



'''simple docstring'''

import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
				import numpy as np
				import torch

				from . import (
				    AlbertForPreTraining,
				    BartForConditionalGeneration,
				    BertForPreTraining,
				    BertForQuestionAnswering,
				    BertForSequenceClassification,
				    CamembertForMaskedLM,
				    CTRLLMHeadModel,
				    DistilBertForMaskedLM,
				    DistilBertForQuestionAnswering,
				    DPRContextEncoder,
				    DPRQuestionEncoder,
				    DPRReader,
				    ElectraForPreTraining,
				    FlaubertWithLMHeadModel,
				    GPTaLMHeadModel,
				    LayoutLMForMaskedLM,
				    LxmertForPreTraining,
				    LxmertVisualFeatureEncoder,
				    OpenAIGPTLMHeadModel,
				    RobertaForMaskedLM,
				    RobertaForSequenceClassification,
				    TaForConditionalGeneration,
				    TransfoXLLMHeadModel,
				    XLMRobertaForMaskedLM,
				    XLMWithLMHeadModel,
				    XLNetLMHeadModel,
				)


logging.set_verbosity_info()

UpperCAmelCase     		=   {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						int=False							,		_SCREAMING_SNAKE_CASE					:						str=True							)      ->						Tuple:







	"""simple docstring"""
	if model_type not in MODEL_CLASSES:
		raise ValueError(f'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()							)}.'							)

	lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     MODEL_CLASSES[model_type]

	# Initialise TF model
	if config_file in aws_config_map:
		lowerCAmelCase   =     cached_file(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		force_download=not use_cached_models							)
	lowerCAmelCase   =     config_class.from_json_file(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     True
	lowerCAmelCase   =     True
	print(f'Building TensorFlow model from configuration: {config}'							)
	lowerCAmelCase   =     model_class(_SCREAMING_SNAKE_CASE							)

	# Load weights from tf checkpoint
	if pytorch_checkpoint_path in aws_config_map.keys():
		lowerCAmelCase   =     cached_file(
		    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		force_download=not use_cached_models							)
	# Load PyTorch checkpoint in tf2 model:
	lowerCAmelCase   =     load_pytorch_checkpoint_in_tfa_model(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	if compare_with_pt_model:
		lowerCAmelCase   =     tf_model(tf_model.dummy_inputs							,		training=_SCREAMING_SNAKE_CASE							)  # build the network

		lowerCAmelCase   =     torch.load(_SCREAMING_SNAKE_CASE							,		map_location="""cpu"""							)
		lowerCAmelCase   =     pt_model_class.from_pretrained(
		    pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE							,		config=_SCREAMING_SNAKE_CASE							,		state_dict=_SCREAMING_SNAKE_CASE							)

		with torch.no_grad():
			lowerCAmelCase   =     pt_model(**pt_model.dummy_inputs							)

		lowerCAmelCase   =     pto[0].numpy()
		lowerCAmelCase   =     tfo[0].numpy()
		lowerCAmelCase   =     np.amax(np.abs(np_pt - np_tf							)							)
		print(f'Max absolute difference between models outputs {diff}'							)
		assert diff <= 2E-2, f'Error, model absolute difference is >2e-2: {diff}'

	# Save pytorch-model
	print(f'Save TensorFlow model to {tf_dump_path}'							)
	tf_model.save_weights(_SCREAMING_SNAKE_CASE							,		save_format="""h5"""							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Optional[int]=None							,		_SCREAMING_SNAKE_CASE					:						int=None							,		_SCREAMING_SNAKE_CASE					:						Dict=False							,		_SCREAMING_SNAKE_CASE					:						str=False							,		_SCREAMING_SNAKE_CASE					:						Union[str, Any]=False							,		_SCREAMING_SNAKE_CASE					:						Dict=False							,		)      ->						int:







