


import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
UpperCamelCase__				       =			logging.getLogger(__name__)

UpperCamelCase__				       =			int(10_000)  # Hardcoded max length to avoid infinite loop

UpperCamelCase__				       =			{
    'gpt2': (GPTaLMHeadModel, GPTaTokenizer),
    'ctrl': (CTRLLMHeadModel, CTRLTokenizer),
    'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
    'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    'xlm': (XLMWithLMHeadModel, XLMTokenizer),
    'gptj': (GPTJForCausalLM, AutoTokenizer),
    'bloom': (BloomForCausalLM, BloomTokenizerFast),
    'llama': (LlamaForCausalLM, LlamaTokenizer),
    'opt': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
UpperCamelCase__				       =			'In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>'

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Tuple:




       '''simple docstring'''






       np.random.seed(args.seed			)
       torch.manual_seed(args.seed			)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed			)

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[str]:




       '''simple docstring'''






       if args.temperature > 0.7:
              logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''			)

       _lowercase					:				Optional[Any]           =			tokenizer.encode(__lowerCamelCase    ,     add_special_tokens=__lowerCamelCase			)
       if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()			):
              logger.info('''WARNING! You are not starting your generation from a control code so you won\'t get good results'''			)
       return prompt_text

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							str:




       '''simple docstring'''






       _lowercase					:				Optional[Any]           =			hasattr(model.config    ,     '''use_lang_emb'''			) and model.config.use_lang_emb
       if hasattr(model.config    ,     '''lang2id'''			) and use_lang_emb:
              _lowercase					:				Optional[int]           =			model.config.langaid.keys()
              if args.xlm_language in available_languages:
                     _lowercase					:				Optional[int]           =			args.xlm_language
              else:
                     _lowercase					:				List[Any]           =			None
                     while language not in available_languages:
                            _lowercase					:				List[str]           =			input('''Using XLM. Select language in ''' + str(list(__lowerCamelCase			)			) + ''' >>> '''			)

              _lowercase					:				Dict           =			model.config.langaid[language]
              # kwargs["language"] = tokenizer.lang2id[language]

       # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
       # XLM masked-language modeling (MLM) models need masked token
       # is_xlm_mlm = "mlm" in args.model_name_or_path
       # if is_xlm_mlm:
       #     kwargs["mask_token_id"] = tokenizer.mask_token_id

       return prompt_text

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Dict:




       '''simple docstring'''






       _lowercase					:				int           =			args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
       _lowercase					:				Optional[int]           =			prefix + prompt_text
       return prompt_text

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Tuple:




       '''simple docstring'''






       _lowercase					:				str           =			args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
       _lowercase					:				Tuple           =			prefix + prompt_text
       return prompt_text


UpperCamelCase__				       =			{
    'ctrl': prepare_ctrl_input,
    'xlm': prepare_xlm_input,
    'xlnet': prepare_xlnet_input,
    'transfo-xl': prepare_transfoxl_input,
}

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							str:




       '''simple docstring'''






       if length < 0 and max_sequence_length > 0:
              _lowercase					:				str           =			max_sequence_length
       elif 0 < max_sequence_length < length:
              _lowercase					:				Dict           =			max_sequence_length  # No generation bigger than model size
       elif length < 0:
              _lowercase					:				List[str]           =			MAX_LENGTH  # avoid infinite loop
       return length

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							str:




       '''simple docstring'''






       _lowercase					:				Dict           =			None
       if hasattr(__lowerCamelCase    ,     '''hidden_size'''			):
              _lowercase					:				List[Any]           =			model_config.hidden_size
       elif hasattr(__lowerCamelCase    ,     '''n_embed'''			):
              _lowercase					:				int           =			model_config.n_embed
       elif hasattr(__lowerCamelCase    ,     '''n_embd'''			):
              _lowercase					:				Any           =			model_config.n_embd

       _lowercase					:				List[Any]           =			None
       if hasattr(__lowerCamelCase    ,     '''num_attention_heads'''			):
              _lowercase					:				Dict           =			model_config.num_attention_heads
       elif hasattr(__lowerCamelCase    ,     '''n_head'''			):
              _lowercase					:				Dict           =			model_config.n_head

       if embedding_size is None or num_head is None or num_head == 0:
              raise ValueError('''Check the model config'''			)

