"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase_:					List[Any]        =					logging.get_logger(__name__)

lowerCAmelCase_:					List[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_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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
lowerCAmelCase_:					Tuple        =					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							{}
     with open(_lowerCamelCase    ,				"r"      ) as file:
          for line_number, line in enumerate(_lowerCamelCase      ):
               lowercase__				 =							line.strip()
               if line:
                    lowercase__				 =							line.split()
                    lowercase__				 =							line_number
                    lowercase__				 =							words[0]
                    lowercase__				 =							value
     return result







def 					__a   (		A    ,				A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



     for attribute in key.split("."      ):
          lowercase__				 =							getattr(_lowerCamelCase    ,				_lowerCamelCase      )

     lowercase__				 =							None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(_lowerCamelCase      ):
               lowercase__				 =							PARAM_MAPPING[full_name.split("."      )[-1]]
               lowercase__				 =							'param'

     if weight_type is not None and weight_type != "param":
          lowercase__				 =							getattr(_lowerCamelCase    ,				_lowerCamelCase      ).shape
     elif weight_type is not None and weight_type == "param":
          lowercase__				 =							hf_pointer
          for attribute in hf_param_name.split("."      ):
               lowercase__				 =							getattr(_lowerCamelCase    ,				_lowerCamelCase      )
          lowercase__				 =							shape_pointer.shape

          # let's reduce dimension
          lowercase__				 =							value[0]
     else:
          lowercase__				 =							hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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":
          lowercase__				 =							value
     elif weight_type == "weight_g":
          lowercase__				 =							value
     elif weight_type == "weight_v":
          lowercase__				 =							value
     elif weight_type == "bias":
          lowercase__				 =							value
     elif weight_type == "param":
          for attribute in hf_param_name.split("."      ):
               lowercase__				 =							getattr(_lowerCamelCase    ,				_lowerCamelCase      )
          lowercase__				 =							value
     else:
          lowercase__				 =							value

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







def 					__a   (		A    ,				A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(_lowerCamelCase      ):
               lowercase__				 =							PARAM_MAPPING[full_name.split("."      )[-1]]
               lowercase__				 =							'param'

     if weight_type is not None and weight_type != "param":
          lowercase__				 =							'.'.join([key, weight_type]      )
     elif weight_type is not None and weight_type == "param":
          lowercase__				 =							'.'.join([key, hf_param_name]      )
     else:
          lowercase__				 =							key

     lowercase__				 =							value if 'lm_head' in full_key else value[0]


lowerCAmelCase_:					List[Any]        =					{
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}







def 					__a   (		A    ,				A    ,				A=None    ,				A=None      ):






     '''simple docstring'''



     lowercase__				 =							False
     for key, mapped_key in MAPPING.items():
          lowercase__				 =							'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
          if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
               lowercase__				 =							True
               if "*" in mapped_key:
                    lowercase__				 =							name.split(_lowerCamelCase      )[0].split("."      )[-2]
                    lowercase__				 =							mapped_key.replace("*"    ,				_lowerCamelCase      )
               if "weight_g" in name:
                    lowercase__				 =							'weight_g'
               elif "weight_v" in name:
                    lowercase__				 =							'weight_v'
               elif "bias" in name:
                    lowercase__				 =							'bias'
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    lowercase__				 =							'weight'
               else:
                    lowercase__				 =							None
               if hf_dict is not None:
                    rename_dict(_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase      )
               else:
                    set_recursively(_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase      )
               return is_used
     return is_used







def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							[]
     lowercase__				 =							fairseq_model.state_dict()

     lowercase__				 =							hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          lowercase__				 =							False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				hf_model.config.feat_extract_norm == "group"    ,				)
               lowercase__				 =							True
          else:
               lowercase__				 =							load_wavaveca_layer(_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase      )
          if not is_used:
               unused_weights.append(_lowerCamelCase      )

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







def 					__a   (		A    ,				A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							full_name.split("conv_layers."      )[-1]
     lowercase__				 =							name.split("."      )
     lowercase__				 =							int(items[0]      )
     lowercase__				 =							int(items[1]      )

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        f'''{full_name} has size {value.shape}, but'''
                        f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''      )
               lowercase__				 =							value
               logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        f'''{full_name} has size {value.shape}, but'''
                        f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''      )
               lowercase__				 =							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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f'''{full_name} has size {value.shape}, but'''
                        f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''      )
               lowercase__				 =							value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f'''{full_name} has size {value.shape}, but'''
                        f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''      )
               lowercase__				 =							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 					__a   (		A    ,				A    ,				A=None    ,				A=None    ,				A=True    ,				A=False      ):






