






import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
	raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
UpperCAmelCase_					:		str           = logging.get_logger(__name__)

UpperCAmelCase_					:		int           = '''Hello world! cécé herlolip'''

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Optional[Any]      ,     __magic_name__   :    Optional[Any]      ,     __magic_name__   :    Union[str, Any]   )						->     Optional[int]:






	"""simple docstring"""



	UpperCamelCase	, UpperCamelCase	:List[Any]		=      os.path.split(UpperCAmelCase_   )
	UpperCamelCase	:str		=      DataaVecTextModel.from_pretrained(
	    UpperCAmelCase_      ,     checkpoint_file=UpperCAmelCase_   )
	dataavec.eval()  # disable dropout
	UpperCamelCase	:Any		=      dataavec.models[0]
	UpperCamelCase	:str		=      dataavec_model.encoder.sentence_encoder
	UpperCamelCase	:Optional[Any]		=      DataaVecTextConfig(
	    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings      ,     hidden_size=dataavec_model.args.encoder_embed_dim      ,     num_hidden_layers=dataavec_model.args.encoder_layers      ,     num_attention_heads=dataavec_model.args.encoder_attention_heads      ,     intermediate_size=dataavec_model.args.encoder_ffn_embed_dim      ,     max_position_embeddings=514      ,     type_vocab_size=1      ,     layer_norm_eps=1E-5      ,     )
	if classification_head:
		UpperCamelCase	:Optional[Any]		=      dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""      ,     UpperCAmelCase_   )

	UpperCamelCase	:int		=      DataaVecTextForSequenceClassification(UpperCAmelCase_   ) if classification_head else DataaVecTextForMaskedLM(UpperCAmelCase_   )
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	UpperCamelCase	:Tuple		=      dataavec_sent_encoder.embed_tokens.weight
	UpperCamelCase	:Tuple		=      dataavec_sent_encoder.embed_positions.weight
	UpperCamelCase	:Optional[Any]		=      torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight   )  # just zero them out b/c data2vec doesn't use them.
	UpperCamelCase	:Optional[Any]		=      dataavec_sent_encoder.layernorm_embedding.weight
	UpperCamelCase	:Union[str, Any]		=      dataavec_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers   ):
		# Encoder: start of layer
		UpperCamelCase	:Union[str, Any]		=      model.dataavec_text.encoder.layer[i]
		UpperCamelCase	:Any		=      dataavec_sent_encoder.layers[i]

		# self attention
		UpperCamelCase	:Optional[Any]		=      layer.attention.self
		assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)   ), (
		    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
		    f""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
		)
		assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)   ), (
		    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
		    f""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
		)
		assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)   ), (
		    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
		    f""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
		)

		UpperCamelCase	:Tuple		=      dataavec_layer.self_attn.q_proj.weight
		UpperCamelCase	:str		=      dataavec_layer.self_attn.q_proj.bias
		UpperCamelCase	:Any		=      dataavec_layer.self_attn.k_proj.weight
		UpperCamelCase	:int		=      dataavec_layer.self_attn.k_proj.bias
		UpperCamelCase	:str		=      dataavec_layer.self_attn.v_proj.weight
		UpperCamelCase	:List[str]		=      dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		UpperCamelCase	:str		=      layer.attention.output
		assert (
		    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
		), f"""Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}"""
		UpperCamelCase	:Union[str, Any]		=      dataavec_layer.self_attn.out_proj.weight
		UpperCamelCase	:str		=      dataavec_layer.self_attn.out_proj.bias
		UpperCamelCase	:Optional[int]		=      dataavec_layer.self_attn_layer_norm.weight
		UpperCamelCase	:str		=      dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		UpperCamelCase	:Optional[int]		=      layer.intermediate
		assert (
		    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
		), f"""Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}"""
		UpperCamelCase	:Tuple		=      dataavec_layer.fca.weight
		UpperCamelCase	:Tuple		=      dataavec_layer.fca.bias

		# output
		UpperCamelCase	:Union[str, Any]		=      layer.output
		assert (
		    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
		), f"""Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}"""
		UpperCamelCase	:Optional[int]		=      dataavec_layer.fca.weight
		UpperCamelCase	:Dict		=      dataavec_layer.fca.bias
		UpperCamelCase	:str		=      dataavec_layer.final_layer_norm.weight
		UpperCamelCase	:List[Any]		=      dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		UpperCamelCase	:str		=      dataavec.model.classification_heads["""mnli"""].dense.weight
		UpperCamelCase	:Optional[int]		=      dataavec.model.classification_heads["""mnli"""].dense.bias
		UpperCamelCase	:str		=      dataavec.model.classification_heads["""mnli"""].out_proj.weight
		UpperCamelCase	:int		=      dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		UpperCamelCase	:List[str]		=      dataavec_model.encoder.lm_head.dense.weight
		UpperCamelCase	:Any		=      dataavec_model.encoder.lm_head.dense.bias
		UpperCamelCase	:Tuple		=      dataavec_model.encoder.lm_head.layer_norm.weight
		UpperCamelCase	:int		=      dataavec_model.encoder.lm_head.layer_norm.bias
		UpperCamelCase	:int		=      dataavec_model.encoder.lm_head.weight
		UpperCamelCase	:List[Any]		=      dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	UpperCamelCase	:List[str]		=      dataavec.encode(UpperCAmelCase_   ).unsqueeze(0   )  # batch of size 1

