


import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase=13,      lowerCamelCase=7,      lowerCamelCase=True,      lowerCamelCase=True,      lowerCamelCase=True,      lowerCamelCase=True,      lowerCamelCase=99,      lowerCamelCase=32,      lowerCamelCase=5,      lowerCamelCase=4,      lowerCamelCase=37,      lowerCamelCase="gelu",      lowerCamelCase=0.1,      lowerCamelCase=0.1,      lowerCamelCase=5_12,      lowerCamelCase=16,      lowerCamelCase=2,      lowerCamelCase=0.0_2,      lowerCamelCase=False,      lowerCamelCase=True,      lowerCamelCase="None",      lowerCamelCase=3,      lowerCamelCase=4,      lowerCamelCase=None,      )       ->   str:




           """simple docstring"""

           _lowercase       :							Dict      		=		parent
           _lowercase       :							Optional[int]      		=		batch_size
           _lowercase       :							Tuple      		=		seq_length
           _lowercase       :							Tuple      		=		is_training
           _lowercase       :							Any      		=		use_input_mask
           _lowercase       :							Optional[Any]      		=		use_token_type_ids
           _lowercase       :							Tuple      		=		use_labels
           _lowercase       :							Any      		=		vocab_size
           _lowercase       :							int      		=		hidden_size
           _lowercase       :							Optional[Any]      		=		num_hidden_layers
           _lowercase       :							Optional[int]      		=		num_attention_heads
           _lowercase       :							Union[str, Any]      		=		intermediate_size
           _lowercase       :							str      		=		hidden_act
           _lowercase       :							List[str]      		=		hidden_dropout_prob
           _lowercase       :							Tuple      		=		attention_probs_dropout_prob
           _lowercase       :							Tuple      		=		max_position_embeddings
           _lowercase       :							int      		=		type_vocab_size
           _lowercase       :							Tuple      		=		type_sequence_label_size
           _lowercase       :							Any      		=		initializer_range
           _lowercase       :							Optional[Any]      		=		num_labels
           _lowercase       :							Tuple      		=		num_choices
           _lowercase       :							Optional[int]      		=		relative_attention
           _lowercase       :							Optional[int]      		=		position_biased_input
           _lowercase       :							Tuple      		=		pos_att_type
           _lowercase       :							Dict      		=		scope
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							str      		=		ids_tensor([self.batch_size, self.seq_length],      self.vocab_size)

           _lowercase       :							Any      		=		None
           if self.use_input_mask:
               _lowercase       :							Optional[int]      		=		ids_tensor([self.batch_size, self.seq_length],      vocab_size=2)

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

           _lowercase       :							List[str]      		=		None
           _lowercase       :							Optional[int]      		=		None
           _lowercase       :							List[Any]      		=		None
           if self.use_labels:
               _lowercase       :							Union[str, Any]      		=		ids_tensor([self.batch_size],      self.type_sequence_label_size)
               _lowercase       :							Any      		=		ids_tensor([self.batch_size, self.seq_length],      self.num_labels)
               _lowercase       :							Optional[Any]      		=		ids_tensor([self.batch_size],      self.num_choices)

           _lowercase       :							Optional[Any]      		=		self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           return DebertaConfig(
               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,      relative_attention=self.relative_attention,      position_biased_input=self.position_biased_input,      pos_att_type=self.pos_att_type,      )
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.get_config()
           _lowercase       :							str      		=		3_00
           return config
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Tuple:




           """simple docstring"""

           self.parent.assertListEqual(list(result.loss.size()),      [])
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		DebertaModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Optional[int]      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase,      token_type_ids=lowerCamelCase)[0]
           _lowercase       :							Union[str, Any]      		=		model(lowerCamelCase,      token_type_ids=lowerCamelCase)[0]
           _lowercase       :							Any      		=		model(lowerCamelCase)[0]

           self.parent.assertListEqual(list(sequence_output.size()),      [self.batch_size, self.seq_length, self.hidden_size])
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		DebertaForMaskedLM(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							str      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase,      token_type_ids=lowerCamelCase,      labels=lowerCamelCase)

           self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.vocab_size))
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.num_labels
           _lowercase       :							int      		=		DebertaForSequenceClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Dict      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase,      token_type_ids=lowerCamelCase,      labels=lowerCamelCase)
           self.parent.assertListEqual(list(result.logits.size()),      [self.batch_size, self.num_labels])
           self.check_loss_output(lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.num_labels
           _lowercase       :							Optional[int]      		=		DebertaForTokenClassification(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							List[str]      		=		model(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 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   int:




