'''simple docstring'''

import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
       raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
lowerCAmelCase_			:			int    	=   logging.get_logger(__name__)

lowerCAmelCase_			:			Dict    	=   "Hello world! cécé herlolip"


def __a			(						__lowerCamelCase      :    str			,		__lowerCamelCase      :    str			,		__lowerCamelCase      :    bool					)						->				int:
      '''simple docstring'''


      lowercase_			,				lowercase_     			=   os.path.split(__lowerCamelCase					)
      lowercase_     			=   DataaVecTextModel.from_pretrained(
          __lowerCamelCase			,		checkpoint_file=__lowerCamelCase					)
      dataavec.eval()  # disable dropout
      lowercase_     			=   dataavec.models[0]
      lowercase_     			=   dataavec_model.encoder.sentence_encoder
      lowercase_     			=   DataaVecTextConfig(
          vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings			,		hidden_size=dataavec_model.args.encoder_embed_dim			,		num_hidden_layers=dataavec_model.args.encoder_layers			,		num_attention_heads=dataavec_model.args.encoder_attention_heads			,		intermediate_size=dataavec_model.args.encoder_ffn_embed_dim			,		max_position_embeddings=514			,		type_vocab_size=1			,		layer_norm_eps=1e-5			,		)
      if classification_head:
            lowercase_     			=   dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
      print("Our BERT config:"			,		__lowerCamelCase					)

      lowercase_     			=   DataaVecTextForSequenceClassification(__lowerCamelCase					) if classification_head else DataaVecTextForMaskedLM(__lowerCamelCase					)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      lowercase_     			=   dataavec_sent_encoder.embed_tokens.weight
      lowercase_     			=   dataavec_sent_encoder.embed_positions.weight
      lowercase_     			=   torch.zeros_like(
          model.dataavec_text.embeddings.token_type_embeddings.weight					)  # just zero them out b/c data2vec doesn't use them.
      lowercase_     			=   dataavec_sent_encoder.layernorm_embedding.weight
      lowercase_     			=   dataavec_sent_encoder.layernorm_embedding.bias

      for i in range(config.num_hidden_layers					):
            # Encoder: start of layer
            lowercase_     			=   model.dataavec_text.encoder.layer[i]
            lowercase_     			=   dataavec_sent_encoder.layers[i]

            # self attention
            lowercase_     			=   layer.attention.self
            assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
                (config.hidden_size, config.hidden_size)					), (
                "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
                f' {torch.Size((config.hidden_size, config.hidden_size)					)}'
            )
            assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
                (config.hidden_size, config.hidden_size)					), (
                "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
                f' {torch.Size((config.hidden_size, config.hidden_size)					)}'
            )
            assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
                (config.hidden_size, config.hidden_size)					), (
                "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
                f' {torch.Size((config.hidden_size, config.hidden_size)					)}'
            )

            lowercase_     			=   dataavec_layer.self_attn.q_proj.weight
            lowercase_     			=   dataavec_layer.self_attn.q_proj.bias
            lowercase_     			=   dataavec_layer.self_attn.k_proj.weight
            lowercase_     			=   dataavec_layer.self_attn.k_proj.bias
            lowercase_     			=   dataavec_layer.self_attn.v_proj.weight
            lowercase_     			=   dataavec_layer.self_attn.v_proj.bias

            # self-attention output
            lowercase_     			=   layer.attention.output
            assert (
                self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
            ), f'Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'
            lowercase_     			=   dataavec_layer.self_attn.out_proj.weight
            lowercase_     			=   dataavec_layer.self_attn.out_proj.bias
            lowercase_     			=   dataavec_layer.self_attn_layer_norm.weight
            lowercase_     			=   dataavec_layer.self_attn_layer_norm.bias

            # intermediate
            lowercase_     			=   layer.intermediate
            assert (
                intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
            ), f'Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'
            lowercase_     			=   dataavec_layer.fca.weight
            lowercase_     			=   dataavec_layer.fca.bias

            # output
            lowercase_     			=   layer.output
            assert (
                bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
            ), f'Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'
            lowercase_     			=   dataavec_layer.fca.weight
            lowercase_     			=   dataavec_layer.fca.bias
            lowercase_     			=   dataavec_layer.final_layer_norm.weight
            lowercase_     			=   dataavec_layer.final_layer_norm.bias
            # end of layer

      if classification_head:
            lowercase_     			=   dataavec.model.classification_heads["mnli"].dense.weight
            lowercase_     			=   dataavec.model.classification_heads["mnli"].dense.bias
            lowercase_     			=   dataavec.model.classification_heads["mnli"].out_proj.weight
            lowercase_     			=   dataavec.model.classification_heads["mnli"].out_proj.bias
      else:
            # LM Head
            lowercase_     			=   dataavec_model.encoder.lm_head.dense.weight
            lowercase_     			=   dataavec_model.encoder.lm_head.dense.bias
            lowercase_     			=   dataavec_model.encoder.lm_head.layer_norm.weight
            lowercase_     			=   dataavec_model.encoder.lm_head.layer_norm.bias
            lowercase_     			=   dataavec_model.encoder.lm_head.weight
            lowercase_     			=   dataavec_model.encoder.lm_head.bias

