import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment


def        __lowerCAmelCase      (	_A			,_A=()			,_A=None			,_A="no"			,_A="29500"       ):



							"""simple docstring"""


							_lowercase					=						False
							_lowercase					=						False
							if any(key.startswith("""KAGGLE"""       ) for key in os.environ.keys()       ):
														_lowercase					=						True
							elif "IPython" in sys.modules:
														_lowercase					=						"""google.colab""" in str(sys.modules["""IPython"""].get_ipython()       )

							try:
														_lowercase					=						PrecisionType(mixed_precision.lower()       )
							except ValueError:
														raise ValueError(
														    f'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''       )

							if (in_colab or in_kaggle) and (os.environ.get("""TPU_NAME"""			,UpperCAmelCase_       ) is not None):
														# TPU launch
														import torch_xla.distributed.xla_multiprocessing as xmp

														if len(AcceleratorState._shared_state       ) > 0:
																					raise ValueError(
																					    """To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside """
																					    """your training function. Restart your notebook and make sure no cells initializes an """
																					    """`Accelerator`."""       )
														if num_processes is None:
																					_lowercase					=						8

														_lowercase					=						PrepareForLaunch(UpperCAmelCase_			,distributed_type="""TPU"""       )
														print(f'''Launching a training on {num_processes} TPU cores.'''       )
														xmp.spawn(UpperCAmelCase_			,args=UpperCAmelCase_			,nprocs=UpperCAmelCase_			,start_method="""fork"""       )
							elif in_colab:
														# No need for a distributed launch otherwise as it's either CPU or one GPU.
														if torch.cuda.is_available():
																					print("""Launching training on one GPU."""       )
														else:
																					print("""Launching training on one CPU."""       )
														function(*UpperCAmelCase_       )
							else:
														if num_processes is None:
																					raise ValueError(
																					    """You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."""       )

														if num_processes > 1:
																					# Multi-GPU launch
																					from torch.multiprocessing import start_processes
																					from torch.multiprocessing.spawn import ProcessRaisedException

																					if len(AcceleratorState._shared_state       ) > 0:
																												raise ValueError(
																												    """To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized """
																												    """inside your training function. Restart your notebook and make sure no cells initializes an """
																												    """`Accelerator`."""       )

																					if torch.cuda.is_initialized():
																												raise ValueError(
																												    """To launch a multi-GPU training from your notebook, you need to avoid running any instruction """
																												    """using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA """
																												    """function."""       )

																					# torch.distributed will expect a few environment variable to be here. We set the ones common to each
																					# process here (the other ones will be set be the launcher).
																					with patch_environment(
																					    world_size=UpperCAmelCase_			,master_addr="""127.0.01"""			,master_port=UpperCAmelCase_			,mixed_precision=UpperCAmelCase_       ):
																												_lowercase					=						PrepareForLaunch(UpperCAmelCase_			,distributed_type="""MULTI_GPU"""       )
																												print(f'''Launching training on {num_processes} GPUs.'''       )
																												try:
																																			start_processes(UpperCAmelCase_			,args=UpperCAmelCase_			,nprocs=UpperCAmelCase_			,start_method="""fork"""       )
																												except ProcessRaisedException as e:
																																			if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																																										raise RuntimeError(
																																										    """CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. """
																																										    """This likely stems from an outside import causing issues once the `notebook_launcher()` is called. """
																																										    """Please review your imports and test them when running the `notebook_launcher()` to identify """
																																										    """which one is problematic."""       ) from e

														else:
																					# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
																					if is_mps_available():
																												_lowercase					=						"""1"""
																												print("""Launching training on MPS."""       )
																					elif torch.cuda.is_available():
																												print("""Launching training on one GPU."""       )
																					else:
																												print("""Launching training on CPU."""       )
																					function(*UpperCAmelCase_       )







def        __lowerCAmelCase      (	_A			,_A=()			,_A=2       ):



