


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()
a					:				Union[str, Any]       =      logging.get_logger(__name__)

a					:				str       =      'Hello world! cécé herlolip'






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->str:


 '''simple docstring'''
 snake_case	,       snake_case :			Optional[Any]							=   os.path.split(__lowerCamelCase							)
 snake_case :			Tuple							=   DataaVecTextModel.from_pretrained(
     __lowerCamelCase			,  checkpoint_file=__lowerCamelCase							)
 dataavec.eval()  # disable dropout
 snake_case :			Union[str, Any]							=   dataavec.models[0]
 snake_case :			List[Any]							=   dataavec_model.encoder.sentence_encoder
 snake_case :			List[Any]							=   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:
  snake_case :			Optional[Any]							=   dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
 print('''Our BERT config:'''			,  __lowerCamelCase							)

 snake_case :			Any							=   DataaVecTextForSequenceClassification(__lowerCamelCase							) if classification_head else DataaVecTextForMaskedLM(__lowerCamelCase							)
 model.eval()

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

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

  # self attention
  snake_case :			BertSelfAttention							=   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)							)}'''
  )

  snake_case :			List[str]							=   dataavec_layer.self_attn.q_proj.weight
  snake_case :			Tuple							=   dataavec_layer.self_attn.q_proj.bias
  snake_case :			int							=   dataavec_layer.self_attn.k_proj.weight
  snake_case :			Dict							=   dataavec_layer.self_attn.k_proj.bias
  snake_case :			Dict							=   dataavec_layer.self_attn.v_proj.weight
  snake_case :			int							=   dataavec_layer.self_attn.v_proj.bias

  # self-attention output
  snake_case :			BertSelfOutput							=   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}'''
  snake_case :			Dict							=   dataavec_layer.self_attn.out_proj.weight
  snake_case :			str							=   dataavec_layer.self_attn.out_proj.bias
  snake_case :			Dict							=   dataavec_layer.self_attn_layer_norm.weight
  snake_case :			int							=   dataavec_layer.self_attn_layer_norm.bias

  # intermediate
  snake_case :			BertIntermediate							=   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}'''
  snake_case :			List[str]							=   dataavec_layer.fca.weight
  snake_case :			List[Any]							=   dataavec_layer.fca.bias

  # output
  snake_case :			BertOutput							=   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}'''
  snake_case :			List[str]							=   dataavec_layer.fca.weight
  snake_case :			Any							=   dataavec_layer.fca.bias
  snake_case :			str							=   dataavec_layer.final_layer_norm.weight
  snake_case :			Union[str, Any]							=   dataavec_layer.final_layer_norm.bias
  # end of layer

 if classification_head:
  snake_case :			List[str]							=   dataavec.model.classification_heads['''mnli'''].dense.weight
  snake_case :			Tuple							=   dataavec.model.classification_heads['''mnli'''].dense.bias
  snake_case :			Optional[int]							=   dataavec.model.classification_heads['''mnli'''].out_proj.weight
  snake_case :			Optional[int]							=   dataavec.model.classification_heads['''mnli'''].out_proj.bias
 else:
  # LM Head
  snake_case :			Union[str, Any]							=   dataavec_model.encoder.lm_head.dense.weight
  snake_case :			str							=   dataavec_model.encoder.lm_head.dense.bias
  snake_case :			Optional[int]							=   dataavec_model.encoder.lm_head.layer_norm.weight
  snake_case :			List[Any]							=   dataavec_model.encoder.lm_head.layer_norm.bias
  snake_case :			Dict							=   dataavec_model.encoder.lm_head.weight
  snake_case :			Optional[int]							=   dataavec_model.encoder.lm_head.bias

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

 snake_case :			str							=   model(__lowerCamelCase							)[0]
 if classification_head:
  snake_case :			Dict							=   dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(__lowerCamelCase							)							)
 else:
  snake_case :			Optional[int]							=   dataavec_model(__lowerCamelCase							)[0]
 print(our_output.shape			,  their_output.shape							)
 snake_case :			Optional[int]							=   torch.max(torch.abs(our_output - their_output							)							).item()
 print(f'''max_absolute_diff = {max_absolute_diff}'''							)  # ~ 1e-7
 snake_case :			Any							=   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__":
  a					:				Tuple       =      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.'
  )
  a					:				Optional[Any]       =      parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )










import functools
import operator

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


a					:				Dict       =      logging.get_logger(__name__)

a					:				str       =      {
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hubert"""

       def __init__(      self						,    a__=32						,    a__=7_68						,    a__=12						,    a__=12						,    a__=30_72						,    a__="gelu"						,    a__=0.1						,    a__=0.1						,    a__=0.1						,    a__=True						,    a__=0.0						,    a__=0.1						,    a__=0.1						,    a__=0.02						,    a__=1e-5						,    a__="group"						,    a__="gelu"						,    a__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)						,    a__=(5, 2, 2, 2, 2, 2, 2)						,    a__=(10, 3, 3, 3, 3, 2, 2)						,    a__=False						,    a__=1_28						,    a__=16						,    a__=False						,    a__=True						,    a__=0.05						,    a__=10						,    a__=2						,    a__=0.0						,    a__=10						,    a__=0						,    a__="sum"						,    a__=False						,    a__=False						,    a__=2_56						,    a__=0						,    a__=1						,    a__=2						,    **a__						,    ):






        '''simple docstring'''






        super().__init__(**a__						,    pad_token_id=a__						,    bos_token_id=a__						,    eos_token_id=a__       )
        snake_case :			List[str]							=   hidden_size
        snake_case :			List[str]							=   feat_extract_norm
        snake_case :			List[str]							=   feat_extract_activation
        snake_case :			Dict							=   list(a__       )
        snake_case :			Tuple							=   list(a__       )
        snake_case :			Dict							=   list(a__       )
        snake_case :			str							=   conv_bias
        snake_case :			Dict							=   num_conv_pos_embeddings
        snake_case :			Union[str, Any]							=   num_conv_pos_embedding_groups
        snake_case :			Optional[int]							=   len(self.conv_dim       )
        snake_case :			Tuple							=   num_hidden_layers
        snake_case :			Any							=   intermediate_size
        snake_case :			Optional[Any]							=   hidden_act
        snake_case :			str							=   num_attention_heads
        snake_case :			List[Any]							=   hidden_dropout
        snake_case :			Union[str, Any]							=   attention_dropout
        snake_case :			Dict							=   activation_dropout
        snake_case :			List[str]							=   feat_proj_layer_norm
        snake_case :			List[str]							=   feat_proj_dropout
        snake_case :			Union[str, Any]							=   final_dropout
        snake_case :			List[str]							=   layerdrop
        snake_case :			str							=   layer_norm_eps
        snake_case :			Tuple							=   initializer_range
        snake_case :			str							=   vocab_size
        snake_case :			Tuple							=   do_stable_layer_norm
        snake_case :			Dict							=   use_weighted_layer_sum
        snake_case :			Optional[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
        snake_case :			Optional[int]							=   apply_spec_augment
        snake_case :			int							=   mask_time_prob
        snake_case :			Dict							=   mask_time_length
        snake_case :			Any							=   mask_time_min_masks
        snake_case :			Any							=   mask_feature_prob
        snake_case :			Union[str, Any]							=   mask_feature_length
        snake_case :			List[Any]							=   mask_feature_min_masks

        # ctc loss
        snake_case :			List[str]							=   ctc_loss_reduction
        snake_case :			Dict							=   ctc_zero_infinity



       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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






