
'''simple docstring'''





import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


UpperCAmelCase    					=   get_logger(__name__)


UpperCAmelCase    					=   r"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"

class 						a   :




				@add_start_docstrings(SCREAMING_SNAKE_CASE_      )
				def __call__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray      ):
										raise NotImplementedError(
										    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""      )




class 						a   :




				@add_start_docstrings(SCREAMING_SNAKE_CASE_      )
				def __call__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray      ):
										raise NotImplementedError(
										    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""      )




class 						a   (				__magic_name__    ):




				@add_start_docstrings(SCREAMING_SNAKE_CASE_      )
				def __call__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int,    **SCREAMING_SNAKE_CASE_	:				Any      ):
										for processor in self:
																snake_case       :     List[Any] =    inspect.signature(processor.__call__      ).parameters
																if len(SCREAMING_SNAKE_CASE_      ) > 3:
																						if not all(arg in kwargs for arg in list(function_args.keys()      )[2:]      ):
																												raise ValueError(
																												    F"""Make sure that all the required parameters: {list(function_args.keys()      )} for """
																												    F"""{processor.__class__} are passed to the logits processor."""      )
																						snake_case       :     List[Any] =    processor(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )
																else:
																						snake_case       :     Dict =    processor(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				float      ):
										if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) or not (temperature > 0):
																raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}"""      )

										snake_case       :     int =    temperature




				def __call__(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     List[Any] =    scores / self.temperature
										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				float,    SCREAMING_SNAKE_CASE_	:				float = -float('''Inf'''      ),    SCREAMING_SNAKE_CASE_	:				int = 1      ):
										if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) or (top_p < 0 or top_p > 1.0):
																raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}"""      )
										if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) or (min_tokens_to_keep < 1):
																raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"""      )

										snake_case       :     Any =    top_p
										snake_case       :     List[str] =    filter_value
										snake_case       :     str =    min_tokens_to_keep




				def __call__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Dict =    lax.top_k(SCREAMING_SNAKE_CASE_,    scores.shape[-1]      )

										snake_case       :     Optional[int] =    jnp.full_like(SCREAMING_SNAKE_CASE_,    self.filter_value      )
										snake_case       :     Tuple =    jax.nn.softmax(SCREAMING_SNAKE_CASE_,    axis=-1      ).cumsum(axis=-1      )
										snake_case       :     str =    cumulative_probs < self.top_p

										# include the token that is higher than top_p as well
										snake_case       :     str =    jnp.roll(SCREAMING_SNAKE_CASE_,    1      )
										score_mask |= score_mask.at[:, 0].set(SCREAMING_SNAKE_CASE_      )

										# min tokens to keep
										snake_case       :     Optional[Any] =    score_mask.at[:, : self.min_tokens_to_keep].set(SCREAMING_SNAKE_CASE_      )

										snake_case       :     Union[str, Any] =    jnp.where(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    jax.lax.sort_key_val(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )[-1]

										return next_scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				float = -float('''Inf'''      ),    SCREAMING_SNAKE_CASE_	:				int = 1      ):
										if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) or top_k <= 0:
																raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}"""      )

										snake_case       :     Dict =    max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    filter_value




				def __call__(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     int =    scores.shape
										snake_case       :     int =    jnp.full(batch_size * vocab_size,    self.filter_value      )

										snake_case       :     Dict =    min(self.top_k,    scores.shape[-1]      )  # Safety check
										snake_case       :     Optional[Any] =    lax.top_k(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    jnp.broadcast_to((jnp.arange(SCREAMING_SNAKE_CASE_      ) * vocab_size)[:, None],    (batch_size, topk)      ).flatten()
										snake_case       :     Dict =    topk_scores.flatten()
										snake_case       :     Optional[Any] =    topk_indices.flatten() + shift

										snake_case       :     Tuple =    next_scores_flat.at[topk_indices_flat].set(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    next_scores_flat.reshape(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										return next_scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     List[Any] =    bos_token_id




				def __call__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     int =    jnp.full(scores.shape,    -float('''inf'''      )      )

										snake_case       :     List[Any] =    1 - jnp.bool_(cur_len - 1      )

										snake_case       :     Tuple =    jnp.where(SCREAMING_SNAKE_CASE_,    new_scores.at[:, self.bos_token_id].set(0      ),    SCREAMING_SNAKE_CASE_      )

										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     List[str] =    max_length
										snake_case       :     Tuple =    eos_token_id




				def __call__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Any =    jnp.full(scores.shape,    -float('''inf'''      )      )

										snake_case       :     Dict =    1 - jnp.bool_(cur_len - self.max_length + 1      )

										snake_case       :     int =    jnp.where(SCREAMING_SNAKE_CASE_,    new_scores.at[:, self.eos_token_id].set(0      ),    SCREAMING_SNAKE_CASE_      )

										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				int      ):
										if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) or min_length < 0:
																raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}"""      )

										if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) or eos_token_id < 0:
																raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}"""      )

										snake_case       :     Optional[int] =    min_length
										snake_case       :     str =    eos_token_id




				def __call__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										# create boolean flag to decide if min length penalty should be applied
										snake_case       :     int =    1 - jnp.clip(cur_len - self.min_length,    0,    1      )

										snake_case       :     Optional[int] =    jnp.where(SCREAMING_SNAKE_CASE_,    scores.at[:, self.eos_token_id].set(-float('''inf'''      )      ),    SCREAMING_SNAKE_CASE_      )