       _lowercase					:				Optional[int]           =			int(embedding_size / num_head			)
       if hasattr(__lowerCamelCase    ,     '''n_layer'''			):
              _lowercase					:				str           =			model_config.n_layer
       elif hasattr(__lowerCamelCase    ,     '''num_hidden_layers'''			):
              _lowercase					:				List[str]           =			model_config.num_hidden_layers
       else:
              raise ValueError('''Number of hidden layers couldn\'t be determined from the model config'''			)

       return num_layer, num_head, num_embedding_size_per_head

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Any:




       '''simple docstring'''






       _lowercase       ,	_lowercase       ,	_lowercase					:				List[Any]           =			sparse_model_config(model.config			)
       if model.config.model_type == "bloom":
              _lowercase					:				List[str]           =			tuple(
                  (
                      torch.empty(int(num_attention_heads * batch_size			)    ,     __lowerCamelCase    ,     __lowerCamelCase			)
                      .to(model.dtype			)
                      .to(model.device			),
                      torch.empty(int(num_attention_heads * batch_size			)    ,     __lowerCamelCase    ,     __lowerCamelCase			)
                      .to(model.dtype			)
                      .to(model.device			),
                  )
                  for _ in range(__lowerCamelCase			)			)
       else:
              _lowercase					:				Optional[int]           =			tuple(
                  (
                      torch.empty(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase			)
                      .to(model.dtype			)
                      .to(model.device			),
                      torch.empty(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase			)
                      .to(model.dtype			)
                      .to(model.device			),
                  )
                  for _ in range(__lowerCamelCase			)			)
       return past_key_values

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[Any]:




       '''simple docstring'''






       _lowercase					:				Optional[int]           =			len(__lowerCamelCase			)
       _lowercase					:				str           =			tokenizer.batch_encode_plus(__lowerCamelCase    ,     return_tensors='''pt'''			)
       _lowercase					:				int           =			dummy_input.to(model.device			)
       if model.config.use_cache:
              _lowercase					:				Union[str, Any]           =			generate_past_key_values(__lowerCamelCase    ,     __lowerCamelCase    ,     1			)
       _lowercase					:				Any           =			torch.cat(
           [
               torch.zeros(dummy_input['''attention_mask'''].shape[0]    ,     1			)
               .to(dummy_input['''attention_mask'''].dtype			)
               .to(model.device			),
               dummy_input['''attention_mask'''],
           ]    ,     -1    ,     )
       return dummy_input

class 						UpperCAmelCase__   (      snake_case_		):



     '''simple docstring'''
     UpperCAmelCase_         =  ('''_optimized''', '''_default''')




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





            """simple docstring"""



            _lowercase					:				Optional[int]           =			optimized
            _lowercase					:				List[str]           =			default




     def __call__( self       :       str	,      *UpperCamelCase       :       Dict	,      **UpperCamelCase       :       Any		):





            """simple docstring"""



            if kwargs["past_key_values"] is None and self._default.config.use_cache:
                   _lowercase					:				Any           =			generate_past_key_values(self._default	,      kwargs['''input_ids'''].shape[0]	,      0		)
            kwargs.pop('''position_ids'''	,      UpperCamelCase		)
            for k in list(kwargs.keys()		):
                   if kwargs[k] is None or isinstance(kwargs[k]	,      UpperCamelCase		):
                          kwargs.pop(UpperCamelCase		)
            _lowercase					:				Optional[int]           =			self._optimized(**UpperCamelCase		)
            _lowercase					:				Dict           =			outputs[0]
            _lowercase					:				Any           =			outputs[1]
            _lowercase					:				int           =			CausalLMOutputWithPast(
                loss=UpperCamelCase	,      logits=UpperCamelCase	,      past_key_values=UpperCamelCase	,      hidden_states=UpperCamelCase	,      attentions=UpperCamelCase	,      )
            return fixed_output




     def __getattr__( self       :       str	,      UpperCamelCase       :       str		):





