


import tempfile
import unittest

from transformers import DPRConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
   from transformers.models.dpr.modeling_dpr import (
       DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
       DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
       DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
   )



class 							a       :





     def __init__(     self       :					str     ,						SCREAMING_SNAKE_CASE_       :					Optional[Any]     ,						SCREAMING_SNAKE_CASE_       :					List[str]=13     ,						SCREAMING_SNAKE_CASE_       :					Optional[Any]=7     ,						SCREAMING_SNAKE_CASE_       :					List[str]=False     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=True     ,						SCREAMING_SNAKE_CASE_       :					str=True     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=True     ,						SCREAMING_SNAKE_CASE_       :					Any=99     ,						SCREAMING_SNAKE_CASE_       :					Dict=32     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=5     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=4     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=37     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]="gelu"     ,						SCREAMING_SNAKE_CASE_       :					Any=0.1     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=0.1     ,						SCREAMING_SNAKE_CASE_       :					Optional[Any]=512     ,						SCREAMING_SNAKE_CASE_       :					Any=16     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=2     ,						SCREAMING_SNAKE_CASE_       :					str=0.02     ,						SCREAMING_SNAKE_CASE_       :					Tuple=3     ,						SCREAMING_SNAKE_CASE_       :					Any=4     ,						SCREAMING_SNAKE_CASE_       :					Tuple=None     ,						SCREAMING_SNAKE_CASE_       :					str=0     ,						):
        __lowerCamelCase:					Optional[Any]	 =						parent
        __lowerCamelCase:					Union[str, Any]	 =						batch_size
        __lowerCamelCase:					str	 =						seq_length
        __lowerCamelCase:					str	 =						is_training
        __lowerCamelCase:					Optional[Any]	 =						use_input_mask
        __lowerCamelCase:					Union[str, Any]	 =						use_token_type_ids
        __lowerCamelCase:					Any	 =						use_labels
        __lowerCamelCase:					str	 =						vocab_size
        __lowerCamelCase:					Tuple	 =						hidden_size
        __lowerCamelCase:					str	 =						num_hidden_layers
        __lowerCamelCase:					Dict	 =						num_attention_heads
        __lowerCamelCase:					int	 =						intermediate_size
        __lowerCamelCase:					Any	 =						hidden_act
        __lowerCamelCase:					Dict	 =						hidden_dropout_prob
        __lowerCamelCase:					str	 =						attention_probs_dropout_prob
        __lowerCamelCase:					Optional[Any]	 =						max_position_embeddings
        __lowerCamelCase:					Optional[Any]	 =						type_vocab_size
        __lowerCamelCase:					Optional[int]	 =						type_sequence_label_size
        __lowerCamelCase:					List[str]	 =						initializer_range
        __lowerCamelCase:					Tuple	 =						num_labels
        __lowerCamelCase:					str	 =						num_choices
        __lowerCamelCase:					Union[str, Any]	 =						scope
        __lowerCamelCase:					Dict	 =						projection_dim





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]				):
        __lowerCamelCase:					int	 =						ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size				)

        __lowerCamelCase:					List[Any]	 =						None
        if self.use_input_mask:
           __lowerCamelCase:					Optional[int]	 =						random_attention_mask([self.batch_size, self.seq_length]				)

        __lowerCamelCase:					int	 =						None
        if self.use_token_type_ids:
           __lowerCamelCase:					Union[str, Any]	 =						ids_tensor([self.batch_size, self.seq_length]     ,						self.type_vocab_size				)

        __lowerCamelCase:					Optional[int]	 =						None
        __lowerCamelCase:					int	 =						None
        __lowerCamelCase:					List[Any]	 =						None
        if self.use_labels:
           __lowerCamelCase:					List[str]	 =						ids_tensor([self.batch_size]     ,						self.type_sequence_label_size				)
           __lowerCamelCase:					str	 =						ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels				)
           __lowerCamelCase:					Optional[int]	 =						ids_tensor([self.batch_size]     ,						self.num_choices				)

