




"""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        lowercase      :







				def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					Any=13      ,     _lowerCamelCase   :					Optional[int]=7      ,     _lowerCamelCase   :					int=True      ,     _lowerCamelCase   :					Optional[int]=True      ,     _lowerCamelCase   :					Optional[int]=True      ,     _lowerCamelCase   :					int=True      ,     _lowerCamelCase   :					Tuple=99      ,     _lowerCamelCase   :					Any=24      ,     _lowerCamelCase   :					Optional[Any]=2      ,     _lowerCamelCase   :					Dict=6      ,     _lowerCamelCase   :					List[Any]=37      ,     _lowerCamelCase   :					Any="gelu"      ,     _lowerCamelCase   :					Tuple=0.1      ,     _lowerCamelCase   :					Any=0.1      ,     _lowerCamelCase   :					Any=5_12      ,     _lowerCamelCase   :					Optional[int]=16      ,     _lowerCamelCase   :					Union[str, Any]=2      ,     _lowerCamelCase   :					Tuple=0.02      ,     _lowerCamelCase   :					Union[str, Any]=3      ,     _lowerCamelCase   :					Any=None      ,     _lowerCamelCase   :					List[Any]=10_00      ,     ):



					"""simple docstring"""

					A_  :				Any	   =		parent
					A_  :				str	   =		batch_size
					A_  :				List[Any]	   =		seq_length
					A_  :				Optional[Any]	   =		is_training
					A_  :				Tuple	   =		use_input_mask
					A_  :				List[str]	   =		use_token_type_ids
					A_  :				Dict	   =		use_labels
					A_  :				Dict	   =		vocab_size
					A_  :				int	   =		hidden_size
					A_  :				List[Any]	   =		num_hidden_layers
					A_  :				Any	   =		num_attention_heads
					A_  :				Optional[int]	   =		intermediate_size
					A_  :				Tuple	   =		hidden_act
					A_  :				Union[str, Any]	   =		hidden_dropout_prob
					A_  :				int	   =		attention_probs_dropout_prob
					A_  :				Optional[Any]	   =		max_position_embeddings
					A_  :				Union[str, Any]	   =		type_vocab_size
					A_  :				Dict	   =		type_sequence_label_size
					A_  :				Tuple	   =		initializer_range
					A_  :				Tuple	   =		num_labels
					A_  :				Union[str, Any]	   =		scope
					A_  :				Union[str, Any]	   =		range_bbox







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				Any	   =		ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size       )

					A_  :				str	   =		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]:
								A_  :				Optional[Any]	   =		bbox[i, j, 3]
								A_  :				int	   =		bbox[i, j, 1]
								A_  :				int	   =		t
							if bbox[i, j, 2] < bbox[i, j, 0]:
								A_  :				Optional[int]	   =		bbox[i, j, 2]
								A_  :				List[Any]	   =		bbox[i, j, 0]
								A_  :				Optional[int]	   =		t

					A_  :				List[str]	   =		None
					if self.use_input_mask:
						A_  :				Tuple	   =		ids_tensor([self.batch_size, self.seq_length]      ,     vocab_size=2       )

					A_  :				List[Any]	   =		None
					if self.use_token_type_ids:
						A_  :				Optional[int]	   =		ids_tensor([self.batch_size, self.seq_length]      ,     self.type_vocab_size       )

					A_  :				int	   =		None
					A_  :				Dict	   =		None
					if self.use_labels:
						A_  :				Union[str, Any]	   =		ids_tensor([self.batch_size]      ,     self.type_sequence_label_size       )
						A_  :				Tuple	   =		ids_tensor([self.batch_size, self.seq_length]      ,     self.num_labels       )

					A_  :				List[str]	   =		self.get_config()

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







				def 					a_  (							self   :					Tuple       ):



					"""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 					a_  (							self   :					Optional[Any]      ,     _lowerCamelCase   :					Optional[Any]      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					Optional[Any]      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					int      ,     ):



					"""simple docstring"""

					A_  :				List[Any]	   =		LiltModel(config=_lowerCamelCase       )
					model.to(_lowerCamelCase       )
					model.eval()
					A_  :				Dict	   =		model(_lowerCamelCase      ,     bbox=_lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		model(_lowerCamelCase      ,     bbox=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase       )
					A_  :				List[str]	   =		model(_lowerCamelCase      ,     bbox=_lowerCamelCase       )

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







				def 					a_  (							self   :					int      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					Optional[int]      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					List[str]      ,     ):



					"""simple docstring"""

					A_  :				int	   =		self.num_labels
					A_  :				Dict	   =		LiltForTokenClassification(config=_lowerCamelCase       )
					model.to(_lowerCamelCase       )
					model.eval()
					A_  :				int	   =		model(
					    _lowerCamelCase      ,     bbox=_lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase      ,     labels=_lowerCamelCase       )
					self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.seq_length, self.num_labels)       )







				def 					a_  (							self   :					List[str]      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					Optional[Any]      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					List[Any]      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					str      ,     ):