      # Let's check that we get the same results.
      lowercase_     			=   dataavec.encode(__lowerCamelCase					).unsqueeze(0					)  # batch of size 1

      lowercase_     			=   model(__lowerCamelCase					)[0]
      if classification_head:
            lowercase_     			=   dataavec.model.classification_heads["mnli"](dataavec.extract_features(__lowerCamelCase					)					)
      else:
            lowercase_     			=   dataavec_model(__lowerCamelCase					)[0]
      print(our_output.shape			,		their_output.shape					)
      lowercase_     			=   torch.max(torch.abs(our_output - their_output					)					).item()
      print(f'max_absolute_diff = {max_absolute_diff}'					)  # ~ 1e-7
      lowercase_     			=   torch.allclose(__lowerCamelCase			,		__lowerCamelCase			,		atol=1e-3					)
      print("Do both models output the same tensors?"			,		"🔥" if success else "💩"					)
      if not success:
            raise Exception("Something went wRoNg"					)

      pathlib.Path(__lowerCamelCase					).mkdir(parents=__lowerCamelCase			,		exist_ok=__lowerCamelCase					)
      print(f'Saving model to {pytorch_dump_folder_path}'					)
      model.save_pretrained(__lowerCamelCase					)


if __name__ == "__main__":
       lowerCAmelCase_			:			Any    	=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--classification_head", action="store_true", help="Whether to convert a final classification head."
       )
       lowerCAmelCase_			:			List[Any]    	=   parser.parse_args()
       convert_dataavec_checkpoint_to_pytorch(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )







'''simple docstring'''

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCAmelCase_			:			Optional[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",
    "mask_emb": "masked_spec_embed",
}


def __a			(						__lowerCamelCase      :    Dict			,		__lowerCamelCase      :    Optional[int]			,		__lowerCamelCase      :    str			,		__lowerCamelCase      :    int			,		__lowerCamelCase      :    Union[str, Any]					)						->				Optional[Any]:
      '''simple docstring'''


      for attribute in key.split("."					):
            lowercase_     			=   getattr(__lowerCamelCase			,		__lowerCamelCase					)

      if weight_type is not None:
            lowercase_     			=   getattr(__lowerCamelCase			,		__lowerCamelCase					).shape
      else:
            lowercase_     			=   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":
            lowercase_     			=   value
      elif weight_type == "weight_g":
            lowercase_     			=   value
      elif weight_type == "weight_v":
            lowercase_     			=   value
      elif weight_type == "bias":
            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			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Optional[int]					)						->				List[Any]:
      '''simple docstring'''


      lowercase_     			=   []
      lowercase_     			=   fairseq_model.state_dict()

      lowercase_     			=   hf_model.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:
                  for key, mapped_key in MAPPING.items():
                        lowercase_     			=   mapped_key

                        if key in name:
                              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 "weight" in name:
                                    lowercase_     			=   "weight"
                              elif "bias" in name:
                                    lowercase_     			=   "bias"
                              else:
                                    lowercase_     			=   None
                              set_recursively(__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase					)
                        continue
            if not is_used:
                  unused_weights.append(__lowerCamelCase					)

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


def __a			(						__lowerCamelCase      :    Any			,		__lowerCamelCase      :    Optional[int]			,		__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Optional[Any]					)						->				Tuple:
      '''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:
                  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.'
                  )
                  lowercase_     			=   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.'
                  )
                  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:
                  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."
                  )
                  lowercase_     			=   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.'
                  )
                  lowercase_     			=   value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
      else:
            unused_weights.append(__lowerCamelCase					)


def __a			(						__lowerCamelCase      :    Optional[int]					)						->				Dict:
      '''simple docstring'''


      lowercase_     			=   HubertConfig()
      lowercase_     			=   model.config

      lowercase_     			=   fs_config.activation_dropout
      lowercase_     			=   False
      lowercase_     			=   fs_config.attention_dropout
      lowercase_     			=   False
      lowercase_     			=   eval(fs_config.extractor_conv_feature_layers					)
      lowercase_     			=   [x[0] for x in conv_layers]
      lowercase_     			=   [x[1] for x in conv_layers]
      lowercase_     			=   [x[2] for x in conv_layers]
      lowercase_     			=   "gelu"
      lowercase_     			=   "layer" if fs_config.extractor_mode == "layer_norm" else "group"
      lowercase_     			=   False
      lowercase_     			=   0.0
      lowercase_     			=   0.0
      lowercase_     			=   fs_config.activation_fn
      lowercase_     			=   fs_config.dropout
      lowercase_     			=   fs_config.encoder_embed_dim
      lowercase_     			=   0.0_2
      lowercase_     			=   fs_config.encoder_ffn_embed_dim
      lowercase_     			=   1e-5
      lowercase_     			=   0.0
      lowercase_     			=   fs_config.encoder_attention_heads
      lowercase_     			=   fs_config.conv_pos_groups
      lowercase_     			=   fs_config.conv_pos
      lowercase_     			=   len(__lowerCamelCase					)
      lowercase_     			=   fs_config.encoder_layers

      return config


@torch.no_grad()
def __a			(						__lowerCamelCase      :    Optional[Any]			,		__lowerCamelCase      :    Any=None					)						->				Any:
      '''simple docstring'''


      lowercase_     			=   distilhubert().model.model

      if config_path is not None:
            lowercase_     			=   HubertConfig.from_pretrained(__lowerCamelCase					)
      else:
            lowercase_     			=   convert_config(__lowerCamelCase					)
      lowercase_     			=   model.eval()

      lowercase_     			=   WavaVecaFeatureExtractor(
          feature_size=1			,		sampling_rate=16_000			,		padding_value=0			,		do_normalize=__lowerCamelCase			,		return_attention_mask=__lowerCamelCase			,		)
      lowercase_     			=   HubertModel(__lowerCamelCase					)

      recursively_load_weights(__lowerCamelCase			,		__lowerCamelCase					)

      feature_extractor.save_pretrained(__lowerCamelCase					)
      hf_model.save_pretrained(__lowerCamelCase					)


if __name__ == "__main__":
       lowerCAmelCase_			:			str    	=   argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       lowerCAmelCase_			:			str    	=   parser.parse_args()
       convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






