
"""simple docstring"""


import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin







class 					lowerCAmelCase_					(   _lowercase		,       _lowercase     ):



						'''simple docstring'''


						@register_to_config
						def __init__(			self  :   List[str]	,A_  :   int = 128	,A_  :   int = 256	,A_  :   float = 20_00.0	,A_  :   int = 768	,A_  :   int = 12	,A_  :   int = 12	,A_  :   int = 64	,A_  :   int = 2048	,A_  :   float = 0.1	,)  -> str:
							super().__init__()

							A     = nn.Sequential(
							    nn.Linear(A_	,d_model * 4	,bias=A_   )	,nn.SiLU()	,nn.Linear(d_model * 4	,d_model * 4	,bias=A_   )	,nn.SiLU()	,)

							A     = nn.Embedding(A_	,A_   )
							A     = False

							A     = nn.Linear(A_	,A_	,bias=A_   )

							A     = nn.Dropout(p=A_   )

							A     = nn.ModuleList()
							for lyr_num in range(A_   ):
								# FiLM conditional T5 decoder
								A     = DecoderLayer(d_model=A_	,d_kv=A_	,num_heads=A_	,d_ff=A_	,dropout_rate=A_   )
								self.decoders.append(A_   )

							A     = TaLayerNorm(A_   )

							A     = nn.Dropout(p=A_   )
							A     = nn.Linear(A_	,A_	,bias=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   List[Any]	,A_  :   int   )  -> Any:
							A     = torch.mul(query_input.unsqueeze(-1   )	,key_input.unsqueeze(-2   )   )
							return mask.unsqueeze(-3   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   Dict	,A_  :   List[Any]	,A_  :   Optional[Any]   )  -> List[Any]:
							A ,   A ,   A     = decoder_input_tokens.shape
							assert decoder_noise_time.shape == (batch,)

							# decoder_noise_time is in [0, 1), so rescale to expected timing range.
							A     = get_timestep_embedding(
							    decoder_noise_time * self.config.max_decoder_noise_time	,embedding_dim=self.config.d_model	,max_period=self.config.max_decoder_noise_time	,).to(dtype=self.dtype   )

							A     = self.conditioning_emb(A_   ).unsqueeze(1   )

							assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

							A     = decoder_input_tokens.shape[1]

							# If we want to use relative positions for audio context, we can just offset
							# this sequence by the length of encodings_and_masks.
							A     = torch.broadcast_to(
							    torch.arange(A_	,device=decoder_input_tokens.device   )	,(batch, seq_length)	,)

							A     = self.position_encoding(A_   )

							A     = self.continuous_inputs_projection(A_   )
							inputs += position_encodings
							A     = self.dropout(A_   )

							# decoder: No padding present.
							A     = torch.ones(
							    decoder_input_tokens.shape[:2]	,device=decoder_input_tokens.device	,dtype=inputs.dtype   )

							# Translate encoding masks to encoder-decoder masks.
							A     = [(x, self.encoder_decoder_mask(A_	,A_   )) for x, y in encodings_and_masks]

							# cross attend style: concat encodings
							A     = torch.cat([x[0] for x in encodings_and_encdec_masks]	,dim=1   )
							A     = torch.cat([x[1] for x in encodings_and_encdec_masks]	,dim=-1   )

							for lyr in self.decoders:
								A     = lyr(
								    A_	,conditioning_emb=A_	,encoder_hidden_states=A_	,encoder_attention_mask=A_	,)[0]

							A     = self.decoder_norm(A_   )
							A     = self.post_dropout(A_   )

							A     = self.spec_out(A_   )
							return spec_out







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   Union[str, Any]	,A_  :   Dict	,A_  :   Optional[int]	,A_  :   Optional[int]	,A_  :   int	,A_  :   Tuple	,A_  :   int=1e-6   )  -> Any:
							super().__init__()
							A     = nn.ModuleList()