            """simple docstring"""



            return getattr(self._default	,      UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       Dict	,      UpperCamelCase       :       int	,      UpperCamelCase       :       Tuple=None	,      UpperCamelCase       :       Any=None	,      UpperCamelCase       :       List[Any]=None	,      **UpperCamelCase       :       List[Any]		):





            """simple docstring"""



            return self._default.prepare_inputs_for_generation(
                UpperCamelCase	,      past_key_values=UpperCamelCase	,      inputs_embeds=UpperCamelCase	,      use_cache=UpperCamelCase	,      **UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       Any	,      UpperCamelCase       :       int		):





            """simple docstring"""



            return self._default._reorder_cache(UpperCamelCase	,      UpperCamelCase		)




def   UpperCamelCase__   (    )						->							List[Any]:




       '''simple docstring'''






       _lowercase					:				Dict           =			argparse.ArgumentParser()
       parser.add_argument(
           '''--model_type'''    ,     default=__lowerCamelCase    ,     type=__lowerCamelCase    ,     required=__lowerCamelCase    ,     help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()			)    ,     )
       parser.add_argument(
           '''--model_name_or_path'''    ,     default=__lowerCamelCase    ,     type=__lowerCamelCase    ,     required=__lowerCamelCase    ,     help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()			)    ,     )

       parser.add_argument('''--prompt'''    ,     type=__lowerCamelCase    ,     default=''''''			)
       parser.add_argument('''--length'''    ,     type=__lowerCamelCase    ,     default=20			)
       parser.add_argument('''--stop_token'''    ,     type=__lowerCamelCase    ,     default=__lowerCamelCase    ,     help='''Token at which text generation is stopped'''			)

       parser.add_argument(
           '''--temperature'''    ,     type=__lowerCamelCase    ,     default=1.0    ,     help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''    ,     )
       parser.add_argument(
           '''--repetition_penalty'''    ,     type=__lowerCamelCase    ,     default=1.0    ,     help='''primarily useful for CTRL model; in that case, use 1.2'''			)
       parser.add_argument('''--k'''    ,     type=__lowerCamelCase    ,     default=0			)
       parser.add_argument('''--p'''    ,     type=__lowerCamelCase    ,     default=0.9			)

       parser.add_argument('''--prefix'''    ,     type=__lowerCamelCase    ,     default=''''''    ,     help='''Text added prior to input.'''			)
       parser.add_argument('''--padding_text'''    ,     type=__lowerCamelCase    ,     default=''''''    ,     help='''Deprecated, the use of `--prefix` is preferred.'''			)
       parser.add_argument('''--xlm_language'''    ,     type=__lowerCamelCase    ,     default=''''''    ,     help='''Optional language when used with the XLM model.'''			)

       parser.add_argument('''--seed'''    ,     type=__lowerCamelCase    ,     default=42    ,     help='''random seed for initialization'''			)
       parser.add_argument('''--no_cuda'''    ,     action='''store_true'''    ,     help='''Avoid using CUDA when available'''			)
       parser.add_argument('''--num_return_sequences'''    ,     type=__lowerCamelCase    ,     default=1    ,     help='''The number of samples to generate.'''			)
       parser.add_argument(
           '''--fp16'''    ,     action='''store_true'''    ,     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''    ,     )
       parser.add_argument('''--jit'''    ,     action='''store_true'''    ,     help='''Whether or not to use jit trace to accelerate inference'''			)
       _lowercase					:				Tuple           =			parser.parse_args()

       _lowercase					:				str           =			torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
       _lowercase					:				int           =			0 if args.no_cuda else torch.cuda.device_count()

       logger.warning(F'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'			)

       set_seed(__lowerCamelCase			)

       # Initialize the model and tokenizer
       try:
              _lowercase					:				List[Any]           =			args.model_type.lower()
              _lowercase       ,	_lowercase					:				str           =			MODEL_CLASSES[args.model_type]
       except KeyError:
              raise KeyError('''the model {} you specified is not supported. You are welcome to add it and open a PR :)'''			)