     '''simple docstring'''



     if config_path is not None:
          lowercase__				 =							WavaVecaConfig.from_pretrained(_lowerCamelCase      )
     else:
          lowercase__				 =							WavaVecaConfig()

     if is_seq_class:
          lowercase__				 =							read_txt_into_dict(_lowerCamelCase      )
          lowercase__				 =							idalabel
          lowercase__				 =							WavaVecaForSequenceClassification(_lowerCamelCase      )
          lowercase__				 =							WavaVecaFeatureExtractor(
              feature_size=1    ,				sampling_rate=1_60_00    ,				padding_value=0    ,				do_normalize=_lowerCamelCase    ,				return_attention_mask=_lowerCamelCase    ,				)
          feature_extractor.save_pretrained(_lowerCamelCase      )

     elif is_finetuned:
          if dict_path:
               lowercase__				 =							Dictionary.load(_lowerCamelCase      )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               lowercase__				 =							target_dict.pad_index
               lowercase__				 =							target_dict.bos_index
               lowercase__				 =							target_dict.eos_index
               lowercase__				 =							len(target_dict.symbols      )
               lowercase__				 =							os.path.join(_lowerCamelCase    ,				"vocab.json"      )
               if not os.path.isdir(_lowerCamelCase      ):
                    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowerCamelCase      )      )
                    return
               os.makedirs(_lowerCamelCase    ,				exist_ok=_lowerCamelCase      )
               lowercase__				 =							target_dict.indices

               # fairseq has the <pad> and <s> switched
               lowercase__				 =							0
               lowercase__				 =							1
               with open(_lowerCamelCase    ,				"w"    ,				encoding="utf-8"      ) as vocab_handle:
                    json.dump(_lowerCamelCase    ,				_lowerCamelCase      )
               lowercase__				 =							WavaVecaCTCTokenizer(
                   _lowerCamelCase    ,				unk_token=target_dict.unk_word    ,				pad_token=target_dict.pad_word    ,				bos_token=target_dict.bos_word    ,				eos_token=target_dict.eos_word    ,				word_delimiter_token="|"    ,				do_lower_case=_lowerCamelCase    ,				)
               lowercase__				 =							True if config.feat_extract_norm == 'layer' else False
               lowercase__				 =							WavaVecaFeatureExtractor(
                   feature_size=1    ,				sampling_rate=1_60_00    ,				padding_value=0    ,				do_normalize=_lowerCamelCase    ,				return_attention_mask=_lowerCamelCase    ,				)
               lowercase__				 =							WavaVecaProcessor(feature_extractor=_lowerCamelCase    ,				tokenizer=_lowerCamelCase      )
               processor.save_pretrained(_lowerCamelCase      )

          lowercase__				 =							WavaVecaForCTC(_lowerCamelCase      )
     else:
          lowercase__				 =							WavaVecaForPreTraining(_lowerCamelCase      )

     if is_finetuned or is_seq_class:
          lowercase__				 =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]    ,				arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
     else:
          lowercase__				 =							argparse.Namespace(task="audio_pretraining"      )
          lowercase__				 =							fairseq.tasks.setup_task(_lowerCamelCase      )

          lowercase__				 =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,				task=_lowerCamelCase      )

     lowercase__				 =							model[0].eval()

     recursively_load_weights(_lowerCamelCase    ,				_lowerCamelCase    ,				not is_finetuned      )

     hf_wavavec.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
 lowerCAmelCase_:					int        =					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("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 parser.add_argument(
     "--is_seq_class",
     action="store_true",
     help="Whether the model to convert is a fine-tuned sequence classification model or not",
 )
 lowerCAmelCase_:					List[Any]        =					parser.parse_args()

 lowerCAmelCase_:					List[Any]        =					not args.not_finetuned and not args.is_seq_class
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.dict_path,
     is_finetuned,
     args.is_seq_class,
 )





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_:					str     =					logging.get_logger(__name__)

lowerCAmelCase_:					List[Any]     =					{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}






class    a__    (   _a	):
   snake_case_							=      "data2vec-vision"


   def __init__(     self,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=224,				_UpperCAmelCase=16,				_UpperCAmelCase=3,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=True,				_UpperCAmelCase=[3, 5, 7, 11],				_UpperCAmelCase=[1, 2, 3, 6],				_UpperCAmelCase=True,				_UpperCAmelCase=0.4,				_UpperCAmelCase=256,				_UpperCAmelCase=1,				_UpperCAmelCase=False,				_UpperCAmelCase=255,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase  )

        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_act
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps

        lowercase__				 =							image_size
        lowercase__				 =							patch_size
        lowercase__				 =							num_channels
        lowercase__				 =							use_mask_token
        lowercase__				 =							use_absolute_position_embeddings
        lowercase__				 =							use_relative_position_bias
        lowercase__				 =							use_shared_relative_position_bias
        lowercase__				 =							layer_scale_init_value
        lowercase__				 =							drop_path_rate
        lowercase__				 =							use_mean_pooling
        # decode head attributes (semantic segmentation)
        lowercase__				 =							out_indices
        lowercase__				 =							pool_scales
        # auxiliary head attributes (semantic segmentation)
        lowercase__				 =							use_auxiliary_head
        lowercase__				 =							auxiliary_loss_weight
        lowercase__				 =							auxiliary_channels
        lowercase__				 =							auxiliary_num_convs
        lowercase__				 =							auxiliary_concat_input
        lowercase__				 =							semantic_loss_ignore_index






class    a__    (   _a	):
   snake_case_							=      version.parse("1.11"	)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]  )


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return 1E-4




