

import math
from collections import OrderedDict

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

from .utils import logging


lowerCAmelCase__             =		logging.get_logger(__name__)







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def __init__(					self):
			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 		A__       (					self						,							__snake_case):
			return nn.functional.gelu(__snake_case						,							approximate='tanh')







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def 		A__       (					self						,							__snake_case):
			return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.0_4_4_7_1_5 * torch.pow(__snake_case						,							3.0))))







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def __init__(					self						,							__snake_case = False):
			super().__init__()
			if use_gelu_python:
				_UpperCamelCase :		List[Any]								=    self._gelu_python
			else:
				_UpperCamelCase :		Optional[int]								=    nn.functional.gelu


		def 		A__       (					self						,							__snake_case):
			return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0)))






		def 		A__       (					self						,							__snake_case):
			return self.act(__snake_case)







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def 		A__       (					self						,							__snake_case):
			return 0.5 * input * (1.0 + torch.tanh(input * 0.7_9_7_8_8_4_5_6_0_8 * (1.0 + 0.0_4_4_7_1_5 * input * input)))







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def 		A__       (					self						,							__snake_case):
			return input * torch.sigmoid(1.7_0_2 * input)







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def __init__(					self						,							__snake_case						,							__snake_case):
			if min > max:
				raise ValueError(f'''min should be < max (got min: {min}, max: {max})''')

			super().__init__()
			_UpperCamelCase :		List[str]								=    min
			_UpperCamelCase :		Optional[Any]								=    max






		def 		A__       (					self						,							__snake_case):
			return torch.clip(gelu(__snake_case)						,							self.min						,							self.max)







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def __init__(					self):
			super().__init__()
			_UpperCamelCase :		Union[str, Any]								=    math.sqrt(2 / math.pi)






		def 		A__       (					self						,							__snake_case):
			return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.0_4_4_7_1_5 * torch.pow(__snake_case						,							3))))







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def 		A__       (					self						,							__snake_case):
			return nn.functional.silu(__snake_case)







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def __init__(					self):
			super().__init__()
			if version.parse(torch.__version__) < version.parse('1.9.0'):
				_UpperCamelCase :		Dict								=    self._mish_python
			else:
				_UpperCamelCase :		Optional[int]								=    nn.functional.mish


		def 		A__       (					self						,							__snake_case):
			return input * torch.tanh(nn.functional.softplus(__snake_case))






		def 		A__       (					self						,							__snake_case):
			return self.act(__snake_case)







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def 		A__       (					self						,							__snake_case):
			return input







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def 		A__       (					self						,							__snake_case						,							__snake_case=0.7_0_7_1_0_7						,							__snake_case=0.2_8_2_0_9_5):
			_UpperCamelCase :		Any								=    (input - mu).div(sigma * math.sqrt(2.0))
			return 0.5 * (1.0 + torch.erf(__snake_case))







class 						lowercase     (					nn.Module				):


		"""simple docstring"""




		def 		A__       (					self						,							__snake_case):
			_UpperCamelCase :		Tuple								=    nn.functional.relu(__snake_case)
			_UpperCamelCase :		int								=    torch.square(__snake_case)
			return squared







class 						lowercase     (					_lowercase				):


		"""simple docstring"""




		def __getitem__(					self						,							__snake_case):
			_UpperCamelCase :		Dict								=    super().__getitem__(__snake_case)
			_UpperCamelCase						,		_UpperCamelCase :		Union[str, Any]								=    content if isinstance(__snake_case						,							__snake_case) else (content, {})
			return cls(**__snake_case)

lowerCAmelCase__             =		{
    """gelu""": GELUActivation,
    """gelu_10""": (ClippedGELUActivation, {"""min""": -1_0, """max""": 1_0}),
    """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,
}
lowerCAmelCase__             =		ClassInstantier(ACTaCLS)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[Any]						)    ->					str:




	'''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
lowerCAmelCase__             =		get_activation("""gelu_python""")
lowerCAmelCase__             =		get_activation("""gelu_new""")
lowerCAmelCase__             =		get_activation("""gelu""")
lowerCAmelCase__             =		get_activation("""gelu_fast""")
lowerCAmelCase__             =		get_activation("""quick_gelu""")
lowerCAmelCase__             =		get_activation("""silu""")
lowerCAmelCase__             =		get_activation("""mish""")







lowerCAmelCase__             =		get_activation("""linear""")












"""simple docstring"""


import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					RoFormerConfig.from_json_file(__UpperCamelCase					)
  print(f'Building PyTorch model from configuration: {config}'					)
  __A		     =					RoFormerForMaskedLM(__UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_roformer(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)

  # Save pytorch-model
  print(f'Save PyTorch model to {pytorch_dump_path}'					)
  torch.save(model.state_dict()						,			__UpperCamelCase						,			_use_new_zipfile_serialization=__UpperCamelCase					)


if __name__ == "__main__":
       lowercase_								=	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       lowercase_								=	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




