




'''simple docstring'''

import math
from collections import OrderedDict

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

from .utils import logging


a__         =     logging.get_logger(__name__)





class __magic_name__(						nn.Module      ):






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



									'''simple docstring'''






									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       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						Tensor       ):



									'''simple docstring'''






									return nn.functional.gelu(lowercase__					,   approximate="""tanh"""       )







class __magic_name__(						nn.Module      ):






						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						Tensor       ):



									'''simple docstring'''






									return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi       ) * (input + 0.04_47_15 * torch.pow(lowercase__					,   3.0       ))       ))







class __magic_name__(						nn.Module      ):






						def __init__(   self						:						List[Any]					,   __UpperCamelCase						:						bool = False       ):



									'''simple docstring'''






									super().__init__()
									if use_gelu_python:
												snake_case__          =						self._gelu_python
									else:
												snake_case__          =						nn.functional.gelu






						def       __lowerCAmelCase(   self						:						List[str]					,   __UpperCamelCase						:						Tensor       ):



									'''simple docstring'''






									return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0       )       ))







						def       __lowerCAmelCase(   self						:						Dict					,   __UpperCamelCase						:						Tensor       ):



									'''simple docstring'''






									return self.act(lowercase__       )







class __magic_name__(						nn.Module      ):






						def       __lowerCAmelCase(   self						:						List[str]					,   __UpperCamelCase						:						Tensor       ):



									'''simple docstring'''






									return 0.5 * input * (1.0 + torch.tanh(input * 0.79_78_84_56_08 * (1.0 + 0.04_47_15 * input * input)       ))







class __magic_name__(						nn.Module      ):






						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						Tensor       ):



									'''simple docstring'''






									return input * torch.sigmoid(1.7_02 * input       )







class __magic_name__(						nn.Module      ):






						def __init__(   self						:						int					,   __UpperCamelCase						:						float					,   __UpperCamelCase						:						float       ):



									'''simple docstring'''






									if min > max:
												raise ValueError(f"""min should be < max (got min: {min}, max: {max})"""       )

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







						def       __lowerCAmelCase(   self						:						List[str]					,   __UpperCamelCase						:						Tensor       ):



									'''simple docstring'''






									return torch.clip(gelu(lowercase__       )					,   self.min					,   self.max       )







class __magic_name__(						nn.Module      ):






						def __init__(   self						:						Optional[int]       ):



									'''simple docstring'''






									super().__init__()
									snake_case__          =						math.sqrt(2 / math.pi       )







						def       __lowerCAmelCase(   self						:						Any					,   __UpperCamelCase						:						Tensor       ):



									'''simple docstring'''






									return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_47_15 * torch.pow(lowercase__					,   3       ))       ))







class __magic_name__(						nn.Module      ):






						def       __lowerCAmelCase(   self						:						Optional[int]					,   __UpperCamelCase						:						Tensor       ):



									'''simple docstring'''






									return nn.functional.silu(lowercase__       )







class __magic_name__(						nn.Module      ):






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



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						List[str]					,   __UpperCamelCase						:						Tensor       ):



									'''simple docstring'''






									return input * torch.tanh(nn.functional.softplus(lowercase__       )       )







						def       __lowerCAmelCase(   self						:						Dict					,   __UpperCamelCase						:						Tensor       ):



									'''simple docstring'''






									return self.act(lowercase__       )







class __magic_name__(						nn.Module      ):






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						Tensor       ):



									'''simple docstring'''






									return input







class __magic_name__(						nn.Module      ):






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						Optional[int]=0.70_71_07					,   __UpperCamelCase						:						str=0.28_20_95       ):



									'''simple docstring'''






									snake_case__          =						(input - mu).div(sigma * math.sqrt(2.0       )       )
									return 0.5 * (1.0 + torch.erf(lowercase__       ))







class __magic_name__(						nn.Module      ):






						def       __lowerCAmelCase(   self						:						List[str]					,   __UpperCamelCase						:						Any       ):



									'''simple docstring'''






									snake_case__          =						nn.functional.relu(lowercase__       )
									snake_case__          =						torch.square(lowercase__       )
									return squared







class __magic_name__(						a__      ):






						def __getitem__(   self						:						Any					,   __UpperCamelCase						:						List[str]       ):



									'''simple docstring'''






									snake_case__          =						super().__getitem__(lowercase__       )
									snake_case__    ,			snake_case__          =						content if isinstance(lowercase__					,   lowercase__       ) else (content, {})
									return cls(**lowercase__       )







a__         =     {
    '''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__         =     ClassInstantier(ACTaCLS)




def    snake_case__     (  a	)			->       Optional[int]:





			'''simple docstring'''






			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__         =     get_activation('''gelu_python''')
a__         =     get_activation('''gelu_new''')
a__         =     get_activation('''gelu''')
a__         =     get_activation('''gelu_fast''')
a__         =     get_activation('''quick_gelu''')
a__         =     get_activation('''silu''')
a__         =     get_activation('''mish''')



a__         =     get_activation('''linear''')





import io
import json
import subprocess


UpperCamelCase__       				=	[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

UpperCamelCase__       				=	8






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_		    =  {}
  for src, tgt in pairs:
    a_		    =  f"{src}-{tgt}"
    a_		    =  f"sacrebleu -t wmt19 -l {pair} --echo src".split()
    a_		    =  subprocess.run(_A      ,      stdout=subprocess.PIPE			).stdout.decode('''utf-8'''			).splitlines()
    a_		    =  f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
    a_		    =  subprocess.run(_A      ,      stdout=subprocess.PIPE			).stdout.decode('''utf-8'''			).splitlines()
    a_		    =  {'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
  return text


UpperCamelCase__       				=	get_all_data(pairs, n_objs)
UpperCamelCase__       				=	'''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
  UpperCamelCase__       				=	json.dump(text, f, indent=2, ensure_ascii=False)
