


import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english







@require_tokenizers
class 	snake_case_       (A__     ,						unittest.TestCase				):
     UpperCAmelCase__ :   List[str]   				=      RoCBertTokenizer
     UpperCAmelCase__ :   Optional[Any]   				=      None
     UpperCAmelCase__ :   Optional[int]   				=      False
     UpperCAmelCase__ :   int   				=      True
     UpperCAmelCase__ :   Tuple   				=      filter_non_english
     def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Tuple:
            super().setUp()

            a__					=					["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
            a__					=					{}
            a__					=					{}
            for i, value in enumerate(__A				):
                   a__					=					i
                   a__					=					i
            a__					=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['vocab_file']				)
            a__					=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['word_shape_file']				)
            a__					=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['word_pronunciation_file']				)
            with open(self.vocab_file      ,'w'      ,encoding='utf-8'				) as vocab_writer:
                   vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)
            with open(self.word_shape_file      ,'w'      ,encoding='utf-8'				) as word_shape_writer:
                   json.dump(__A      ,__A      ,ensure_ascii=__A				)
            with open(self.word_pronunciation_file      ,'w'      ,encoding='utf-8'				) as word_pronunciation_writer:
                   json.dump(__A      ,__A      ,ensure_ascii=__A				)
     def 			lowerCamelCase__(				self       :List[Any]				) ->   Dict:
            a__					=					self.tokenizer_class(self.vocab_file      ,self.word_shape_file      ,self.word_pronunciation_file				)

            a__					=					tokenizer.tokenize('你好[SEP]你是谁'				)
            self.assertListEqual(__A      ,['你', '好', '[SEP]', '你', '是', '谁']				)
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__A				)      ,[5, 6, 2, 5, 7, 8]				)
            self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(__A				)      ,[5, 6, 2, 5, 7, 8]				)
            self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(__A				)      ,[5, 6, 2, 5, 7, 8]				)
     def 			lowerCamelCase__(				self       :Dict				) ->   int:
            a__					=					RoCBertBasicTokenizer()

            self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'				)      ,['ah', '\u535A', '\u63A8', 'zz']				)
     def 			lowerCamelCase__(				self       :Optional[int]				) ->   Dict:
            a__					=					RoCBertBasicTokenizer(do_lower_case=__A				)

            self.assertListEqual(
                tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '				)      ,['hello', '!', 'how', 'are', 'you', '?']				)
            self.assertListEqual(tokenizer.tokenize('H\u00E9llo'				)      ,['hello']				)
     def 			lowerCamelCase__(				self       :Optional[Any]				) ->   str:
            a__					=					RoCBertBasicTokenizer(do_lower_case=__A      ,strip_accents=__A				)

            self.assertListEqual(
                tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '				)      ,['hällo', '!', 'how', 'are', 'you', '?']				)
            self.assertListEqual(tokenizer.tokenize('H\u00E9llo'				)      ,['h\u00E9llo']				)
     def 			lowerCamelCase__(				self       :Optional[int]				) ->   Union[str, Any]:
            a__					=					RoCBertBasicTokenizer(do_lower_case=__A      ,strip_accents=__A				)

            self.assertListEqual(
                tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '				)      ,['hallo', '!', 'how', 'are', 'you', '?']				)
            self.assertListEqual(tokenizer.tokenize('H\u00E9llo'				)      ,['hello']				)
     def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   int:
            a__					=					RoCBertBasicTokenizer(do_lower_case=__A				)

            self.assertListEqual(
                tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '				)      ,['hallo', '!', 'how', 'are', 'you', '?']				)
            self.assertListEqual(tokenizer.tokenize('H\u00E9llo'				)      ,['hello']				)
     def 			lowerCamelCase__(				self       :List[Any]				) ->   Union[str, Any]:
            a__					=					RoCBertBasicTokenizer(do_lower_case=__A				)

