




"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase      		=			logging.get_logger(__name__)

UpperCAmelCase      		=			{
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      List[Any]        =   '''wavlm'''






       def __init__(   self  :    int  ,     snake_case__  :    int=3_2  ,     snake_case__  :    Optional[int]=7_6_8  ,     snake_case__  :    Optional[int]=1_2  ,     snake_case__  :    List[Any]=1_2  ,     snake_case__  :    List[Any]=3_0_7_2  ,     snake_case__  :    Tuple="gelu"  ,     snake_case__  :    List[str]=0.1  ,     snake_case__  :    Any=0.1  ,     snake_case__  :    int=0.1  ,     snake_case__  :    Tuple=0.0  ,     snake_case__  :    Any=0.1  ,     snake_case__  :    str=0.1  ,     snake_case__  :    Any=0.02  ,     snake_case__  :    Optional[int]=1e-5  ,     snake_case__  :    int="group"  ,     snake_case__  :    List[str]="gelu"  ,     snake_case__  :    int=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)  ,     snake_case__  :    int=(5, 2, 2, 2, 2, 2, 2)  ,     snake_case__  :    Any=(1_0, 3, 3, 3, 3, 2, 2)  ,     snake_case__  :    List[str]=False  ,     snake_case__  :    Any=1_2_8  ,     snake_case__  :    Optional[int]=1_6  ,     snake_case__  :    Dict=3_2_0  ,     snake_case__  :    str=8_0_0  ,     snake_case__  :    int=False  ,     snake_case__  :    Union[str, Any]=True  ,     snake_case__  :    str=0.05  ,     snake_case__  :    Optional[int]=1_0  ,     snake_case__  :    Dict=2  ,     snake_case__  :    Dict=0.0  ,     snake_case__  :    Tuple=1_0  ,     snake_case__  :    str=3_2_0  ,     snake_case__  :    Dict=2  ,     snake_case__  :    Tuple=0.1  ,     snake_case__  :    Optional[int]=1_0_0  ,     snake_case__  :    Optional[Any]=2_5_6  ,     snake_case__  :    List[str]=2_5_6  ,     snake_case__  :    Tuple=0.1  ,     snake_case__  :    List[str]="mean"  ,     snake_case__  :    Tuple=False  ,     snake_case__  :    Optional[int]=False  ,     snake_case__  :    Dict=2_5_6  ,     snake_case__  :    Any=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)  ,     snake_case__  :    List[str]=(5, 3, 3, 1, 1)  ,     snake_case__  :    List[str]=(1, 2, 3, 1, 1)  ,     snake_case__  :    Optional[Any]=5_1_2  ,     snake_case__  :    Any=8_0  ,     snake_case__  :    Optional[int]=0  ,     snake_case__  :    int=1  ,     snake_case__  :    List[str]=2  ,     snake_case__  :    Union[str, Any]=False  ,     snake_case__  :    Optional[int]=3  ,     snake_case__  :    Dict=2  ,     snake_case__  :    Tuple=3  ,     snake_case__  :    Any=None  ,     **snake_case__  :    int  ,     ):

              '''simple docstring'''






              super().__init__(**__SCREAMING_SNAKE_CASE  ,     pad_token_id=__SCREAMING_SNAKE_CASE  ,     bos_token_id=__SCREAMING_SNAKE_CASE  ,     eos_token_id=__SCREAMING_SNAKE_CASE				)
              lowercase		:Any       							=				hidden_size
              lowercase		:Optional[int]       							=				feat_extract_norm
              lowercase		:Tuple       							=				feat_extract_activation
              lowercase		:List[str]       							=				list(__SCREAMING_SNAKE_CASE				)
              lowercase		:Tuple       							=				list(__SCREAMING_SNAKE_CASE				)
              lowercase		:List[Any]       							=				list(__SCREAMING_SNAKE_CASE				)
              lowercase		:Tuple       							=				conv_bias
              lowercase		:List[str]       							=				num_buckets
              lowercase		:Optional[int]       							=				max_bucket_distance
              lowercase		:int       							=				num_conv_pos_embeddings
              lowercase		:int       							=				num_conv_pos_embedding_groups
              lowercase		:Optional[Any]       							=				len(self.conv_dim				)
              lowercase		:List[Any]       							=				num_hidden_layers
              lowercase		:Optional[Any]       							=				intermediate_size
              lowercase		:Tuple       							=				hidden_act
              lowercase		:int       							=				num_attention_heads
              lowercase		:str       							=				hidden_dropout
              lowercase		:List[str]       							=				attention_dropout
              lowercase		:str       							=				activation_dropout
              lowercase		:List[Any]       							=				feat_proj_dropout
              lowercase		:Union[str, Any]       							=				final_dropout
              lowercase		:Optional[Any]       							=				layerdrop
              lowercase		:Optional[int]       							=				layer_norm_eps
              lowercase		:Union[str, Any]       							=				initializer_range
              lowercase		:Union[str, Any]       							=				num_ctc_classes
              lowercase		:Union[str, Any]       							=				vocab_size
              lowercase		:List[Any]       							=				do_stable_layer_norm
              lowercase		:List[str]       							=				use_weighted_layer_sum
              lowercase		:Union[str, Any]       							=				classifier_proj_size

              if (
                  (len(self.conv_stride				) != self.num_feat_extract_layers)
                  or (len(self.conv_kernel				) != self.num_feat_extract_layers)
                  or (len(self.conv_dim				) != self.num_feat_extract_layers)
              ):
                     raise ValueError(
                         '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                         ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                         f""" {len(self.conv_dim				)}`, `len(config.conv_stride) = {len(self.conv_stride				)}`,"""
                         f""" `len(config.conv_kernel) = {len(self.conv_kernel				)}`."""				)

              # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
              lowercase		:Any       							=				apply_spec_augment
              lowercase		:Union[str, Any]       							=				mask_time_prob
              lowercase		:Optional[int]       							=				mask_time_length
              lowercase		:Any       							=				mask_time_min_masks
              lowercase		:List[Any]       							=				mask_feature_prob
              lowercase		:Tuple       							=				mask_feature_length

              # parameters for pretraining with codevector quantized representations
              lowercase		:int       							=				num_codevectors_per_group
              lowercase		:Any       							=				num_codevector_groups
              lowercase		:str       							=				contrastive_logits_temperature
              lowercase		:Optional[int]       							=				num_negatives
              lowercase		:List[Any]       							=				codevector_dim
              lowercase		:int       							=				proj_codevector_dim
              lowercase		:Dict       							=				diversity_loss_weight

              # ctc loss
              lowercase		:Optional[int]       							=				ctc_loss_reduction
              lowercase		:Union[str, Any]       							=				ctc_zero_infinity

              # adapter
              lowercase		:List[Any]       							=				add_adapter
              lowercase		:Dict       							=				adapter_kernel_size
              lowercase		:Tuple       							=				adapter_stride
              lowercase		:str       							=				num_adapter_layers
              lowercase		:Union[str, Any]       							=				output_hidden_size or hidden_size

              # SequenceClassification-specific parameter. Feel free to ignore for other classes.
              lowercase		:Dict       							=				classifier_proj_size

              # XVector-specific parameters. Feel free to ignore for other classes.
              lowercase		:str       							=				list(__SCREAMING_SNAKE_CASE				)
              lowercase		:Tuple       							=				list(__SCREAMING_SNAKE_CASE				)
              lowercase		:int       							=				list(__SCREAMING_SNAKE_CASE				)
              lowercase		:Any       							=				xvector_output_dim







       @property
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              return functools.reduce(operator.mul  ,     self.conv_stride  ,     1				)





"""simple docstring"""



import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCAmelCase      		=			[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]






def       lowerCamelCase					(a_						:Union[str, Any])     ->							Optional[Any]:
       for pegasus_name, hf_name in PATTERNS:
              lowercase		:List[Any]       							=				k.replace(a_				,				a_)
       return k






def       lowerCamelCase					(a_						:dict				,				a_						:dict)     ->							PegasusForConditionalGeneration:
       lowercase		:Union[str, Any]       							=				DEFAULTS.copy()
       cfg_kwargs.update(a_)
       lowercase		:Optional[Any]       							=				PegasusConfig(**a_)
       lowercase		:int       							=				PegasusForConditionalGeneration(a_)
       lowercase		:Optional[int]       							=				torch_model.model.state_dict()
       lowercase		:Tuple       							=				{}
       for k, v in tf_weights.items():
              lowercase		:Dict       							=				rename_state_dict_key(a_)
              if new_k not in sd:
                     raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""")

              if "dense" in k or "proj" in new_k:
                     lowercase		:Union[str, Any]       							=				v.T
              lowercase		:List[str]       							=				torch.tensor(a_				,				dtype=sd[new_k].dtype)
              assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
       # make sure embedding.padding_idx is respected
       lowercase		:Optional[int]       							=				torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1])
       lowercase		:Union[str, Any]       							=				mapping['''shared.weight''']
       lowercase		:Dict       							=				mapping['''shared.weight''']
       lowercase		:int       							=				{k: torch.zeros_like(a_) for k, v in sd.items() if k.endswith('''bias''') and k not in mapping}
       mapping.update(**a_)
       lowercase							,  lowercase		:Tuple       							=				torch_model.model.load_state_dict(a_				,				strict=a_)
       lowercase		:Dict       							=				[
           k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
       ]
       assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
       assert extra == [], F"""no matches found for the following tf keys {extra}"""
       return torch_model






def       lowerCamelCase					(a_						:Union[str, Any]="./ckpt/aeslc/model.ckpt-32000")     ->							Dict:
       lowercase		:Any       							=				tf.train.list_variables(a_)
       lowercase		:str       							=				{}
       lowercase		:Optional[Any]       							=				['''Adafactor''', '''global_step''']
       for name, shape in tqdm(a_				,				desc='''converting tf checkpoint to dict'''):
              lowercase		:str       							=				any(pat in name for pat in ignore_name)
              if skip_key:
                     continue
              lowercase		:str       							=				tf.train.load_variable(a_				,				a_)
              lowercase		:Dict       							=				array
       return tf_weights






def       lowerCamelCase					(a_						:str				,				a_						:str)     ->							List[str]:
       # save tokenizer first
       lowercase		:List[str]       							=				Path(a_).parent.name
       lowercase		:List[Any]       							=				task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
       lowercase		:List[Any]       							=				PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''				,				model_max_length=a_)
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(a_)

       # convert model
       lowercase		:Any       							=				get_tf_weights_as_numpy(a_)
       lowercase		:str       							=				task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              lowercase		:int       							=				task_specific_params
       lowercase		:List[Any]       							=				convert_pegasus(a_				,				a_)
       torch_model.save_pretrained(a_)
       lowercase		:Union[str, Any]       							=				torch_model.state_dict()
       sd.pop('''model.decoder.embed_positions.weight''')
       sd.pop('''model.encoder.embed_positions.weight''')
       torch.save(a_				,				Path(a_) / '''pytorch_model.bin''')


if __name__ == "__main__":
 UpperCAmelCase      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
 parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
 UpperCAmelCase      		=			parser.parse_args()
 if args.save_dir is None:
  UpperCAmelCase      		=			Path(args.tf_ckpt_path).parent.name
  UpperCAmelCase      		=			os.path.join('''pegasus''', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)

