
'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase    					=   {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase    					=   {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = RobertaTokenizer




				def __init__(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Optional[int]=None,    SCREAMING_SNAKE_CASE_	:				List[Any]=None,    SCREAMING_SNAKE_CASE_	:				List[Any]=None,    SCREAMING_SNAKE_CASE_	:				Optional[int]="replace",    SCREAMING_SNAKE_CASE_	:				Any="<s>",    SCREAMING_SNAKE_CASE_	:				List[Any]="</s>",    SCREAMING_SNAKE_CASE_	:				str="</s>",    SCREAMING_SNAKE_CASE_	:				Optional[int]="<s>",    SCREAMING_SNAKE_CASE_	:				Optional[int]="<unk>",    SCREAMING_SNAKE_CASE_	:				Union[str, Any]="<pad>",    SCREAMING_SNAKE_CASE_	:				List[Any]="<mask>",    SCREAMING_SNAKE_CASE_	:				Any=False,    SCREAMING_SNAKE_CASE_	:				Tuple=True,    **SCREAMING_SNAKE_CASE_	:				List[str],    ):
										super().__init__(
										    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    errors=SCREAMING_SNAKE_CASE_,    bos_token=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    sep_token=SCREAMING_SNAKE_CASE_,    cls_token=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    pad_token=SCREAMING_SNAKE_CASE_,    mask_token=SCREAMING_SNAKE_CASE_,    add_prefix_space=SCREAMING_SNAKE_CASE_,    trim_offsets=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										snake_case       :     Optional[int] =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																snake_case       :     List[Any] =    getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('''type'''      )      )
																snake_case       :     Any =    add_prefix_space
																snake_case       :     Union[str, Any] =    pre_tok_class(**SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[Any] =    add_prefix_space

										snake_case       :     Optional[Any] =    '''post_processor'''
										snake_case       :     Any =    getattr(self.backend_tokenizer,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										if tokenizer_component_instance:
																snake_case       :     List[str] =    json.loads(tokenizer_component_instance.__getstate__()      )

																# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																if "sep" in state:
																						snake_case       :     Union[str, Any] =    tuple(state['''sep''']      )
																if "cls" in state:
																						snake_case       :     Union[str, Any] =    tuple(state['''cls''']      )

																snake_case       :     Union[str, Any] =    False

																if state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																						snake_case       :     Tuple =    add_prefix_space
																						snake_case       :     List[str] =    True

																if state.get('''trim_offsets''',    SCREAMING_SNAKE_CASE_      ) != trim_offsets:
																						snake_case       :     Optional[Any] =    trim_offsets
																						snake_case       :     str =    True

																if changes_to_apply:
																						snake_case       :     Any =    getattr(SCREAMING_SNAKE_CASE_,    state.pop('''type'''      )      )
																						snake_case       :     int =    component_class(**SCREAMING_SNAKE_CASE_      )
																						setattr(self.backend_tokenizer,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )




				@property
				def 	__snake_case							(							self	:				Union[str, Any]      ):
										if self._mask_token is None:
																if self.verbose:
																						logger.error('''Using mask_token, but it is not set yet.'''      )
																return None
										return str(self._mask_token      )




				@mask_token.setter
				def 	__snake_case							(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										snake_case       :     Tuple =    AddedToken(SCREAMING_SNAKE_CASE_,    lstrip=SCREAMING_SNAKE_CASE_,    rstrip=SCREAMING_SNAKE_CASE_      ) if isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) else value
										snake_case       :     Dict =    value




				def 	__snake_case							(							self	:				str,    *SCREAMING_SNAKE_CASE_	:				int,    **SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										snake_case       :     Optional[int] =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )
										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				int,    *SCREAMING_SNAKE_CASE_	:				List[Any],    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										snake_case       :     List[Any] =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )

										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										snake_case       :     str =    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_      )
										return tuple(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Optional[int]=None      ):
										snake_case       :     Optional[Any] =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
																return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[int],    SCREAMING_SNAKE_CASE_	:				Optional[List[int]] = None      ):
										snake_case       :     Union[str, Any] =    [self.sep_token_id]
										snake_case       :     Tuple =    [self.cls_token_id]

										if token_ids_a is None:
																return len(cls + token_ids_a + sep      ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]



'''simple docstring'''





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()
UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {
    "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",
}
UpperCAmelCase    					=   [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def      A (       A_							:					Dict    ,							A_							:					Any    ,							A_							:					List[str]    ,							A_							:					Tuple    ,							A_							:					Optional[Any]       ):
						for attribute in key.split('''.'''       ):
												snake_case       :     Tuple =    getattr(A_    ,							A_       )

						if weight_type is not None:
												snake_case       :     Optional[Any] =    getattr(A_    ,							A_       ).shape
						else:
												snake_case       :     Any =    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":
												snake_case       :     Optional[Any] =    value
						elif weight_type == "weight_g":
												snake_case       :     Any =    value
						elif weight_type == "weight_v":
												snake_case       :     int =    value
						elif weight_type == "bias":
												snake_case       :     Any =    value
						else:
												snake_case       :     Optional[int] =    value

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

def      A (       A_							:					str    ,							A_							:					str       ):
						snake_case       :     Dict =    []
						snake_case       :     Optional[Any] =    fairseq_model.state_dict()

						snake_case       :     int =    hf_model.feature_extractor

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

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

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

def      A (       A_							:					Tuple    ,							A_							:					Any    ,							A_							:					int    ,							A_							:					Dict    ,							A_							:					Optional[Any]       ):
						snake_case       :     List[str] =    full_name.split('''conv_layers.'''       )[-1]
						snake_case       :     List[Any] =    name.split('''.'''       )
						snake_case       :     Dict =    int(items[0]       )
						snake_case       :     Any =    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."""
																		)
																		snake_case       :     Tuple =    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."""
																		)
																		snake_case       :     Dict =    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."
																		)
																		snake_case       :     str =    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."""
																		)
																		snake_case       :     Tuple =    value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
						else:
												unused_weights.append(A_       )

@torch.no_grad()
def      A (       A_							:					Tuple    ,							A_							:					Any    ,							A_							:					Optional[Any]=None       ):
						# load the pre-trained checkpoints
						snake_case       :     Any =    torch.load(A_       )
						snake_case       :     List[str] =    WavLMConfigOrig(checkpoint['''cfg''']       )
						snake_case       :     Union[str, Any] =    WavLMOrig(A_       )
						model.load_state_dict(checkpoint['''model''']       )
						model.eval()

						if config_path is not None:
												snake_case       :     Union[str, Any] =    WavLMConfig.from_pretrained(A_       )
						else:
												snake_case       :     Any =    WavLMConfig()

						snake_case       :     Optional[int] =    WavLMModel(A_       )

						recursively_load_weights(A_    ,							A_       )

						hf_wavlm.save_pretrained(A_       )


if __name__ == "__main__":
						UpperCAmelCase    					=   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")
						UpperCAmelCase    					=   parser.parse_args()
						convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