							"""simple docstring"""


							from torch.multiprocessing import start_processes

							with tempfile.NamedTemporaryFile() as tmp_file:
														# torch.distributed will expect a few environment variable to be here. We set the ones common to each
														# process here (the other ones will be set be the launcher).
														with patch_environment(
														    world_size=UpperCAmelCase_			,master_addr="""127.0.01"""			,master_port="""29500"""			,accelerate_mixed_precision="""no"""			,accelerate_debug_rdv_file=tmp_file.name			,accelerate_use_cpu="""yes"""			,):
																					_lowercase					=						PrepareForLaunch(UpperCAmelCase_			,debug=UpperCAmelCase_       )
																					start_processes(UpperCAmelCase_			,args=UpperCAmelCase_			,nprocs=UpperCAmelCase_			,start_method="""fork"""       )









'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


a__					:			str          =     logging.get_logger(__name__)

a__					:			Any          =     {
    '''xlm-mlm-en-2048''': '''https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json''',
    '''xlm-mlm-ende-1024''': '''https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-enfr-1024''': '''https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json''',
    '''xlm-mlm-enro-1024''': '''https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json''',
    '''xlm-mlm-tlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-mlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-clm-enfr-1024''': '''https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json''',
    '''xlm-clm-ende-1024''': '''https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-17-1280''': '''https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json''',
    '''xlm-mlm-100-1280''': '''https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json''',
}






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  '''xlm'''
	__lowerCAmelCase       						=  {
	    '''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_=3_0145      ,       UpperCamelCase_=2048      ,       UpperCamelCase_=12      ,       UpperCamelCase_=16      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=True      ,       UpperCamelCase_=False      ,       UpperCamelCase_=False      ,       UpperCamelCase_=False      ,       UpperCamelCase_=1      ,       UpperCamelCase_=True      ,       UpperCamelCase_=512      ,       UpperCamelCase_=2048**-0.5      ,       UpperCamelCase_=1E-1_2      ,       UpperCamelCase_=0.0_2      ,       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_      ,       )      ->		List[str]:
		snake_case__	       =   vocab_size
		snake_case__	       =   emb_dim
		snake_case__	       =   n_layers
		snake_case__	       =   n_heads
		snake_case__	       =   dropout
		snake_case__	       =   attention_dropout
		snake_case__	       =   gelu_activation
		snake_case__	       =   sinusoidal_embeddings
		snake_case__	       =   causal
		snake_case__	       =   asm
		snake_case__	       =   n_langs
		snake_case__	       =   use_lang_emb
		snake_case__	       =   layer_norm_eps
		snake_case__	       =   bos_index
		snake_case__	       =   eos_index
		snake_case__	       =   pad_index
		snake_case__	       =   unk_index
		snake_case__	       =   mask_index
		snake_case__	       =   is_encoder
		snake_case__	       =   max_position_embeddings
		snake_case__	       =   embed_init_std
		snake_case__	       =   init_std
		snake_case__	       =   summary_type
		snake_case__	       =   summary_use_proj
		snake_case__	       =   summary_activation
		snake_case__	       =   summary_proj_to_labels
		snake_case__	       =   summary_first_dropout
		snake_case__	       =   start_n_top
		snake_case__	       =   end_n_top
		snake_case__	       =   mask_token_id
		snake_case__	       =   lang_id

		if "n_words" in kwargs:
			snake_case__	       =   kwargs['n_words']

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






class 				__snake_case     (					__magic_name__							):





	@property
	def   _snake_case   (		self		)      ->		Mapping[str, Mapping[int, str]]:
		if self.task == "multiple-choice":
			snake_case__	       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
		else:
			snake_case__	       =   {0: 'batch', 1: 'sequence'}
		return OrderedDict(
		    [
		        ('input_ids', dynamic_axis),
		        ('attention_mask', dynamic_axis),
		        ('token_type_ids', dynamic_axis),
		    ]		)



