import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


__snake_case						=			'''scheduler_config.json'''







class 	__snake_case      (   lowerCamelCase__      ):
 __lowerCamelCase			:      Optional[Any]           =					1
 __lowerCamelCase			:      List[Any]           =					2
 __lowerCamelCase			:      Any           =					3
 __lowerCamelCase			:      List[str]           =					4
 __lowerCamelCase			:      Any           =					5
 __lowerCamelCase			:      Optional[int]           =					6
 __lowerCamelCase			:      Optional[Any]           =					7
 __lowerCamelCase			:      int           =					8
 __lowerCamelCase			:      Optional[Any]           =					9
 __lowerCamelCase			:      Dict           =					10
 __lowerCamelCase			:      Tuple           =					11
 __lowerCamelCase			:      Dict           =					12
 __lowerCamelCase			:      Dict           =					13
 __lowerCamelCase			:      Union[str, Any]           =					14







@dataclass
class 	__snake_case      (   lowerCamelCase__      ):
 __lowerCamelCase			:      str           =					42







class 	__snake_case      :
 __lowerCamelCase			:      int           =					SCHEDULER_CONFIG_NAME
 __lowerCamelCase			:      str           =					[]
 __lowerCamelCase			:      Optional[int]           =					True
 @classmethod
 def 		UpperCAmelCase__				(     cls						,      snake_case__ = None						,      snake_case__ = None						,      snake_case__=False						,      **snake_case__						,      )		->							int:
       '''simple docstring'''





       UpperCAmelCase     ,  UpperCAmelCase     ,  UpperCAmelCase			:       List[Any]			     =cls.load_config(
           pretrained_model_name_or_path=__UpperCAmelCase						,      subfolder=__UpperCAmelCase						,      return_unused_kwargs=__UpperCAmelCase						,      return_commit_hash=__UpperCAmelCase						,      **__UpperCAmelCase						,      )
       return cls.from_config(__UpperCAmelCase						,      return_unused_kwargs=__UpperCAmelCase						,      **__UpperCAmelCase  )
 def 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__ = False						,      **snake_case__  )		->							List[Any]:
       '''simple docstring'''





       self.save_config(save_directory=__UpperCAmelCase						,      push_to_hub=__UpperCAmelCase						,      **__UpperCAmelCase  )
 @property
 def 		UpperCAmelCase__				(     self  )		->							List[Any]:
       '''simple docstring'''





       return self._get_compatibles()






 @classmethod
 def 		UpperCAmelCase__				(     cls  )		->							List[str]:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     =list(set([cls.__name__] + cls._compatibles  )  )
       UpperCAmelCase			:       str			     =importlib.import_module(__name__.split('''.'''  )[0]  )
       UpperCAmelCase			:       Optional[int]			     =[
           getattr(__UpperCAmelCase						,      __UpperCAmelCase  ) for c in compatible_classes_str if hasattr(__UpperCAmelCase						,      __UpperCAmelCase  )
       ]
       return compatible_classes








from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch






@require_torch
@require_tf
class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						str  )    ->int:




       """simple docstring"""


       a		  =      SMALL_MODEL_IDENTIFIER
       a		  =      '''pt'''
       a		  =      '''tf'''







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						str  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      AutoModel.from_pretrained(self.test_model  )
       model_pt.save_pretrained(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Any ,					__UpperCAmelCase      :						Union[str, Any]  )    ->List[str]:




       """simple docstring"""


       a		  =      TFAutoModel.from_pretrained(self.test_model ,					from_pt=__UpperCAmelCase  )
       model_tf.save_pretrained(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->int:




       """simple docstring"""


       a		  =      '''mock_framework'''

       # Framework provided - return whatever the user provides
       a		  =      FeaturesManager.determine_framework(self.test_model ,					__UpperCAmelCase  )
       self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase  )

       # Local checkpoint and framework provided - return provided framework
       # PyTorch checkpoint
       with TemporaryDirectory() as local_pt_ckpt:
        self._setup_pt_ckpt(__UpperCAmelCase  )
        a		  =      FeaturesManager.determine_framework(__UpperCAmelCase ,					__UpperCAmelCase  )
        self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase  )

       # TensorFlow checkpoint
       with TemporaryDirectory() as local_tf_ckpt:
        self._setup_tf_ckpt(__UpperCAmelCase  )
        a		  =      FeaturesManager.determine_framework(__UpperCAmelCase ,					__UpperCAmelCase  )
        self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						str  )    ->int:




       """simple docstring"""


       with TemporaryDirectory() as local_pt_ckpt:
        self._setup_pt_ckpt(__UpperCAmelCase  )
        a		  =      FeaturesManager.determine_framework(__UpperCAmelCase  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_pt  )

       # TensorFlow checkpoint
       with TemporaryDirectory() as local_tf_ckpt:
        self._setup_tf_ckpt(__UpperCAmelCase  )
        a		  =      FeaturesManager.determine_framework(__UpperCAmelCase  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_tf  )

       # Invalid local checkpoint
       with TemporaryDirectory() as local_invalid_ckpt:
        with self.assertRaises(__UpperCAmelCase  ):
         a		  =      FeaturesManager.determine_framework(__UpperCAmelCase  )







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




       """simple docstring"""


       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       with patch('''transformers.onnx.features.is_tf_available''' ,					__UpperCAmelCase  ):
        a		  =      FeaturesManager.determine_framework(self.test_model  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_pt  )

       # PyTorch not in environment -> use TensorFlow
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       with patch('''transformers.onnx.features.is_torch_available''' ,					__UpperCAmelCase  ):
        a		  =      FeaturesManager.determine_framework(self.test_model  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_tf  )

       # Both in environment -> use PyTorch
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       with patch('''transformers.onnx.features.is_tf_available''' ,					__UpperCAmelCase  ), patch(
           '''transformers.onnx.features.is_torch_available''' ,					__UpperCAmelCase  ):
        a		  =      FeaturesManager.determine_framework(self.test_model  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_pt  )

       # Both not in environment -> raise error
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       with patch('''transformers.onnx.features.is_tf_available''' ,					__UpperCAmelCase  ), patch(
           '''transformers.onnx.features.is_torch_available''' ,					__UpperCAmelCase  ):
        with self.assertRaises(__UpperCAmelCase  ):
         a		  =      FeaturesManager.determine_framework(self.test_model  )


