






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		:	Optional[Any]      					=			logging.get_logger(__name__)

a		:	str      					=			"Hello world! cécé herlolip"






def     lowerCamelCase__				(   __lowerCamelCase      :					str  ,		__lowerCamelCase      :					str  ,		__lowerCamelCase      :					bool		):
  __UpperCAmelCase				,			__UpperCAmelCase							:     Optional[int]									=  os.path.split(__lowerCamelCase		)
  __UpperCAmelCase							:     List[Any]									=  DataaVecTextModel.from_pretrained(
      __lowerCamelCase  ,		checkpoint_file=__lowerCamelCase		)
  dataavec.eval()  # disable dropout
  __UpperCAmelCase							:     str									=  dataavec.models[0]
  __UpperCAmelCase							:     Tuple									=  dataavec_model.encoder.sentence_encoder
  __UpperCAmelCase							:     Optional[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:
    __UpperCAmelCase							:     Any									=  dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
  print("""Our BERT config:"""  ,		__lowerCamelCase		)

  __UpperCAmelCase							:     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[int]									=  dataavec_sent_encoder.embed_positions.weight
  __UpperCAmelCase							:     Dict									=  torch.zeros_like(
      model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
  __UpperCAmelCase							:     Tuple									=  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							:     int									=  dataavec_layer.self_attn.q_proj.weight
    __UpperCAmelCase							:     int									=  dataavec_layer.self_attn.q_proj.bias
    __UpperCAmelCase							:     Any									=  dataavec_layer.self_attn.k_proj.weight
    __UpperCAmelCase							:     Dict									=  dataavec_layer.self_attn.k_proj.bias
    __UpperCAmelCase							:     str									=  dataavec_layer.self_attn.v_proj.weight
    __UpperCAmelCase							:     List[Any]									=  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							:     Tuple									=  dataavec_layer.self_attn.out_proj.weight
    __UpperCAmelCase							:     List[Any]									=  dataavec_layer.self_attn.out_proj.bias
    __UpperCAmelCase							:     Optional[int]									=  dataavec_layer.self_attn_layer_norm.weight
    __UpperCAmelCase							:     Dict									=  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							:     Any									=  dataavec_layer.fca.weight
    __UpperCAmelCase							:     Union[str, Any]									=  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							:     Optional[int]									=  dataavec_layer.fca.weight
    __UpperCAmelCase							:     Tuple									=  dataavec_layer.fca.bias
    __UpperCAmelCase							:     Optional[Any]									=  dataavec_layer.final_layer_norm.weight
    __UpperCAmelCase							:     Any									=  dataavec_layer.final_layer_norm.bias
    # end of layer

  if classification_head:
    __UpperCAmelCase							:     str									=  dataavec.model.classification_heads["""mnli"""].dense.weight
    __UpperCAmelCase							:     List[Any]									=  dataavec.model.classification_heads["""mnli"""].dense.bias
    __UpperCAmelCase							:     int									=  dataavec.model.classification_heads["""mnli"""].out_proj.weight
    __UpperCAmelCase							:     Any									=  dataavec.model.classification_heads["""mnli"""].out_proj.bias
  else:
    # LM Head
    __UpperCAmelCase							:     Optional[int]									=  dataavec_model.encoder.lm_head.dense.weight
    __UpperCAmelCase							:     Dict									=  dataavec_model.encoder.lm_head.dense.bias
    __UpperCAmelCase							:     Optional[int]									=  dataavec_model.encoder.lm_head.layer_norm.weight
    __UpperCAmelCase							:     int									=  dataavec_model.encoder.lm_head.layer_norm.bias
    __UpperCAmelCase							:     Union[str, Any]									=  dataavec_model.encoder.lm_head.weight
    __UpperCAmelCase							:     int									=  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							:     Optional[int]									=  model(__lowerCamelCase		)[0]
  if classification_head:
    __UpperCAmelCase							:     Any									=  dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__lowerCamelCase		)		)
  else:
    __UpperCAmelCase							:     List[Any]									=  dataavec_model(__lowerCamelCase		)[0]
  print(our_output.shape  ,		their_output.shape		)
  __UpperCAmelCase							:     int									=  torch.max(torch.abs(our_output - their_output		)		).item()
  print(f"""max_absolute_diff = {max_absolute_diff}"""		)  # ~ 1e-7
  __UpperCAmelCase							:     Optional[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		:	Any      					=			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
       )










def     lowerCamelCase__				(   ):

  for a in range(300		):
    for b in range(a + 1  ,		400		):
      for c in range(b + 1  ,		500		):
        if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
          return a * b * c

  return -1






def     lowerCamelCase__				(   ):

  for a in range(300		):
    for b in range(400		):
      __UpperCAmelCase							:     Optional[Any]									=  1000 - a - b
      if a < b < c and (a**2) + (b**2) == (c**2):
        return a * b * c

  return -1






def     lowerCamelCase__				(   ):
  import timeit

  print(
      timeit.timeit("""solution()"""  ,		setup="""from __main__ import solution"""  ,		number=1000		)		)
  print(
      timeit.timeit(
          """solution_fast()"""  ,		setup="""from __main__ import solution_fast"""  ,		number=1000		)		)


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


