





import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel

class       __A     :


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=13   , UpperCAmelCase_=7   , UpperCAmelCase_=True   , UpperCAmelCase_=True   , UpperCAmelCase_=True   , UpperCAmelCase_=True   , UpperCAmelCase_=99   , UpperCAmelCase_=32   , UpperCAmelCase_=5   , UpperCAmelCase_=4   , UpperCAmelCase_=4   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.1   , UpperCAmelCase_=True   , UpperCAmelCase_=512   , UpperCAmelCase_=16   , UpperCAmelCase_=2   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=3   , UpperCAmelCase_=4   , UpperCAmelCase_=None   , ):
								lowerCamelCase	 =parent
								lowerCamelCase	 =batch_size
								lowerCamelCase	 =seq_length
								lowerCamelCase	 =is_training
								lowerCamelCase	 =use_input_mask
								lowerCamelCase	 =use_token_type_ids
								lowerCamelCase	 =use_labels
								lowerCamelCase	 =vocab_size
								lowerCamelCase	 =hidden_size
								lowerCamelCase	 =num_hidden_layers
								lowerCamelCase	 =num_attention_heads
								lowerCamelCase	 =intermediate_multiple_size
								lowerCamelCase	 =hidden_act
								lowerCamelCase	 =hidden_dropout
								lowerCamelCase	 =attention_dropout
								lowerCamelCase	 =weight_tying
								lowerCamelCase	 =max_position_embeddings
								lowerCamelCase	 =type_vocab_size
								lowerCamelCase	 =type_sequence_label_size
								lowerCamelCase	 =initializer_range
								lowerCamelCase	 =num_labels
								lowerCamelCase	 =num_choices
								lowerCamelCase	 =scope


				def _snake_case       (		self							):
								lowerCamelCase	 =ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size							)

								lowerCamelCase	 =None
								if self.use_input_mask:
												lowerCamelCase	 =random_attention_mask([self.batch_size, self.seq_length]							)

								lowerCamelCase	 =None
								if self.use_labels:
												lowerCamelCase	 =ids_tensor([self.batch_size, self.seq_length]   , self.num_labels							)

								lowerCamelCase	 =self.get_config()

								return config, input_ids, input_mask, token_labels


				def _snake_case       (		self							):
								return GPTNeoXJapaneseConfig(
								    vocab_size=self.vocab_size   , hidden_size=self.hidden_size   , num_hidden_layers=self.num_hidden_layers   , num_attention_heads=self.num_attention_heads   , intermediate_multiple_size=self.intermediate_multiple_size   , hidden_act=self.hidden_act   , hidden_dropout=self.hidden_dropout   , attention_dropout=self.attention_dropout   , weight_tying=self.weight_tying   , max_position_embeddings=self.max_position_embeddings   , type_vocab_size=self.type_vocab_size   , is_decoder=UpperCAmelCase_   , initializer_range=self.initializer_range   , )


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self.prepare_config_and_inputs()

								lowerCamelCase	 =True

								return config, input_ids, input_mask, token_labels


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =GPTNeoXJapaneseModel(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_   , attention_mask=UpperCAmelCase_							)
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =True
								lowerCamelCase	 =GPTNeoXJapaneseModel(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_   , attention_mask=UpperCAmelCase_							)
								self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =GPTNeoXJapaneseForCausalLM(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_   , attention_mask=UpperCAmelCase_   , labels=UpperCAmelCase_							)
								self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =True
								lowerCamelCase	 =GPTNeoXJapaneseForCausalLM(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()

								# first forward pass
								lowerCamelCase	 =model(UpperCAmelCase_   , attention_mask=UpperCAmelCase_   , use_cache=UpperCAmelCase_							)
								lowerCamelCase	 =outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								lowerCamelCase	 =ids_tensor((self.batch_size, 3)   , config.vocab_size							)
								lowerCamelCase	 =ids_tensor((self.batch_size, 3)   , vocab_size=2							)