										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Dict      ):
										snake_case       :     Union[str, Any] =    list(SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[Any] =    begin_index




				def __call__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Optional[int] =    1 - jnp.bool_(cur_len - self.begin_index      )

										snake_case       :     int =    jnp.where(SCREAMING_SNAKE_CASE_,    scores.at[:, self.begin_suppress_tokens].set(-float('''inf'''      )      ),    SCREAMING_SNAKE_CASE_      )

										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				list      ):
										snake_case       :     Optional[int] =    list(SCREAMING_SNAKE_CASE_      )




				def __call__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     List[Any] =    scores.at[..., self.suppress_tokens].set(-float('''inf'''      )      )

										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Dict =    dict(SCREAMING_SNAKE_CASE_      )
										# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
										# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
										# Indexes without forced tokens will have a negative value.
										snake_case       :     Tuple =    jnp.ones((max(force_token_map.keys()      ) + 1),    dtype=jnp.intaa      ) * -1
										for index, token in force_token_map.items():
																if token is not None:
																						snake_case       :     Any =    force_token_array.at[index].set(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Any =    jnp.intaa(SCREAMING_SNAKE_CASE_      )




				def __call__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										def _force_token(SCREAMING_SNAKE_CASE_	:				Dict      ):
																snake_case       :     Union[str, Any] =    scores.shape[0]
																snake_case       :     Optional[Any] =    self.force_token_array[generation_idx]

																snake_case       :     List[str] =    jnp.ones_like(SCREAMING_SNAKE_CASE_,    dtype=scores.dtype      ) * -float('''inf'''      )
																snake_case       :     Optional[Any] =    jnp.zeros((batch_size, 1),    dtype=scores.dtype      )
																snake_case       :     List[str] =    lax.dynamic_update_slice(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    (0, current_token)      )
																return new_scores

										snake_case       :     List[Any] =    lax.cond(
										    cur_len >= self.force_token_array.shape[0],    lambda: scores,    lambda: lax.cond(
										        self.force_token_array[cur_len] >= 0,    lambda: _force_token(SCREAMING_SNAKE_CASE_      ),    lambda: scores,    ),    )
										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				List[str]      ):
										snake_case       :     int =    generate_config.eos_token_id
										snake_case       :     Any =    generate_config.no_timestamps_token_id
										snake_case       :     Tuple =    generate_config.no_timestamps_token_id + 1

										snake_case       :     int =    decoder_input_length + 1

										if generate_config.is_multilingual:
																# room for language token and task token
																self.begin_index += 2
										if hasattr(SCREAMING_SNAKE_CASE_,    '''max_initial_timestamp_index'''      ):
																snake_case       :     int =    generate_config.max_initial_timestamp_index
										else:
																snake_case       :     Optional[int] =    model_config.vocab_size
										if self.max_initial_timestamp_index is None:
																snake_case       :     str =    model_config.vocab_size




				def __call__(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				int      ):
										# suppress <|notimestamps|> which is handled by without_timestamps
										snake_case       :     Optional[Any] =    scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''      )      )

										def handle_pairs(SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
																snake_case       :     int =    jnp.where((cur_len - self.begin_index) >= 1,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
																snake_case       :     Dict =    jnp.where(
																    input_ids_k[cur_len - 1] >= self.timestamp_begin,    True and last_was_timestamp,    SCREAMING_SNAKE_CASE_,    )

																snake_case       :     Optional[int] =    jnp.where((cur_len - self.begin_index) < 2,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
																snake_case       :     Dict =    jnp.where(
																    input_ids_k[cur_len - 2] >= self.timestamp_begin,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    )

																return jnp.where(
																    SCREAMING_SNAKE_CASE_,    jnp.where(
																        penultimate_was_timestamp > 0,    scores_k.at[self.timestamp_begin :].set(-float('''inf'''      )      ),    scores_k.at[: self.eos_token_id].set(-float('''inf'''      )      ),    ),    SCREAMING_SNAKE_CASE_,    )

										snake_case       :     Optional[Any] =    jax.vmap(SCREAMING_SNAKE_CASE_      )(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )

										snake_case       :     Tuple =    jnp.where(cur_len == self.begin_index,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[int] =    jnp.where(
										    self.max_initial_timestamp_index is not None,    True and apply_max_initial_timestamp,    SCREAMING_SNAKE_CASE_,    )

										snake_case       :     int =    self.timestamp_begin + self.max_initial_timestamp_index

										snake_case       :     List[str] =    jnp.where(
										    SCREAMING_SNAKE_CASE_,    scores.at[:, last_allowed + 1 :].set(-float('''inf'''      )      ),    SCREAMING_SNAKE_CASE_,    )

										# if sum of probability over timestamps is above any other token, sample timestamp
										snake_case       :     Tuple =    jax.nn.log_softmax(SCREAMING_SNAKE_CASE_,    axis=-1      )

										def handle_cumulative_probs(SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
																snake_case       :     Union[str, Any] =    jax.nn.logsumexp(logprobs_k[self.timestamp_begin :],    axis=-1      )
																snake_case       :     Dict =    jnp.max(logprobs_k[: self.timestamp_begin]      )
																return jnp.where(
																    timestamp_logprob > max_text_token_logprob,    scores_k.at[: self.timestamp_begin].set(-float('''inf'''      )      ),    SCREAMING_SNAKE_CASE_,    )

										snake_case       :     Any =    jax.vmap(SCREAMING_SNAKE_CASE_      )(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )

										return scores





'''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)