        __lowerCamelCase:					int	 =						self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        return DPRConfig(
            projection_dim=self.projection_dim     ,						vocab_size=self.vocab_size     ,						hidden_size=self.hidden_size     ,						num_hidden_layers=self.num_hidden_layers     ,						num_attention_heads=self.num_attention_heads     ,						intermediate_size=self.intermediate_size     ,						hidden_act=self.hidden_act     ,						hidden_dropout_prob=self.hidden_dropout_prob     ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,						max_position_embeddings=self.max_position_embeddings     ,						type_vocab_size=self.type_vocab_size     ,						initializer_range=self.initializer_range     ,						)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					str     ,						SCREAMING_SNAKE_CASE_       :					int     ,						SCREAMING_SNAKE_CASE_       :					Dict     ,						SCREAMING_SNAKE_CASE_       :					Tuple     ,						SCREAMING_SNAKE_CASE_       :					Optional[Any]     ,						SCREAMING_SNAKE_CASE_       :					Optional[Any]     ,						SCREAMING_SNAKE_CASE_       :					Dict     ,						SCREAMING_SNAKE_CASE_       :					List[str]				):
        __lowerCamelCase:					Union[str, Any]	 =						DPRContextEncoder(config=SCREAMING_SNAKE_CASE_				)
        model.to(SCREAMING_SNAKE_CASE_				)
        model.eval()
        __lowerCamelCase:					int	 =						model(SCREAMING_SNAKE_CASE_     ,						attention_mask=SCREAMING_SNAKE_CASE_     ,						token_type_ids=SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Optional[Any]	 =						model(SCREAMING_SNAKE_CASE_     ,						token_type_ids=SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Any	 =						model(SCREAMING_SNAKE_CASE_				)
        self.parent.assertEqual(result.pooler_output.shape     ,						(self.batch_size, self.projection_dim or self.hidden_size)				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]     ,						SCREAMING_SNAKE_CASE_       :					List[Any]     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					int     ,						SCREAMING_SNAKE_CASE_       :					Dict     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					Tuple				):
        __lowerCamelCase:					List[Any]	 =						DPRQuestionEncoder(config=SCREAMING_SNAKE_CASE_				)
        model.to(SCREAMING_SNAKE_CASE_				)
        model.eval()
        __lowerCamelCase:					int	 =						model(SCREAMING_SNAKE_CASE_     ,						attention_mask=SCREAMING_SNAKE_CASE_     ,						token_type_ids=SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Optional[int]	 =						model(SCREAMING_SNAKE_CASE_     ,						token_type_ids=SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Union[str, Any]	 =						model(SCREAMING_SNAKE_CASE_				)
        self.parent.assertEqual(result.pooler_output.shape     ,						(self.batch_size, self.projection_dim or self.hidden_size)				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					Optional[Any]     ,						SCREAMING_SNAKE_CASE_       :					Dict     ,						SCREAMING_SNAKE_CASE_       :					List[Any]     ,						SCREAMING_SNAKE_CASE_       :					Optional[Any]				):
        __lowerCamelCase:					Optional[Any]	 =						DPRReader(config=SCREAMING_SNAKE_CASE_				)
        model.to(SCREAMING_SNAKE_CASE_				)
        model.eval()
        __lowerCamelCase:					Any	 =						model(
            SCREAMING_SNAKE_CASE_     ,						attention_mask=SCREAMING_SNAKE_CASE_     ,						)

        self.parent.assertEqual(result.start_logits.shape     ,						(self.batch_size, self.seq_length)				)
        self.parent.assertEqual(result.end_logits.shape     ,						(self.batch_size, self.seq_length)				)
        self.parent.assertEqual(result.relevance_logits.shape     ,						(self.batch_size,)				)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]				):
        __lowerCamelCase:					Any	 =						self.prepare_config_and_inputs()
        (
            (
            __lowerCamelCase
        ) ,  (
            __lowerCamelCase
        ) ,  (
            __lowerCamelCase
        ) ,  (
            __lowerCamelCase
        ) ,  (
            __lowerCamelCase
        ) ,  (
            __lowerCamelCase
        ) ,  (
            __lowerCamelCase
        ) ,  
        ):					Dict	 =						config_and_inputs
        __lowerCamelCase:					List[str]	 =						{"""input_ids""": input_ids}
        return config, inputs_dict



@require_torch
class 							a       (      _UpperCAmelCase  ,_UpperCAmelCase  ,unittest.TestCase ):
     UpperCAmelCase__			:	Dict       =						(
         (
             DPRContextEncoder,
             DPRQuestionEncoder,
             DPRReader,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase__			:	List[str]       =						{"feature-extraction": DPRQuestionEncoder} if is_torch_available() else {}

     UpperCAmelCase__			:	str       =						False
     UpperCAmelCase__			:	Optional[Any]       =						False  # why?
     UpperCAmelCase__			:	List[Any]       =						False
     UpperCAmelCase__			:	int       =						False





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]				):
        __lowerCamelCase:					str	 =						DPRModelTester(self				)
        __lowerCamelCase:					Dict	 =						ConfigTester(self     ,						config_class=SCREAMING_SNAKE_CASE_     ,						hidden_size=37				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        self.config_tester.run_common_tests()





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					Optional[Any]	 =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_context_encoder(*SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int				):
        __lowerCamelCase:					Any	 =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_question_encoder(*SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Dict				):
        __lowerCamelCase:					str	 =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_reader(*SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]				):
        __lowerCamelCase:					str	 =						self.model_tester.prepare_config_and_inputs()[0]

