
"""simple docstring"""


import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test




class  __lowerCamelCase  ( unittest.TestCase			):




				@parameterized.expand([(None,), ('''foo.json''',)]   )
				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  GenerationConfig(
							    do_sample=UpperCAmelCase							,						temperature=0.7							,						length_penalty=1.0							,						bad_words_ids=[[1, 2, 3], [4, 5]]							,						)
							with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(UpperCAmelCase							,						config_name=UpperCAmelCase   )
										lowerCamelCase_					=  GenerationConfig.from_pretrained(UpperCAmelCase							,						config_name=UpperCAmelCase   )

							# Checks parameters that were specified
							self.assertEqual(loaded_config.do_sample							,						UpperCAmelCase   )
							self.assertEqual(loaded_config.temperature							,						0.7   )
							self.assertEqual(loaded_config.length_penalty							,						1.0   )
							self.assertEqual(loaded_config.bad_words_ids							,						[[1, 2, 3], [4, 5]]   )

							# Checks parameters that were not specified (defaults)
							self.assertEqual(loaded_config.top_k							,						50   )
							self.assertEqual(loaded_config.max_length							,						20   )
							self.assertEqual(loaded_config.max_time							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoConfig.from_pretrained('''gpt2'''   )
							lowerCamelCase_					=  GenerationConfig.from_model_config(UpperCAmelCase   )
							lowerCamelCase_					=  GenerationConfig()

							# The generation config has loaded a few non-default parameters from the model config
							self.assertNotEqual(UpperCAmelCase							,						UpperCAmelCase   )

							# One of those parameters is eos_token_id -- check if it matches
							self.assertNotEqual(generation_config_from_model.eos_token_id							,						default_generation_config.eos_token_id   )
							self.assertEqual(generation_config_from_model.eos_token_id							,						model_config.eos_token_id   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig()
							lowerCamelCase_					=  {
							    '''max_new_tokens''': 1024,
							    '''foo''': '''bar''',
							}
							lowerCamelCase_					=  copy.deepcopy(UpperCAmelCase   )
							lowerCamelCase_					=  generation_config.update(**UpperCAmelCase   )

							# update_kwargs was not modified (no side effects)
							self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )

							# update_kwargs was used to update the config on valid attributes
							self.assertEqual(generation_config.max_new_tokens							,						1024   )

							# `.update()` returns a dictionary of unused kwargs
							self.assertEqual(UpperCAmelCase							,						{'''foo''': '''bar'''}   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig()
							lowerCamelCase_					=  '''bar'''

							with tempfile.TemporaryDirectory('''test-generation-config'''   ) as tmp_dir:
										generation_config.save_pretrained(UpperCAmelCase   )

										lowerCamelCase_					=  GenerationConfig.from_pretrained(UpperCAmelCase   )
							# update_kwargs was used to update the config on valid attributes
							self.assertEqual(new_config.foo							,						'''bar'''   )

							lowerCamelCase_					=  GenerationConfig.from_model_config(UpperCAmelCase   )
							assert not hasattr(UpperCAmelCase							,						'''foo'''   )  # no new kwargs should be initialized if from config





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig()
							self.assertEqual(default_config.temperature							,						1.0   )
							self.assertEqual(default_config.do_sample							,						UpperCAmelCase   )
							self.assertEqual(default_config.num_beams							,						1   )

							lowerCamelCase_					=  GenerationConfig(
							    do_sample=UpperCAmelCase							,						temperature=0.7							,						length_penalty=1.0							,						bad_words_ids=[[1, 2, 3], [4, 5]]							,						)
							self.assertEqual(config.temperature							,						0.7   )
							self.assertEqual(config.do_sample							,						UpperCAmelCase   )
							self.assertEqual(config.num_beams							,						1   )

							with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(UpperCAmelCase   )
										lowerCamelCase_					=  GenerationConfig.from_pretrained(UpperCAmelCase							,						temperature=1.0   )

							self.assertEqual(loaded_config.temperature							,						1.0   )
							self.assertEqual(loaded_config.do_sample							,						UpperCAmelCase   )
							self.assertEqual(loaded_config.num_beams							,						1   )  # default value




@is_staging_test
class  __lowerCamelCase  ( unittest.TestCase			):




