



'''simple docstring'''



import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

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


lowerCAmelCase_  :							str             =    4
lowerCAmelCase_  :							List[str]             =    3
class 						lowerCamelCase_				(       a_      ):
						pass


def        UpperCAmelCase (    A	:					Any					):
							for shard in shards:
														for i in range(A_					):
																					yield {"i": i, "shard": shard}
def        UpperCAmelCase (    ):
							SCREAMING_SNAKE_CASE       :   List[str]         =      int(os.environ['''RANK''']					)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      int(os.environ['''WORLD_SIZE''']					)

							SCREAMING_SNAKE_CASE       :   Tuple         =      ArgumentParser()
							parser.add_argument('''--streaming'''       ,		type=A_					)
							parser.add_argument('''--local_rank'''       ,		type=A_					)
							parser.add_argument('''--num_workers'''       ,		type=A_       ,		default=0					)
							SCREAMING_SNAKE_CASE       :   Tuple         =      parser.parse_args()
							SCREAMING_SNAKE_CASE       :   Tuple         =      args.streaming
							SCREAMING_SNAKE_CASE       :   Any         =      args.num_workers

							SCREAMING_SNAKE_CASE       :   Dict         =      {'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(A_					)]}
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      IterableDataset.from_generator(A_       ,		gen_kwargs=A_					)
							if not streaming:
														SCREAMING_SNAKE_CASE       :   Dict         =      Dataset.from_list(list(A_					)					)

							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      split_dataset_by_node(A_       ,		rank=A_       ,		world_size=A_					)
							SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.utils.data.DataLoader(A_       ,		num_workers=A_					)

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      NUM_SHARDS * NUM_ITEMS_PER_SHARD
							SCREAMING_SNAKE_CASE       :   List[str]         =      full_size // world_size
							expected_local_size += int(rank < (full_size % world_size)					)

							SCREAMING_SNAKE_CASE       :   int         =      sum(1 for _ in dataloader					)
							if local_size != expected_local_size:
														raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""					)


if __name__ == "__main__":
							main()







"""simple docstring"""





import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
							import onnxruntime as ort


__UpperCamelCase    : int				 =						logging.get_logger(__name__)

__UpperCamelCase    : Optional[Any]				 =						{
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}







class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					def __init__(			self     :							Optional[Any]  ,lowercase_     :							Union[str, Any]=None  ,**lowercase_     :							List[str]						):
						logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''						)
						lowerCAmelCase__      :		List[str]    							=      model
						lowerCAmelCase__      :		Optional[Any]    							=      kwargs.get('''model_save_dir'''  ,lowercase_						)
						lowerCAmelCase__      :		str    							=      kwargs.get('''latest_model_name'''  ,lowercase_						)







					def __call__(			self     :							Any  ,**lowercase_     :							Dict						):
						lowerCAmelCase__      :		List[Any]    							=      {k: np.array(lowercase_						) for k, v in kwargs.items()}
						return self.model.run(lowercase_  ,lowercase_						)







					@staticmethod
					def 			__lowerCAmelCase					(			lowercase_     :							Union[str, Path]  ,lowercase_     :							str=None  ,lowercase_     :							str=None						):
						if provider is None:
							logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''						)
							lowerCAmelCase__      :		Optional[Any]    							=      '''CPUExecutionProvider'''

						return ort.InferenceSession(lowercase_  ,providers=[provider]  ,sess_options=lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							Union[str, Path]  ,lowercase_     :							Optional[str] = None  ,**lowercase_     :							str						):
						lowerCAmelCase__      :		Any    							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME

						lowerCAmelCase__      :		Tuple    							=      self.model_save_dir.joinpath(self.latest_model_name						)
						lowerCAmelCase__      :		List[Any]    							=      Path(lowercase_						).joinpath(lowercase_						)
						try:
							shutil.copyfile(lowercase_  ,lowercase_						)
						except shutil.SameFileError:
							pass

						# copy external weights (for models >2GB)
						lowerCAmelCase__      :		Dict    							=      self.model_save_dir.joinpath(lowercase_						)
						if src_path.exists():
							lowerCAmelCase__      :		Union[str, Any]    							=      Path(lowercase_						).joinpath(lowercase_						)
							try:
								shutil.copyfile(lowercase_  ,lowercase_						)
							except shutil.SameFileError:
								pass







					def 			__lowerCAmelCase					(			self     :							Optional[int]  ,lowercase_     :							Union[str, os.PathLike]  ,**lowercase_     :							Union[str, Any]  ,):
						if os.path.isfile(lowercase_						):
							logger.error(F'Provided path ({save_directory}) should be a directory, not a file'						)
							return

						os.makedirs(lowercase_  ,exist_ok=lowercase_						)

						# saving model weights/files
						self._save_pretrained(lowercase_  ,**lowercase_						)







					@classmethod
					def 			__lowerCAmelCase					(			cls     :							Dict  ,lowercase_     :							Union[str, Path]  ,lowercase_     :							Optional[Union[bool, str, None]] = None  ,lowercase_     :							Optional[Union[str, None]] = None  ,lowercase_     :							bool = False  ,lowercase_     :							Optional[str] = None  ,lowercase_     :							Optional[str] = None  ,lowercase_     :							Optional[str] = None  ,lowercase_     :							Optional["ort.SessionOptions"] = None  ,**lowercase_     :							Optional[int]  ,):
						lowerCAmelCase__      :		Optional[Any]    							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME
						# load model from local directory
						if os.path.isdir(lowercase_						):
							lowerCAmelCase__      :		str    							=      OnnxRuntimeModel.load_model(
							    os.path.join(lowercase_  ,lowercase_						)  ,provider=lowercase_  ,sess_options=lowercase_						)
							lowerCAmelCase__      :		Any    							=      Path(lowercase_						)
						# load model from hub
						else:
							# download model
							lowerCAmelCase__      :		Optional[Any]    							=      hf_hub_download(
							    repo_id=lowercase_  ,filename=lowercase_  ,use_auth_token=lowercase_  ,revision=lowercase_  ,cache_dir=lowercase_  ,force_download=lowercase_  ,)
							lowerCAmelCase__      :		str    							=      Path(lowercase_						).parent
							lowerCAmelCase__      :		Union[str, Any]    							=      Path(lowercase_						).name
							lowerCAmelCase__      :		Optional[Any]    							=      OnnxRuntimeModel.load_model(lowercase_  ,provider=lowercase_  ,sess_options=lowercase_						)
						return cls(model=lowercase_  ,**lowercase_						)







					@classmethod
					def 			__lowerCAmelCase					(			cls     :							Any  ,lowercase_     :							Union[str, Path]  ,lowercase_     :							bool = True  ,lowercase_     :							Optional[str] = None  ,lowercase_     :							Optional[str] = None  ,**lowercase_     :							Any  ,):
						lowerCAmelCase__      :		Union[str, Any]    							=      None
						if len(str(lowercase_						).split('''@'''						)						) == 2:
							lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      model_id.split('''@'''						)

						return cls._from_pretrained(
						    model_id=lowercase_  ,revision=lowercase_  ,cache_dir=lowercase_  ,force_download=lowercase_  ,use_auth_token=lowercase_  ,**lowercase_  ,)