        __lowerCamelCase:					Optional[int]	 =						DPRQuestionEncoder(config=SCREAMING_SNAKE_CASE_				)
        model.to(SCREAMING_SNAKE_CASE_				)
        model.eval()

        with tempfile.TemporaryDirectory() as tmp_dirname:
           model.save_pretrained(SCREAMING_SNAKE_CASE_				)
           __lowerCamelCase:					List[Any]	 =						DPRQuestionEncoder.from_pretrained(SCREAMING_SNAKE_CASE_     ,						projection_dim=512				)

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)







     @slow
     def 			SCREAMING_SNAKE_CASE__							(     self       :					Dict				):
        for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:					Dict	 =						DPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE_				)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)

        for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:					Any	 =						DPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE_				)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)

        for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:					Tuple	 =						DPRQuestionEncoder.from_pretrained(SCREAMING_SNAKE_CASE_				)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)

        for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:					Optional[int]	 =						DPRReader.from_pretrained(SCREAMING_SNAKE_CASE_				)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)



@require_torch
class 							a       (      unittest.TestCase ):





     @slow
     def 			SCREAMING_SNAKE_CASE__							(     self       :					Union[str, Any]				):
        __lowerCamelCase:					Union[str, Any]	 =						DPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""     ,						return_dict=SCREAMING_SNAKE_CASE_				)
        model.to(SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					Tuple	 =						torch.tensor(
            [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]     ,						dtype=torch.long     ,						device=SCREAMING_SNAKE_CASE_				)  # [CLS] hello, is my dog cute? [SEP]
        __lowerCamelCase:					Tuple	 =						model(SCREAMING_SNAKE_CASE_				)[0]  # embedding shape = (1, 768)
        # compare the actual values for a slice.
        __lowerCamelCase:					Dict	 =						torch.tensor(
            [
                [
                    0.0323_6253,
                    0.1275_3335,
                    0.1681_8509,
                    0.0027_9786,
                    0.389_6933,
                    0.2426_4945,
                    0.217_8971,
                    -0.0233_5227,
                    -0.0848_1959,
                    -0.1432_4117,
                ]
            ]     ,						dtype=torch.float     ,						device=SCREAMING_SNAKE_CASE_     ,						)
        self.assertTrue(torch.allclose(output[:, :10]     ,						SCREAMING_SNAKE_CASE_     ,						atol=1E-4				)				)







     @slow
     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]				):
        __lowerCamelCase:					Dict	 =						DPRReaderTokenizer.from_pretrained("""facebook/dpr-reader-single-nq-base"""				)
        __lowerCamelCase:					Optional[Any]	 =						DPRReader.from_pretrained("""facebook/dpr-reader-single-nq-base"""				)
        model.to(SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					int	 =						tokenizer(
            questions="""What is love ?"""     ,						titles="""Haddaway"""     ,						texts="""What Is Love is a song recorded by the artist Haddaway"""     ,						padding=SCREAMING_SNAKE_CASE_     ,						return_tensors="""pt"""     ,						)
        encoded_inputs.to(SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					Optional[Any]	 =						model(**SCREAMING_SNAKE_CASE_				)

        # compare the actual values for a slice.
        __lowerCamelCase:					Optional[Any]	 =						torch.tensor(
            [[-10.3005, -10.7765, -11.4872, -11.6841, -11.9312, -10.3002, -9.8544, -11.7378, -12.0821, -10.2975]]     ,						dtype=torch.float     ,						device=SCREAMING_SNAKE_CASE_     ,						)

        __lowerCamelCase:					Dict	 =						torch.tensor(
            [[-11.0684, -11.7041, -11.5397, -10.3465, -10.8791, -6.8443, -11.9959, -11.0364, -10.0096, -6.8405]]     ,						dtype=torch.float     ,						device=SCREAMING_SNAKE_CASE_     ,						)
        self.assertTrue(torch.allclose(outputs.start_logits[:, :10]     ,						SCREAMING_SNAKE_CASE_     ,						atol=1E-4				)				)
        self.assertTrue(torch.allclose(outputs.end_logits[:, :10]     ,						SCREAMING_SNAKE_CASE_     ,						atol=1E-4				)				)






import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


_A		:					Optional[int]          =							logging.get_logger(__name__)



class 							a       (      nn.Module ):





     def __init__(     self       :					Dict				):
        super().__init__()
        if version.parse(torch.__version__				) < version.parse("""1.12.0"""				):
           raise ImportError(
               F'You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '
               """PytorchGELUTanh. Please upgrade torch."""				)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					int     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return nn.functional.gelu(SCREAMING_SNAKE_CASE_     ,						approximate="""tanh"""				)