       _lowercase					:				Tuple           =			tokenizer_class.from_pretrained(args.model_name_or_path			)
       if tokenizer.pad_token is None:
              _lowercase					:				Optional[Any]           =			tokenizer.eos_token
       _lowercase					:				Any           =			model_class.from_pretrained(args.model_name_or_path			)
       model.to(args.device			)

       if args.fpaa:
              model.half()
       _lowercase					:				str           =			getattr(model.config    ,     '''max_position_embeddings'''    ,     0			)
       _lowercase					:				Optional[Any]           =			adjust_length_to_model(args.length    ,     max_sequence_length=__lowerCamelCase			)
       logger.info(__lowerCamelCase			)

       _lowercase					:				Optional[Any]           =			args.prompt if args.prompt else input('''Model prompt >>> '''			)

       # Different models need different input formatting and/or extra arguments
       _lowercase					:				Union[str, Any]           =			args.model_type in PREPROCESSING_FUNCTIONS.keys()
       if requires_preprocessing:
              _lowercase					:				Union[str, Any]           =			PREPROCESSING_FUNCTIONS.get(args.model_type			)
              _lowercase					:				Optional[Any]           =			prepare_input(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase			)

              if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
                     _lowercase					:				Dict           =			{'''add_space_before_punct_symbol''': True}
              else:
                     _lowercase					:				Optional[Any]           =			{}

              _lowercase					:				Optional[Any]           =			tokenizer.encode(
                  __lowerCamelCase    ,     add_special_tokens=__lowerCamelCase    ,     return_tensors='''pt'''    ,     **__lowerCamelCase			)
       else:
              _lowercase					:				List[str]           =			args.prefix if args.prefix else args.padding_text
              _lowercase					:				int           =			tokenizer.encode(prefix + prompt_text    ,     add_special_tokens=__lowerCamelCase    ,     return_tensors='''pt'''			)
       _lowercase					:				Optional[Any]           =			encoded_prompt.to(args.device			)

       if encoded_prompt.size()[-1] == 0:
              _lowercase					:				Optional[Any]           =			None
       else:
              _lowercase					:				Optional[int]           =			encoded_prompt

       if args.jit:
              _lowercase					:				Any           =			['''enable jit''']
              _lowercase					:				List[Any]           =			prepare_jit_inputs(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase			)
              torch._C._jit_set_texpr_fuser_enabled(__lowerCamelCase			)
              _lowercase					:				Optional[Any]           =			False
              if hasattr(__lowerCamelCase    ,     '''forward'''			):
                     _lowercase					:				Any           =			inspect.signature(model.forward			)
              else:
                     _lowercase					:				List[Any]           =			inspect.signature(model.__call__			)
              _lowercase					:				int           =			tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(__lowerCamelCase    ,     __lowerCamelCase			) is not None			)
              _lowercase					:				Any           =			torch.jit.trace(__lowerCamelCase    ,     __lowerCamelCase    ,     strict=__lowerCamelCase			)
              _lowercase					:				Any           =			torch.jit.freeze(traced_model.eval()			)
              traced_model(*__lowerCamelCase			)
              traced_model(*__lowerCamelCase			)

              _lowercase					:				str           =			_ModelFallbackWrapper(__lowerCamelCase    ,     __lowerCamelCase			)

       _lowercase					:				Dict           =			model.generate(
           input_ids=__lowerCamelCase    ,     max_length=args.length + len(encoded_prompt[0]			)    ,     temperature=args.temperature    ,     top_k=args.k    ,     top_p=args.p    ,     repetition_penalty=args.repetition_penalty    ,     do_sample=__lowerCamelCase    ,     num_return_sequences=args.num_return_sequences    ,     )

       # Remove the batch dimension when returning multiple sequences
       if len(output_sequences.shape			) > 2:
              output_sequences.squeeze_()

       _lowercase					:				Any           =			[]

       for generated_sequence_idx, generated_sequence in enumerate(__lowerCamelCase			):
              print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='			)
              _lowercase					:				List[str]           =			generated_sequence.tolist()

