





"""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()
SCREAMING_SNAKE_CASE__ =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__ =       "Hello world! cécé herlolip"


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						str							,						SCREAMING_SNAKE_CASE					:						str							,						SCREAMING_SNAKE_CASE					:						bool      ):







      '''simple docstring'''




      lowerCAmelCase						,		lowerCAmelCase  						=	os.path.split(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	DataaVecTextModel.from_pretrained(
          SCREAMING_SNAKE_CASE							,						checkpoint_file=SCREAMING_SNAKE_CASE      )
      dataavec.eval()  # disable dropout
      lowerCAmelCase  						=	dataavec.models[0]
      lowerCAmelCase  						=	dataavec_model.encoder.sentence_encoder
      lowerCAmelCase  						=	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=5_14							,						type_vocab_size=1							,						layer_norm_eps=1e-5							,						)
      if classification_head:
            lowerCAmelCase  						=	dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
      print("""Our BERT config:"""							,						SCREAMING_SNAKE_CASE      )

      lowerCAmelCase  						=	DataaVecTextForSequenceClassification(SCREAMING_SNAKE_CASE      ) if classification_head else DataaVecTextForMaskedLM(SCREAMING_SNAKE_CASE      )
      model.eval()

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

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

            # self attention
            lowerCAmelCase  						=	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)      )}'
            )

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

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

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

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

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

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

      lowerCAmelCase  						=	model(SCREAMING_SNAKE_CASE      )[0]
      if classification_head:
            lowerCAmelCase  						=	dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(SCREAMING_SNAKE_CASE      )      )
      else:
            lowerCAmelCase  						=	dataavec_model(SCREAMING_SNAKE_CASE      )[0]
      print(our_output.shape							,						their_output.shape      )
      lowerCAmelCase  						=	torch.max(torch.abs(our_output - their_output      )      ).item()
      print(F'max_absolute_diff = {max_absolute_diff}'      )  # ~ 1e-7
      lowerCAmelCase  						=	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__":
  SCREAMING_SNAKE_CASE__ =       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."
  )
  SCREAMING_SNAKE_CASE__ =       parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )













"""simple docstring"""





import argparse
import json
import re
import string
import sys
from collections import Counter


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						List[Any]      ):







      '''simple docstring'''




      def remove_articles(SCREAMING_SNAKE_CASE					:						int      ):
            return re.sub(R"""\b(a|an|the)\b"""							,						""" """							,						SCREAMING_SNAKE_CASE      )

      def white_space_fix(SCREAMING_SNAKE_CASE					:						int      ):
            return " ".join(text.split()      )

      def remove_punc(SCREAMING_SNAKE_CASE					:						Union[str, Any]      ):
            lowerCAmelCase  						=	set(string.punctuation      )
            return "".join(ch for ch in text if ch not in exclude      )

      def lower(SCREAMING_SNAKE_CASE					:						Optional[Any]      ):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE      )      )      )      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Union[str, Any]							,						SCREAMING_SNAKE_CASE					:						Optional[int]      ):







      '''simple docstring'''




      lowerCAmelCase  						=	normalize_answer(SCREAMING_SNAKE_CASE      ).split()
      lowerCAmelCase  						=	normalize_answer(SCREAMING_SNAKE_CASE      ).split()
      lowerCAmelCase  						=	Counter(SCREAMING_SNAKE_CASE      ) & Counter(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	sum(common.values()      )
      if num_same == 0:
            return 0
      lowerCAmelCase  						=	1.0 * num_same / len(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	1.0 * num_same / len(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	(2 * precision * recall) / (precision + recall)
      return fa


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Tuple							,						SCREAMING_SNAKE_CASE					:						Tuple      ):







      '''simple docstring'''




      return normalize_answer(SCREAMING_SNAKE_CASE      ) == normalize_answer(SCREAMING_SNAKE_CASE      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						List[Any]							,						SCREAMING_SNAKE_CASE					:						Any							,						SCREAMING_SNAKE_CASE					:						str      ):







      '''simple docstring'''




      lowerCAmelCase  						=	[]
      for ground_truth in ground_truths:
            lowerCAmelCase  						=	metric_fn(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )
            scores_for_ground_truths.append(SCREAMING_SNAKE_CASE      )
      return max(SCREAMING_SNAKE_CASE      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Optional[int]							,						SCREAMING_SNAKE_CASE					:						Dict      ):







      '''simple docstring'''




      lowerCAmelCase  						=	lowerCAmelCase  						=	lowerCAmelCase  						=	0
      for article in dataset:
            for paragraph in article["paragraphs"]:
                  for qa in paragraph["qas"]:
                        total += 1
                        if qa["id"] not in predictions:
                              lowerCAmelCase  						=	"""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
                              print(SCREAMING_SNAKE_CASE							,						file=sys.stderr      )
                              continue
                        lowerCAmelCase  						=	[x["""text"""] for x in qa["""answers"""]]
                        lowerCAmelCase  						=	predictions[qa["""id"""]]
                        exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )
                        fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )

      lowerCAmelCase  						=	1_00.0 * exact_match / total
      lowerCAmelCase  						=	1_00.0 * fa / total

      return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__ =       "1.1"
       SCREAMING_SNAKE_CASE__ =       argparse.ArgumentParser(description="Evaluation for SQuAD " + expected_version)
       parser.add_argument("dataset_file", help="Dataset file")
       parser.add_argument("prediction_file", help="Prediction File")
       SCREAMING_SNAKE_CASE__ =       parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              SCREAMING_SNAKE_CASE__ =       json.load(dataset_file)
              if dataset_json["version"] != expected_version:
                     print(
                         "Evaluation expects v-" + expected_version + ", but got dataset with v-" + dataset_json["version"],
                         file=sys.stderr,
                     )
              SCREAMING_SNAKE_CASE__ =       dataset_json["data"]
       with open(args.prediction_file) as prediction_file:
              SCREAMING_SNAKE_CASE__ =       json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))






