



"""simple docstring"""






import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
   import jax

   from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
   from transformers.models.bert.modeling_flax_bert import FlaxBertModel
   from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel






@require_flax
class        __snake_case       (	unittest.TestCase):




  @slow
  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]		):






         """simple docstring"""






         for model_name in ["bert-base-cased", "bert-large-uncased"]:
                with self.subTest(UpperCamelCase__		):
                       _lowerCamelCase						:				Optional[Any]    			= AutoConfig.from_pretrained(UpperCamelCase__		)
                       self.assertIsNotNone(UpperCamelCase__		)
                       self.assertIsInstance(UpperCamelCase__						,					UpperCamelCase__		)

                       _lowerCamelCase						:				List[str]    			= FlaxAutoModel.from_pretrained(UpperCamelCase__		)
                       self.assertIsNotNone(UpperCamelCase__		)
                       self.assertIsInstance(UpperCamelCase__						,					UpperCamelCase__		)




  @slow
  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         for model_name in ["roberta-base", "roberta-large"]:
                with self.subTest(UpperCamelCase__		):
                       _lowerCamelCase						:				List[Any]    			= AutoConfig.from_pretrained(UpperCamelCase__		)
                       self.assertIsNotNone(UpperCamelCase__		)
                       self.assertIsInstance(UpperCamelCase__						,					UpperCamelCase__		)

                       _lowerCamelCase						:				List[Any]    			= FlaxAutoModel.from_pretrained(UpperCamelCase__		)
                       self.assertIsNotNone(UpperCamelCase__		)
                       self.assertIsInstance(UpperCamelCase__						,					UpperCamelCase__		)




  @slow
  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         for model_name in ["bert-base-cased", "bert-large-uncased"]:
                _lowerCamelCase						:				List[Any]    			= AutoTokenizer.from_pretrained(UpperCamelCase__		)
                _lowerCamelCase						:				Optional[Any]    			= FlaxBertModel.from_pretrained(UpperCamelCase__		)
                _lowerCamelCase						:				List[str]    			= tokenizer('''Do you support jax jitted function?'''						,					return_tensors=TensorType.JAX		)

                @jax.jit
                def eval(**__lowerCAmelCase     :						str		):
                       return model(**UpperCamelCase__		)

                eval(**UpperCamelCase__		).block_until_ready()




  @slow
  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         for model_name in ["roberta-base", "roberta-large"]:
                _lowerCamelCase						:				Any    			= AutoTokenizer.from_pretrained(UpperCamelCase__		)
                _lowerCamelCase						:				Dict    			= FlaxRobertaModel.from_pretrained(UpperCamelCase__		)
                _lowerCamelCase						:				str    			= tokenizer('''Do you support jax jitted function?'''						,					return_tensors=TensorType.JAX		)

                @jax.jit
                def eval(**__lowerCAmelCase     :						str		):
                       return model(**UpperCamelCase__		)

                eval(**UpperCamelCase__		).block_until_ready()




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         with self.assertRaisesRegex(
             UpperCamelCase__						,					'''bert-base is not a local folder and is not a valid model identifier'''		):
                _lowerCamelCase						:				Any    			= FlaxAutoModel.from_pretrained('''bert-base'''		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         with self.assertRaisesRegex(
             UpperCamelCase__						,					R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''		):
                _lowerCamelCase						:				Any    			= FlaxAutoModel.from_pretrained(UpperCamelCase__						,					revision='''aaaaaa'''		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         with self.assertRaisesRegex(
             UpperCamelCase__						,					'''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'''						,					):
                _lowerCamelCase						:				Optional[Any]    			= FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''		)




  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






         with self.assertRaisesRegex(UpperCamelCase__						,					'''Use `from_pt=True` to load this model'''		):
                _lowerCamelCase						:				str    			= FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''		)







'''simple docstring'''


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  2






    @register_to_config
    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					float = 0.0_2  ,						UpperCamelCase__      :					float = 1_0_0  ,						UpperCamelCase__      :					float = 1.0_0_7  ,						UpperCamelCase__      :					float = 8_0  ,						UpperCamelCase__      :					float = 0.0_5  ,						UpperCamelCase__      :					float = 5_0  ,						):


       """simple docstring"""







       UpperCamelCase					=  sigma_max

       # setable values
       UpperCamelCase					=  None
       UpperCamelCase					=  None
       UpperCamelCase					=  None  # sigma(t_i)






    def  A       (  self      :					str  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







       UpperCamelCase					=  num_inference_steps
       UpperCamelCase					=  np.arange(0  ,						self.num_inference_steps )[::-1].copy()
       UpperCamelCase					=  torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )
       UpperCamelCase					=  [
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       UpperCamelCase					=  torch.tensor(UpperCamelCase__  ,						dtype=torch.floataa  ,						device=UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					Optional[torch.Generator] = None ):


       """simple docstring"""







       if self.config.s_min <= sigma <= self.config.s_max:
          UpperCamelCase					=  min(self.config.s_churn / self.num_inference_steps  ,						2**0.5 - 1 )
       else:
          UpperCamelCase					=  0

       # sample eps ~ N(0, S_noise^2 * I)
       UpperCamelCase					=  self.config.s_noise * randn_tensor(sample.shape  ,						generator=UpperCamelCase__ ).to(sample.device )
       UpperCamelCase					=  sigma + gamma * sigma
       UpperCamelCase					=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat






    def  A       (  self      :					str  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  sample_hat + sigma_hat * model_output
       UpperCamelCase					=  (sample_hat - pred_original_sample) / sigma_hat
       UpperCamelCase					=  sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
          return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=UpperCamelCase__  ,						derivative=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  sample_prev + sigma_prev * model_output
       UpperCamelCase					=  (sample_prev - pred_original_sample) / sigma_prev
       UpperCamelCase					=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
          return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=UpperCamelCase__  ,						derivative=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       raise NotImplementedError()