              # Decode text
              _lowercase					:				List[Any]           =			tokenizer.decode(__lowerCamelCase    ,     clean_up_tokenization_spaces=__lowerCamelCase			)

              # Remove all text after the stop token
              _lowercase					:				List[str]           =			text[: text.find(args.stop_token			) if args.stop_token else None]

              # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
              _lowercase					:				Optional[Any]           =			(
                  prompt_text + text[len(tokenizer.decode(encoded_prompt[0]    ,     clean_up_tokenization_spaces=__lowerCamelCase			)			) :]
              )

              generated_sequences.append(__lowerCamelCase			)
              print(__lowerCamelCase			)

       return generated_sequences


if __name__ == "__main__":
  main()



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

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCamelCase__				       =			'\nDistills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class\nnames. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but\nwhen only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled\ndataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided\nclass names.\n'

UpperCamelCase__				       =			logging.getLogger(__name__)

@dataclass
class 						UpperCAmelCase__   :



     '''simple docstring'''
     UpperCAmelCase_         =  field(
         default='''roberta-large-mnli'''      ,						metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}		)
     UpperCAmelCase_         =  field(
         default='''This example is {}.'''      ,						metadata={
             '''help''': (
                 '''Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}'''
                 '''where class name is inserted.'''
             )
         }      ,						)
     UpperCAmelCase_         =  field(
         default=3_2      ,						metadata={'''help''': '''Batch size for generating teacher predictions.'''}		)
     UpperCAmelCase_         =  field(
         default=A_      ,						metadata={
             '''help''': (
                 '''Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'''
                 '''multi-class multi-label classification).'''
             )
         }      ,						)
     UpperCAmelCase_         =  field(
         default=1.0      ,						metadata={'''help''': '''Temperature applied to teacher softmax for distillation.'''}		)




@dataclass
class 						UpperCAmelCase__   :



     '''simple docstring'''
     UpperCAmelCase_         =  field(
         default='''distilbert-base-uncased'''      ,						metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}		)




@dataclass
class 						UpperCAmelCase__   :



     '''simple docstring'''
     UpperCAmelCase_         =  field(metadata={'''help''': '''Text file with one unlabeled instance per line.'''}		)
     UpperCAmelCase_         =  field(metadata={'''help''': '''Text file with one class name per line.'''}		)
     UpperCAmelCase_         =  field(
         default=A_      ,						metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'''}      ,						)




@dataclass
class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''
     UpperCAmelCase_         =  field(
         default=A_      ,						metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}      ,						)
     UpperCAmelCase_         =  field(
         default=3_2      ,						metadata={'''help''': '''Batch size per GPU/TPU core/CPU for training.'''}		)
     UpperCAmelCase_         =  field(
         default=1_2_8      ,						metadata={'''help''': '''Batch size per GPU/TPU core/CPU for evaluation.'''}		)
     UpperCAmelCase_         =  field(default=1.0      ,						metadata={'''help''': '''Total number of training epochs to perform.'''}		)
     UpperCAmelCase_         =  field(default=A_      ,						metadata={'''help''': '''Whether to run training of student model.'''}		)
     UpperCAmelCase_         =  field(
         default=A_      ,						metadata={
             '''help''': (
                 '''Whether to evaluate the agreement of the final student predictions and the teacher predictions'''
                 '''after training.'''
             )
         }      ,						)
     UpperCAmelCase_         =  field(
         default=0      ,						metadata={
             '''help''': (
                 '''Limit the total amount of checkpoints.'''
                 '''Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints).'''
             )
         }      ,						)




class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''




     def   lowerCAmelCase_   ( self       :       Dict	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       List[str]	,      UpperCamelCase       :       Dict=False		):





            """simple docstring"""



            _lowercase					:				Optional[Any]           =			inputs['''labels''']
            _lowercase					:				List[Any]           =			model(inputs['''input_ids''']	,      attention_mask=inputs['''attention_mask''']		)
            _lowercase					:				int           =			outputs[0]

