






import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_a				     =  logging.get_logger(__name__)


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase     =      WavLMForSequenceClassification.from_pretrained(__lowerCAmelCase			,		config=__lowerCAmelCase     )
						_UpperCAmelCase     =      downstream_dict['projector.weight']
						_UpperCAmelCase     =      downstream_dict['projector.bias']
						_UpperCAmelCase     =      downstream_dict['model.post_net.linear.weight']
						_UpperCAmelCase     =      downstream_dict['model.post_net.linear.bias']
						return model


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Any:





						"""simple docstring"""



						_UpperCAmelCase     =      WavLMForAudioFrameClassification.from_pretrained(__lowerCAmelCase			,		config=__lowerCAmelCase     )
						_UpperCAmelCase     =      downstream_dict['model.linear.weight']
						_UpperCAmelCase     =      downstream_dict['model.linear.bias']
						return model


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase     =      WavLMForXVector.from_pretrained(__lowerCAmelCase			,		config=__lowerCAmelCase     )
						_UpperCAmelCase     =      downstream_dict['connector.weight']
						_UpperCAmelCase     =      downstream_dict['connector.bias']
						for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
												_UpperCAmelCase     =      downstream_dict[
												    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
												]
												_UpperCAmelCase     =      downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

						_UpperCAmelCase     =      downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
						_UpperCAmelCase     =      downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
						_UpperCAmelCase     =      downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
						_UpperCAmelCase     =      downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
						_UpperCAmelCase     =      downstream_dict['objective.W']
						return model


@torch.no_grad()
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Tuple:





						"""simple docstring"""



						_UpperCAmelCase     =      torch.load(__lowerCAmelCase			,		map_location='cpu'     )

						_UpperCAmelCase     =      checkpoint['Downstream']

						_UpperCAmelCase     =      WavLMConfig.from_pretrained(__lowerCAmelCase     )
						_UpperCAmelCase     =      WavaVecaFeatureExtractor.from_pretrained(
						    __lowerCAmelCase			,		return_attention_mask=__lowerCAmelCase			,		do_normalize=__lowerCAmelCase     )

						_UpperCAmelCase     =      hf_config.architectures[0]
						if arch.endswith('ForSequenceClassification'     ):
												_UpperCAmelCase     =      convert_classification(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						elif arch.endswith('ForAudioFrameClassification'     ):
												_UpperCAmelCase     =      convert_diarization(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						elif arch.endswith('ForXVector'     ):
												_UpperCAmelCase     =      convert_xvector(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						else:
												raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""     )

						if hf_config.use_weighted_layer_sum:
												_UpperCAmelCase     =      checkpoint['Featurizer']['weights']

						hf_feature_extractor.save_pretrained(__lowerCAmelCase     )
						hf_model.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
		_a				     =  argparse.ArgumentParser()
		parser.add_argument(
		    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
		)
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
		parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
		_a				     =  parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=				MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	UpperCamelCase__								=				TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							if isinstance(model.config	,   UpperCAmelCase   ):
													_UpperCAmelCase ,			_UpperCAmelCase     =      list(tokenizer.lang_code_to_id.keys()   )[:2]
													_UpperCAmelCase     =      TranslationPipeline(model=UpperCAmelCase	,   tokenizer=UpperCAmelCase	,   src_lang=UpperCAmelCase	,   tgt_lang=UpperCAmelCase   )
							else:
													_UpperCAmelCase     =      TranslationPipeline(model=UpperCAmelCase	,   tokenizer=UpperCAmelCase   )
							return translator, ["Some string", "Some other text"]

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      translator('Some string'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}]   )

							_UpperCAmelCase     =      translator(['Some string']   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}]   )

							_UpperCAmelCase     =      translator(['Some string', 'other string']   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}, {'translation_text': ANY(UpperCAmelCase   )}]   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_ro'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='pt'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
							                ' Beide Beide'
							            )
							        }
							    ]	,   )

	@require_tf
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_ro'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='tf'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
							                ' Beide Beide'
							            )
							        }
							    ]	,   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_de'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='pt'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
							                ' urine urine urine urine urine urine urine'
							            )
							        }
							    ]	,   )

	@require_tf
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_de'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='tf'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
							                ' urine urine urine urine urine urine urine'
							            )
							        }
							    ]	,   )





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	@require_torch
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							with self.assertRaises(UpperCAmelCase   ):
													pipeline(task='translation_cn_to_ar'   )

							# but we do for this one
							_UpperCAmelCase     =      pipeline(task='translation_en_to_de'   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'en'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'de'   )

	@require_torch
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'   )
							_UpperCAmelCase     =      MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'   )

							_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase	,   tokenizer=UpperCAmelCase   )
							# Missing src_lang, tgt_lang
							with self.assertRaises(UpperCAmelCase   ):
													translator('This is a test'   )

							_UpperCAmelCase     =      translator('This is a test'	,   src_lang='en_XX'	,   tgt_lang='ar_AR'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'هذا إختبار'}]   )

							_UpperCAmelCase     =      translator('This is a test'	,   src_lang='en_XX'	,   tgt_lang='hi_IN'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'यह एक परीक्षण है'}]   )

							# src_lang, tgt_lang can be defined at pipeline call time
							_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase	,   tokenizer=UpperCAmelCase	,   src_lang='en_XX'	,   tgt_lang='ar_AR'   )
							_UpperCAmelCase     =      translator('This is a test'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'هذا إختبار'}]   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'patrickvonplaten/t5-tiny-random'
							_UpperCAmelCase     =      pipeline(task='translation_cn_to_ar'	,   model=UpperCAmelCase   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'cn'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'ar'   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'patrickvonplaten/t5-tiny-random'
							with pytest.warns(UpperCAmelCase	,   match=R'.*translation_en_to_de.*'   ):
													_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase   )
							self.assertEqual(translator.task	,   'translation_en_to_de'   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'en'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'de'   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							with self.assertRaises(UpperCAmelCase   ):
													pipeline(task='translation'   )
