




import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase			 =						get_tests_dir('fixtures/spiece.model')



@require_sentencepiece
@require_tokenizers
class 			a     (   _A	,   unittest.TestCase			):

     '''simple docstring'''





     lowerCAmelCase				:       Optional[Any]   		=  ErnieMTokenizer
     lowerCAmelCase				:       Union[str, Any]   		=  False
     lowerCAmelCase				:       Any   		=  True
     lowerCAmelCase				:       Any   		=  False
     lowerCAmelCase				:       Optional[Any]   		=  False
     def   lowerCamelCase_   (	self   :			Tuple				):
         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCAmelCase_							 =     ErnieMTokenizer(__snake_case					,    unk_token='''<unk>'''					,    pad_token='''<pad>'''				)
         tokenizer.save_pretrained(self.tmpdirname				)
     def   lowerCamelCase_   (	self   :			Optional[int]					,    __snake_case   :			int				):
         UpperCAmelCase_							 =     '''this is a test'''
         UpperCAmelCase_							 =     '''this is a test'''
         return input_text, output_text
     def   lowerCamelCase_   (	self   :			Optional[int]				):
         UpperCAmelCase_							 =     '''<pad>'''
         UpperCAmelCase_							 =     0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case				)					,    __snake_case				)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case				)					,    __snake_case				)
     def   lowerCamelCase_   (	self   :			int				):
         UpperCAmelCase_							 =     list(self.get_tokenizer().get_vocab().keys()				)

         self.assertEqual(vocab_keys[0]					,    '''<pad>'''				)
         self.assertEqual(vocab_keys[1]					,    '''<unk>'''				)
         self.assertEqual(vocab_keys[-1]					,    '''▁eloquent'''				)
         self.assertEqual(len(__snake_case				)					,    3_00_00				)
     def   lowerCamelCase_   (	self   :			Optional[int]				):
         self.assertEqual(self.get_tokenizer().vocab_size					,    3_00_00				)
     def   lowerCamelCase_   (	self   :			str				):
         if not self.test_rust_tokenizer:
             return

         UpperCAmelCase_							 =     self.get_tokenizer()
         UpperCAmelCase_							 =     self.get_rust_tokenizer()

         UpperCAmelCase_							 =     '''I was born in 92000, and this is falsé.'''

         UpperCAmelCase_							 =     tokenizer.tokenize(__snake_case				)
         UpperCAmelCase_							 =     rust_tokenizer.tokenize(__snake_case				)
         self.assertListEqual(__snake_case					,    __snake_case				)

         UpperCAmelCase_							 =     tokenizer.encode(__snake_case					,    add_special_tokens=__snake_case				)
         UpperCAmelCase_							 =     rust_tokenizer.encode(__snake_case					,    add_special_tokens=__snake_case				)
         self.assertListEqual(__snake_case					,    __snake_case				)

         UpperCAmelCase_							 =     self.get_rust_tokenizer()
         UpperCAmelCase_							 =     tokenizer.encode(__snake_case				)
         UpperCAmelCase_							 =     rust_tokenizer.encode(__snake_case				)
         self.assertListEqual(__snake_case					,    __snake_case				)
     def   lowerCamelCase_   (	self   :			Any				):
         UpperCAmelCase_							 =     ErnieMTokenizer(__snake_case					,    do_lower_case=__snake_case					,    unk_token='''<unk>'''					,    pad_token='''<pad>'''				)

         UpperCAmelCase_							 =     tokenizer.tokenize('''This is a test'''				)
         self.assertListEqual(__snake_case					,    ['''▁this''', '''▁is''', '''▁a''', '''▁test''']				)

         self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case				)					,    [48, 25, 21, 12_89]				)

         UpperCAmelCase_							 =     tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
         # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
         self.assertListEqual(
             __snake_case					,    ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']				)
         UpperCAmelCase_							 =     tokenizer.convert_tokens_to_ids(__snake_case				)
         self.assertListEqual(__snake_case					,    [31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]				)

         UpperCAmelCase_							 =     tokenizer.convert_ids_to_tokens(__snake_case				)
         self.assertListEqual(
             __snake_case					,    ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']					,    )
     def   lowerCamelCase_   (	self   :			Any				):
         UpperCAmelCase_							 =     ErnieMTokenizer(__snake_case					,    unk_token='''<unk>'''					,    pad_token='''<pad>'''				)

         UpperCAmelCase_							 =     tokenizer.encode('''sequence builders'''				)
         UpperCAmelCase_							 =     tokenizer.encode('''multi-sequence build'''				)

         UpperCAmelCase_							 =     tokenizer.build_inputs_with_special_tokens(__snake_case				)
         UpperCAmelCase_							 =     tokenizer.build_inputs_with_special_tokens(__snake_case					,    __snake_case				)

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
             tokenizer.sep_token_id
         ] + text_a + [tokenizer.sep_token_id]




     @slow
     def   lowerCamelCase_   (	self   :			List[str]				):
         # fmt: off
         UpperCAmelCase_							 =     {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 9, 3_04, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 5, 5, 5, 16, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 64_60, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 35_59, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=__snake_case					,    model_name='''susnato/ernie-m-base_pytorch'''					,    sequences=[
                 '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
                 '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
                 '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
                 '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
                 '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
                 '''conditioning on both left and right context in all layers.''',
                 '''The quick brown fox jumps over the lazy dog.''',
             ]					,    )






import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    torch.load(__lowercase					,			map_location="cpu"						)
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

						_snake_case						:     List[str]				=    HubertConfig.from_pretrained(__lowercase						)
						_snake_case						:     Union[str, Any]				=    HubertForSequenceClassification.from_pretrained(__lowercase					,			config=__lowercase						)
						_snake_case						:     Union[str, Any]				=    WavaVecaFeatureExtractor.from_pretrained(
						    __lowercase					,			return_attention_mask=__lowercase					,			do_normalize=__lowercase						)

						if hf_congfig.use_weighted_layer_sum:
												_snake_case						:     Optional[int]				=    checkpoint["Featurizer"]["weights"]

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[int]     = 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.')
				__SCREAMING_SNAKE_CASE						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)