed=__SCREAMING_SNAKE_CASE ,			)

												# Check metrics
												_UpperCamelCase			     =     TrainerState.load_from_json(os.path.join(__SCREAMING_SNAKE_CASE ,			'''trainer_state.json'''		)		).log_history
												_UpperCamelCase			     =     [log for log in logs if '''eval_loss''' in log.keys()]
												_UpperCamelCase			     =     eval_metrics[0]
												_UpperCamelCase			     =     eval_metrics[-1]

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

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







							@slow
							@require_bitsandbytes
							def  UpperCamelCase_						(   self      :     int		):
												from transformers.training_args import OptimizerNames

												def train_and_return_metrics(_A      :     str		) -> Tuple[int, float]:
																	_UpperCamelCase			     =     '''--skip_memory_metrics 0'''

																	_UpperCamelCase			     =     self.run_trainer(
																	    max_len=128 ,			model_name=__SCREAMING_SNAKE_CASE ,			learning_rate=3e-4 ,			num_train_epochs=1 ,			optim=__SCREAMING_SNAKE_CASE ,			distributed=__SCREAMING_SNAKE_CASE ,			extra_args_str=__SCREAMING_SNAKE_CASE ,			do_eval=__SCREAMING_SNAKE_CASE ,			do_predict=__SCREAMING_SNAKE_CASE ,			n_gpus_to_use=1 ,			)

																	# Check metrics
																	_UpperCamelCase			     =     TrainerState.load_from_json(Path(__SCREAMING_SNAKE_CASE ,			'''trainer_state.json'''		)		).log_history
																	_UpperCamelCase			     =     int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20		)
																	_UpperCamelCase			     =     int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20		)

																	_UpperCamelCase			     =     logs[0]['''train_loss''']
																	return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

												_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value		)
												_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     train_and_return_metrics(OptimizerNames.ADAMW_BNB.value		)

												_UpperCamelCase			     =     gpu_alloc_mem_orig - gpu_alloc_mem_bnb

												_UpperCamelCase			     =     gpu_peak_mem_orig + gpu_alloc_mem_orig
												_UpperCamelCase			     =     gpu_peak_mem_bnb + gpu_alloc_mem_bnb
												_UpperCamelCase			     =     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			     =     120

												# 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(
												    __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			'''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(
												    __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			'''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(
												    __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""		)







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

												_UpperCamelCase			     =     F"""\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(__SCREAMING_SNAKE_CASE		)}\n        """.split()

												_UpperCamelCase			     =     '''
            --do_predict
        '''.split()

												_UpperCamelCase			     =     []
												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			     =     get_gpu_count()
																	_UpperCamelCase			     =     get_torch_dist_unique_port()
																	_UpperCamelCase			     =     F"""\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            """.split()
																	_UpperCamelCase			     =     [sys.executable] + distributed_args + args
																	# keep for quick debug
																	# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
																	execute_subprocess_async(__SCREAMING_SNAKE_CASE ,			env=self.get_env()		)
												else:
																	_UpperCamelCase			     =     ['''run_translation.py'''] + args
																	with patch.object(__SCREAMING_SNAKE_CASE ,			'''argv''' ,			__SCREAMING_SNAKE_CASE		):
																						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     )     )






