

'''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


lowerCamelCase			:				List[Any] 			=					logging.get_logger(__name__)

lowerCamelCase			:				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 							A__   :





  def __init__(		self				:     str							, _a				:     List[str]=None							, **_a				:     Dict			)     ->   Optional[int]:





       '''simple docstring'''






       logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'			)
       _SCREAMING_SNAKE_CASE							   =model
       _SCREAMING_SNAKE_CASE							   =kwargs.get('model_save_dir'							, lowercase_			)
       _SCREAMING_SNAKE_CASE							   =kwargs.get('latest_model_name'							, lowercase_			)





  def __call__(		self				:     Dict							, **_a				:     Tuple			)     ->   Optional[int]:





       '''simple docstring'''






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





  @staticmethod
  def 						A  (		_a				:     List[str]							, _a				:     List[str]=None							, _a				:     Optional[Any]=None			)     ->   Optional[Any]:





       '''simple docstring'''






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

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





  def 						A  (		self				:     Any							, _a				:     List[Any]							, _a				:     Dict = None							, **_a				:     int			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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





  def 						A  (		self				:     Tuple							, _a				:     Optional[int]							, **_a				:     Dict							, )     ->   Dict:





       '''simple docstring'''






       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 						A  (		cls				:     Tuple							, _a				:     Union[str, Any]							, _a				:     List[str] = None							, _a				:     List[str] = None							, _a				:     int = False							, _a				:     Any = None							, _a				:     Any = None							, _a				:     str = None							, _a				:     int = None							, **_a				:     Dict							, )     ->   List[Any]:





       '''simple docstring'''






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





  @classmethod
  def 						A  (		cls				:     Union[str, Any]							, _a				:     Optional[int]							, _a				:     Any = True							, _a				:     Any = None							, _a				:     int = None							, **_a				:     Tuple							, )     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =None
       if len(str(lowercase_			).split('@'			)			) == 2:
            _SCREAMING_SNAKE_CASE							   =model_id.split('@'			)

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



'''simple docstring'''
import os
import sys
import unittest


lowerCamelCase			:				Optional[Any] 			=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase			:				Optional[int] 			=					os.path.join(git_repo_path, "src", "transformers")

lowerCamelCase			:				Union[str, Any] 			=					"\n{0} = None\n"

lowerCamelCase			:				Optional[Any] 			=					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


lowerCamelCase			:				List[Any] 			=					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"







class 							A__   (	unittest.TestCase  ):





  def 						A  (		self				:     Optional[int]			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'			)
       self.assertIsNone(_a			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not is_tokenizers_available():'			)
       self.assertEqual(_a							, 'tokenizers'			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not is_tensorflow_text_available():'			)
       self.assertEqual(_a							, 'tensorflow_text'			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tokenizers'			)

       _SCREAMING_SNAKE_CASE							   =find_backend(
           '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tensorflow_text'			)

       _SCREAMING_SNAKE_CASE							   =find_backend(
           '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tokenizers_and_vision'			)





  def 						A  (		self				:     Optional[int]			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =read_init()
       # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
       self.assertIn('torch'							, _a			)
       self.assertIn('tensorflow_text'							, _a			)
       self.assertIn('sentencepiece_and_tokenizers'							, _a			)

       # Likewise, we can't assert on the exact content of a key
       self.assertIn('BertModel'							, objects['torch']			)
       self.assertIn('TFBertModel'							, objects['tf']			)
       self.assertIn('FlaxBertModel'							, objects['flax']			)
       self.assertIn('BertModel'							, objects['torch']			)
       self.assertIn('TFBertTokenizer'							, objects['tensorflow_text']			)
       self.assertIn('convert_slow_tokenizer'							, objects['sentencepiece_and_tokenizers']			)





  def 						A  (		self				:     Optional[int]			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =create_dummy_object('CONSTANT'							, '\'torch\''			)
       self.assertEqual(_a							, '\nCONSTANT = None\n'			)

       _SCREAMING_SNAKE_CASE							   =create_dummy_object('function'							, '\'torch\''			)
       self.assertEqual(
           _a							, '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'			)

       _SCREAMING_SNAKE_CASE							   ='\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
       _SCREAMING_SNAKE_CASE							   =create_dummy_object('FakeClass'							, '\'torch\''			)
       self.assertEqual(_a							, _a			)





  def 						A  (		self				:     Optional[int]			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ='# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
       _SCREAMING_SNAKE_CASE							   =create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}			)
       self.assertEqual(dummy_files['torch']							, _a			)