           """simple docstring"""

           _lowercase       :							Tuple      		=		DebertaForQuestionAnswering(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase       :							Optional[Any]      		=		model(
               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 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.prepare_config_and_inputs()
           (
               (
               _lowercase
           )		,				(
               _lowercase
           )		,				(
               _lowercase
           )		,				(
               _lowercase
           )		,				(
               _lowercase
           )		,				(
               _lowercase
           )		,				(
               _lowercase
           )		,				
           )       :							int      		=		config_and_inputs
           _lowercase       :							Dict      		=		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(						_a, _a, unittest.TestCase   ):
       lowercase_ :	Optional[int]     =			(
           (
               DebertaModel,
               DebertaForMaskedLM,
               DebertaForSequenceClassification,
               DebertaForTokenClassification,
               DebertaForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       lowercase_ :	Dict     =			(
           {
               """feature-extraction""": DebertaModel,
               """fill-mask""": DebertaForMaskedLM,
               """question-answering""": DebertaForQuestionAnswering,
               """text-classification""": DebertaForSequenceClassification,
               """token-classification""": DebertaForTokenClassification,
               """zero-shot""": DebertaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )

       lowercase_ :	Union[str, Any]     =			True
       lowercase_ :	Optional[int]     =			False
       lowercase_ :	Union[str, Any]     =			False
       lowercase_ :	Optional[int]     =			False
       lowercase_ :	Dict     =			False
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							Any      		=		DebertaModelTester(self)
           _lowercase       :							List[str]      		=		ConfigTester(self,      config_class=lowerCamelCase,      hidden_size=37)
       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							Tuple      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_model(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							Any      		=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase)




       @slow
       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase       :							Optional[int]      		=		DebertaModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)






@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase(						unittest.TestCase   ):
       @unittest.skip(reason='Model not available yet')
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           pass




       @slow
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		DebertaModel.from_pretrained('microsoft/deberta-base')

           _lowercase       :							List[Any]      		=		torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
           _lowercase       :							int      		=		torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
           with torch.no_grad():
               _lowercase       :							List[str]      		=		model(lowerCamelCase,      attention_mask=lowerCamelCase)[0]
           # compare the actual values for a slice.
           _lowercase       :							Optional[Any]      		=		torch.tensor(
               [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]])
           self.assertTrue(torch.allclose(output[:, 1:4, 1:4],      lowerCamelCase,      atol=1E-4),      F'''{output[:, 1:4, 1:4]}''')






"""simple docstring"""


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel
class   _UpperCamelCase  (   pl.LightningModule		):
							'''simple docstring'''







							def __init__(     self    ,   __a	):
										super().__init__()
										__lowerCAmelCase				    =			model
										__lowerCAmelCase				    =			2
										__lowerCAmelCase				    =			nn.Linear(self.model.config.hidden_size    ,   self.num_labels	)



							def 			snake_case				(     self	):
										pass


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			LongformerModel.from_pretrained(_UpperCamelCase      )
			__lowerCAmelCase				    =			LightningModel(_UpperCamelCase      )

			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location=torch.device("cpu"      )      )
			lightning_model.load_state_dict(ckpt["state_dict"]      )

			# init longformer question answering model
			__lowerCAmelCase				    =			LongformerForQuestionAnswering.from_pretrained(_UpperCamelCase      )

			# transfer weights
			longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()      )
			longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()      )
			longformer_for_qa.eval()

			# save model
			longformer_for_qa.save_pretrained(_UpperCamelCase      )

			print(f"Conversion successful. Model saved under {pytorch_dump_folder_path}"      )


if __name__ == "__main__":
	A     :   Tuple			    =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--longformer_model",
	    default=None,
	    type=str,
	    required=True,
	    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
	)
	parser.add_argument(
	    "--longformer_question_answering_ckpt_path",
	    default=None,
	    type=str,
	    required=True,
	    help="Path the official PyTorch Lightning Checkpoint.",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
	)
	A     :   Optional[int]			    =			parser.parse_args()
	convert_longformer_qa_checkpoint_to_pytorch(
	    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
	)




