

'''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     a			(				unittest.TestCase       ):






						"""simple docstring"""




						@slow
						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								for model_name in ["bert-base-cased", "bert-large-uncased"]:
										with self.subTest(snake_case_  ):
												snake_case__						:       Dict       						=  AutoConfig.from_pretrained(snake_case_  )
												self.assertIsNotNone(snake_case_  )
												self.assertIsInstance(snake_case_			,			snake_case_  )

												snake_case__						:       str       						=  FlaxAutoModel.from_pretrained(snake_case_  )
												self.assertIsNotNone(snake_case_  )
												self.assertIsInstance(snake_case_			,			snake_case_  )







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



								'''simple docstring'''



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

												snake_case__						:       Union[str, Any]       						=  FlaxAutoModel.from_pretrained(snake_case_  )
												self.assertIsNotNone(snake_case_  )
												self.assertIsInstance(snake_case_			,			snake_case_  )







						@slow
						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



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

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

										eval(**snake_case_  ).block_until_ready()







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



								'''simple docstring'''



								for model_name in ["roberta-base", "roberta-large"]:
										snake_case__						:       List[Any]       						=  AutoTokenizer.from_pretrained(snake_case_  )
										snake_case__						:       Optional[int]       						=  FlaxRobertaModel.from_pretrained(snake_case_  )
										snake_case__						:       Optional[int]       						=  tokenizer('''Do you support jax jitted function?'''			,			return_tensors=TensorType.JAX  )

										@jax.jit
										def eval(**snake_case_					:      List[str]  ):
												return model(**snake_case_  )

										eval(**snake_case_  ).block_until_ready()







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								with self.assertRaisesRegex(
								    snake_case_			,			R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''  ):
										snake_case__						:       Optional[int]       						=  FlaxAutoModel.from_pretrained(snake_case_			,			revision='''aaaaaa'''  )







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								with self.assertRaisesRegex(snake_case_			,			'''Use `from_pt=True` to load this model'''  ):
										snake_case__						:       Optional[int]       						=  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     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    None





class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    2







						@register_to_config
						def __init__(							self					:      int			,			snake_case_					:      float = 0.0_2			,			snake_case_					:      float = 1_0_0			,			snake_case_					:      float = 1.0_0_7			,			snake_case_					:      float = 8_0			,			snake_case_					:      float = 0.0_5			,			snake_case_					:      float = 5_0			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  sigma_max

								# setable values
								snake_case__						:       int       						=  None
								snake_case__						:       np.IntTensor       						=  None
								snake_case__						:       torch.FloatTensor       						=  None  # sigma(t_i)







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      Optional[int] = None  ):



								'''simple docstring'''



								return sample







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      int			,			snake_case_					:      Union[str, torch.device] = None  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  num_inference_steps
								snake_case__						:       Tuple       						=  np.arange(0			,			self.num_inference_steps  )[::-1].copy()
								snake_case__						:       int       						=  torch.from_numpy(snake_case_  ).to(snake_case_  )
								snake_case__						:       List[str]       						=  [
								    (
								        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
								]
								snake_case__						:       Union[str, Any]       						=  torch.tensor(snake_case_			,			dtype=torch.floataa			,			device=snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      Optional[torch.Generator] = None  ):



								'''simple docstring'''



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

								# sample eps ~ N(0, S_noise^2 * I)
								snake_case__						:       Any       						=  self.config.s_noise * randn_tensor(sample.shape			,			generator=snake_case_  ).to(sample.device  )
								snake_case__						:       Tuple       						=  sigma + gamma * sigma
								snake_case__						:       Dict       						=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      float			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  sample_hat + sigma_hat * model_output
								snake_case__						:       List[Any]       						=  (sample_hat - pred_original_sample) / sigma_hat
								snake_case__						:       Any       						=  sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
										return (sample_prev, derivative)

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







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      float			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       str       						=  sample_prev + sigma_prev * model_output
								snake_case__						:       List[Any]       						=  (sample_prev - pred_original_sample) / sigma_prev
								snake_case__						:       Any       						=  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=snake_case_			,			derivative=snake_case_			,			pred_original_sample=snake_case_  )







						def       __magic_name__							(							self					:      str			,			snake_case_					:      List[Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								raise NotImplementedError()