            _lowercase					:				List[str]           =			-torch.sum(target_p * logits.log_softmax(dim=-1		)	,      axis=-1		).mean()

            if return_outputs:
                   return loss, outputs

            return loss




def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Tuple:




       '''simple docstring'''






       _lowercase					:				Tuple           =			[]
       with open(UpperCAmelCase_    ,     '''r'''			) as f:
              for line in f:
                     _lowercase					:				Optional[int]           =			line.strip()
                     if len(UpperCAmelCase_			) > 0:
                            lines.append(UpperCAmelCase_			)
       return lines

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Dict:




       '''simple docstring'''






       _lowercase					:				Any           =			[]
       _lowercase					:				int           =			[]
       for example in examples:
              for name in class_names:
                     premises.append(UpperCAmelCase_			)
                     hypotheses.append(hypothesis_template.format(UpperCAmelCase_			)			)
       return premises, hypotheses

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							List[Any]:




       '''simple docstring'''






       for label, ind in config.labelaid.items():
              if label.lower().startswith('''entail'''			):
                     return ind
       logger.warning('''Could not identify entailment dimension from teacher config label2id. Setting to -1.'''			)
       return -1

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     )						->							int:




       '''simple docstring'''






       _lowercase					:				Optional[Any]           =			AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_			)
       _lowercase					:				Any           =			model.config
       if not no_cuda and torch.cuda.is_available():
              _lowercase					:				int           =			nn.DataParallel(model.cuda()			)
              batch_size *= len(model.device_ids			)
       _lowercase					:				Optional[int]           =			AutoTokenizer.from_pretrained(UpperCAmelCase_    ,     use_fast=UpperCAmelCase_			)

       _lowercase       ,	_lowercase					:				Any           =			get_premise_hypothesis_pairs(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
       _lowercase					:				List[str]           =			[]

       for i in tqdm(range(0    ,     len(UpperCAmelCase_			)    ,     UpperCAmelCase_			)			):
              _lowercase					:				Tuple           =			premises[i : i + batch_size]
              _lowercase					:				Optional[int]           =			hypotheses[i : i + batch_size]

              _lowercase					:				Union[str, Any]           =			tokenizer(
                  UpperCAmelCase_    ,     UpperCAmelCase_    ,     padding=UpperCAmelCase_    ,     truncation='''only_first'''    ,     return_tensors='''pt'''    ,     )

              with torch.cuda.amp.autocast(enabled=UpperCAmelCase_			):
                     with torch.no_grad():
                            _lowercase					:				Dict           =			model(**UpperCAmelCase_			)
              logits.append(outputs.logits.detach().cpu().float()			)

       _lowercase					:				Any           =			get_entailment_id(UpperCAmelCase_			)
       _lowercase					:				Tuple           =			-1 if entail_id == 0 else 0
       _lowercase					:				Any           =			torch.cat(UpperCAmelCase_    ,     dim=0			)  # N*K x 3
       _lowercase					:				List[Any]           =			logits.reshape(len(UpperCAmelCase_			)    ,     len(UpperCAmelCase_			)    ,     -1			)[..., [contr_id, entail_id]]  # N x K x 2

       if multi_label:
              # softmax over (contr, entail) logits for each class independently
              _lowercase					:				Optional[int]           =			(nli_logits / temperature).softmax(-1			)
       else:
              # softmax over entail logits across classes s.t. class probabilities sum to 1.
              _lowercase					:				Union[str, Any]           =			(nli_logits / temperature).softmax(1			)

       return nli_prob[..., 1]  # N x K

def   UpperCamelCase__   (    )						->							Union[str, Any]:




       '''simple docstring'''






       _lowercase					:				Optional[Any]           =			HfArgumentParser(
           (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)    ,     description=UpperCAmelCase_    ,     )

       if len(sys.argv			) == 2 and sys.argv[1].endswith('''.json'''			):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase					:				Union[str, Any]           =			parser.parse_json_file(
                  json_file=os.path.abspath(sys.argv[1]			)			)
       else:
              _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase					:				Tuple           =			parser.parse_args_into_dataclasses()