            self.assertListEqual(
                tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '				)      ,['HeLLo', '!', 'how', 'Are', 'yoU', '?']				)
     def 			lowerCamelCase__(				self       :str				) ->   List[Any]:
            a__					=					RoCBertBasicTokenizer(do_lower_case=__A      ,strip_accents=__A				)

            self.assertListEqual(
                tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '				)      ,['HäLLo', '!', 'how', 'Are', 'yoU', '?']				)
     def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Any:
            a__					=					RoCBertBasicTokenizer(do_lower_case=__A      ,strip_accents=__A				)

            self.assertListEqual(
                tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '				)      ,['HaLLo', '!', 'how', 'Are', 'yoU', '?']				)
     def 			lowerCamelCase__(				self       :Any				) ->   Tuple:
            a__					=					RoCBertBasicTokenizer(do_lower_case=__A      ,never_split=['[UNK]']				)

            self.assertListEqual(
                tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'				)      ,['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']				)
     def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   str:
            a__					=					["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

            a__					=					{}
            for i, token in enumerate(__A				):
                   a__					=					i
            a__					=					RoCBertWordpieceTokenizer(vocab=__A      ,unk_token='[UNK]'				)

            self.assertListEqual(tokenizer.tokenize(''				)      ,[]				)

            self.assertListEqual(tokenizer.tokenize('unwanted running'				)      ,['un', '##want', '##ed', 'runn', '##ing']				)

            self.assertListEqual(tokenizer.tokenize('unwantedX running'				)      ,['[UNK]', 'runn', '##ing']				)
     def 			lowerCamelCase__(				self       :Tuple				) ->   Tuple:
            self.assertTrue(_is_whitespace(' '				)				)
            self.assertTrue(_is_whitespace('\t'				)				)
            self.assertTrue(_is_whitespace('\r'				)				)
            self.assertTrue(_is_whitespace('\n'				)				)
            self.assertTrue(_is_whitespace('\u00A0'				)				)

            self.assertFalse(_is_whitespace('A'				)				)
            self.assertFalse(_is_whitespace('-'				)				)
     def 			lowerCamelCase__(				self       :Any				) ->   Optional[Any]:
            self.assertTrue(_is_control('\u0005'				)				)

            self.assertFalse(_is_control('A'				)				)
            self.assertFalse(_is_control(' '				)				)
            self.assertFalse(_is_control('\t'				)				)
            self.assertFalse(_is_control('\r'				)				)
     def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Tuple:
            self.assertTrue(_is_punctuation('-'				)				)
            self.assertTrue(_is_punctuation('$'				)				)
            self.assertTrue(_is_punctuation('`'				)				)
            self.assertTrue(_is_punctuation('.'				)				)

            self.assertFalse(_is_punctuation('A'				)				)
            self.assertFalse(_is_punctuation(' '				)				)
     def 			lowerCamelCase__(				self       :Any				) ->   List[str]:
            a__					=					self.get_tokenizer()

            # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
            self.assertListEqual([tokenizer.tokenize(__A				) for t in ['Test', '\xad', 'test']]      ,[['[UNK]'], [], ['[UNK]']]				)

            if self.test_rust_tokenizer:
                   a__					=					self.get_rust_tokenizer()
                   self.assertListEqual(
                       [rust_tokenizer.tokenize(__A				) for t in ['Test', '\xad', 'test']]      ,[['[UNK]'], [], ['[UNK]']]				)
     def 			lowerCamelCase__(				self       :Optional[Any]				) ->   int:
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
                          a__					=					self.rust_tokenizer_class.from_pretrained(__A      ,**__A				)

                          a__					=					F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
                          a__					=					tokenizer_r.encode_plus(
                              __A      ,return_attention_mask=__A      ,return_token_type_ids=__A      ,return_offsets_mapping=__A      ,add_special_tokens=__A      ,)

