




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__	:







     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  ,		)  ->   int:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



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

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

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

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

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

          a     =				self.get_config()

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







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''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  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		)  ->   List[str]:



          '''simple docstring'''



          a     =				LiltModel(config=A			)
          model.to(A			)
          model.eval()
          a     =				model(A  ,		bbox=A  ,		attention_mask=A  ,		token_type_ids=A			)
          a     =				model(A  ,		bbox=A  ,		token_type_ids=A			)
          a     =				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       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		)  ->   Any:



          '''simple docstring'''



          a     =				self.num_labels
          a     =				LiltForTokenClassification(config=A			)
          model.to(A			)
          model.eval()
          a     =				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       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				LiltForQuestionAnswering(config=A			)
          model.to(A			)
          model.eval()
          a     =				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       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.prepare_config_and_inputs()
          (
              (
              a				
          )  ,				(
              a				
          )  ,				(
              a				
          )  ,				(
              a				
          )  ,				(
              a				
          )  ,				(
              a				
          )  ,				(
              a				
          )  ,								
          )     =				config_and_inputs
          a     =				{
              "input_ids": input_ids,
              "bbox": bbox,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict







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







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A			)  ->   str:



          '''simple docstring'''



          return True







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          a     =				LiltModelTester(self			)
          a     =				ConfigTester(self  ,		config_class=A  ,		hidden_size=37			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          self.config_tester.run_common_tests()







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A			)







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



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



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







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







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



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

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

          # forward pass
          with torch.no_grad():
               a     =				model(input_ids=A  ,		bbox=A			)

          a     =				torch.Size([1, 2, 768]			)
          a     =				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			)			)









import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class       a__	( pl.LightningModule				):







     def __init__(     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          super().__init__()
          a     =				model
          a     =				2
          a     =				nn.Linear(self.model.config.hidden_size  ,		self.num_labels			)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          pass







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     # load longformer model from model identifier
     a     =				LongformerModel.from_pretrained(__UpperCamelCase)
     a     =				LightningModel(__UpperCamelCase)

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

     # init longformer question answering model
     a     =				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__":
   lowercase__				:      Optional[Any]	   = 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."
   )
   lowercase__				:      Union[str, Any]	   = parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )

