
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A							=       logging.get_logger(__name__)

_A							=       {
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					Optional[Any]    =     """flaubert"""
 A__    :					str    =     {
     """hidden_size""": """emb_dim""",
     """num_attention_heads""": """n_heads""",
     """num_hidden_layers""": """n_layers""",
     """n_words""": """vocab_size""",  # For backward compatibility
 }

 def __init__(   self  ,						__UpperCamelCase=False  ,						__UpperCamelCase=0.0  ,						__UpperCamelCase=3_0_1_4_5  ,						__UpperCamelCase=2_0_4_8  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase=1_6  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=True  ,						__UpperCamelCase=False  ,						__UpperCamelCase=False  ,						__UpperCamelCase=False  ,						__UpperCamelCase=1  ,						__UpperCamelCase=True  ,						__UpperCamelCase=5_1_2  ,						__UpperCamelCase=2_0_4_8**-0.5  ,						__UpperCamelCase=1e-12  ,						__UpperCamelCase=0.02  ,						__UpperCamelCase=0  ,						__UpperCamelCase=1  ,						__UpperCamelCase=2  ,						__UpperCamelCase=3  ,						__UpperCamelCase=5  ,						__UpperCamelCase=True  ,						__UpperCamelCase="first"  ,						__UpperCamelCase=True  ,						__UpperCamelCase=None  ,						__UpperCamelCase=True  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=5  ,						__UpperCamelCase=5  ,						__UpperCamelCase=0  ,						__UpperCamelCase=0  ,						__UpperCamelCase=2  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        UpperCamelCase_											=							pre_norm
        UpperCamelCase_											=							layerdrop
        UpperCamelCase_											=							vocab_size
        UpperCamelCase_											=							emb_dim
        UpperCamelCase_											=							n_layers
        UpperCamelCase_											=							n_heads
        UpperCamelCase_											=							dropout
        UpperCamelCase_											=							attention_dropout
        UpperCamelCase_											=							gelu_activation
        UpperCamelCase_											=							sinusoidal_embeddings
        UpperCamelCase_											=							causal
        UpperCamelCase_											=							asm
        UpperCamelCase_											=							n_langs
        UpperCamelCase_											=							use_lang_emb
        UpperCamelCase_											=							layer_norm_eps
        UpperCamelCase_											=							bos_index
        UpperCamelCase_											=							eos_index
        UpperCamelCase_											=							pad_index
        UpperCamelCase_											=							unk_index
        UpperCamelCase_											=							mask_index
        UpperCamelCase_											=							is_encoder
        UpperCamelCase_											=							max_position_embeddings
        UpperCamelCase_											=							embed_init_std
        UpperCamelCase_											=							init_std
        UpperCamelCase_											=							summary_type
        UpperCamelCase_											=							summary_use_proj
        UpperCamelCase_											=							summary_activation
        UpperCamelCase_											=							summary_proj_to_labels
        UpperCamelCase_											=							summary_first_dropout
        UpperCamelCase_											=							start_n_top
        UpperCamelCase_											=							end_n_top
        UpperCamelCase_											=							mask_token_id
        UpperCamelCase_											=							lang_id

        if "n_words" in kwargs:
               UpperCamelCase_											=							kwargs["""n_words"""]

        super().__init__(pad_token_id=__UpperCamelCase  ,						bos_token_id=__UpperCamelCase  ,						**__UpperCamelCase  )



class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        if self.task == "multiple-choice":
               UpperCamelCase_											=							{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
               UpperCamelCase_											=							{0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]  )





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''  )
       with patch_environment(
           world_size=a__						,   master_addr="""127.0.01"""						,   master_port="""29500"""						,   mixed_precision=args[1].mixed_precision  ):
              UpperCamelCase_											=							PrepareForLaunch(a__						,   distributed_type="""MULTI_GPU"""  )
              torch.multiprocessing.start_processes(a__						,   args=a__						,   nprocs=a__						,   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
      _A							=       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
      _A							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      _A							=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      _A							=       {'''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'])



