
import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
SCREAMING_SNAKE_CASE__     :    Dict      							=					logging.getLogger(__name__)




def 				__magic_name__				(     __lowerCAmelCase			:		Optional[Any]							)    ->     int:
    np.random.seed(args.seed							)
    torch.manual_seed(args.seed							)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed							)




def 				__magic_name__				(     )    ->     List[str]:
    __lowerCamelCase					      =			argparse.ArgumentParser()
    parser.add_argument(
        '''--model_name_or_path'''	,						default=__A	,						type=__A	,						required=__A	,						)
    parser.add_argument('''--prompt'''	,						type=__A	,						default=''''''							)
    parser.add_argument('''--length'''	,						type=__A	,						default=20							)
    parser.add_argument('''--stop_token'''	,						type=__A	,						default=__A	,						help='''Token at which text generation is stopped'''							)
    parser.add_argument(
        '''--temperature'''	,						type=__A	,						default=1.0	,						help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''	,						)
    parser.add_argument(
        '''--repetition_penalty'''	,						type=__A	,						default=1.0	,						help='''primarily useful for CTRL model; in that case, use 1.2'''							)
    parser.add_argument('''--k'''	,						type=__A	,						default=0							)
    parser.add_argument('''--penalty_alpha'''	,						type=__A	,						default=0.0							)
    parser.add_argument('''--p'''	,						type=__A	,						default=0.9							)

    parser.add_argument('''--prefix'''	,						type=__A	,						default=''''''	,						help='''Text added prior to input.'''							)
    parser.add_argument('''--padding_text'''	,						type=__A	,						default=''''''	,						help='''Deprecated, the use of `--prefix` is preferred.'''							)
    parser.add_argument('''--xlm_language'''	,						type=__A	,						default=''''''	,						help='''Optional language when used with the XLM model.'''							)

    parser.add_argument('''--seed'''	,						type=__A	,						default=42	,						help='''random seed for initialization'''							)
    parser.add_argument('''--no_cuda'''	,						action='''store_true'''	,						help='''Avoid using CUDA when available'''							)
    parser.add_argument(
        '''--fp16'''	,						action='''store_true'''	,						help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''	,						)
    __lowerCamelCase					      =			parser.parse_args()

    __lowerCamelCase					      =			torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
    __lowerCamelCase					      =			0 if args.no_cuda else torch.cuda.device_count()

    logger.warning(f'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''							)

    set_seed(__A							)

    # Initialize the model and tokenizer
    __lowerCamelCase					      =			AutoTokenizer.from_pretrained(args.model_name_or_path							)
    __lowerCamelCase					      =			AutoModelForCausalLM.from_pretrained(args.model_name_or_path							)

    # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
    # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
    model.to(args.device							)

    if args.fpaa:
        model.half()

    logger.info(__A							)
    __lowerCamelCase					      =			args.prompt if args.prompt else input('''Model prompt >>> '''							)

    __lowerCamelCase					      =			tokenizer(__A	,						return_tensors='''pt'''	,						add_special_tokens=__A							)
    __lowerCamelCase					      =			{key: value.to(args.device							) for key, value in inputs.items()}

    __lowerCamelCase					      =			model.generate(
        **__A	,						max_length=args.length + len(inputs['''input_ids'''][0]							)	,						penalty_alpha=args.penalty_alpha	,						top_k=args.k	,						)

    __lowerCamelCase					      =			[]
    for generated_sequence_idx, generated_sequence in enumerate(__A							):
        print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''							)
        __lowerCamelCase					      =			generated_sequence.tolist()

        # Decode text
        __lowerCamelCase					      =			tokenizer.decode(__A	,						clean_up_tokenization_spaces=__A	,						add_special_tokens=__A							)

        # Remove all text after the stop token
        __lowerCamelCase					      =			text[: text.find(args.stop_token							) if args.stop_token else None]

        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
        __lowerCamelCase					      =			(
            prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]	,						clean_up_tokenization_spaces=__A							)							) :]
        )

        generated_sequences.append(__A							)
        print(__A							)

    return generated_sequences


if __name__ == "__main__":
     main()





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def 				__magic_name__				(     )    ->     Union[str, Any]:
				__lowerCamelCase					      =			Dataset.from_dict({'''i''': range(17							)}							)
				__lowerCamelCase					      =			len(__lowerCAmelCase							)
				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(__lowerCAmelCase							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 				__magic_name__				(     )    ->     Optional[Any]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(list(__lowerCAmelCase							)							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




@pytest.mark.parametrize('''shards_per_node'''	,						[1, 2, 3]							)
def 				__magic_name__				(     __lowerCAmelCase			:		Any							)    ->     Union[str, Any]:
				def gen(__lowerCAmelCase			:		Union[str, Any]							):
								for shard in shards:
												yield from ({"i": i, "shard": shard} for i in range(17							))

				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			shards_per_node * world_size
				__lowerCamelCase					      =			{'''shards''': [f'''shard_{shard_idx}.txt''' for shard_idx in range(__lowerCAmelCase							)]}
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase	,						gen_kwargs=__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				assert full_ds.n_shards == world_size * shards_per_node
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
				assert sum(len(list(__lowerCAmelCase							)							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 				__magic_name__				(     )    ->     Optional[int]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)

				__lowerCamelCase					      =			split_dataset_by_node(__lowerCAmelCase	,						rank=0	,						world_size=__lowerCAmelCase							).shuffle(seed=42							)
				assert len(list(__lowerCAmelCase							)							) == 1 + full_size // world_size
				with pytest.raises(__lowerCAmelCase							):
								split_dataset_by_node(__lowerCAmelCase	,						rank=0	,						world_size=__lowerCAmelCase							).shuffle()

				__lowerCamelCase					      =			split_dataset_by_node(full_ds.shuffle(seed=42							)	,						rank=0	,						world_size=__lowerCAmelCase							)
				assert len(list(__lowerCAmelCase							)							) == 1 + full_size // world_size
				with pytest.raises(__lowerCAmelCase							):
								split_dataset_by_node(full_ds.shuffle()	,						rank=0	,						world_size=__lowerCAmelCase							)




@pytest.mark.parametrize('''streaming'''	,						[False, True]							)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''	,						reason='''execute_subprocess_async doesn\'t support windows'''							)
@pytest.mark.integration
def 				__magic_name__				(     __lowerCAmelCase			:		List[Any]							)    ->     Optional[int]:
				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						env=os.environ.copy()							)







@pytest.mark.parametrize(
    '''nproc_per_node, num_workers'''	,						[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]	,						)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''	,						reason='''execute_subprocess_async doesn\'t support windows'''							)
@pytest.mark.integration
def 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Any							)    ->     int:
				__lowerCamelCase					      =			True
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						env=os.environ.copy()							)



