






"""simple docstring"""







import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(_snake_case				,	_snake_case							)

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	emb.weight.shape
      UpperCAmelCase          =	nn.Linear(_snake_case				,	_snake_case				,	bias=_snake_case							)
      UpperCAmelCase          =	emb.weight.data
      return lin_layer

def _a			(		_snake_case				,	_snake_case=None							):







      """simple docstring"""






      UpperCAmelCase          =	{}
      for old_key in state_dict.keys():
            UpperCAmelCase          =	old_key
            if "moe_layer.experts." in key:
                  if expert_idx is not None:
                        UpperCAmelCase          =	key.replace("""moe_layer.experts.0"""				,	F'''ffn.experts.expert_{expert_idx}'''							)
                  else:
                        UpperCAmelCase          =	key.replace("""moe_layer.experts."""				,	"""ffn.experts.expert_"""							)
            if "gate" in key:
                  UpperCAmelCase          =	key.replace(""".moe_layer.gate.wg"""				,	""".ffn.router.classifier"""							)
            if "fc2" and "experts" not in key:
                  UpperCAmelCase          =	key.replace(""".fc2."""				,	""".ffn.fc2."""							)
            if "fc1" and "experts" not in key:
                  UpperCAmelCase          =	key.replace(""".fc1."""				,	""".ffn.fc1."""							)
            if ".encoder_attn." in key:
                  UpperCAmelCase          =	key.replace(""".encoder_attn."""				,	""".cross_attention."""							)
            if "encoder_attn_layer_norm" in key:
                  UpperCAmelCase          =	key.replace("""encoder_attn_layer_norm"""				,	"""cross_attention_layer_norm"""							)
            if "final_layer_norm" in key:
                  UpperCAmelCase          =	key.replace("""final_layer_norm"""				,	"""ff_layer_norm"""							)
            UpperCAmelCase          =	state_dict[old_key]
      return new_dict

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case = WEIGHTS_NAME							):







      """simple docstring"""






      UpperCAmelCase          =	[]
      UpperCAmelCase          =	0
      os.makedirs(_snake_case				,	exist_ok=_snake_case							)

      for expert in range(_snake_case							):
            UpperCAmelCase          =	switch_checkpoint_path + F'''-rank-{expert}.pt'''
            if os.path.isfile(_snake_case							):
                  UpperCAmelCase          =	torch.load(_snake_case							)["""model"""]
                  remove_ignore_keys_(_snake_case							)
                  UpperCAmelCase          =	rename_fairseq_keys(_snake_case				,	_snake_case							)
                  UpperCAmelCase          =	os.path.join(
                      _snake_case				,	weights_name.replace(""".bin"""				,	F'''-{len(_snake_case							)+1:05d}-of-???.bin'''							)							)
                  torch.save(_snake_case				,	_snake_case							)
                  sharded_state_dicts.append(expert_state.keys()							)
                  total_size += sum([value.numel() for key, value in expert_state.items()]							) * dtype_byte_size(
                      expert_state[list(_snake_case							)[0]].dtype							)

    # Add the last block
      UpperCAmelCase          =	os.path.join(_snake_case				,	weights_name.replace(""".bin"""				,	F'''-{len(_snake_case							)+1:05d}-of-???.bin'''							)							)
      UpperCAmelCase          =	torch.load(switch_checkpoint_path + """-shared.pt"""							)["""model"""]
      remove_ignore_keys_(_snake_case							)
      UpperCAmelCase          =	rename_fairseq_keys(_snake_case				,	_snake_case							)
      UpperCAmelCase          =	shared_weights["""decoder.embed_tokens.weight"""]
      sharded_state_dicts.append(shared_weights.keys()							)

      # If we only have the shared weights (dummy model/experts saved on the same file)
      if len(_snake_case							) == 1:
            UpperCAmelCase          =	os.path.join(_snake_case				,	_snake_case							)
            torch.save(_snake_case				,	_snake_case							)
            return {weights_name: sharded_state_dicts[0]}, None
      else:
            torch.save(_snake_case				,	_snake_case							)
      # Otherwise, let's build the index
      UpperCAmelCase          =	{}
      for idx, shard in enumerate(_snake_case							):
            UpperCAmelCase          =	weights_name.replace(""".bin"""				,	F'''-{idx+1:05d}-of-{len(_snake_case							):05d}.bin'''							)
            UpperCAmelCase          =	os.path.join(_snake_case				,	weights_name.replace(""".bin"""				,	F'''-{idx+1:05d}-of-???.bin'''							)							)
            os.rename(_snake_case				,	os.path.join(_snake_case				,	_snake_case							)							)
            for key in shard:
                  UpperCAmelCase          =	shard_file

    # Add the metadata
      UpperCAmelCase          =	{"""total_size""": total_size}
      UpperCAmelCase          =	{"""metadata""": metadata, """weight_map""": weight_map}

      with open(os.path.join(_snake_case				,	_snake_case							)				,	"""w"""				,	encoding="""utf-8"""							) as f:
            UpperCAmelCase          =	json.dumps(_snake_case				,	indent=2				,	sort_keys=_snake_case							) + """\n"""
            f.write(_snake_case							)

      return metadata, index


if __name__ == "__main__":
  _UpperCamelCase					      =  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--nllb_moe_checkpoint_path""",
      default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
      type=str,
      required=False,
      help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
  )
  parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
      type=str,
      required=False,
      help="""Path to the output pytorch model.""",
  )
  _UpperCamelCase					      =  parser.parse_args()
  _UpperCamelCase      ,  _UpperCamelCase					      =  shard_on_the_fly(
      args.nllb_moe_checkpoint_path,
      args.pytorch_dump_folder_path,
      128,
      args.dtype,
  )

  _UpperCamelCase					      =  NllbMoeConfig.from_pretrained(
      """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
  )
  config.save_pretrained(args.pytorch_dump_folder_path)
  _UpperCamelCase					      =  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
  print("""Done""")
  model.save_pretrained(args.pytorch_dump_folder_path)











"""simple docstring"""







import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_UpperCamelCase					      =  pd.read_csv(
    """https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"""
    """position_salaries.csv"""
)
_UpperCamelCase					      =  dataset.iloc[:, 1:2].values
_UpperCamelCase					      =  dataset.iloc[:, 2].values


_UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase					      =  train_test_split(X, y, test_size=0.2, random_state=0)


_UpperCamelCase					      =  PolynomialFeatures(degree=4)
_UpperCamelCase					      =  poly_reg.fit_transform(X)
_UpperCamelCase					      =  LinearRegression()
pol_reg.fit(X_poly, y)

def _a			(		):







      """simple docstring"""






      plt.scatter(_snake_case				,	_snake_case				,	color="""red"""							)
      plt.plot(_snake_case				,	pol_reg.predict(poly_reg.fit_transform(_snake_case							)							)				,	color="""blue"""							)
      plt.title("""Truth or Bluff (Linear Regression)"""							)
      plt.xlabel("""Position level"""							)
      plt.ylabel("""Salary"""							)
      plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003