	"""simple docstring"""
	if args_model_type is None:
		lowerCAmelCase   =     list(MODEL_CLASSES.keys()							)
	else:
		lowerCAmelCase   =     [args_model_type]

	for j, model_type in enumerate(_SCREAMING_SNAKE_CASE							,		start=1							):
		print("""=""" * 100							)
		print(f' Converting model type {j}/{len(_SCREAMING_SNAKE_CASE							)}: {model_type}'							)
		print("""=""" * 100							)
		if model_type not in MODEL_CLASSES:
			raise ValueError(f'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()							)}.'							)

		lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     MODEL_CLASSES[model_type]

		if model_shortcut_names_or_path is None:
			lowerCAmelCase   =     list(aws_model_maps.keys()							)
		if config_shortcut_names_or_path is None:
			lowerCAmelCase   =     model_shortcut_names_or_path

		for i, (model_shortcut_name, config_shortcut_name) in enumerate(
		    zip(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)							,		start=1							):
			print("""-""" * 100							)
			if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
				if not only_convert_finetuned_models:
					print(f'    Skipping finetuned checkpoint {model_shortcut_name}'							)
					continue
				lowerCAmelCase   =     model_shortcut_name
			elif only_convert_finetuned_models:
				print(f'    Skipping not finetuned checkpoint {model_shortcut_name}'							)
				continue
			print(
			    f'    Converting checkpoint {i}/{len(_SCREAMING_SNAKE_CASE							)}: {model_shortcut_name} - model_type {model_type}'							)
			print("""-""" * 100							)

			if config_shortcut_name in aws_config_map:
				lowerCAmelCase   =     cached_file(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		force_download=not use_cached_models							)
			else:
				lowerCAmelCase   =     config_shortcut_name

			if model_shortcut_name in aws_model_maps:
				lowerCAmelCase   =     cached_file(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		force_download=not use_cached_models							)
			else:
				lowerCAmelCase   =     model_shortcut_name

			if os.path.isfile(_SCREAMING_SNAKE_CASE							):
				lowerCAmelCase   =     """converted_model"""

			convert_pt_checkpoint_to_tf(
			    model_type=_SCREAMING_SNAKE_CASE							,		pytorch_checkpoint_path=_SCREAMING_SNAKE_CASE							,		config_file=_SCREAMING_SNAKE_CASE							,		tf_dump_path=os.path.join(_SCREAMING_SNAKE_CASE							,		model_shortcut_name + """-tf_model.h5"""							)							,		compare_with_pt_model=_SCREAMING_SNAKE_CASE							,		)
			if remove_cached_files:
				os.remove(_SCREAMING_SNAKE_CASE							)
				os.remove(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
				UpperCAmelCase     		=   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
				)
				parser.add_argument(
				    '--model_type',
				    default=None,
				    type=str,
				    help=(
				        F'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
				        'convert all the models from AWS.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_checkpoint_path',
				    default=None,
				    type=str,
				    help=(
				        'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
				        'If not given, will download and convert all the checkpoints from AWS.'
				    ),
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    help=(
				        'The config json file corresponding to the pre-trained model. \n'
				        'This specifies the model architecture. If not given and '
				        '--pytorch_checkpoint_path is not given or is a shortcut name '
				        'use the configuration associated to the shortcut name on the AWS'
				    ),
				)
				parser.add_argument(
				    '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
				)
				parser.add_argument(
				    '--use_cached_models',
				    action='store_true',
				    help='Use cached models if possible instead of updating to latest checkpoint versions.',
				)
				parser.add_argument(
				    '--remove_cached_files',
				    action='store_true',
				    help='Remove pytorch models after conversion (save memory when converting in batches).',
				)
				parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
				UpperCAmelCase     		=   parser.parse_args()

				# if args.pytorch_checkpoint_path is not None:
				#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
				#                                 args.pytorch_checkpoint_path,
				#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
				#                                 args.tf_dump_path,
				#                                 compare_with_pt_model=args.compare_with_pt_model,
				#                                 use_cached_models=args.use_cached_models)
				# else:
				convert_all_pt_checkpoints_to_tf(
				    args.model_type.lower() if args.model_type is not None else None,
				    args.tf_dump_path,
				    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
				    if args.pytorch_checkpoint_path is not None
				    else None,
				    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
				    compare_with_pt_model=args.compare_with_pt_model,
				    use_cached_models=args.use_cached_models,
				    remove_cached_files=args.remove_cached_files,
				    only_convert_finetuned_models=args.only_convert_finetuned_models,
				)




