
'''simple docstring'''
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






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

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

'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowerCAmelCase  							= logging.get_logger(__name__)

__lowerCAmelCase  							= {
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		"m2m_100"
							lowerCAmelCase_      		=		["past_key_values"]
							lowerCAmelCase_      		=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

							def __init__(self						,						UpperCAmelCase=128112						,						UpperCAmelCase=1024						,						UpperCAmelCase=12						,						UpperCAmelCase=4096						,						UpperCAmelCase=16						,						UpperCAmelCase=12						,						UpperCAmelCase=4096						,						UpperCAmelCase=16						,						UpperCAmelCase=0.05						,						UpperCAmelCase=0.05						,						UpperCAmelCase=True						,						UpperCAmelCase=True						,						UpperCAmelCase="relu"						,						UpperCAmelCase=1024						,						UpperCAmelCase=0.1						,						UpperCAmelCase=0.1						,						UpperCAmelCase=0.0						,						UpperCAmelCase=0.02						,						UpperCAmelCase=2						,						UpperCAmelCase=True						,						UpperCAmelCase=1						,						UpperCAmelCase=0						,						UpperCAmelCase=2						,						**UpperCAmelCase						,						)  ->     int:
														_snake_case     =						vocab_size
														_snake_case     =						max_position_embeddings
														_snake_case     =						d_model
														_snake_case     =						encoder_ffn_dim
														_snake_case     =						encoder_layers
														_snake_case     =						encoder_attention_heads
														_snake_case     =						decoder_ffn_dim
														_snake_case     =						decoder_layers
														_snake_case     =						decoder_attention_heads
														_snake_case     =						dropout
														_snake_case     =						attention_dropout
														_snake_case     =						activation_dropout
														_snake_case     =						activation_function
														_snake_case     =						init_std
														_snake_case     =						encoder_layerdrop
														_snake_case     =						decoder_layerdrop
														_snake_case     =						use_cache
														_snake_case     =						encoder_layers
														_snake_case     =						scale_embedding  # scale factor will be sqrt(d_model) if True

														super().__init__(
														    pad_token_id=UpperCAmelCase						,						bos_token_id=UpperCAmelCase						,						eos_token_id=UpperCAmelCase						,						is_encoder_decoder=UpperCAmelCase						,						decoder_start_token_id=UpperCAmelCase						,						**UpperCAmelCase						,						)


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							@property
							def  lowercase							(self						)  ->     Mapping[str, Mapping[int, str]]:
														_snake_case     =						OrderedDict(
														    [
														        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
														        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
														    ]						)

														if self.use_past:
																					_snake_case     =						{0: """batch"""}
																					_snake_case     =						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
														else:
																					_snake_case     =						{0: """batch""", 1: """decoder_sequence"""}
																					_snake_case     =						{0: """batch""", 1: """decoder_sequence"""}

														if self.use_past:
																					self.fill_with_past_key_values_(UpperCAmelCase						,						direction="""inputs"""						)
														return common_inputs

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = -1						,						UpperCAmelCase = -1						,						UpperCAmelCase = False						,						UpperCAmelCase = None						,						)  ->     Mapping[str, Any]:
														# Copied from OnnxConfig.generate_dummy_inputs
														# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														_snake_case     =						compute_effective_axis_dimension(
														    UpperCAmelCase						,						fixed_dimension=OnnxConfig.default_fixed_batch						,						num_token_to_add=0						)

														# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
														_snake_case     =						tokenizer.num_special_tokens_to_add(UpperCAmelCase						)
														_snake_case     =						compute_effective_axis_dimension(
														    UpperCAmelCase						,						fixed_dimension=OnnxConfig.default_fixed_sequence						,						num_token_to_add=UpperCAmelCase						)

														# Generate dummy inputs according to compute batch and sequence
														_snake_case     =						[""" """.join([tokenizer.unk_token]						) * seq_length] * batch_size
														_snake_case     =						dict(tokenizer(UpperCAmelCase						,						return_tensors=UpperCAmelCase						)						)
														return common_inputs

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = -1						,						UpperCAmelCase = -1						,						UpperCAmelCase = False						,						UpperCAmelCase = None						,						)  ->     Mapping[str, Any]:
														_snake_case     =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)

														# Generate decoder inputs
														_snake_case     =						seq_length if not self.use_past else 1
														_snake_case     =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)
														_snake_case     =						{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
														_snake_case     =						dict(**UpperCAmelCase						,						**UpperCAmelCase						)

														if self.use_past:
																					if not is_torch_available():
																												raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
																					else:
																												import torch
																					_snake_case,							_snake_case     =						common_inputs["""input_ids"""].shape
																					_snake_case     =						common_inputs["""decoder_input_ids"""].shape[1]
																					_snake_case,							_snake_case     =						self.num_attention_heads
																					_snake_case     =						(
																					    batch,
																					    num_encoder_attention_heads,
																					    encoder_seq_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)
																					_snake_case     =						decoder_seq_length + 3
																					_snake_case     =						(
																					    batch,
																					    num_decoder_attention_heads,
																					    decoder_past_length,
																					    self._config.hidden_size // num_decoder_attention_heads,
																					)

																					_snake_case     =						torch.cat(
																					    [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCAmelCase						,						UpperCAmelCase						)]						,						dim=1						)

																					_snake_case     =						[]
																					# If the number of encoder and decoder layers are present in the model configuration, both are considered
																					_snake_case,							_snake_case     =						self.num_layers
																					_snake_case     =						min(UpperCAmelCase						,						UpperCAmelCase						)
																					_snake_case     =						max(UpperCAmelCase						,						UpperCAmelCase						) - min_num_layers
																					_snake_case     =						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

																					for _ in range(UpperCAmelCase						):
																												common_inputs["past_key_values"].append(
																												    (
																												        torch.zeros(UpperCAmelCase						),
																												        torch.zeros(UpperCAmelCase						),
																												        torch.zeros(UpperCAmelCase						),
																												        torch.zeros(UpperCAmelCase						),
																												    )						)
																					# TODO: test this.
																					_snake_case     =						encoder_shape if remaining_side_name == """encoder""" else decoder_shape
																					for _ in range(UpperCAmelCase						,						UpperCAmelCase						):
																												common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase						), torch.zeros(UpperCAmelCase						))						)
														return common_inputs





							lowerCAmelCase_      		=		_generate_dummy_inputs_for_default_and_seqaseq_lm