				@classmethod
				def        UpperCAmelCase__						(				cls   ):
							lowerCamelCase_					=  TOKEN
							HfFolder.save_token(UpperCAmelCase   )




				@classmethod
				def        UpperCAmelCase__						(				cls   ):
							try:
										delete_repo(token=cls._token							,						repo_id='''test-generation-config'''   )
							except HTTPError:
										pass

							try:
										delete_repo(token=cls._token							,						repo_id='''valid_org/test-generation-config-org'''   )
							except HTTPError:
										pass




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig(
							    do_sample=UpperCAmelCase							,						temperature=0.7							,						length_penalty=1.0							,						)
							config.push_to_hub('''test-generation-config'''							,						use_auth_token=self._token   )

							lowerCamelCase_					=  GenerationConfig.from_pretrained(f"{USER}/test-generation-config"   )
							for k, v in config.to_dict().items():
										if k != "transformers_version":
													self.assertEqual(UpperCAmelCase							,						getattr(UpperCAmelCase							,						UpperCAmelCase   )   )

        # Reset repo
							delete_repo(token=self._token							,						repo_id='''test-generation-config'''   )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(
										    UpperCAmelCase							,						repo_id='''test-generation-config'''							,						push_to_hub=UpperCAmelCase							,						use_auth_token=self._token   )

							lowerCamelCase_					=  GenerationConfig.from_pretrained(f"{USER}/test-generation-config"   )
							for k, v in config.to_dict().items():
										if k != "transformers_version":
													self.assertEqual(UpperCAmelCase							,						getattr(UpperCAmelCase							,						UpperCAmelCase   )   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig(
							    do_sample=UpperCAmelCase							,						temperature=0.7							,						length_penalty=1.0							,						)
							config.push_to_hub('''valid_org/test-generation-config-org'''							,						use_auth_token=self._token   )

							lowerCamelCase_					=  GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''   )
							for k, v in config.to_dict().items():
										if k != "transformers_version":
													self.assertEqual(UpperCAmelCase							,						getattr(UpperCAmelCase							,						UpperCAmelCase   )   )

        # Reset repo
							delete_repo(token=self._token							,						repo_id='''valid_org/test-generation-config-org'''   )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(
										    UpperCAmelCase							,						repo_id='''valid_org/test-generation-config-org'''							,						push_to_hub=UpperCAmelCase							,						use_auth_token=self._token   )

							lowerCamelCase_					=  GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''   )
							for k, v in config.to_dict().items():
										if k != "transformers_version":
													self.assertEqual(UpperCAmelCase							,						getattr(UpperCAmelCase							,						UpperCAmelCase   )   )











'''simple docstring'''
import argparse

import torch

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


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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  downstream_dict["projector.weight"]
   lowercase_						:     str							     =  downstream_dict["projector.bias"]
   lowercase_						:     int							     =  downstream_dict["model.post_net.linear.weight"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["model.post_net.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Any							     =  downstream_dict["model.linear.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["model.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  WavaVecaForXVector.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     str							     =  downstream_dict["connector.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
      lowercase_						:     Union[str, Any]							     =  downstream_dict[
          F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      lowercase_						:     Dict							     =  downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   lowercase_						:     List[Any]							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["objective.W"]
   return model




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  torch.load(_UpperCamelCase    ,		map_location="cpu"				)

   lowercase_						:     Dict							     =  checkpoint["Downstream"]

   lowercase_						:     Optional[Any]							     =  WavaVecaConfig.from_pretrained(_UpperCamelCase				)
   lowercase_						:     Dict							     =  WavaVecaFeatureExtractor.from_pretrained(
       _UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		do_normalize=_UpperCamelCase				)

   lowercase_						:     Dict							     =  hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"				):
      lowercase_						:     Any							     =  convert_classification(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForAudioFrameClassification"				):
      lowercase_						:     Optional[int]							     =  convert_diarization(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForXVector"				):
      lowercase_						:     List[Any]							     =  convert_xvector(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   else:
      raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""				)

   if hf_config.use_weighted_layer_sum:
      lowercase_						:     List[str]							     =  checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(_UpperCamelCase				)
   hf_model.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
 UpperCamelCase__			=      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.')
 UpperCamelCase__			=      parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




