




import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           CpmAntConfig,
           CpmAntForCausalLM,
           CpmAntModel,
           CpmAntTokenizer,
       )





@require_torch
class        lowerCamelCase__:
      def __init__(							self:					Union[str, Any]       ,			UpperCamelCase_:					Optional[int]       ,			UpperCamelCase_:					Tuple=2       ,			UpperCamelCase_:					Union[str, Any]=8       ,			UpperCamelCase_:					List[str]=True       ,			UpperCamelCase_:					Tuple=False       ,			UpperCamelCase_:					str=False       ,			UpperCamelCase_:					Union[str, Any]=False       ,			UpperCamelCase_:					int=False       ,			UpperCamelCase_:					Union[str, Any]=99       ,			UpperCamelCase_:					Dict=32       ,			UpperCamelCase_:					str=3       ,			UpperCamelCase_:					Any=4       ,			UpperCamelCase_:					int=37       ,			UpperCamelCase_:					Any=32       ,			UpperCamelCase_:					Dict=1_28       ,			UpperCamelCase_:					str=8       ,			UpperCamelCase_:					int=8       ,			UpperCamelCase_:					int=8       ,			UpperCamelCase_:					List[str]=1.0       ,			UpperCamelCase_:					List[str]=True       ,			):
             __lowerCamelCase  =		parent
             __lowerCamelCase  =		batch_size
             __lowerCamelCase  =		seq_length
             __lowerCamelCase  =		is_training
             __lowerCamelCase  =		use_token_type_ids
             __lowerCamelCase  =		use_input_mask
             __lowerCamelCase  =		use_labels
             __lowerCamelCase  =		use_mc_token_ids
             __lowerCamelCase  =		vocab_size
             __lowerCamelCase  =		hidden_size
             __lowerCamelCase  =		num_hidden_layers
             __lowerCamelCase  =		num_attention_heads
             __lowerCamelCase  =		intermediate_size
             __lowerCamelCase  =		num_buckets
             __lowerCamelCase  =		max_distance
             __lowerCamelCase  =		prompt_length
             __lowerCamelCase  =		prompt_types
             __lowerCamelCase  =		segment_types
             __lowerCamelCase  =		init_std
             __lowerCamelCase  =		return_dict
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             __lowerCamelCase  =		{}
             __lowerCamelCase  =		ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size  ).type(torch.intaa  )
             __lowerCamelCase  =		False

             __lowerCamelCase  =		self.get_config()

             return (config, input_ids)
      def      lowerCAmelCase__		(							self:					Optional[int]  ):
             return CpmAntConfig(
                 vocab_size=self.vocab_size       ,			hidden_size=self.hidden_size       ,			num_hidden_layers=self.num_hidden_layers       ,			num_attention_heads=self.num_attention_heads       ,			dim_ff=self.intermediate_size       ,			position_bias_num_buckets=self.num_buckets       ,			position_bias_max_distance=self.max_distance       ,			prompt_types=self.prompt_types       ,			prompt_length=self.prompt_length       ,			segment_types=self.segment_types       ,			use_cache=UpperCamelCase_       ,			init_std=self.init_std       ,			return_dict=self.return_dict       ,			)
      def      lowerCAmelCase__		(							self:					List[str]       ,			UpperCamelCase_:					Optional[Any]       ,			UpperCamelCase_:					List[Any]       ,			*UpperCamelCase_:					List[str]  ):
             __lowerCamelCase  =		CpmAntModel(config=UpperCamelCase_  )
             model.to(UpperCamelCase_  )
             model.eval()

             __lowerCamelCase  =		model(**UpperCamelCase_  ).last_hidden_state

             self.parent.assertEqual(hidden_states.shape       ,			(self.batch_size, self.seq_length, config.hidden_size)  )
      def      lowerCAmelCase__		(							self:					Tuple       ,			UpperCamelCase_:					Any       ,			UpperCamelCase_:					Dict       ,			*UpperCamelCase_:					List[Any]  ):
             __lowerCamelCase  =		CpmAntForCausalLM(UpperCamelCase_  )
             model.to(UpperCamelCase_  )
             __lowerCamelCase  =		input_ids["""input_ids"""].to(UpperCamelCase_  )
             model.eval()

             __lowerCamelCase  =		model(**UpperCamelCase_  )
             self.parent.assertEqual(
                 model_output.logits.shape       ,			(self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)       ,			)




      def      lowerCAmelCase__		(							self:					int  ):
             __lowerCamelCase,  __lowerCamelCase  =		self.prepare_config_and_inputs()
             return config, inputs_dict





@require_torch
class        lowerCamelCase__(   __lowerCamelCase					,	__lowerCamelCase					,	unittest.TestCase):
      UpperCAmelCase__ :      str       						=  (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
      UpperCAmelCase__ :      Optional[Any]       						=  (
          {'feature-extraction': CpmAntModel, 'text-generation': CpmAntForCausalLM} if is_torch_available() else {}
      )

