





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_A					:    Union[str, Any]   =  {"LayoutLMv2Config", "LayoutLMv3Config"}


@is_pipeline_test
class    __SCREAMING_SNAKE_CASE			( unittest.TestCase					):
       _UpperCAmelCase      :  Union[str, Any]     =    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       _UpperCAmelCase      :  Tuple     =    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              _UpperCAmelCase      :  Optional[Any]     =    {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              _UpperCAmelCase      :  Any     =    {
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }







       @require_torch
       def 						__lowerCamelCase    (      self     :	Any			)   ->Dict:
          lowerCamelCase__   :						List[Any]					   =							pipeline(
              task='''text-classification'''		,					model='''hf-internal-testing/tiny-random-distilbert'''		,					framework='''pt'''			)

          lowerCamelCase__   :						Any					   =							text_classifier('''This is great !'''			)
          self.assertEqual(nested_simplify(A			)		,					[{'''label''': '''LABEL_0''', '''score''': 0.5_04}]			)

          lowerCamelCase__   :						str					   =							text_classifier('''This is great !'''		,					top_k=2			)
          self.assertEqual(
              nested_simplify(A			)		,					[{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}]			)

          lowerCamelCase__   :						Optional[int]					   =							text_classifier(['''This is great !''', '''This is bad''']		,					top_k=2			)
          self.assertEqual(
              nested_simplify(A			)		,					[
                  [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}],
                  [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}],
              ]		,					)

          lowerCamelCase__   :						Optional[int]					   =							text_classifier('''This is great !'''		,					top_k=1			)
          self.assertEqual(nested_simplify(A			)		,					[{'''label''': '''LABEL_0''', '''score''': 0.5_04}]			)

          # Legacy behavior
          lowerCamelCase__   :						Tuple					   =							text_classifier('''This is great !'''		,					return_all_scores=A			)
          self.assertEqual(nested_simplify(A			)		,					[{'''label''': '''LABEL_0''', '''score''': 0.5_04}]			)

          lowerCamelCase__   :						str					   =							text_classifier('''This is great !'''		,					return_all_scores=A			)
          self.assertEqual(
              nested_simplify(A			)		,					[[{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}]]			)

          lowerCamelCase__   :						List[str]					   =							text_classifier(['''This is great !''', '''Something else''']		,					return_all_scores=A			)
          self.assertEqual(
              nested_simplify(A			)		,					[
                  [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}],
                  [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}],
              ]		,					)

          lowerCamelCase__   :						Any					   =							text_classifier(['''This is great !''', '''Something else''']		,					return_all_scores=A			)
          self.assertEqual(
              nested_simplify(A			)		,					[
                  {'''label''': '''LABEL_0''', '''score''': 0.5_04},
                  {'''label''': '''LABEL_0''', '''score''': 0.5_04},
              ]		,					)







       @require_torch
       def 						__lowerCamelCase    (      self     :	Optional[Any]			)   ->Union[str, Any]:
          import torch

          lowerCamelCase__   :						List[str]					   =							pipeline(
              task='''text-classification'''		,					model='''hf-internal-testing/tiny-random-distilbert'''		,					framework='''pt'''		,					device=torch.device('''cpu'''			)		,					)

          lowerCamelCase__   :						str					   =							text_classifier('''This is great !'''			)
          self.assertEqual(nested_simplify(A			)		,					[{'''label''': '''LABEL_0''', '''score''': 0.5_04}]			)







       @require_tf
       def 						__lowerCamelCase    (      self     :	List[Any]			)   ->int:
          lowerCamelCase__   :						int					   =							pipeline(
              task='''text-classification'''		,					model='''hf-internal-testing/tiny-random-distilbert'''		,					framework='''tf'''			)

          lowerCamelCase__   :						Tuple					   =							text_classifier('''This is great !'''			)
          self.assertEqual(nested_simplify(A			)		,					[{'''label''': '''LABEL_0''', '''score''': 0.5_04}]			)







       @slow
       @require_torch
       def 						__lowerCamelCase    (      self     :	Any			)   ->Optional[int]:
          lowerCamelCase__   :						Optional[Any]					   =							pipeline('''text-classification'''			)

          lowerCamelCase__   :						Any					   =							text_classifier('''This is great !'''			)
          self.assertEqual(nested_simplify(A			)		,					[{'''label''': '''POSITIVE''', '''score''': 1.0}]			)
          lowerCamelCase__   :						Dict					   =							text_classifier('''This is bad !'''			)
          self.assertEqual(nested_simplify(A			)		,					[{'''label''': '''NEGATIVE''', '''score''': 1.0}]			)
          lowerCamelCase__   :						Union[str, Any]					   =							text_classifier('''Birds are a type of animal'''			)
          self.assertEqual(nested_simplify(A			)		,					[{'''label''': '''POSITIVE''', '''score''': 0.9_88}]			)







