

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


snake_case_    						=				logging.get_logger(__name__)

snake_case_    						=				{
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}


class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
    __lowerCamelCase		: str														=  """m2m_100"""
    __lowerCamelCase		: str														=  ["""past_key_values"""]
    __lowerCamelCase		: Optional[Any]														=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





    def __init__(   self		,     a=12_8112		,     a=1024		,     a=12		,     a=4096		,     a=16		,     a=12		,     a=4096		,     a=16		,     a=0.05		,     a=0.05		,     a=True		,     a=True		,     a="relu"		,     a=1024		,     a=0.1		,     a=0.1		,     a=0.0		,     a=0.02		,     a=2		,     a=True		,     a=1		,     a=0		,     a=2		,     **a		,     ):
         lowercase__					:     str   = vocab_size
         lowercase__					:     Optional[Any]   = max_position_embeddings
         lowercase__					:     Optional[Any]   = d_model
         lowercase__					:     Tuple   = encoder_ffn_dim
         lowercase__					:     Optional[int]   = encoder_layers
         lowercase__					:     Optional[int]   = encoder_attention_heads
         lowercase__					:     List[str]   = decoder_ffn_dim
         lowercase__					:     List[Any]   = decoder_layers
         lowercase__					:     Union[str, Any]   = decoder_attention_heads
         lowercase__					:     Dict   = dropout
         lowercase__					:     Dict   = attention_dropout
         lowercase__					:     str   = activation_dropout
         lowercase__					:     Any   = activation_function
         lowercase__					:     str   = init_std
         lowercase__					:     Tuple   = encoder_layerdrop
         lowercase__					:     List[Any]   = decoder_layerdrop
         lowercase__					:     List[str]   = use_cache
         lowercase__					:     Union[str, Any]   = encoder_layers
         lowercase__					:     Dict   = scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             pad_token_id=a		,     bos_token_id=a		,     eos_token_id=a		,     is_encoder_decoder=a		,     decoder_start_token_id=a		,     **a		,     )



class    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    @property
    def    snake_case_       (   self):
         lowercase__					:     str   = OrderedDict(
             [
                 ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                 ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
             ])

         if self.use_past:
              lowercase__					:     Union[str, Any]   = {0: 'batch'}
              lowercase__					:     List[str]   = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
              lowercase__					:     List[str]   = {0: 'batch', 1: 'decoder_sequence'}
              lowercase__					:     Dict   = {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
              self.fill_with_past_key_values_(a		,     direction='inputs')
         return common_inputs





    def    snake_case_       (   self		,     a		,     a = -1		,     a = -1		,     a = False		,     a = None		,     ):
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         lowercase__					:     Optional[int]   = compute_effective_axis_dimension(
             a		,     fixed_dimension=OnnxConfig.default_fixed_batch		,     num_token_to_add=0)

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         lowercase__					:     Optional[Any]   = tokenizer.num_special_tokens_to_add(a)
         lowercase__					:     Union[str, Any]   = compute_effective_axis_dimension(
             a		,     fixed_dimension=OnnxConfig.default_fixed_sequence		,     num_token_to_add=a)

         # Generate dummy inputs according to compute batch and sequence
         lowercase__					:     Tuple   = [' '.join([tokenizer.unk_token]) * seq_length] * batch_size
         lowercase__					:     Optional[Any]   = dict(tokenizer(a		,     return_tensors=a))
         return common_inputs





    def    snake_case_       (   self		,     a		,     a = -1		,     a = -1		,     a = False		,     a = None		,     ):
         lowercase__					:     str   = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             a		,     a		,     a		,     a		,     a)

         # Generate decoder inputs
         lowercase__					:     Tuple   = seq_length if not self.use_past else 1
         lowercase__					:     List[Any]   = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             a		,     a		,     a		,     a		,     a)
         lowercase__					:     int   = {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         lowercase__					:     Optional[int]   = dict(**a		,     **a)

