

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

_A						=     """Hello world! cécé herlolip"""




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   List[Any]:
      UpperCAmelCase__				,		UpperCAmelCase__ :       List[Any]						=						os.path.split(lowerCAmelCase						)
      UpperCAmelCase__ :       List[Any]						=						DataaVecTextModel.from_pretrained(
          lowerCAmelCase ,  checkpoint_file=lowerCAmelCase						)
      dataavec.eval()  # disable dropout
      UpperCAmelCase__ :       Dict						=						dataavec.models[0]
      UpperCAmelCase__ :       Tuple						=						dataavec_model.encoder.sentence_encoder
      UpperCAmelCase__ :       Dict						=						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:
            UpperCAmelCase__ :       Optional[int]						=						dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
      print("""Our BERT config:""" ,  lowerCAmelCase						)

      UpperCAmelCase__ :       Union[str, Any]						=						DataaVecTextForSequenceClassification(lowerCAmelCase						) if classification_head else DataaVecTextForMaskedLM(lowerCAmelCase						)
      model.eval()

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

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

            # self attention
            UpperCAmelCase__ :       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)						)}"""
            )

            UpperCAmelCase__ :       Optional[int]						=						dataavec_layer.self_attn.q_proj.weight
            UpperCAmelCase__ :       Union[str, Any]						=						dataavec_layer.self_attn.q_proj.bias
            UpperCAmelCase__ :       Union[str, Any]						=						dataavec_layer.self_attn.k_proj.weight
            UpperCAmelCase__ :       Optional[int]						=						dataavec_layer.self_attn.k_proj.bias
            UpperCAmelCase__ :       Union[str, Any]						=						dataavec_layer.self_attn.v_proj.weight
            UpperCAmelCase__ :       Tuple						=						dataavec_layer.self_attn.v_proj.bias

            # self-attention output
            UpperCAmelCase__ :       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}"""
            UpperCAmelCase__ :       Optional[Any]						=						dataavec_layer.self_attn.out_proj.weight
            UpperCAmelCase__ :       Dict						=						dataavec_layer.self_attn.out_proj.bias
            UpperCAmelCase__ :       Union[str, Any]						=						dataavec_layer.self_attn_layer_norm.weight
            UpperCAmelCase__ :       Tuple						=						dataavec_layer.self_attn_layer_norm.bias

            # intermediate
            UpperCAmelCase__ :       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}"""
            UpperCAmelCase__ :       Optional[Any]						=						dataavec_layer.fca.weight
            UpperCAmelCase__ :       List[str]						=						dataavec_layer.fca.bias

            # output
            UpperCAmelCase__ :       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}"""
            UpperCAmelCase__ :       Dict						=						dataavec_layer.fca.weight
            UpperCAmelCase__ :       List[str]						=						dataavec_layer.fca.bias
            UpperCAmelCase__ :       int						=						dataavec_layer.final_layer_norm.weight
            UpperCAmelCase__ :       Dict						=						dataavec_layer.final_layer_norm.bias
            # end of layer

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

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

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




"""simple docstring"""
from __future__ import annotations




def 				a__	(	lowerCAmelCase						)  ->   set[int]:
      UpperCAmelCase__ :       List[Any]						=						{1}
      UpperCAmelCase__ :       Optional[int]						=						[1]
      for _ in range(2 ,  depth + 1						):
            UpperCAmelCase__ :       Tuple						=						[*previous_coefficients, 0]
            UpperCAmelCase__ :       List[str]						=						[0, *previous_coefficients]
            UpperCAmelCase__ :       Dict						=						[]
            for x, y in zip(lowerCAmelCase ,  lowerCAmelCase						):
                  coefficients.add(x + y						)
                  previous_coefficients.append(x + y						)
      return coefficients




def 				a__	(	lowerCAmelCase						)  ->   set[int]:
      UpperCAmelCase__ :       List[str]						=						set()
      for number in unique_coefficients:
            UpperCAmelCase__ :       int						=						2
            UpperCAmelCase__ :       List[Any]						=						number
            while divisor**2 <= copy_number:
                  UpperCAmelCase__ :       Tuple						=						0
                  while copy_number % divisor == 0:
                        copy_number //= divisor
                        multiplicity += 1
                  if multiplicity >= 2:
                        non_squarefrees.add(lowerCAmelCase						)
                        break
                  divisor += 1

      return unique_coefficients.difference(lowerCAmelCase						)




def 				a__	(	lowerCAmelCase = 51						)  ->   int:
      UpperCAmelCase__ :       Any						=						get_pascal_triangle_unique_coefficients(lowerCAmelCase						)
      UpperCAmelCase__ :       Dict						=						get_squarefrees(lowerCAmelCase						)
      return sum(lowerCAmelCase						)


if __name__ == "__main__":
    print(f'''{solution() = }''')

