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


__lowerCamelCase	:					Optional[int]			  =					"""scheduler_config.json"""



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :int						   =       1
      _UpperCAmelCase     :int						   =       2
      _UpperCAmelCase     :str						   =       3
      _UpperCAmelCase     :Optional[Any]						   =       4
      _UpperCAmelCase     :List[Any]						   =       5
      _UpperCAmelCase     :int						   =       6
      _UpperCAmelCase     :Dict						   =       7
      _UpperCAmelCase     :Optional[Any]						   =       8
      _UpperCAmelCase     :int						   =       9
      _UpperCAmelCase     :Union[str, Any]						   =       1_0
      _UpperCAmelCase     :Optional[int]						   =       1_1
      _UpperCAmelCase     :int						   =       1_2
      _UpperCAmelCase     :int						   =       1_3
      _UpperCAmelCase     :str						   =       1_4



@dataclass
class  A__       (	__snake_case      ):
      _UpperCAmelCase     :torch.FloatTensor



class  A__       :
      _UpperCAmelCase     :Union[str, Any]						   =       SCHEDULER_CONFIG_NAME
      _UpperCAmelCase     :Any						   =       []
      _UpperCAmelCase     :Optional[int]						   =       True





      @classmethod
      def 	__UpperCamelCase(			cls	,							A_ = None	,							A_ = None	,							A_=False	,							**A_	,							):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       cls.load_config(
           pretrained_model_name_or_path=A_	,							subfolder=A_	,							return_unused_kwargs=A_	,							return_commit_hash=A_	,							**A_	,							)
       return cls.from_config(A_	,							return_unused_kwargs=A_	,							**A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_ = False	,							**A_       ):
       '''simple docstring'''




       self.save_config(save_directory=A_	,							push_to_hub=A_	,							**A_       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._get_compatibles()





      @classmethod
      def 	__UpperCamelCase(			cls       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       list(set([cls.__name__] + cls._compatibles       )       )
       UpperCamelCase     : Optional[int]	     =       importlib.import_module(__name__.split("."       )[0]       )
       UpperCamelCase     : Dict	     =       [
           getattr(A_	,							A_       ) for c in compatible_classes_str if hasattr(A_	,							A_       )
       ]
       return compatible_classes







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import tensorflow as tf

    from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       )
       UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained("google/mt5-small"       )

       UpperCamelCase     : Dict	     =       tokenizer("Hello there"	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       tokenizer("Hi I am"	,							return_tensors="tf"       ).input_ids

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							labels=A_       ).loss
       UpperCamelCase     : List[str]	     =       -tf.math.reduce_mean(A_       ).numpy()

       UpperCamelCase     : Union[str, Any]	     =       -21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 2e-4       )
