"""simple docstring"""





import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class  _snake_case     (					SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__		):
      snake_case__			  =			1






      @register_to_config
      def __init__(       self      :			Any							,					UpperCAmelCase      :			Dict=2000							,					UpperCAmelCase      :			Optional[Any]=0.1							,					UpperCAmelCase      :			Tuple=20							,					UpperCAmelCase      :			str=1E-3    ):
             __lowerCamelCase					:		str		   =     None
             __lowerCamelCase					:		Union[str, Any]		   =     None
             __lowerCamelCase					:		Optional[int]		   =     None






      def       lowerCamelCase__     (       self      :			List[Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Union[str, Any] = None    ):
             __lowerCamelCase					:		List[Any]		   =     torch.linspace(1							,					self.config.sampling_eps							,					_lowercase							,					device=_lowercase    )






      def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Optional[int]=None    ):

             if self.timesteps is None:
                    raise ValueError(
                        "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

             # TODO(Patrick) better comments + non-PyTorch
             # postprocess model score
             __lowerCamelCase					:		Optional[Any]		   =     (
                 -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
             )
             __lowerCamelCase					:		Dict		   =     torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff    )    )
             __lowerCamelCase					:		str		   =     std.flatten()
             while len(std.shape    ) < len(score.shape    ):
                    __lowerCamelCase					:		List[str]		   =     std.unsqueeze(-1    )
             __lowerCamelCase					:		str		   =     -score / std

             # compute
             __lowerCamelCase					:		Any		   =     -1.0 / len(self.timesteps    )

             __lowerCamelCase					:		List[str]		   =     self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
             __lowerCamelCase					:		Tuple		   =     beta_t.flatten()
             while len(beta_t.shape    ) < len(x.shape    ):
                    __lowerCamelCase					:		Union[str, Any]		   =     beta_t.unsqueeze(-1    )
             __lowerCamelCase					:		List[str]		   =     -0.5 * beta_t * x

             __lowerCamelCase					:		Dict		   =     torch.sqrt(_lowercase    )
             __lowerCamelCase					:		Any		   =     drift - diffusion**2 * score
             __lowerCamelCase					:		str		   =     x + drift * dt

             # add noise
             __lowerCamelCase					:		Dict		   =     randn_tensor(x.shape							,					layout=x.layout							,					generator=_lowercase							,					device=x.device							,					dtype=x.dtype    )
             __lowerCamelCase					:		str		   =     x_mean + diffusion * math.sqrt(-dt    ) * noise

             return x, x_mean







      def __len__(       self      :			Optional[Any]    ):
             return self.config.num_train_timesteps
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A  =				{
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''BertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''BertForMaskedLM''',
				    '''BertForMultipleChoice''',
				    '''BertForNextSentencePrediction''',
				    '''BertForPreTraining''',
				    '''BertForQuestionAnswering''',
				    '''BertForSequenceClassification''',
				    '''BertForTokenClassification''',
				    '''BertLayer''',
				    '''BertLMHeadModel''',
				    '''BertModel''',
				    '''BertPreTrainedModel''',
				    '''load_tf_weights_in_bert''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFBertEmbeddings''',
				    '''TFBertForMaskedLM''',
				    '''TFBertForMultipleChoice''',
				    '''TFBertForNextSentencePrediction''',
				    '''TFBertForPreTraining''',
				    '''TFBertForQuestionAnswering''',
				    '''TFBertForSequenceClassification''',
				    '''TFBertForTokenClassification''',
				    '''TFBertLMHeadModel''',
				    '''TFBertMainLayer''',
				    '''TFBertModel''',
				    '''TFBertPreTrainedModel''',
				]
try:
				if not is_tensorflow_text_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''TFBertTokenizer''']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''FlaxBertForCausalLM''',
				    '''FlaxBertForMaskedLM''',
				    '''FlaxBertForMultipleChoice''',
				    '''FlaxBertForNextSentencePrediction''',
				    '''FlaxBertForPreTraining''',
				    '''FlaxBertForQuestionAnswering''',
				    '''FlaxBertForSequenceClassification''',
				    '''FlaxBertForTokenClassification''',
				    '''FlaxBertModel''',
				    '''FlaxBertPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
				from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_fast import BertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bert import (
								    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BertForMaskedLM,
								    BertForMultipleChoice,
								    BertForNextSentencePrediction,
								    BertForPreTraining,
								    BertForQuestionAnswering,
								    BertForSequenceClassification,
								    BertForTokenClassification,
								    BertLayer,
								    BertLMHeadModel,
								    BertModel,
								    BertPreTrainedModel,
								    load_tf_weights_in_bert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bert import (
								    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFBertEmbeddings,
								    TFBertForMaskedLM,
								    TFBertForMultipleChoice,
								    TFBertForNextSentencePrediction,
								    TFBertForPreTraining,
								    TFBertForQuestionAnswering,
								    TFBertForSequenceClassification,
								    TFBertForTokenClassification,
								    TFBertLMHeadModel,
								    TFBertMainLayer,
								    TFBertModel,
								    TFBertPreTrainedModel,
								)

				try:
								if not is_tensorflow_text_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_tf import TFBertTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bert import (
								    FlaxBertForCausalLM,
								    FlaxBertForMaskedLM,
								    FlaxBertForMultipleChoice,
								    FlaxBertForNextSentencePrediction,
								    FlaxBertForPreTraining,
								    FlaxBertForQuestionAnswering,
								    FlaxBertForSequenceClassification,
								    FlaxBertForTokenClassification,
								    FlaxBertModel,
								    FlaxBertPreTrainedModel,
								)

else:
				import sys

				__A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)