								# append to next input_ids and
								lowerCamelCase	 =torch.cat([input_ids, next_tokens]   , dim=-1							)
								lowerCamelCase	 =torch.cat([input_mask, next_mask]   , dim=-1							)

								lowerCamelCase	 =model(UpperCAmelCase_   , attention_mask=UpperCAmelCase_   , output_hidden_states=UpperCAmelCase_							)
								lowerCamelCase	 =output_from_no_past["""hidden_states"""][0]
								lowerCamelCase	 =model(
								    UpperCAmelCase_   , attention_mask=UpperCAmelCase_   , past_key_values=UpperCAmelCase_   , output_hidden_states=UpperCAmelCase_   , )["""hidden_states"""][0]

								# select random slice
								lowerCamelCase	 =ids_tensor((1,)   , output_from_past.shape[-1]							).item()
								lowerCamelCase	 =output_from_no_past[:, -3:, random_slice_idx].detach()
								lowerCamelCase	 =output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(UpperCAmelCase_   , UpperCAmelCase_   , atol=1E-3							)							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.prepare_config_and_inputs()
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =config_and_inputs
								lowerCamelCase	 ={"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_torch
class       __A     (						a    ,					a    ,					unittest.TestCase    ):
				__A      =				(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
				__A      =				(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
				__A      =				(
				    {"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
				    if is_torch_available()
				    else {}
				)
				__A      =				False
				__A      =				False
				__A      =				False
				__A      =				False


				def _snake_case       (		self							):
								lowerCamelCase	 =GPTNeoXJapaneseModelTester(self							)
								lowerCamelCase	 =ConfigTester(self   , config_class=UpperCAmelCase_   , hidden_size=37							)


				def _snake_case       (		self							):
								self.config_tester.run_common_tests()


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								# This regression test was failing with PyTorch < 1.3
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_decoder()

								lowerCamelCase	 =None

								self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase_							)


				@slow
				def _snake_case       (		self							):
								lowerCamelCase	 ="""abeja/gpt-neox-japanese-2.7b"""

								lowerCamelCase	 =["""データサイエンティストとは、""", """100年後に必要とされる会社は、""", """フルリモートの環境で働くために必要なことは、""", """国境の長いトンネルを抜けると""", """美味しい日本食といえば、"""]

								lowerCamelCase	 =[
								    """データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。""",
								    """100年後に必要とされる会社は、「人」が中心の会社です。""",
								    """フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。""",
								    """国境の長いトンネルを抜けると、そこは雪国だった。""",
								    """美味しい日本食といえば、やっぱりお寿司ですよね。""",
								]

								lowerCamelCase	 =GPTNeoXJapaneseTokenizer.from_pretrained(UpperCAmelCase_							)
								lowerCamelCase	 =GPTNeoXJapaneseForCausalLM.from_pretrained(UpperCAmelCase_							)

								lowerCamelCase	 =[]
								for prompt in prompts:
												lowerCamelCase	 =tokenizer(UpperCAmelCase_   , return_tensors="""pt"""							).input_ids
												lowerCamelCase	 =model.generate(UpperCAmelCase_   , max_length=50							)
												lowerCamelCase	 =tokenizer.batch_decode(UpperCAmelCase_   , skip_special_tokens=UpperCAmelCase_							)
												predicted_outputs += generated_string
								self.assertListEqual(UpperCAmelCase_   , UpperCAmelCase_							)












import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__						:			int									=logging.get_logger(__name__)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				lowerCamelCase	 =UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  int:
				lowerCamelCase	 =UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""model.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				lowerCamelCase	 =UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  str:
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      map_location="""cpu"""				)

				lowerCamelCase	 =checkpoint["""Downstream"""]

				lowerCamelCase	 =UniSpeechSatConfig.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(
				    _UpperCAmelCase			,      return_attention_mask=_UpperCAmelCase			,      do_normalize=_UpperCAmelCase				)

				lowerCamelCase	 =hf_config.architectures[0]
				if arch.endswith("""ForSequenceClassification"""				):
								lowerCamelCase	 =convert_classification(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForAudioFrameClassification"""				):
								lowerCamelCase	 =convert_diarization(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForXVector"""				):
								lowerCamelCase	 =convert_xvector(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				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(_UpperCAmelCase				)
				hf_model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=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__						:			List[Any]									=parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





