



import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
_lowercase: List[Any]      			=		logging.get_logger(__name__)






def     a(      A       :    List[Any] ,     A       :    Optional[int] ,     A       :    int       )		->       List[Any]:
					"""simple docstring"""





					a					     =   WavaVecaForSequenceClassification.from_pretrained(A ,     config=A       )
					a					     =   downstream_dict["projector.weight"]
					a					     =   downstream_dict["projector.bias"]
					a					     =   downstream_dict["model.post_net.linear.weight"]
					a					     =   downstream_dict["model.post_net.linear.bias"]
					return model






def     a(      A       :    int ,     A       :    int ,     A       :    Dict       )		->       Union[str, Any]:
					"""simple docstring"""





					a					     =   WavaVecaForAudioFrameClassification.from_pretrained(A ,     config=A       )
					a					     =   downstream_dict["model.linear.weight"]
					a					     =   downstream_dict["model.linear.bias"]
					return model






def     a(      A       :    int ,     A       :    int ,     A       :    Optional[Any]       )		->       Optional[int]:
					"""simple docstring"""





					a					     =   WavaVecaForXVector.from_pretrained(A ,     config=A       )
					a					     =   downstream_dict["connector.weight"]
					a					     =   downstream_dict["connector.bias"]
					for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
										a					     =   downstream_dict[
										    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										a					     =   downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

					a					     =   downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
					a					     =   downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
					a					     =   downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
					a					     =   downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
					a					     =   downstream_dict["objective.W"]
					return model






@torch.no_grad()
def     a(      A       :    Optional[Any] ,     A       :    List[str] ,     A       :    List[str] ,     A       :    List[Any]       )		->       List[Any]:
					"""simple docstring"""





					a					     =   torch.load(A ,     map_location="cpu"       )

					a					     =   checkpoint["Downstream"]

					a					     =   WavaVecaConfig.from_pretrained(A       )
					a					     =   WavaVecaFeatureExtractor.from_pretrained(
					    A ,     return_attention_mask=A ,     do_normalize=A       )

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

					if hf_config.use_weighted_layer_sum:
										a					     =   checkpoint["Featurizer"]["weights"]

					hf_feature_extractor.save_pretrained(A       )
					hf_model.save_pretrained(A       )


if __name__ == "__main__":
				_lowercase: str      			=		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.")
				_lowercase: List[str]      			=		parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowercase: Union[str, Any]      			=		get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
				_lowercase: List[str]      			=		json.load(f)



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_		).to(lowerCamelCase_		)
						if torch_device == "cuda":
											model.half()
						return model




	@parameterized.expand(
	    [
	        ["en-ru", 26.0],
	        ["ru-en", 22.0],
	        ["en-de", 22.0],
	        ["de-en", 29.0],
	    ]		)
	@slow
	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   F'''facebook/wmt19-{pair}'''
						a					     =   self.get_tokenizer(lowerCamelCase_		)
						a					     =   self.get_model(lowerCamelCase_		)

						a					     =   bleu_data[pair]["src"]
						a					     =   bleu_data[pair]["tgt"]

						a					     =   tokenizer(lowerCamelCase_		,       return_tensors="pt"		,       truncation=lowerCamelCase_		,       padding="longest"		).to(lowerCamelCase_		)
						a					     =   model.generate(
						    input_ids=batch.input_ids		,       num_beams=8		,       )
						a					     =   tokenizer.batch_decode(
						    lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       clean_up_tokenization_spaces=lowerCamelCase_		)
						a					     =   calculate_bleu(lowerCamelCase_		,       lowerCamelCase_		)
						print(lowerCamelCase_		)
						self.assertGreaterEqual(scores["bleu"]		,       lowerCamelCase_		)
