



'''simple docstring'''







import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def  lowerCamelCase_					(       A_    ,				A_				):
	__lowerCamelCase		       =		torch.load(lowerCamelCase_    ,				map_location='''cpu'''				)

	__lowerCamelCase		       =		chkpt['''model''']

	# We have the base model one level deeper than the original XLM repository
	__lowerCamelCase		       =		{}
	for k, v in state_dict.items():
		if "pred_layer" in k:
			__lowerCamelCase		       =		v
		else:
			__lowerCamelCase		       =		v

	__lowerCamelCase		       =		chkpt['''params''']
	__lowerCamelCase		       =		{n: v for n, v in config.items() if not isinstance(lowerCamelCase_    ,				(torch.FloatTensor, numpy.ndarray)				)}

	__lowerCamelCase		       =		chkpt['''dico_word2id''']
	__lowerCamelCase		       =		{s + '''</w>''' if s.find('''@@'''				) == -1 and i > 13 else s.replace('''@@'''    ,				''''''				): i for s, i in vocab.items()}

	# Save pytorch-model
	__lowerCamelCase		       =		pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
	__lowerCamelCase		       =		pytorch_dump_folder_path + '''/''' + CONFIG_NAME
	__lowerCamelCase		       =		pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

	print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''				)
	torch.save(lowerCamelCase_    ,				lowerCamelCase_				)

	print(f'''Save configuration file to {pytorch_config_dump_path}'''				)
	with open(lowerCamelCase_    ,				'''w'''    ,				encoding='''utf-8'''				) as f:
		f.write(json.dumps(lowerCamelCase_    ,				indent=2				) + '''\n'''				)

	print(f'''Save vocab file to {pytorch_config_dump_path}'''				)
	with open(lowerCamelCase_    ,				'''w'''    ,				encoding='''utf-8'''				) as f:
		f.write(json.dumps(lowerCamelCase_    ,				indent=2				) + '''\n'''				)


if __name__ == "__main__":
				_UpperCamelCase					:  Dict        =argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
				)
				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_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)









import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
				lowercase__											=			TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Any,    lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     AudioClassificationPipeline(model=lowerCamelCase,    feature_extractor=lowerCamelCase    )

								# test with a raw waveform
								lowercase__  					=     np.zeros((34_000,)    )
								lowercase__  					=     np.zeros((14_000,)    )
								return audio_classifier, [audioa, audio]




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     examples
								lowercase__  					=     audio_classifier(lowerCamelCase    )
								# by default a model is initialized with num_labels=2
								self.assertEqual(
								    lowerCamelCase,    [
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								    ],    )
								lowercase__  					=     audio_classifier(lowerCamelCase,    top_k=1    )
								self.assertEqual(
								    lowerCamelCase,    [
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								    ],    )

								self.run_torchaudio(lowerCamelCase    )




				@require_torchaudio
				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								import datasets

								# test with a local file
								lowercase__  					=     datasets.load_dataset('''hf-internal-testing/librispeech_asr_dummy''',    '''clean''',    split='''validation'''    )
								lowercase__  					=     dataset[0]['''audio''']['''array''']
								lowercase__  					=     audio_classifier(lowerCamelCase    )
								self.assertEqual(
								    lowerCamelCase,    [
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								    ],    )




				@require_torch
				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     '''anton-l/wav2vec2-random-tiny-classifier'''

								lowercase__  					=     pipeline('''audio-classification''',    model=lowerCamelCase    )

								lowercase__  					=     np.ones((8_000,)    )
								lowercase__  					=     audio_classifier(lowerCamelCase,    top_k=4    )

								lowercase__  					=     [
								    {'''score''': 0.0842, '''label''': '''no'''},
								    {'''score''': 0.0838, '''label''': '''up'''},
								    {'''score''': 0.0837, '''label''': '''go'''},
								    {'''score''': 0.0834, '''label''': '''right'''},
								]
								lowercase__  					=     [
								    {'''score''': 0.0845, '''label''': '''stop'''},
								    {'''score''': 0.0844, '''label''': '''on'''},
								    {'''score''': 0.0841, '''label''': '''right'''},
								    {'''score''': 0.0834, '''label''': '''left'''},
								]
								self.assertIn(nested_simplify(lowerCamelCase,    decimals=4    ),    [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]    )

								lowercase__  					=     {'''array''': np.ones((8_000,)    ), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
								lowercase__  					=     audio_classifier(lowerCamelCase,    top_k=4    )
								self.assertIn(nested_simplify(lowerCamelCase,    decimals=4    ),    [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]    )




				@require_torch
				@slow
				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								import datasets

								lowercase__  					=     '''superb/wav2vec2-base-superb-ks'''

								lowercase__  					=     pipeline('''audio-classification''',    model=lowerCamelCase    )
								lowercase__  					=     datasets.load_dataset('''anton-l/superb_dummy''',    '''ks''',    split='''test'''    )

								lowercase__  					=     np.array(dataset[3]['''speech'''],    dtype=np.floataa    )
								lowercase__  					=     audio_classifier(lowerCamelCase,    top_k=4    )
								self.assertEqual(
								    nested_simplify(lowerCamelCase,    decimals=3    ),    [
								        {'''score''': 0.981, '''label''': '''go'''},
								        {'''score''': 0.007, '''label''': '''up'''},
								        {'''score''': 0.006, '''label''': '''_unknown_'''},
								        {'''score''': 0.001, '''label''': '''down'''},
								    ],    )




				@require_tf
				@unittest.skip('''Audio classification is not implemented for TF'''    )
				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								pass