       # Detecting last checkpoint.
       _lowercase					:				Tuple           =			None
       if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
              _lowercase					:				Optional[Any]           =			get_last_checkpoint(training_args.output_dir			)
              if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
                     raise ValueError(
                         F'Output directory ({training_args.output_dir}) already exists and is not empty. '
                         '''Use --overwrite_output_dir to overcome.'''			)
              elif last_checkpoint is not None:
                     logger.info(
                         F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                         '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''			)

    # Setup logging
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,     datefmt='''%m/%d/%Y %H:%M:%S'''    ,     handlers=[logging.StreamHandler(sys.stdout			)]    ,     )
       logger.setLevel(logging.INFO if is_main_process(training_args.local_rank			) else logging.WARN			)

       # Log on each process the small summary:
       logger.warning(
           F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
           + F'distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'			)
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank			):
              utils.logging.set_verbosity_info()
              utils.logging.enable_default_handler()
              utils.logging.enable_explicit_format()

       if training_args.local_rank != -1:
              raise ValueError('''Distributed training is not currently supported.'''			)
       if training_args.tpu_num_cores is not None:
              raise ValueError('''TPU acceleration is not currently supported.'''			)

       logger.info(F'Training/evaluation parameters {training_args}'			)

       # Set seed before initializing model.
       set_seed(training_args.seed			)

       # 1. read in data
       _lowercase					:				str           =			read_lines(data_args.data_file			)
       _lowercase					:				List[str]           =			read_lines(data_args.class_names_file			)

       # 2. get teacher predictions and load into dataset
       logger.info('''Generating predictions from zero-shot teacher model'''			)
       _lowercase					:				Optional[int]           =			get_teacher_predictions(
           teacher_args.teacher_name_or_path    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     teacher_args.hypothesis_template    ,     teacher_args.teacher_batch_size    ,     teacher_args.temperature    ,     teacher_args.multi_label    ,     data_args.use_fast_tokenizer    ,     training_args.no_cuda    ,     training_args.fpaa    ,     )
       _lowercase					:				Optional[Any]           =			Dataset.from_dict(
           {
               '''text''': examples,
               '''labels''': teacher_soft_preds,
           }			)

       # 3. create student
       logger.info('''Initializing student model'''			)
       _lowercase					:				List[Any]           =			AutoModelForSequenceClassification.from_pretrained(
           student_args.student_name_or_path    ,     num_labels=len(UpperCAmelCase_			)			)
       _lowercase					:				Optional[Any]           =			AutoTokenizer.from_pretrained(student_args.student_name_or_path    ,     use_fast=data_args.use_fast_tokenizer			)
       _lowercase					:				Optional[int]           =			dict(enumerate(UpperCAmelCase_			)			)
       _lowercase					:				Union[str, Any]           =			{label: i for i, label in enumerate(UpperCAmelCase_			)}

       # 4. train student on teacher predictions
       _lowercase					:				List[str]           =			dataset.map(UpperCAmelCase_    ,     input_columns='''text'''			)
       dataset.set_format('''torch'''			)

       def compute_metrics(UpperCAmelCase_    ,     UpperCAmelCase_=False			):
              _lowercase					:				Optional[Any]           =			p.predictions.argmax(-1			)
              _lowercase					:				List[Any]           =			p.label_ids.argmax(-1			)  # "label_ids" are actually distributions
              return {"agreement": (preds == proxy_labels).mean().item()}

       _lowercase					:				Optional[int]           =			DistillationTrainer(
           model=UpperCAmelCase_    ,     tokenizer=UpperCAmelCase_    ,     args=UpperCAmelCase_    ,     train_dataset=UpperCAmelCase_    ,     compute_metrics=UpperCAmelCase_    ,     )

       if training_args.do_train:
              logger.info('''Training student model on teacher predictions'''			)
              trainer.train()

       if training_args.do_eval:
              _lowercase					:				Tuple           =			trainer.evaluate(eval_dataset=UpperCAmelCase_			)['''eval_agreement''']
              logger.info(F'Agreement of student and teacher predictions: {agreement * 100:0.2f}%'			)

       trainer.save_model()


if __name__ == "__main__":
   main()