
"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_a									=				logging.getLogger(__name__)


@dataclass
class A_      :




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		str    	=						field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
  SCREAMING_SNAKE_CASE__     :		Optional[str]    	=						field(
      default=lowercase__							,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
  SCREAMING_SNAKE_CASE__     :		Optional[str]    	=						field(
      default=lowercase__							,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
  SCREAMING_SNAKE_CASE__     :		Optional[str]    	=						field(
      default=lowercase__							,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,)




@dataclass
class A_      :




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		str    	=						field(
      metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys() )} )
  SCREAMING_SNAKE_CASE__     :		str    	=						field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""} )
  SCREAMING_SNAKE_CASE__     :		int    	=						field(
      default=128							,metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }							,)
  SCREAMING_SNAKE_CASE__     :		bool    	=						field(
      default=lowercase__							,metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )







def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   Dict     	=       default_data_collator(__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       batch.pop("pairID",						__lowerCamelCase    )
   return batch







def       __a	(						):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   UpperCAmelCase_   :   str     	=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
   UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Tuple     	=       parser.parse_args_into_dataclasses()

   if (
       os.path.exists(training_args.output_dir    )
       and os.listdir(training_args.output_dir    )
       and training_args.do_train
       and not training_args.overwrite_output_dir
   ):
      raise ValueError(
          f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
          " --overwrite_output_dir to overcome."    )

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",						datefmt="%m/%d/%Y %H:%M:%S",						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,						)
   logger.warning(
       "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",						training_args.local_rank,						training_args.device,						training_args.n_gpu,						bool(training_args.local_rank != -1    ),						training_args.fpaa,						)
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank    ):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
   logger.info("Training/evaluation parameters %s",						__lowerCamelCase    )

   # Set seed
   set_seed(training_args.seed    )

   try:
      UpperCAmelCase_   :   List[Any]     	=       hans_tasks_num_labels[data_args.task_name]
   except KeyError:
      raise ValueError("Task not found: %s" % (data_args.task_name)    )

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   UpperCAmelCase_   :   Optional[Any]     	=       AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path,						num_labels=__lowerCamelCase,						finetuning_task=data_args.task_name,						cache_dir=model_args.cache_dir,						)
   UpperCAmelCase_   :   Any     	=       AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,						cache_dir=model_args.cache_dir,						)
   UpperCAmelCase_   :   str     	=       AutoModelForSequenceClassification.from_pretrained(
       model_args.model_name_or_path,						from_tf=bool(".ckpt" in model_args.model_name_or_path    ),						config=__lowerCamelCase,						cache_dir=model_args.cache_dir,						)

   # Get datasets
   UpperCAmelCase_   :   Optional[Any]     	=       (
       HansDataset(
           data_dir=data_args.data_dir,						tokenizer=__lowerCamelCase,						task=data_args.task_name,						max_seq_length=data_args.max_seq_length,						overwrite_cache=data_args.overwrite_cache,						)
       if training_args.do_train
       else None
   )
   UpperCAmelCase_   :   Optional[int]     	=       (
       HansDataset(
           data_dir=data_args.data_dir,						tokenizer=__lowerCamelCase,						task=data_args.task_name,						max_seq_length=data_args.max_seq_length,						overwrite_cache=data_args.overwrite_cache,						evaluate=__lowerCamelCase,						)
       if training_args.do_eval
       else None
   )

   # Initialize our Trainer
   UpperCAmelCase_   :   str     	=       Trainer(
       model=__lowerCamelCase,						args=__lowerCamelCase,						train_dataset=__lowerCamelCase,						eval_dataset=__lowerCamelCase,						data_collator=__lowerCamelCase,						)

   # Training
   if training_args.do_train:
      trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
      trainer.save_model()
      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      if trainer.is_world_master():
         tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
   if training_args.do_eval:
      logger.info("*** Evaluate ***"    )

      UpperCAmelCase_   :   List[Any]     	=       trainer.predict(__lowerCamelCase    )
      UpperCAmelCase_   :   List[str]     	=       output.predictions
      UpperCAmelCase_   :   Union[str, Any]     	=       np.argmax(__lowerCamelCase,						axis=1    )

      UpperCAmelCase_   :   Tuple     	=       [ex.pairID for ex in eval_dataset]
      UpperCAmelCase_   :   str     	=       os.path.join(training_args.output_dir,						"hans_predictions.txt"    )
      UpperCAmelCase_   :   Union[str, Any]     	=       eval_dataset.get_labels()
      if trainer.is_world_master():
         with open(__lowerCamelCase,						"w"    ) as writer:
            writer.write("pairID,gold_label\n"    )
            for pid, pred in zip(__lowerCamelCase,						__lowerCamelCase    ):
               writer.write("ex" + str(__lowerCamelCase    ) + "," + label_list[int(__lowerCamelCase    )] + "\n"    )

      trainer._log(output.metrics    )







def       __a	(						__lowerCamelCase    ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()









import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


UpperCamelCase__ :			Optional[int]       	=			[nn.LayerNorm]

UpperCamelCase__ :			int       	=			logging.get_logger(__name__)

UpperCamelCase__ :			Dict       	=			version.parse(version.parse(torch.__version__).base_version)

