





import math
from collections import OrderedDict

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

from .utils import logging


lowercase_			       =				logging.get_logger(__name__)


class       a_  (       nn.Module ):

    '''simple docstring'''


    def __init__(	self )	->     Optional[int]:
        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     snake_case_(	self				,	A )	->     Tensor:
        return nn.functional.gelu(A				,	approximate="""tanh""" )




class       a_  (       nn.Module ):

    '''simple docstring'''


    def     snake_case_(	self				,	A )	->     Tensor:
        return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.04_4715 * torch.pow(A				,	3.0 )) ))




class       a_  (       nn.Module ):

    '''simple docstring'''


    def __init__(	self				,	A = False )	->     List[Any]:
        super().__init__()
        if use_gelu_python:
            _SCREAMING_SNAKE_CASE             =    self._gelu_python
        else:
            _SCREAMING_SNAKE_CASE             =    nn.functional.gelu


    def     snake_case_(	self				,	A )	->     Tensor:
        return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0 ) ))





    def     snake_case_(	self				,	A )	->     Tensor:
        return self.act(A )




class       a_  (       nn.Module ):

    '''simple docstring'''


    def     snake_case_(	self				,	A )	->     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 ):

    '''simple docstring'''


    def     snake_case_(	self				,	A )	->     Tensor:
        return input * torch.sigmoid(1.702 * input )




class       a_  (       nn.Module ):

    '''simple docstring'''


    def __init__(	self				,	A				,	A )	->     Union[str, Any]:
        if min > max:
            raise ValueError(f'min should be < max (got min: {min}, max: {max})' )

        super().__init__()
        _SCREAMING_SNAKE_CASE             =    min
        _SCREAMING_SNAKE_CASE             =    max





    def     snake_case_(	self				,	A )	->     Tensor:
        return torch.clip(gelu(A )				,	self.min				,	self.max )




class       a_  (       nn.Module ):

    '''simple docstring'''


    def __init__(	self )	->     Optional[Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE             =    math.sqrt(2 / math.pi )





    def     snake_case_(	self				,	A )	->     Tensor:
        return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(A				,	3 )) ))




class       a_  (       nn.Module ):

    '''simple docstring'''


    def     snake_case_(	self				,	A )	->     Tensor:
        return nn.functional.silu(A )




class       a_  (       nn.Module ):

    '''simple docstring'''


    def __init__(	self )	->     Tuple:
        super().__init__()
        if version.parse(torch.__version__ ) < version.parse("""1.9.0""" ):
            _SCREAMING_SNAKE_CASE             =    self._mish_python
        else:
            _SCREAMING_SNAKE_CASE             =    nn.functional.mish


    def     snake_case_(	self				,	A )	->     Tensor:
        return input * torch.tanh(nn.functional.softplus(A ) )





    def     snake_case_(	self				,	A )	->     Tensor:
        return self.act(A )




class       a_  (       nn.Module ):

    '''simple docstring'''


    def     snake_case_(	self				,	A )	->     Tensor:
        return input




class       a_  (       nn.Module ):

    '''simple docstring'''


    def     snake_case_(	self				,	A				,	A=0.70_7107				,	A=0.28_2095 )	->     Optional[int]:
        _SCREAMING_SNAKE_CASE             =    (input - mu).div(sigma * math.sqrt(2.0 ) )
        return 0.5 * (1.0 + torch.erf(A ))




class       a_  (       nn.Module ):

    '''simple docstring'''


    def     snake_case_(	self				,	A )	->     Tuple:
        _SCREAMING_SNAKE_CASE             =    nn.functional.relu(A )
        _SCREAMING_SNAKE_CASE             =    torch.square(A )
        return squared




class       a_  (       snake_case_ ):

    '''simple docstring'''


    def __getitem__(	self				,	A )	->     Any:
        _SCREAMING_SNAKE_CASE             =    super().__getitem__(A )
        _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    content if isinstance(A				,	A ) else (content, {})
        return cls(**A )




lowercase_			       =				{
    """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,
}
lowercase_			       =				ClassInstantier(ACTaCLS)





def lowerCamelCase		(  __lowerCamelCase				:					int					)		->Optional[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
lowercase_			       =				get_activation("""gelu_python""")
lowercase_			       =				get_activation("""gelu_new""")
lowercase_			       =				get_activation("""gelu""")
lowercase_			       =				get_activation("""gelu_fast""")
lowercase_			       =				get_activation("""quick_gelu""")
lowercase_			       =				get_activation("""silu""")
lowercase_			       =				get_activation("""mish""")






lowercase_			       =				get_activation("""linear""")










'''simple docstring'''

import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class       a_  (       unittest.TestCase ):

    '''simple docstring'''


    def     snake_case_(	self )	->     int:
        _SCREAMING_SNAKE_CASE             =    {
            """model_details""": {
                """Organization""": """testing""",
                """Model date""": """today""",
                """Model version""": """v2.1, Developed by Test Corp in 2019.""",
                """Architecture""": """Convolutional Neural Network.""",
            },
            """metrics""": """BLEU and ROUGE-1""",
            """evaluation_data""": {
                """Datasets""": {"""BLEU""": """My-great-dataset-v1""", """ROUGE-1""": """My-short-dataset-v2.1"""},
                """Preprocessing""": """See details on https://arxiv.org/pdf/1810.03993.pdf""",
            },
            """training_data""": {
                """Dataset""": """English Wikipedia dump dated 2018-12-01""",
                """Preprocessing""": (
                    """Using SentencePiece vocabulary of size 52k tokens. See details on"""
                    """ https://arxiv.org/pdf/1810.03993.pdf"""
                ),
            },
            """quantitative_analyses""": {"""BLEU""": 55.1, """ROUGE-1""": 76},
        }


    def     snake_case_(	self )	->     Tuple:
        _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
        self.assertTrue(hasattr(A				,	"""model_details""" ) )
        self.assertTrue(hasattr(A				,	"""intended_use""" ) )
        self.assertTrue(hasattr(A				,	"""factors""" ) )
        self.assertTrue(hasattr(A				,	"""metrics""" ) )
        self.assertTrue(hasattr(A				,	"""evaluation_data""" ) )
        self.assertTrue(hasattr(A				,	"""training_data""" ) )
        self.assertTrue(hasattr(A				,	"""quantitative_analyses""" ) )
        self.assertTrue(hasattr(A				,	"""ethical_considerations""" ) )
        self.assertTrue(hasattr(A				,	"""caveats_and_recommendations""" ) )


    def     snake_case_(	self )	->     List[str]:
        _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
        _SCREAMING_SNAKE_CASE             =    json.loads(modelcard.to_json_string() )
        for key, value in self.inputs_dict.items():
            self.assertEqual(obj[key]				,	A )


    def     snake_case_(	self )	->     Any:
        _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )

        with tempfile.TemporaryDirectory() as tmpdirname:
            _SCREAMING_SNAKE_CASE             =    os.path.join(A				,	"""modelcard.json""" )
            model_card_first.to_json_file(A )
            _SCREAMING_SNAKE_CASE             =    ModelCard.from_json_file(A )

        self.assertEqual(model_card_second.to_dict()				,	model_card_first.to_dict() )





    def     snake_case_(	self )	->     List[Any]:
        _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )

        with tempfile.TemporaryDirectory() as tmpdirname:
            model_card_first.save_pretrained(A )
            _SCREAMING_SNAKE_CASE             =    ModelCard.from_pretrained(A )

        self.assertEqual(model_card_second.to_dict()				,	model_card_first.to_dict() )



