


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


__snake_case							   =  logging.get_logger(__name__)

__snake_case							   =  {
    '''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      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_=None					,     **UpperCamelCase_							):

							'''simple docstring'''


							logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''							)
							UpperCamelCase__     :Any							      =				model
							UpperCamelCase__     :Optional[int]							      =				kwargs.get('''model_save_dir'''					,     UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				kwargs.get('''latest_model_name'''					,     UpperCamelCase_							)




		def __call__(	self					,     **UpperCamelCase_							):

							'''simple docstring'''


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




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_=None					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if provider is None:
												logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''							)
												UpperCamelCase__     :Tuple							      =				'''CPUExecutionProvider'''

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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				file_name if file_name is not None else ONNX_WEIGHTS_NAME

							UpperCamelCase__     :Optional[int]							      =				self.model_save_dir.joinpath(self.latest_model_name							)
							UpperCamelCase__     :Any							      =				Path(UpperCamelCase_							).joinpath(UpperCamelCase_							)
							try:
												shutil.copyfile(UpperCamelCase_					,     UpperCamelCase_							)
							except shutil.SameFileError:
												pass

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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     **UpperCamelCase_					,     ):

							'''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 					lowerCAmelCase__       (	cls					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = False					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				file_name if file_name is not None else ONNX_WEIGHTS_NAME
							# load model from local directory
							if os.path.isdir(UpperCamelCase_							):
												UpperCamelCase__     :str							      =				OnnxRuntimeModel.load_model(
												    os.path.join(UpperCamelCase_					,     UpperCamelCase_							)					,     provider=UpperCamelCase_					,     sess_options=UpperCamelCase_							)
												UpperCamelCase__     :List[str]							      =				Path(UpperCamelCase_							)
							# load model from hub
							else:
												# download model
												UpperCamelCase__     :List[Any]							      =				hf_hub_download(
												    repo_id=UpperCamelCase_					,     filename=UpperCamelCase_					,     use_auth_token=UpperCamelCase_					,     revision=UpperCamelCase_					,     cache_dir=UpperCamelCase_					,     force_download=UpperCamelCase_					,     )
												UpperCamelCase__     :Union[str, Any]							      =				Path(UpperCamelCase_							).parent
												UpperCamelCase__     :List[Any]							      =				Path(UpperCamelCase_							).name
												UpperCamelCase__     :Dict							      =				OnnxRuntimeModel.load_model(UpperCamelCase_					,     provider=UpperCamelCase_					,     sess_options=UpperCamelCase_							)
							return cls(model=UpperCamelCase_					,     **UpperCamelCase_							)




		@classmethod
		def 					lowerCAmelCase__       (	cls					,     UpperCamelCase_					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				None
							if len(str(UpperCamelCase_							).split('''@'''							)							) == 2:
												UpperCamelCase__     :Union[str, Any]							      =				model_id.split('''@'''							)

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



'''simple docstring'''
import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch
@require_torch
@require_optimum
@slow
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :int							      =				AutoTokenizer.from_pretrained(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :int							      =				tokenizer('''This is me'''					,     return_tensors='''pt'''							)

							UpperCamelCase__     :Optional[int]							      =				model.to_bettertransformer()

							self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							UpperCamelCase__     :int							      =				model.generate(**UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				model.reverse_bettertransformer()

							self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

												self.assertFalse(
												    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()							)							)

												UpperCamelCase__     :Tuple							      =				model_reloaded.generate(**UpperCamelCase_							)
												self.assertTrue(torch.allclose(UpperCamelCase_					,     UpperCamelCase_							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :Dict							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
												with self.assertRaises(UpperCamelCase_							):
																	model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				model.reverse_bettertransformer()
												model.save_pretrained(UpperCamelCase_							)