UpperCamelCase__ :			str       	=			parsed_torch_version_base >= version.parse("""2.0""")
UpperCamelCase__ :			List[Any]       	=			parsed_torch_version_base >= version.parse("""1.12""")
UpperCamelCase__ :			List[Any]       	=			parsed_torch_version_base >= version.parse("""1.11""")
UpperCamelCase__ :			Any       	=			parsed_torch_version_base < version.parse("""1.11""")




def  A_(      A					,   A					,   A					,   A					,   self ):
	from torch import _softmax_backward_data

	if is_torch_less_than_1_11:
		return _softmax_backward_data(A					,   A					,   parent.dim					,   self )
	else:
		return _softmax_backward_data(A					,   A					,   parent.dim					,   self.dtype )




def  A_(      A					,   A					,   A = 0 ):
	UpperCAmelCase_		    =  index.to(layer.weight.device )
	UpperCAmelCase_		    =  layer.weight.index_select(A					,   A ).clone().detach()
	if layer.bias is not None:
		if dim == 1:
			UpperCAmelCase_		    =  layer.bias.clone().detach()
		else:
			UpperCAmelCase_		    =  layer.bias[index].clone().detach()
	UpperCAmelCase_		    =  list(layer.weight.size() )
	UpperCAmelCase_		    =  len(A )
	UpperCAmelCase_		    =  nn.Linear(new_size[1]					,   new_size[0]					,   bias=layer.bias is not None ).to(layer.weight.device )
	UpperCAmelCase_		    =  False
	new_layer.weight.copy_(W.contiguous() )
	UpperCAmelCase_		    =  True
	if layer.bias is not None:
		UpperCAmelCase_		    =  False
		new_layer.bias.copy_(b.contiguous() )
		UpperCAmelCase_		    =  True
	return new_layer







class 	_UpperCamelCase	(							nn.Module	):







					'''simple docstring'''

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






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_		    =  nf
						UpperCAmelCase_		    =  nn.Parameter(torch.empty(__lowercase			,       __lowercase		)		)
						UpperCAmelCase_		    =  nn.Parameter(torch.zeros(__lowercase		)		)
						nn.init.normal_(self.weight			,       std=0.02		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	str			,       __lowercase     :	Tuple		):






						'''simple docstring'''







						UpperCAmelCase_		    =  x.size()[:-1] + (self.nf,)
						UpperCAmelCase_		    =  torch.addmm(self.bias			,       x.view(-1			,       x.size(-1		)		)			,       self.weight		)
						UpperCAmelCase_		    =  x.view(__lowercase		)
						return x




def  A_(      A					,   A					,   A = 1 ):
	UpperCAmelCase_		    =  index.to(layer.weight.device )
	UpperCAmelCase_		    =  layer.weight.index_select(A					,   A ).clone().detach()
	if dim == 0:
		UpperCAmelCase_		    =  layer.bias.clone().detach()
	else:
		UpperCAmelCase_		    =  layer.bias[index].clone().detach()
	UpperCAmelCase_		    =  list(layer.weight.size() )
	UpperCAmelCase_		    =  len(A )
	UpperCAmelCase_		    =  ConvaD(new_size[1]					,   new_size[0] ).to(layer.weight.device )
	UpperCAmelCase_		    =  False
	new_layer.weight.copy_(W.contiguous() )
	UpperCAmelCase_		    =  True
	UpperCAmelCase_		    =  False
	new_layer.bias.copy_(b.contiguous() )
	UpperCAmelCase_		    =  True
	return new_layer




def  A_(      A					,   A					,   A = None ):
	if isinstance(A					,   nn.Linear ):
		return prune_linear_layer(A					,   A					,   dim=0 if dim is None else dim )
	elif isinstance(A					,   A ):
		return prune_convad_layer(A					,   A					,   dim=1 if dim is None else dim )
	else:
		raise ValueError(f"""Can't prune layer of class {layer.__class__}""" )




def  A_(      A					,   A					,   A					,   *A ):
	assert len(A ) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

	# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
	UpperCAmelCase_		    =  len(inspect.signature(A ).parameters )
	if num_args_in_forward_chunk_fn != len(A ):
		raise ValueError(
		    f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(A )} input """
		    """tensors are given""" )

	if chunk_size > 0:
		UpperCAmelCase_		    =  input_tensors[0].shape[chunk_dim]
		for input_tensor in input_tensors:
			if input_tensor.shape[chunk_dim] != tensor_shape:
				raise ValueError(
				    f"""All input tenors have to be of the same shape: {tensor_shape}, """
				    f"""found shape {input_tensor.shape[chunk_dim]}""" )

		if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
			raise ValueError(
			    f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
			    f"""size {chunk_size}""" )

		UpperCAmelCase_		    =  input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		UpperCAmelCase_		    =  tuple(input_tensor.chunk(A					,   dim=A ) for input_tensor in input_tensors )
		# apply forward fn to every tuple
		UpperCAmelCase_		    =  tuple(forward_fn(*A ) for input_tensors_chunk in zip(*A ) )
		# concatenate output at same dimension
		return torch.cat(A					,   dim=A )

	return forward_fn(*A )




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  torch.ones(A					,   A )
	UpperCAmelCase_		    =  set(A ) - already_pruned_heads  # Convert to set and remove already pruned heads
	for head in heads:
		# Compute how many pruned heads are before the head and move the index accordingly
		UpperCAmelCase_		    =  head - sum(1 if h < head else 0 for h in already_pruned_heads )
		UpperCAmelCase_		    =  0
	UpperCAmelCase_		    =  mask.view(-1 ).contiguous().eq(1 )
	UpperCAmelCase_		    =  torch.arange(len(A ) )[mask].long()
	return heads, index




def  A_(      *A					,   A = None ):
	return torch.meshgrid(*A					,   indexing=A )




def  A_(      A ):
	return tensor.device, storage_ptr(A ), storage_size(A )