							# cond self attention: layer 0
							self.layer.append(
							    TaLayerSelfAttentionCond(d_model=A_	,d_kv=A_	,num_heads=A_	,dropout_rate=A_   )   )

							# cross attention: layer 1
							self.layer.append(
							    TaLayerCrossAttention(
							        d_model=A_	,d_kv=A_	,num_heads=A_	,dropout_rate=A_	,layer_norm_epsilon=A_	,)   )

							# Film Cond MLP + dropout: last layer
							self.layer.append(
							    TaLayerFFCond(d_model=A_	,d_ff=A_	,dropout_rate=A_	,layer_norm_epsilon=A_   )   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   List[str]	,A_  :   str=None	,A_  :   Any=None	,A_  :   Optional[Any]=None	,A_  :   List[str]=None	,A_  :   Optional[int]=None	,)  -> Optional[int]:
							A     = self.layer[0](
							    A_	,conditioning_emb=A_	,attention_mask=A_	,)

							if encoder_hidden_states is not None:
								A     = torch.where(encoder_attention_mask > 0	,0	,-1e10   ).to(
								    encoder_hidden_states.dtype   )

								A     = self.layer[1](
								    A_	,key_value_states=A_	,attention_mask=A_	,)

							# Apply Film Conditional Feed Forward layer
							A     = self.layer[-1](A_	,A_   )

							return (hidden_states,)







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   int	,A_  :   Optional[int]	,A_  :   str	,A_  :   int	,A_  :   int   )  -> Optional[int]:
							super().__init__()
							A     = TaLayerNorm(A_   )
							A     = TaFiLMLayer(in_features=d_model * 4	,out_features=A_   )
							A     = Attention(query_dim=A_	,heads=A_	,dim_head=A_	,out_bias=A_	,scale_qk=A_   )
							A     = nn.Dropout(A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   int	,A_  :   str=None	,A_  :   Optional[Any]=None	,)  -> List[Any]:
							# pre_self_attention_layer_norm
							A     = self.layer_norm(A_   )

							if conditioning_emb is not None:
								A     = self.FiLMLayer(A_	,A_   )

							# Self-attention block
							A     = self.attention(A_   )

							A     = hidden_states + self.dropout(A_   )

							return hidden_states







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   Any	,A_  :   Any	,A_  :   Any	,A_  :   Optional[int]	,A_  :   int	,A_  :   str   )  -> List[str]:
							super().__init__()
							A     = Attention(query_dim=A_	,heads=A_	,dim_head=A_	,out_bias=A_	,scale_qk=A_   )
							A     = TaLayerNorm(A_	,eps=A_   )
							A     = nn.Dropout(A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   Union[str, Any]	,A_  :   Optional[Any]=None	,A_  :   Tuple=None	,)  -> List[str]:
							A     = self.layer_norm(A_   )
							A     = self.attention(
							    A_	,encoder_hidden_states=A_	,attention_mask=attention_mask.squeeze(1   )	,)
							A     = hidden_states + self.dropout(A_   )
							return layer_output







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   Optional[int]	,A_  :   str	,A_  :   str	,A_  :   List[Any]	,A_  :   Tuple   )  -> int:
							super().__init__()
							A     = TaDenseGatedActDense(d_model=A_	,d_ff=A_	,dropout_rate=A_   )
							A     = TaFiLMLayer(in_features=d_model * 4	,out_features=A_   )
							A     = TaLayerNorm(A_	,eps=A_   )
							A     = nn.Dropout(A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Optional[int]	,A_  :   Optional[int]=None   )  -> List[str]:
							A     = self.layer_norm(A_   )
							if conditioning_emb is not None:
								A     = self.film(A_	,A_   )

							A     = self.DenseReluDense(A_   )
							A     = hidden_states + self.dropout(A_   )
							return hidden_states







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   Optional[Any]	,A_  :   Tuple	,A_  :   int	,A_  :   str   )  -> Optional[int]:
							super().__init__()
							A     = nn.Linear(A_	,A_	,bias=A_   )
							A     = nn.Linear(A_	,A_	,bias=A_   )
							A     = nn.Linear(A_	,A_	,bias=A_   )
							A     = nn.Dropout(A_   )
							A     = NewGELUActivation()