       @slow
       @require_tf
       def 						__lowerCamelCase    (      self     :	int			)   ->Tuple:
          lowerCamelCase__   :						str					   =							pipeline('''text-classification'''		,					framework='''tf'''			)

          lowerCamelCase__   :						List[Any]					   =							text_classifier('''This is great !'''			)
          self.assertEqual(nested_simplify(A			)		,					[{'''label''': '''POSITIVE''', '''score''': 1.0}]			)
          lowerCamelCase__   :						int					   =							text_classifier('''This is bad !'''			)
          self.assertEqual(nested_simplify(A			)		,					[{'''label''': '''NEGATIVE''', '''score''': 1.0}]			)
          lowerCamelCase__   :						List[str]					   =							text_classifier('''Birds are a type of animal'''			)
          self.assertEqual(nested_simplify(A			)		,					[{'''label''': '''POSITIVE''', '''score''': 0.9_88}]			)







       def 						__lowerCamelCase    (      self     :	Tuple		,					A     :	List[str]		,					A     :	str		,					A     :	Any			)   ->List[Any]:
          lowerCamelCase__   :						Any					   =							TextClassificationPipeline(model=A		,					tokenizer=A			)
          return text_classifier, ["HuggingFace is in", "This is another test"]







       def 						__lowerCamelCase    (      self     :	Dict		,					A     :	List[Any]		,					A     :	Dict			)   ->Optional[Any]:
          lowerCamelCase__   :						Optional[int]					   =							text_classifier.model
          # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
          lowerCamelCase__   :						List[Any]					   =							'''HuggingFace is in'''
          lowerCamelCase__   :						int					   =							text_classifier(A			)

          self.assertEqual(nested_simplify(A			)		,					[{'''label''': ANY(A			), '''score''': ANY(A			)}]			)
          self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()			)

          lowerCamelCase__   :						Tuple					   =							['''HuggingFace is in ''', '''Paris is in France''']
          lowerCamelCase__   :						int					   =							text_classifier(A			)
          self.assertEqual(
              nested_simplify(A			)		,					[{'''label''': ANY(A			), '''score''': ANY(A			)}, {'''label''': ANY(A			), '''score''': ANY(A			)}]		,					)
          self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()			)
          self.assertTrue(outputs[1]['''label'''] in model.config.idalabel.values()			)

          # Forcing to get all results with `top_k=None`
          # This is NOT the legacy format
          lowerCamelCase__   :						str					   =							text_classifier(A		,					top_k=A			)
          lowerCamelCase__   :						Optional[int]					   =							len(model.config.idalabel.values()			)
          self.assertEqual(
              nested_simplify(A			)		,					[[{'''label''': ANY(A			), '''score''': ANY(A			)}] * N, [{'''label''': ANY(A			), '''score''': ANY(A			)}] * N]		,					)

          lowerCamelCase__   :						List[str]					   =							{'''text''': '''HuggingFace is in ''', '''text_pair''': '''Paris is in France'''}
          lowerCamelCase__   :						Any					   =							text_classifier(A			)
          self.assertEqual(
              nested_simplify(A			)		,					{'''label''': ANY(A			), '''score''': ANY(A			)}		,					)
          self.assertTrue(outputs['''label'''] in model.config.idalabel.values()			)

          # This might be used a text pair, but tokenizer + pipe interaction
          # makes it hard to understand that it's not using the pair properly
          # https://github.com/huggingface/transformers/issues/17305
          # We disabled this usage instead as it was outputting wrong outputs.
          lowerCamelCase__   :						Optional[int]					   =							[['''HuggingFace is in ''', '''Paris is in France''']]
          with self.assertRaises(A			):
             text_classifier(A			)

          # This used to be valid for doing text pairs
          # We're keeping it working because of backward compatibility
          lowerCamelCase__   :						List[Any]					   =							text_classifier([[['''HuggingFace is in ''', '''Paris is in France''']]]			)
          self.assertEqual(
              nested_simplify(A			)		,					[{'''label''': ANY(A			), '''score''': ANY(A			)}]		,					)
          self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()			)









import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 		__lowerCAmelCase	(	pl.LightningModule ):
							def __init__(						self						:Union[str, Any]		,      __magic_name__						:Optional[int]				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								pass







def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[int]:
	# load longformer model from model identifier
	a										=							LongformerModel.from_pretrained(__lowerCamelCase    )
	a										=							LightningModel(__lowerCamelCase    )

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

	# init longformer question answering model
	a										=							LongformerForQuestionAnswering.from_pretrained(__lowerCamelCase    )

	# 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(__lowerCamelCase    )

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


if __name__ == "__main__":
		__UpperCamelCase					:       List[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."
		)
		__UpperCamelCase					:       List[Any]    					=      parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)