'''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 __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=13     ,	A_=7     ,	A_=True     ,	A_=True     ,	A_=True     ,	A_=True     ,	A_=99     ,	A_=24     ,	A_=2     ,	A_=6     ,	A_=37     ,	A_="gelu"     ,	A_=0.1     ,	A_=0.1     ,	A_=512     ,	A_=16     ,	A_=2     ,	A_=0.0_2     ,	A_=3     ,	A_=None     ,	A_=1000     ,	)     ->     Union[str, Any]:
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     batch_size
							lowerCAmelCase   =     seq_length
							lowerCAmelCase   =     is_training
							lowerCAmelCase   =     use_input_mask
							lowerCAmelCase   =     use_token_type_ids
							lowerCAmelCase   =     use_labels
							lowerCAmelCase   =     vocab_size
							lowerCAmelCase   =     hidden_size
							lowerCAmelCase   =     num_hidden_layers
							lowerCAmelCase   =     num_attention_heads
							lowerCAmelCase   =     intermediate_size
							lowerCAmelCase   =     hidden_act
							lowerCAmelCase   =     hidden_dropout_prob
							lowerCAmelCase   =     attention_probs_dropout_prob
							lowerCAmelCase   =     max_position_embeddings
							lowerCAmelCase   =     type_vocab_size
							lowerCAmelCase   =     type_sequence_label_size
							lowerCAmelCase   =     initializer_range
							lowerCAmelCase   =     num_labels
							lowerCAmelCase   =     scope
							lowerCAmelCase   =     range_bbox


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size				)

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

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

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

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

							lowerCAmelCase   =     self.get_config()

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


						def     __snake_case   ( self				)     ->     int:
							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     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	)     ->     int:
							lowerCAmelCase   =     LiltModel(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_     ,	bbox=A_     ,	attention_mask=A_     ,	token_type_ids=A_				)
							lowerCAmelCase   =     model(A_     ,	bbox=A_     ,	token_type_ids=A_				)
							lowerCAmelCase   =     model(A_     ,	bbox=A_				)

							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     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.num_labels
							lowerCAmelCase   =     LiltForTokenClassification(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(
							    A_     ,	bbox=A_     ,	attention_mask=A_     ,	token_type_ids=A_     ,	labels=A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.num_labels)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	)     ->     Optional[Any]:
							lowerCAmelCase   =     LiltForQuestionAnswering(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(
							    A_     ,	bbox=A_     ,	attention_mask=A_     ,	token_type_ids=A_     ,	start_positions=A_     ,	end_positions=A_     ,	)
							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     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       
							)   =     config_and_inputs
							lowerCAmelCase   =     {
							    """input_ids""": input_ids,
							    """bbox""": bbox,
							    """token_type_ids""": token_type_ids,
							    """attention_mask""": input_mask,
							}
							return config, inputs_dict







@require_torch
class __snake_case(				_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



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


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Union[str, Any]:
							return True


						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     LiltModelTester(self				)
							lowerCAmelCase   =     ConfigTester(self     ,	config_class=A_     ,	hidden_size=37				)


						def     __snake_case   ( self				)     ->     Any:
							self.config_tester.run_common_tests()


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_				)


						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
								lowerCAmelCase   =     type
								self.model_tester.create_and_check_model(*A_				)


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*A_				)


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*A_				)




						@slow
						def     __snake_case   ( self				)     ->     Union[str, Any]:
							for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCAmelCase   =     LiltModel.from_pretrained(A_				)
								self.assertIsNotNone(A_				)







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


						'''simple docstring'''



						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""				).to(A_				)

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

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

							lowerCAmelCase   =     torch.Size([1, 2, 768]				)
							lowerCAmelCase   =     torch.tensor(
							    [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]     ,	device=A_     ,	)

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