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.04_4715 * torch.pow(SCREAMING_SNAKE_CASE_     ,						3.0				))				))



class 							a       (      nn.Module ):





     def __init__(     self       :					List[Any]     ,						SCREAMING_SNAKE_CASE_       :					bool = False				):
        super().__init__()
        if use_gelu_python:
           __lowerCamelCase:					Union[str, Any]	 =						self._gelu_python
        else:
           __lowerCamelCase:					Tuple	 =						nn.functional.gelu





     def 			SCREAMING_SNAKE_CASE__							(     self       :					str     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0				)				))







     def 			SCREAMING_SNAKE_CASE__							(     self       :					Union[str, Any]     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return self.act(SCREAMING_SNAKE_CASE_				)



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return 0.5 * input * (1.0 + torch.tanh(input * 0.79_7884_5608 * (1.0 + 0.04_4715 * input * input)				))



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return input * torch.sigmoid(1.702 * input				)



class 							a       (      nn.Module ):





     def __init__(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					float     ,						SCREAMING_SNAKE_CASE_       :					float				):
        if min > max:
           raise ValueError(F'min should be < max (got min: {min}, max: {max})'				)

        super().__init__()
        __lowerCamelCase:					Optional[int]	 =						min
        __lowerCamelCase:					str	 =						max







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return torch.clip(gelu(SCREAMING_SNAKE_CASE_				)     ,						self.min     ,						self.max				)



class 							a       (      nn.Module ):





     def __init__(     self       :					int				):
        super().__init__()
        __lowerCamelCase:					List[str]	 =						math.sqrt(2 / math.pi				)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(SCREAMING_SNAKE_CASE_     ,						3				))				))



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return nn.functional.silu(SCREAMING_SNAKE_CASE_				)



class 							a       (      nn.Module ):





     def __init__(     self       :					Any				):
        super().__init__()
        if version.parse(torch.__version__				) < version.parse("""1.9.0"""				):
           __lowerCamelCase:					str	 =						self._mish_python
        else:
           __lowerCamelCase:					Any	 =						nn.functional.mish





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return input * torch.tanh(nn.functional.softplus(SCREAMING_SNAKE_CASE_				)				)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return self.act(SCREAMING_SNAKE_CASE_				)



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return input



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=0.70_7107     ,						SCREAMING_SNAKE_CASE_       :					Tuple=0.28_2095				):
        __lowerCamelCase:					Optional[Any]	 =						(input - mu).div(sigma * math.sqrt(2.0				)				)
        return 0.5 * (1.0 + torch.erf(SCREAMING_SNAKE_CASE_				))



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int     ,						SCREAMING_SNAKE_CASE_       :					str				):
        __lowerCamelCase:					Any	 =						nn.functional.relu(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					List[str]	 =						torch.square(SCREAMING_SNAKE_CASE_				)
        return squared



class 							a       (      _UpperCAmelCase ):





     def __getitem__(     self       :					str     ,						SCREAMING_SNAKE_CASE_       :					Tuple				):
        __lowerCamelCase:					Optional[Any]	 =						super().__getitem__(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase ,  __lowerCamelCase:					Optional[Any]	 =						content if isinstance(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				) else (content, {})
        return cls(**SCREAMING_SNAKE_CASE_				)



_A		:					Optional[Any]          =							{
    '''gelu''': GELUActivation,
    '''gelu_10''': (ClippedGELUActivation, {'''min''': -10, '''max''': 10}),
    '''gelu_fast''': FastGELUActivation,
    '''gelu_new''': NewGELUActivation,
    '''gelu_python''': (GELUActivation, {'''use_gelu_python''': True}),
    '''gelu_pytorch_tanh''': PytorchGELUTanh,
    '''gelu_accurate''': AccurateGELUActivation,
    '''laplace''': LaplaceActivation,
    '''linear''': LinearActivation,
    '''mish''': MishActivation,
    '''quick_gelu''': QuickGELUActivation,
    '''relu''': nn.ReLU,
    '''relu2''': ReLUSquaredActivation,
    '''relu6''': nn.ReLUa,
    '''sigmoid''': nn.Sigmoid,
    '''silu''': SiLUActivation,
    '''swish''': SiLUActivation,
    '''tanh''': nn.Tanh,
}
_A		:					Optional[int]          =							ClassInstantier(ACTaCLS)



def      __lowerCAmelCase    (				snake_case						:     Any			)							->				int:
   if activation_string in ACTaFN:
      return ACTaFN[activation_string]
   else:
      raise KeyError(f'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()			)}'			)


# For backwards compatibility with: from activations import gelu_python
_A		:					Optional[int]          =							get_activation('''gelu_python''')
_A		:					Dict          =							get_activation('''gelu_new''')
_A		:					List[str]          =							get_activation('''gelu''')
_A		:					Tuple          =							get_activation('''gelu_fast''')
_A		:					int          =							get_activation('''quick_gelu''')
_A		:					Tuple          =							get_activation('''silu''')
_A		:					Optional[Any]          =							get_activation('''mish''')







_A		:					Dict          =							get_activation('''linear''')


