




'''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()
UpperCAmelCase_       				=      logging.get_logger(__name__)

UpperCAmelCase_       				=      'Hello world! cécé herlolip'

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      bool       ):
 '''simple docstring'''





 UpperCAmelCase__							,			UpperCAmelCase__										=    os.path.split(SCREAMING_SNAKE_CASE__       )
 UpperCAmelCase__										=    DataaVecTextModel.from_pretrained(
     SCREAMING_SNAKE_CASE__       ,						checkpoint_file=SCREAMING_SNAKE_CASE__       )
 dataavec.eval()  # disable dropout
 UpperCAmelCase__										=    dataavec.models[0]
 UpperCAmelCase__										=    dataavec_model.encoder.sentence_encoder
 UpperCAmelCase__										=    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:
  UpperCAmelCase__										=    dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
 print("""Our BERT config:"""       ,						SCREAMING_SNAKE_CASE__       )

 UpperCAmelCase__										=    DataaVecTextForSequenceClassification(SCREAMING_SNAKE_CASE__       ) if classification_head else DataaVecTextForMaskedLM(SCREAMING_SNAKE_CASE__       )
 model.eval()

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

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

  # self attention
  UpperCAmelCase__										=    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)       )}'''
  )

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

  # self-attention output
  UpperCAmelCase__										=    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}'''
  UpperCAmelCase__										=    dataavec_layer.self_attn.out_proj.weight
  UpperCAmelCase__										=    dataavec_layer.self_attn.out_proj.bias
  UpperCAmelCase__										=    dataavec_layer.self_attn_layer_norm.weight
  UpperCAmelCase__										=    dataavec_layer.self_attn_layer_norm.bias

  # intermediate
  UpperCAmelCase__										=    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}'''
  UpperCAmelCase__										=    dataavec_layer.fca.weight
  UpperCAmelCase__										=    dataavec_layer.fca.bias

  # output
  UpperCAmelCase__										=    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}'''
  UpperCAmelCase__										=    dataavec_layer.fca.weight
  UpperCAmelCase__										=    dataavec_layer.fca.bias
  UpperCAmelCase__										=    dataavec_layer.final_layer_norm.weight
  UpperCAmelCase__										=    dataavec_layer.final_layer_norm.bias
  # end of layer

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

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

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

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


if __name__ == "__main__":
    UpperCAmelCase_       				=      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.'
    )
    UpperCAmelCase_       				=      parser.parse_args()
    convert_dataavec_checkpoint_to_pytorch(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )






'''simple docstring'''




from collections import namedtuple

UpperCAmelCase_       				=      namedtuple('from_to', 'from_ to')

UpperCAmelCase_       				=      {
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

UpperCAmelCase_       				=      {
    'mm': from_to(0.001, 1_0_0_0),
    'cm': from_to(0.01, 1_0_0),
    'm': from_to(1, 1),
    'km': from_to(1_0_0_0, 0.001),
    'in': from_to(0.0254, 39.3701),
    'ft': from_to(0.3048, 3.2_8084),
    'yd': from_to(0.9144, 1.0_9361),
    'mi': from_to(1609.34, 0.0_0062_1371),
}

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      float       ,						SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      str       ):
 '''simple docstring'''





 UpperCAmelCase__										=    from_type.lower().rstrip("""s"""       )
 UpperCAmelCase__										=    TYPE_CONVERSION.get(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )
 UpperCAmelCase__										=    to_type.lower().rstrip("""s"""       )
 UpperCAmelCase__										=    TYPE_CONVERSION.get(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )
 if new_from not in METRIC_CONVERSION:
  UpperCAmelCase__										=    (
      F'''Invalid \'from_type\' value: {from_type!r}.\n'''
      F'''Conversion abbreviations are: {', '.join(SCREAMING_SNAKE_CASE__       )}'''
  )
  raise ValueError(SCREAMING_SNAKE_CASE__       )
 if new_to not in METRIC_CONVERSION:
  UpperCAmelCase__										=    (
      F'''Invalid \'to_type\' value: {to_type!r}.\n'''
      F'''Conversion abbreviations are: {', '.join(SCREAMING_SNAKE_CASE__       )}'''
  )
  raise ValueError(SCREAMING_SNAKE_CASE__       )
 return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
    import doctest

    doctest.testmod()