	UpperCamelCase	:Union[str, Any]		=      model(UpperCAmelCase_   )[0]
	if classification_head:
		UpperCamelCase	:Optional[Any]		=      dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(UpperCAmelCase_   )   )
	else:
		UpperCamelCase	:Any		=      dataavec_model(UpperCAmelCase_   )[0]
	print(our_output.shape      ,     their_output.shape   )
	UpperCamelCase	:int		=      torch.max(torch.abs(our_output - their_output   )   ).item()
	print(f"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
	UpperCamelCase	:Union[str, Any]		=      torch.allclose(UpperCAmelCase_      ,     UpperCAmelCase_      ,     atol=1E-3   )
	print("""Do both models output the same tensors?"""      ,     """🔥""" if success else """💩"""   )
	if not success:
		raise Exception("""Something went wRoNg"""   )

	pathlib.Path(UpperCAmelCase_   ).mkdir(parents=UpperCAmelCase_      ,     exist_ok=UpperCAmelCase_   )
	print(f"""Saving model to {pytorch_dump_folder_path}"""   )
	model.save_pretrained(UpperCAmelCase_   )


if __name__ == "__main__":
	UpperCAmelCase_					:		str           = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	parser.add_argument(
	    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
	)
	UpperCAmelCase_					:		str           = parser.parse_args()
	convert_dataavec_checkpoint_to_pytorch(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)






import math
from collections import OrderedDict

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

from .utils import logging


SCREAMING_SNAKE_CASE_    :       Tuple       =							logging.get_logger(__name__)







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							int     )      ->      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 					__SCREAMING_SNAKE_CASE		(   self			:							str    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return nn.functional.gelu(A    ,					approximate="""tanh"""     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							str    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi     ) * (input + 0.0_4_4_7_1_5 * torch.pow(A    ,					3.0     ))     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Optional[Any]    ,					A			:							bool = False     )      ->      Optional[Any]:






         """simple docstring"""
         super().__init__()
         if use_gelu_python:
             a__  			=							self._gelu_python
         else:
             a__  			=							nn.functional.gelu

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict    ,					A			:							Tensor     )      ->      Tensor:






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






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return self.act(A     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         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 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input * torch.sigmoid(1.7_0_2 * input     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Tuple    ,					A			:							float    ,					A			:							float     )      ->      Optional[Any]:






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

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






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return torch.clip(gelu(A     )    ,					self.min    ,					self.max     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Tuple     )      ->      List[Any]:






         """simple docstring"""
         super().__init__()
         a__  			=							math.sqrt(2 / math.pi     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.0_4_4_7_1_5 * torch.pow(A    ,					3     ))     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return nn.functional.silu(A     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def __init__(   self			:							Optional[Any]     )      ->      List[str]:






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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input * torch.tanh(nn.functional.softplus(A     )     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return self.act(A     )







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]    ,					A			:							Tensor     )      ->      Tensor:






         """simple docstring"""
         return input







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]    ,					A			:							str    ,					A			:							Dict=0.7_0_7_1_0_7    ,					A			:							Union[str, Any]=0.2_8_2_0_9_5     )      ->      int:






         """simple docstring"""
         a__  			=							(input - mu).div(sigma * math.sqrt(2.0     )     )
         return 0.5 * (1.0 + torch.erf(A     ))







class 		__UpperCAmelCase(		nn.Module			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]    ,					A			:							List[Any]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							nn.functional.relu(A     )
         a__  			=							torch.square(A     )
         return squared







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     def __getitem__(   self			:							List[str]    ,					A			:							int     )      ->      Any:






         """simple docstring"""
         a__  			=							super().__getitem__(A     )
         a__			,		a__  			=							content if isinstance(A    ,					A     ) else (content, {})
         return cls(**A     )






SCREAMING_SNAKE_CASE_    :       List[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,
}
SCREAMING_SNAKE_CASE_    :       Any       =							ClassInstantier(ACTaCLS)
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    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
SCREAMING_SNAKE_CASE_    :       Optional[int]       =							get_activation('''gelu_python''')
SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							get_activation('''gelu_new''')
SCREAMING_SNAKE_CASE_    :       str       =							get_activation('''gelu''')
SCREAMING_SNAKE_CASE_    :       Optional[Any]       =							get_activation('''gelu_fast''')
SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							get_activation('''quick_gelu''')
SCREAMING_SNAKE_CASE_    :       Tuple       =							get_activation('''silu''')
SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							get_activation('''mish''')







SCREAMING_SNAKE_CASE_    :       Tuple       =							get_activation('''linear''')






