code: 
  
  """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
      )
  
  
style_context: 
  
  """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() = }''')
  
  