                          a__					=					tokenizer_r.do_lower_case if hasattr(__A      ,'do_lower_case'				) else False
                          a__					=					(
                              [
                                  ((0, 0), tokenizer_r.cls_token),
                                  ((0, 1), """A"""),
                                  ((1, 2), ""","""),
                                  ((3, 5), """na"""),
                                  ((5, 6), """##ï"""),
                                  ((6, 8), """##ve"""),
                                  ((9, 15), tokenizer_r.mask_token),
                                  ((16, 21), """Allen"""),
                                  ((21, 23), """##NL"""),
                                  ((23, 24), """##P"""),
                                  ((25, 33), """sentence"""),
                                  ((33, 34), """."""),
                                  ((0, 0), tokenizer_r.sep_token),
                              ]
                              if not do_lower_case
                              else [
                                  ((0, 0), tokenizer_r.cls_token),
                                  ((0, 1), """a"""),
                                  ((1, 2), ""","""),
                                  ((3, 8), """naive"""),
                                  ((9, 15), tokenizer_r.mask_token),
                                  ((16, 21), """allen"""),
                                  ((21, 23), """##nl"""),
                                  ((23, 24), """##p"""),
                                  ((25, 33), """sentence"""),
                                  ((33, 34), """."""),
                                  ((0, 0), tokenizer_r.sep_token),
                              ]
                          )

                          self.assertEqual(
                              [e[1] for e in expected_results]      ,tokenizer_r.convert_ids_to_tokens(tokens['input_ids']				)				)
                          self.assertEqual([e[0] for e in expected_results]      ,tokens['offset_mapping']				)
     def 			lowerCamelCase__(				self       :int				) ->   List[Any]:
            a__					=					["""的""", """人""", """有"""]
            a__					=					"""""".join(__A				)
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
                          a__					=					True
                          a__					=					self.tokenizer_class.from_pretrained(__A      ,**__A				)
                          a__					=					self.rust_tokenizer_class.from_pretrained(__A      ,**__A				)

                          a__					=					tokenizer_p.encode(__A      ,add_special_tokens=__A				)
                          a__					=					tokenizer_r.encode(__A      ,add_special_tokens=__A				)

                          a__					=					tokenizer_r.convert_ids_to_tokens(__A				)
                          a__					=					tokenizer_p.convert_ids_to_tokens(__A				)

                          # it is expected that each Chinese character is not preceded by "##"
                          self.assertListEqual(__A      ,__A				)
                          self.assertListEqual(__A      ,__A				)

                          a__					=					False
                          a__					=					self.rust_tokenizer_class.from_pretrained(__A      ,**__A				)
                          a__					=					self.tokenizer_class.from_pretrained(__A      ,**__A				)

                          a__					=					tokenizer_r.encode(__A      ,add_special_tokens=__A				)
                          a__					=					tokenizer_p.encode(__A      ,add_special_tokens=__A				)

                          a__					=					tokenizer_r.convert_ids_to_tokens(__A				)
                          a__					=					tokenizer_p.convert_ids_to_tokens(__A				)

                          # it is expected that only the first Chinese character is not preceded by "##".
                          a__					=					[
                              F'##{token}' if idx != 0 else token for idx, token in enumerate(__A				)
                          ]
                          self.assertListEqual(__A      ,__A				)
                          self.assertListEqual(__A      ,__A				)
     @slow
     def 			lowerCamelCase__(				self       :Any				) ->   Any:
            a__					=					self.tokenizer_class(self.vocab_file      ,self.word_shape_file      ,self.word_pronunciation_file				)

            a__					=					tokenizer.encode('你好'      ,add_special_tokens=__A				)
            a__					=					tokenizer.encode('你是谁'      ,add_special_tokens=__A				)

            a__					=					tokenizer.build_inputs_with_special_tokens(__A				)
            a__					=					tokenizer.build_inputs_with_special_tokens(__A      ,__A				)

            assert encoded_sentence == [1] + text + [2]
            assert encoded_pair == [1] + text + [2] + text_a + [2]



     def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
            a__					=					self.get_tokenizers(do_lower_case=__A				)
            for tokenizer in tokenizers:
                   with self.subTest(F'{tokenizer.__class__.__name__}'				):
                          a__					=					"""你好，你是谁"""
                          a__					=					tokenizer.tokenize(__A				)
                          a__					=					tokenizer.convert_tokens_to_ids(__A				)
                          a__					=					tokenizer.convert_tokens_to_shape_ids(__A				)
                          a__					=					tokenizer.convert_tokens_to_pronunciation_ids(__A				)
                          a__					=					tokenizer.prepare_for_model(
                              __A      ,__A      ,__A      ,add_special_tokens=__A				)

