'''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()
snake_case  =			logging.get_logger(__name__)

snake_case  =			'''Hello world! cécé herlolip'''





def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str  ,  _lowerCamelCase			:							bool    ):
      _lowerCAmelCase   ,  _lowerCAmelCase		   =   os.path.split(_lowerCamelCase    )
      _lowerCAmelCase		   =   DataaVecTextModel.from_pretrained(
          _lowerCamelCase  ,  checkpoint_file=_lowerCamelCase    )
      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=514  ,  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:'  ,  _lowerCamelCase    )

      _lowerCAmelCase		   =   DataaVecTextForSequenceClassification(_lowerCamelCase    ) if classification_head else DataaVecTextForMaskedLM(_lowerCamelCase    )
      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(_lowerCamelCase    ).unsqueeze(0    )  # batch of size 1

      _lowerCAmelCase		   =   model(_lowerCamelCase    )[0]
      if classification_head:
            _lowerCAmelCase		   =   dataavec.model.classification_heads['mnli'](dataavec.extract_features(_lowerCamelCase    )    )
      else:
            _lowerCAmelCase		   =   dataavec_model(_lowerCamelCase    )[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(_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__":
 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.'''
 )
 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 runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment







def 			A_   ( *_lowerCamelCase			:							Union[str, Any]    ):
      _lowerCAmelCase		   =   torch.cuda.device_count()
      print(F"Device count: {num_processes}"    )
      with patch_environment(
          world_size=_lowerCamelCase  ,  master_addr='127.0.01'  ,  master_port='29500'  ,  mixed_precision=args[1].mixed_precision    ):
            _lowerCAmelCase		   =   PrepareForLaunch(_lowerCamelCase  ,  distributed_type='MULTI_GPU'    )
            torch.multiprocessing.start_processes(_lowerCamelCase  ,  args=_lowerCamelCase  ,  nprocs=_lowerCamelCase  ,  start_method='spawn'    )


if __name__ == "__main__":
 # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
 # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

 # on-demand GPU
 # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
 snake_case  =			rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
 gpu.up_if_not()

 # on-prem GPU
 # gpu = rh.cluster(
 #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
 #       )

 # Set up remote function
 snake_case  =			[
     '''pip:./''',
     '''transformers''',
     '''datasets''',
     '''evaluate''',
     '''tqdm''',
     '''scipy''',
     '''scikit-learn''',
     '''tensorboard''',
     '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
 ]
 snake_case  =			rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

 # Define train args/config, run train function
 snake_case  =			argparse.Namespace(cpu=False, mixed_precision='''fp16''')
 snake_case  =			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 launch_train_gpu(config, train_args, stream_logs=True)

 # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
 # gpu.install_packages(reqs)
 # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])





