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__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{
    '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      A							:


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						int=None       ,  **__UpperCAmelCase     :						Optional[int]		)						->				int:
         """simple docstring"""



         logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."		)
         UpperCAmelCase__								=  model
         UpperCAmelCase__								=  kwargs.get("model_save_dir"       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  kwargs.get("latest_model_name"       ,  __UpperCAmelCase		)


   def __call__(self     :						Tuple       ,  **__UpperCAmelCase     :						int		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  {k: np.array(__UpperCAmelCase		) for k, v in kwargs.items()}
         return self.model.run(__UpperCAmelCase       ,  __UpperCAmelCase		)


   @staticmethod
   def 			lowercase_      (__UpperCAmelCase     :						Union[str, Path]       ,  __UpperCAmelCase     :						Tuple=None       ,  __UpperCAmelCase     :						Dict=None		)						->				List[str]:
         """simple docstring"""



         if provider is None:
               logger.info("No onnxruntime provider specified, using CPUExecutionProvider"		)
               UpperCAmelCase__								=  "CPUExecutionProvider"

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


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Union[str, Path]       ,  __UpperCAmelCase     :						Optional[str] = None       ,  **__UpperCAmelCase     :						List[str]		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  file_name if file_name is not None else ONNX_WEIGHTS_NAME

         UpperCAmelCase__								=  self.model_save_dir.joinpath(self.latest_model_name		)
         UpperCAmelCase__								=  Path(__UpperCAmelCase		).joinpath(__UpperCAmelCase		)
         try:
               shutil.copyfile(__UpperCAmelCase       ,  __UpperCAmelCase		)
         except shutil.SameFileError:
               pass

         # copy external weights (for models >2GB)
         UpperCAmelCase__								=  self.model_save_dir.joinpath(__UpperCAmelCase		)
         if src_path.exists():
               UpperCAmelCase__								=  Path(__UpperCAmelCase		).joinpath(__UpperCAmelCase		)
               try:
                     shutil.copyfile(__UpperCAmelCase       ,  __UpperCAmelCase		)
               except shutil.SameFileError:
                     pass


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						Union[str, os.PathLike]       ,  **__UpperCAmelCase     :						Tuple       ,  )						->				Union[str, Any]:
         """simple docstring"""



         if os.path.isfile(__UpperCAmelCase		):
               logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
               return

         os.makedirs(__UpperCAmelCase       ,  exist_ok=__UpperCAmelCase		)

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


   @classmethod
   def 			lowercase_      (cls     :						List[Any]       ,  __UpperCAmelCase     :						Union[str, Path]       ,  __UpperCAmelCase     :						Optional[Union[bool, str, None]] = None       ,  __UpperCAmelCase     :						Optional[Union[str, None]] = None       ,  __UpperCAmelCase     :						bool = False       ,  __UpperCAmelCase     :						Optional[str] = None       ,  __UpperCAmelCase     :						Optional[str] = None       ,  __UpperCAmelCase     :						Optional[str] = None       ,  __UpperCAmelCase     :						Optional["ort.SessionOptions"] = None       ,  **__UpperCAmelCase     :						Union[str, Any]       ,  )						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  file_name if file_name is not None else ONNX_WEIGHTS_NAME
         # load model from local directory
         if os.path.isdir(__UpperCAmelCase		):
               UpperCAmelCase__								=  OnnxRuntimeModel.load_model(
                   os.path.join(__UpperCAmelCase       ,  __UpperCAmelCase		)       ,  provider=__UpperCAmelCase       ,  sess_options=__UpperCAmelCase		)
               UpperCAmelCase__								=  Path(__UpperCAmelCase		)
         # load model from hub
         else:
               # download model
               UpperCAmelCase__								=  hf_hub_download(
                   repo_id=__UpperCAmelCase       ,  filename=__UpperCAmelCase       ,  use_auth_token=__UpperCAmelCase       ,  revision=__UpperCAmelCase       ,  cache_dir=__UpperCAmelCase       ,  force_download=__UpperCAmelCase       ,  )
               UpperCAmelCase__								=  Path(__UpperCAmelCase		).parent
               UpperCAmelCase__								=  Path(__UpperCAmelCase		).name
               UpperCAmelCase__								=  OnnxRuntimeModel.load_model(__UpperCAmelCase       ,  provider=__UpperCAmelCase       ,  sess_options=__UpperCAmelCase		)
         return cls(model=__UpperCAmelCase       ,  **__UpperCAmelCase		)





   @classmethod
   def 			lowercase_      (cls     :						Union[str, Any]       ,  __UpperCAmelCase     :						Union[str, Path]       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						Optional[str] = None       ,  __UpperCAmelCase     :						Optional[str] = None       ,  **__UpperCAmelCase     :						int       ,  )						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  None
         if len(str(__UpperCAmelCase		).split("@"		)		) == 2:
               UpperCAmelCase__		,      UpperCAmelCase__								=  model_id.split("@"		)

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




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment





class      A							(				unittest.TestCase     ):


   def 			lowercase_      (self     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  inspect.getfile(accelerate.test_utils		)
         UpperCAmelCase__								=  os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_script.py"]		)
         UpperCAmelCase__								=  os.path.sep.join(
             mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_distributed_data_loop.py"]		)
         UpperCAmelCase__								=  os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_ops.py"]		)


   @require_multi_gpu
   def 			lowercase_      (self     :						List[str]		)						->				Any:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices."""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


   @require_multi_gpu
   def 			lowercase_      (self     :						str		)						->				str:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices."""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
         print(f"""Command: {cmd}"""		)
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


   @require_multi_gpu
   def 			lowercase_      (self     :						Tuple		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__		)]
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)





   @require_multi_gpu
   def 			lowercase_      (self     :						Dict		)						->				str:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
         with patch_environment(omp_num_threads=1       ,  cuda_visible_devices="0,1"		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


if __name__ == "__main__":
      UpperCamelCase__											=					Accelerator()
      UpperCamelCase__											=					(accelerator.state.process_index + 2, 1_0)
      UpperCamelCase__											=					torch.randint(0, 1_0, shape).to(accelerator.device)

      UpperCamelCase__											=					''

      UpperCamelCase__											=					accelerator.pad_across_processes(tensor)
      if tensora.shape[0] != accelerator.state.num_processes + 1:
            error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
      if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
            error_msg += "Tensors have different values."
      if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
            error_msg += "Padding was not done with the right value (0)."

      UpperCamelCase__											=					accelerator.pad_across_processes(tensor, pad_first=True)
      if tensora.shape[0] != accelerator.state.num_processes + 1:
            error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
      UpperCamelCase__											=					accelerator.state.num_processes - accelerator.state.process_index - 1
      if not torch.equal(tensora[index:], tensor):
            error_msg += "Tensors have different values."
      if not torch.all(tensora[:index] == 0):
            error_msg += "Padding was not done with the right value (0)."

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)



