


'''simple docstring'''

import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow





@is_pipeline_test
@require_torch
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  @require_torch
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  pipeline(
       task="zero-shot-audio-classification"			,  model="hf-internal-testing/tiny-clap-htsat-unfused"     )
   lowerCamelCase_      =  load_dataset("ashraq/esc50"     )
   lowerCamelCase_      =  dataset["train"]["audio"][-1]["array"]
   lowerCamelCase_      =  audio_classifier(__a			,  candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]     )
   self.assertEqual(
       nested_simplify(__a     )			,  [{"score": 0.501, "label": "Sound of a dog"}, {"score": 0.499, "label": "Sound of vaccum cleaner"}]			,  )






  @unittest.skip("No models are available in TF"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  @slow
  @require_torch
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  pipeline(
       task="zero-shot-audio-classification"			,  model="laion/clap-htsat-unfused"			,  )
   # This is an audio of a dog
   lowerCamelCase_      =  load_dataset("ashraq/esc50"     )
   lowerCamelCase_      =  dataset["train"]["audio"][-1]["array"]
   lowerCamelCase_      =  audio_classifier(__a			,  candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]     )

   self.assertEqual(
       nested_simplify(__a     )			,  [
           {"score": 0.999, "label": "Sound of a dog"},
           {"score": 0.001, "label": "Sound of vaccum cleaner"},
       ]			,  )

   lowerCamelCase_      =  audio_classifier([audio] * 5			,  candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]     )
   self.assertEqual(
       nested_simplify(__a     )			,  [
           [
               {"score": 0.999, "label": "Sound of a dog"},
               {"score": 0.001, "label": "Sound of vaccum cleaner"},
           ],
       ]
       * 5			,  )
   lowerCamelCase_      =  audio_classifier(
       [audio] * 5			,  candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]			,  batch_size=5     )
   self.assertEqual(
       nested_simplify(__a     )			,  [
           [
               {"score": 0.999, "label": "Sound of a dog"},
               {"score": 0.001, "label": "Sound of vaccum cleaner"},
           ],
       ]
       * 5			,  )







  @unittest.skip("No models are available in TF"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass







"""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
	)




