
'''simple docstring'''





import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCAmelCase    					=   logging.get_logger(__name__)

class 						a   :




				def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										snake_case       :     str =    question_encoder
										snake_case       :     Tuple =    generator
										snake_case       :     Optional[int] =    self.question_encoder




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):

										if os.path.isfile(__SCREAMING_SNAKE_CASE      ):
																raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""      )
										os.makedirs(__SCREAMING_SNAKE_CASE,    exist_ok=__SCREAMING_SNAKE_CASE      )
										snake_case       :     List[str] =    os.path.join(__SCREAMING_SNAKE_CASE,    '''question_encoder_tokenizer'''      )
										snake_case       :     Union[str, Any] =    os.path.join(__SCREAMING_SNAKE_CASE,    '''generator_tokenizer'''      )
										self.question_encoder.save_pretrained(__SCREAMING_SNAKE_CASE      )
										self.generator.save_pretrained(__SCREAMING_SNAKE_CASE      )




				@classmethod
				def 	__snake_case							(							cls	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				str,    **SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										from ..auto.tokenization_auto import AutoTokenizer

										snake_case       :     Optional[Any] =    kwargs.pop('''config''',    __SCREAMING_SNAKE_CASE      )

										if config is None:
																snake_case       :     int =    RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE      )

										snake_case       :     List[str] =    AutoTokenizer.from_pretrained(
										    __SCREAMING_SNAKE_CASE,    config=config.question_encoder,    subfolder='''question_encoder_tokenizer'''      )
										snake_case       :     Tuple =    AutoTokenizer.from_pretrained(
										    __SCREAMING_SNAKE_CASE,    config=config.generator,    subfolder='''generator_tokenizer'''      )
										return cls(question_encoder=__SCREAMING_SNAKE_CASE,    generator=__SCREAMING_SNAKE_CASE      )




				def __call__(							self	:				Tuple,    *SCREAMING_SNAKE_CASE_	:				str,    **SCREAMING_SNAKE_CASE_	:				int      ):
										return self.current_tokenizer(*__SCREAMING_SNAKE_CASE,    **__SCREAMING_SNAKE_CASE      )




				def 	__snake_case							(							self	:				Dict,    *SCREAMING_SNAKE_CASE_	:				Union[str, Any],    **SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										return self.generator.batch_decode(*__SCREAMING_SNAKE_CASE,    **__SCREAMING_SNAKE_CASE      )




				def 	__snake_case							(							self	:				Tuple,    *SCREAMING_SNAKE_CASE_	:				Optional[Any],    **SCREAMING_SNAKE_CASE_	:				List[str]      ):
										return self.generator.decode(*__SCREAMING_SNAKE_CASE,    **__SCREAMING_SNAKE_CASE      )




				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Optional[Any] =    self.question_encoder




				def 	__snake_case							(							self	:				int      ):
										snake_case       :     Dict =    self.generator




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[List[str]] = None,    SCREAMING_SNAKE_CASE_	:				Optional[int] = None,    SCREAMING_SNAKE_CASE_	:				Optional[int] = None,    SCREAMING_SNAKE_CASE_	:				str = "longest",    SCREAMING_SNAKE_CASE_	:				str = None,    SCREAMING_SNAKE_CASE_	:				bool = True,    **SCREAMING_SNAKE_CASE_	:				int,    ):
										warnings.warn(
										    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
										    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
										    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
										    '''details''',    __SCREAMING_SNAKE_CASE,    )
										if max_length is None:
																snake_case       :     Tuple =    self.current_tokenizer.model_max_length
										snake_case       :     List[Any] =    self(
										    __SCREAMING_SNAKE_CASE,    add_special_tokens=__SCREAMING_SNAKE_CASE,    return_tensors=__SCREAMING_SNAKE_CASE,    max_length=__SCREAMING_SNAKE_CASE,    padding=__SCREAMING_SNAKE_CASE,    truncation=__SCREAMING_SNAKE_CASE,    **__SCREAMING_SNAKE_CASE,    )
										if tgt_texts is None:
																return model_inputs
										# Process tgt_texts
										if max_target_length is None:
																snake_case       :     Union[str, Any] =    self.current_tokenizer.model_max_length
										snake_case       :     Dict =    self(
										    text_target=__SCREAMING_SNAKE_CASE,    add_special_tokens=__SCREAMING_SNAKE_CASE,    return_tensors=__SCREAMING_SNAKE_CASE,    padding=__SCREAMING_SNAKE_CASE,    max_length=__SCREAMING_SNAKE_CASE,    truncation=__SCREAMING_SNAKE_CASE,    **__SCREAMING_SNAKE_CASE,    )
										snake_case       :     Union[str, Any] =    labels['''input_ids''']
										return model_inputs





'''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)