                          a__					=					tokenizer.encode_plus(__A      ,add_special_tokens=__A				)

                          self.assertEqual(__A      ,__A				)








import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
snake_case							:	str					      =				logging.get_logger(__name__)

snake_case							:	Optional[Any]					      =				{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
snake_case							:	Dict					      =				[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Dict       ):
							for attribute in key.split('.'       ):
														a__					=					getattr(__lowerCAmelCase ,   __lowerCAmelCase       )

							if weight_type is not None:
														a__					=					getattr(__lowerCAmelCase ,   __lowerCAmelCase       ).shape
							else:
														a__					=					hf_pointer.shape

							assert hf_shape == value.shape, (
							    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
							    F' {value.shape} for {full_name}'
							)

							if weight_type == "weight":
														a__					=					value
							elif weight_type == "weight_g":
														a__					=					value
							elif weight_type == "weight_v":
														a__					=					value
							elif weight_type == "bias":
														a__					=					value
							else:
														a__					=					value

							logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'       )






def 							__lowercase       (			__lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    List[Any]       ):
							a__					=					[]
							a__					=					fairseq_model.state_dict()

							a__					=					hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														a__					=					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   hf_model.config.feat_extract_norm == 'group' ,   )
																					a__					=					True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('w2v_model.'       )[-1] == name.split('.'       )[0]:
																																			a__					=					True
																																			if "*" in mapped_key:
																																										a__					=					name.split(__lowerCAmelCase       )[0].split('.'       )[-2]
																																										a__					=					mapped_key.replace('*' ,   __lowerCAmelCase       )
																																			if "weight_g" in name:
																																										a__					=					'weight_g'
																																			elif "weight_v" in name:
																																										a__					=					'weight_v'
																																			elif "bias" in name and "relative_attention_bias" not in name:
																																										a__					=					'bias'
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										a__					=					'weight'
																																			else:
																																										a__					=					None

																																			set_recursively(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
																												continue
														if not is_used:
																					unused_weights.append(__lowerCAmelCase       )

							logger.warning(F'Unused weights: {unused_weights}'       )






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Optional[int]       ):
							a__					=					full_name.split('conv_layers.'       )[-1]
							a__					=					name.split('.'       )
							a__					=					int(items[0]       )
							a__					=					int(items[1]       )

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
																					)
																					a__					=					value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
																					)
																					a__					=					value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
																					    " found."
																					)
																					a__					=					value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
																					)
																					a__					=					value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
							else:
														unused_weights.append(__lowerCAmelCase       )






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    List[Any]=None       ):
							# load the pre-trained checkpoints
							a__					=					torch.load(__lowerCAmelCase       )
							a__					=					WavLMConfigOrig(checkpoint['cfg']       )
							a__					=					WavLMOrig(__lowerCAmelCase       )
							model.load_state_dict(checkpoint['model']       )
							model.eval()

							if config_path is not None:
														a__					=					WavLMConfig.from_pretrained(__lowerCAmelCase       )
							else:
														a__					=					WavLMConfig()

							a__					=					WavLMModel(__lowerCAmelCase       )

							recursively_load_weights(__lowerCAmelCase ,   __lowerCAmelCase       )

							hf_wavlm.save_pretrained(__lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	Union[str, Any]					      =				argparse.ArgumentParser()
						parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
						parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
						snake_case							:	Union[str, Any]					      =				parser.parse_args()
						convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




