


from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
				from .deepspeed import (
				    DeepSpeedEngineWrapper,
				    DeepSpeedOptimizerWrapper,
				    DeepSpeedSchedulerWrapper,
				    DummyOptim,
				    DummyScheduler,
				    HfDeepSpeedConfig,
				)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers







"""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      __A     :





							'''simple docstring'''

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



														"""simple docstring"""



														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		seq_length
														snake_case_           =		is_training
														snake_case_           =		use_input_mask
														snake_case_           =		use_token_type_ids
														snake_case_           =		use_labels
														snake_case_           =		vocab_size
														snake_case_           =		hidden_size
														snake_case_           =		num_hidden_layers
														snake_case_           =		num_attention_heads
														snake_case_           =		intermediate_size
														snake_case_           =		hidden_act
														snake_case_           =		hidden_dropout_prob
														snake_case_           =		attention_probs_dropout_prob
														snake_case_           =		max_position_embeddings
														snake_case_           =		type_vocab_size
														snake_case_           =		type_sequence_label_size
														snake_case_           =		initializer_range
														snake_case_           =		num_labels
														snake_case_           =		scope
														snake_case_           =		range_bbox



							def 		lowerCAmelCase       (				self							:      Tuple       )					->int:



														"""simple docstring"""



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

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

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

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

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

														snake_case_           =		self.get_config()

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



							def 		lowerCAmelCase       (				self							:      int       )					->Optional[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 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[int]		,       )					->str:



														"""simple docstring"""



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

														self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, self.seq_length, self.hidden_size)       )
														self.parent.assertEqual(result.pooler_output.shape		,       (self.batch_size, self.hidden_size)       )



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[Any]		,       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.num_labels
														snake_case_           =		LiltForTokenClassification(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(
														    UpperCAmelCase_		,       bbox=UpperCAmelCase_		,       attention_mask=UpperCAmelCase_		,       token_type_ids=UpperCAmelCase_		,       labels=UpperCAmelCase_       )
														self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.seq_length, self.num_labels)       )



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      List[str]		,       )					->Dict:



														"""simple docstring"""



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



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



														"""simple docstring"""



														snake_case_           =		self.prepare_config_and_inputs()
														(
														    (
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						
														)           =		config_and_inputs
														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      __A     (snake_case__						,		snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Optional[int]							      =    (
							    (
							        LiltModel,
							        LiltForSequenceClassification,
							        LiltForTokenClassification,
							        LiltForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							__lowercase:		Optional[Any]							      =    (
							    {
							        """feature-extraction""": LiltModel,
							        """question-answering""": LiltForQuestionAnswering,
							        """text-classification""": LiltForSequenceClassification,
							        """token-classification""": LiltForTokenClassification,
							        """zero-shot""": LiltForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__lowercase:		Union[str, Any]							      =    False
							__lowercase:		List[str]							      =    False



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Optional[int]       )					->Optional[int]:



														"""simple docstring"""



														return True



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



														"""simple docstring"""



														snake_case_           =		LiltModelTester(self       )
														snake_case_           =		ConfigTester(self		,       config_class=UpperCAmelCase_		,       hidden_size=37       )



							def 		lowerCAmelCase       (				self							:      str       )					->List[Any]:



														"""simple docstring"""



														self.config_tester.run_common_tests()



							def 		lowerCAmelCase       (				self							:      List[str]       )					->int:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase_       )



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



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_       )



							@slow
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



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





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





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



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

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

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

														snake_case_           =		torch.Size([1, 2, 768]       )
														snake_case_           =		torch.tensor(
														    [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]]		,       device=UpperCAmelCase_		,       )

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