					"""simple docstring"""

					A_  :				List[Any]	   =		LiltForQuestionAnswering(config=_lowerCamelCase       )
					model.to(_lowerCamelCase       )
					model.eval()
					A_  :				Any	   =		model(
					    _lowerCamelCase      ,     bbox=_lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     token_type_ids=_lowerCamelCase      ,     start_positions=_lowerCamelCase      ,     end_positions=_lowerCamelCase      ,     )
					self.parent.assertEqual(result.start_logits.shape      ,     (self.batch_size, self.seq_length)       )
					self.parent.assertEqual(result.end_logits.shape      ,     (self.batch_size, self.seq_length)       )







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_  :				str	   =		self.prepare_config_and_inputs()
					(
					    (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , 
					)  :				Optional[Any]	   =		config_and_inputs
					A_  :				Dict	   =		{
					    '''input_ids''': input_ids,
					    '''bbox''': bbox,
					    '''token_type_ids''': token_type_ids,
					    '''attention_mask''': input_mask,
					}
					return config, inputs_dict

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







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					List[Any]      ,     _lowerCamelCase   :					Any       ):



					"""simple docstring"""

					return True







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Tuple	   =		LiltModelTester(self       )
					A_  :				Optional[int]	   =		ConfigTester(self      ,     config_class=_lowerCamelCase      ,     hidden_size=37       )







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					self.config_tester.run_common_tests()







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				str	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_lowerCamelCase       )







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
						A_  :				Tuple	   =		type
						self.model_tester.create_and_check_model(*_lowerCamelCase       )







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase       )







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				int	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase       )







				@slow
				def 					a_  (							self   :					str       ):



					"""simple docstring"""

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

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







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Dict	   =		LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''       ).to(_lowerCamelCase       )

					A_  :				int	   =		torch.tensor([[1, 2]]      ,     device=_lowerCamelCase       )
					A_  :				Tuple	   =		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]      ,     device=_lowerCamelCase       )

					# forward pass
					with torch.no_grad():
						A_  :				Optional[int]	   =		model(input_ids=_lowerCamelCase      ,     bbox=_lowerCamelCase       )

					A_  :				List[str]	   =		torch.Size([1, 2, 7_68]       )
					A_  :				Tuple	   =		torch.tensor(
					    [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]      ,     device=_lowerCamelCase      ,     )

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











"""simple docstring"""

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		[
	    '''decoder.version''',
	    '''decoder.output_projection.weight''',
	    '''_float_tensor''',
	    '''decoder.embed_positions._float_tensor''',
	]
	for k in ignore_keys:
		state_dict.pop(_UpperCAmelCase  ,      _UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_   , A_  :				List[Any]	   =		emb.weight.shape
	A_  :				List[Any]	   =		nn.Linear(_UpperCAmelCase  ,      _UpperCAmelCase  ,      bias=_UpperCAmelCase	)
	A_  :				Any	   =		emb.weight.data
	return lin_layer

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
	A_  :				Any	   =		Namespace(**checkpoint['''cfg''']['''model''']	)
	A_  :				List[str]	   =		checkpoint['''model''']
	remove_ignore_keys_(_UpperCAmelCase	)
	A_  :				Union[str, Any]	   =		state_dict['''decoder.embed_tokens.weight'''].shape[0]

	A_  :				List[str]	   =		{key.replace('''decoder'''  ,      '''model'''	): val for key, val in state_dict.items()}

	A_  :				int	   =		XGLMConfig(
	    vocab_size=_UpperCAmelCase  ,      max_position_embeddings=args.max_target_positions  ,      num_layers=args.decoder_layers  ,      attention_heads=args.decoder_attention_heads  ,      ffn_dim=args.decoder_ffn_embed_dim  ,      d_model=args.decoder_embed_dim  ,      layerdrop=args.decoder_layerdrop  ,      dropout=args.dropout  ,      attention_dropout=args.attention_dropout  ,      activation_dropout=args.activation_dropout  ,      activation_function='''gelu'''  ,      scale_embedding=not args.no_scale_embedding  ,      tie_word_embeddings=args.share_decoder_input_output_embed  ,      )

	A_  :				Any	   =		XGLMForCausalLM(_UpperCAmelCase	)
	A_  :				int	   =		model.load_state_dict(_UpperCAmelCase  ,      strict=_UpperCAmelCase	)
	print(_UpperCAmelCase	)
	A_  :				int	   =		make_linear_from_emb(model.model.embed_tokens	)

	return model


if __name__ == "__main__":
		_lowerCamelCase  :			Any        =       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
		parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		_lowerCamelCase  :			int        =       parser.parse_args()
		_lowerCamelCase  :			Optional[Any]        =       convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