						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   Tuple   )  -> List[str]:
							A     = self.act(self.wi_a(A_   )   )
							A     = self.wi_a(A_   )
							A     = hidden_gelu * hidden_linear
							A     = self.dropout(A_   )

							A     = self.wo(A_   )
							return hidden_states







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   List[Any]	,A_  :   Optional[int]	,A_  :   Tuple=1e-6   )  -> Any:
							super().__init__()
							A     = nn.Parameter(torch.ones(A_   )   )
							A     = eps




						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   List[str]   )  -> str:
							# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
							# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
							# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
							# half-precision inputs is done in fp32

							A     = hidden_states.to(torch.floataa   ).pow(2   ).mean(-1	,keepdim=A_   )
							A     = hidden_states * torch.rsqrt(variance + self.variance_epsilon   )

							# convert into half-precision if necessary
							if self.weight.dtype in [torch.floataa, torch.bfloataa]:
								A     = hidden_states.to(self.weight.dtype   )

							return self.weight * hidden_states







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   torch.Tensor   )  -> torch.Tensor:
							return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.04_47_15 * torch.pow(A_	,3.0   ))   ))







class 					lowerCAmelCase_					(   nn.Module     ):



						'''simple docstring'''


						def __init__(			self  :   str	,A_  :   List[Any]	,A_  :   str   )  -> Tuple:
							super().__init__()
							A     = nn.Linear(A_	,out_features * 2	,bias=A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   Dict	,A_  :   Tuple   )  -> List[Any]:
							A     = self.scale_bias(A_   )
							A ,   A     = torch.chunk(A_	,2	,-1   )
							A     = x * (1 + scale) + shift
							return x

'''simple docstring'''
import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowercase__											={
    'cola': 2,
    'mnli': 3,
    'mrpc': 2,
    'sst-2': 2,
    'sts-b': 1,
    'qqp': 2,
    'qnli': 2,
    'rte': 2,
    'wnli': 2,
}


logging.set_verbosity_info()






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__=None						):
	# Initialise PyTorch model
	a_      				=						XLNetConfig.from_json_file(A__						)

	a_      				=						finetuning_task.lower() if finetuning_task is not None else """"""
	if finetuning_task in GLUE_TASKS_NUM_LABELS:
		print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''						)
		a_      				=						finetuning_task
		a_      				=						GLUE_TASKS_NUM_LABELS[finetuning_task]
		a_      				=						XLNetForSequenceClassification(A__						)
	elif "squad" in finetuning_task:
		a_      				=						finetuning_task
		a_      				=						XLNetForQuestionAnswering(A__						)
	else:
		a_      				=						XLNetLMHeadModel(A__						)

	# Load weights from tf checkpoint
	load_tf_weights_in_xlnet(A__  ,      A__  ,      A__						)

	# Save pytorch-model
	a_      				=						os.path.join(A__  ,      A__						)
	a_      				=						os.path.join(A__  ,      A__						)
	print(F'''Save PyTorch model to {os.path.abspath(A__						)}'''						)
	torch.save(model.state_dict()  ,      A__						)
	print(F'''Save configuration file to {os.path.abspath(A__						)}'''						)
	with open(A__  ,      """w"""  ,      encoding="""utf-8"""						) as f:
		f.write(config.to_json_string()						)


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(
		    '--xlnet_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained XLNet model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=str,
		    required=True,
		    help='Path to the folder to store the PyTorch model or dataset/vocab.',
		)
		parser.add_argument(
		    '--finetuning_task',
		    default=None,
		    type=str,
		    help='Name of a task on which the XLNet TensorFlow model was fine-tuned',
		)
		lowercase__											=parser.parse_args()
		print(args)

		convert_xlnet_checkpoint_to_pytorch(
		    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
		)