      UpperCAmelCase__ :      int       						=  False
      UpperCAmelCase__ :      List[str]       						=  False
      UpperCAmelCase__ :      Dict       						=  False
      UpperCAmelCase__ :      int       						=  False
      UpperCAmelCase__ :      Optional[Any]       						=  False
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		CpmAntModelTester(self  )
             __lowerCamelCase  =		ConfigTester(self       ,			config_class=UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             self.config_tester.create_and_test_config_common_properties()
             self.config_tester.create_and_test_config_to_json_string()
             self.config_tester.create_and_test_config_to_json_file()
             self.config_tester.create_and_test_config_from_and_save_pretrained()
             self.config_tester.check_config_can_be_init_without_params()
             self.config_tester.check_config_arguments_init()
      def      lowerCAmelCase__		(							self:					Optional[int]  ):
             unittest.skip("""CPMAnt doesn't support input_embeds."""  )(self.test_inputs_embeds  )
      def      lowerCAmelCase__		(							self:					Dict  ):
             unittest.skip(
                 """CPMAnt doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model."""  )(self.test_retain_grad_hidden_states_attentions  )
      def      lowerCAmelCase__		(							self:					Dict  ):
             __lowerCamelCase,  __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_cpmant_model(UpperCamelCase_       ,			UpperCamelCase_  )




      def      lowerCAmelCase__		(							self:					Tuple  ):
             __lowerCamelCase,  __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_lm_head_model(UpperCamelCase_       ,			UpperCamelCase_  )





@require_torch
class        lowerCamelCase__(   unittest.TestCase):
      @tooslow
      def      lowerCAmelCase__		(							self:					Any  ):
             __lowerCamelCase  =		"""今天天气真好！"""
             __lowerCamelCase  =		"""openbmb/cpm-ant-10b"""
             __lowerCamelCase  =		CpmAntModel.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		CpmAntTokenizer.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		tokenizer(UpperCamelCase_       ,			return_tensors="""pt"""  )
             __lowerCamelCase  =		model(**UpperCamelCase_  ).last_hidden_state

             __lowerCamelCase  =		torch.tensor(
                 [[[6.1708, 5.9244, 1.0835], [6.5207, 6.2893, -11.3324], [-1.0107, -0.0576, -5.9577]]]       ,			)
             self.assertTrue(torch.allclose(hidden_states[:, :3, :3]       ,			UpperCamelCase_       ,			atol=1E-2  )  )





@require_torch
class        lowerCamelCase__(   unittest.TestCase):
      @tooslow
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		"""今天天气真好！"""
             __lowerCamelCase  =		"""openbmb/cpm-ant-10b"""
             __lowerCamelCase  =		CpmAntForCausalLM.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		CpmAntTokenizer.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		tokenizer(UpperCamelCase_       ,			return_tensors="""pt"""  )
             __lowerCamelCase  =		model(**UpperCamelCase_  ).logits

             __lowerCamelCase  =		torch.tensor(
                 [[[-6.4267, -6.4083, -6.3958], [-5.8802, -5.9447, -5.7811], [-5.3896, -5.4820, -5.4295]]]       ,			)
             self.assertTrue(torch.allclose(hidden_states[:, :3, :3]       ,			UpperCamelCase_       ,			atol=1E-2  )  )
      @tooslow
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		"""openbmb/cpm-ant-10b"""
             __lowerCamelCase  =		CpmAntForCausalLM.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		CpmAntTokenizer.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		"""今天天气不错，"""
             __lowerCamelCase  =		"""今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"""
             __lowerCamelCase  =		tokenizer(UpperCamelCase_       ,			return_tensors="""pt"""  )
             __lowerCamelCase  =		model.generate(**UpperCamelCase_  )
             __lowerCamelCase  =		tokenizer.batch_decode(UpperCamelCase_  )
             self.assertEqual(UpperCamelCase_       ,			UpperCamelCase_  )




      @tooslow
      def      lowerCAmelCase__		(							self:					int  ):
             __lowerCamelCase  =		"""openbmb/cpm-ant-10b"""
             __lowerCamelCase  =		CpmAntForCausalLM.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		CpmAntTokenizer.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		["""今天天气不错，""", """新年快乐，万事如意！"""]
             __lowerCamelCase  =		[
                 """今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的""",
                 """新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的""",
             ]
             __lowerCamelCase  =		tokenizer(UpperCamelCase_       ,			return_tensors="""pt"""       ,			padding=UpperCamelCase_  )
             __lowerCamelCase  =		model.generate(**UpperCamelCase_  )
             __lowerCamelCase  =		tokenizer.batch_decode(UpperCamelCase_  )
             self.assertEqual(UpperCamelCase_       ,			UpperCamelCase_  )







import argparse

import torch

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


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




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Any    ,     A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForSequenceClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""projector.weight"""]
       __lowerCamelCase  =		downstream_dict["""projector.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Any    ,     A__			:   List[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForAudioFrameClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""model.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Dict    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



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

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




@torch.no_grad()
def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Tuple    ,     A__			:   int    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.load(A__    ,     map_location="""cpu"""      )

       __lowerCamelCase  =		checkpoint["""Downstream"""]

       __lowerCamelCase  =		WavLMConfig.from_pretrained(A__      )
       __lowerCamelCase  =		WavaVecaFeatureExtractor.from_pretrained(
           A__    ,     return_attention_mask=A__    ,     do_normalize=A__      )

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

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

       hf_feature_extractor.save_pretrained(A__      )
       hf_model.save_pretrained(A__      )


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)