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
              else:
                   import torch
              lowercase__					:     Optional[Any]   = common_inputs['input_ids'].shape
              lowercase__					:     Optional[int]   = common_inputs['decoder_input_ids'].shape[1]
              lowercase__					:     Dict   = self.num_attention_heads
              lowercase__					:     Tuple   = (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              lowercase__					:     List[str]   = decoder_seq_length + 3
              lowercase__					:     List[Any]   = (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              lowercase__					:     Optional[Any]   = torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(a		,     a)]		,     dim=1)

              lowercase__					:     Optional[int]   = []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              lowercase__					:     Tuple   = self.num_layers
              lowercase__					:     Dict   = min(a		,     a)
              lowercase__					:     str   = max(a		,     a) - min_num_layers
              lowercase__					:     Union[str, Any]   = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

              for _ in range(a):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(a),
                           torch.zeros(a),
                           torch.zeros(a),
                           torch.zeros(a),
                       ))
              # TODO: test this.
              lowercase__					:     Union[str, Any]   = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
              for _ in range(a		,     a):
                   common_inputs["past_key_values"].append((torch.zeros(a), torch.zeros(a)))
         return common_inputs

    __lowerCamelCase		: List[Any]														=  _generate_dummy_inputs_for_default_and_seqaseq_lm




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


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

snake_case_    						=				logging.getLogger(__name__)


@dataclass
class    SCREAMING_SNAKE_CASE__      :
    __lowerCamelCase		: Optional[str]														=  field(
        default="""roberta-large-mnli"""	, metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}  )
    __lowerCamelCase		: Optional[str]														=  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."""
            )
        }	, )
    __lowerCamelCase		: Optional[int]														=  field(
        default=32	, metadata={"""help""": """Batch size for generating teacher predictions."""}  )
    __lowerCamelCase		: Optional[bool]														=  field(
        default=__snake_case	, metadata={
            """help""": (
                """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
                """multi-class multi-label classification)."""
            )
        }	, )
    __lowerCamelCase		: Optional[float]														=  field(
        default=1.0	, metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}  )



@dataclass
class    SCREAMING_SNAKE_CASE__      :
    __lowerCamelCase		: Optional[str]														=  field(
        default="""distilbert-base-uncased"""	, metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}  )



@dataclass
class    SCREAMING_SNAKE_CASE__      :
    __lowerCamelCase		: str														=  field(metadata={"""help""": """Text file with one unlabeled instance per line."""}  )
    __lowerCamelCase		: str														=  field(metadata={"""help""": """Text file with one class name per line."""}  )
    __lowerCamelCase		: bool														=  field(
        default=__snake_case	, metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""}	, )



@dataclass
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
    __lowerCamelCase		: Optional[str]														=  field(
        default=__snake_case	, metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}	, )
    __lowerCamelCase		: int														=  field(
        default=32	, metadata={"""help""": """Batch size per GPU/TPU core/CPU for training."""}  )
    __lowerCamelCase		: int														=  field(
        default=128	, metadata={"""help""": """Batch size per GPU/TPU core/CPU for evaluation."""}  )
    __lowerCamelCase		: float														=  field(default=1.0	, metadata={"""help""": """Total number of training epochs to perform."""}  )
    __lowerCamelCase		: bool														=  field(default=__snake_case	, metadata={"""help""": """Whether to run training of student model."""}  )
    __lowerCamelCase		: bool														=  field(
        default=__snake_case	, metadata={
            """help""": (
                """Whether to evaluate the agreement of the final student predictions and the teacher predictions"""
                """after training."""
            )
        }	, )
    __lowerCamelCase		: Optional[int]														=  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    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    def    snake_case_       (   self		,     a		,     a		,     a=False):
         lowercase__					:     Tuple   = inputs['labels']
         lowercase__					:     Any   = model(inputs['input_ids']		,     attention_mask=inputs['attention_mask'])
         lowercase__					:     Optional[int]   = outputs[0]

         lowercase__					:     Any   = -torch.sum(target_p * logits.log_softmax(dim=-1)		,     axis=-1).mean()

         if return_outputs:
              return loss, outputs

         return loss



def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple    ):



     '''simple docstring'''





     lowercase__					:     Any   = []
     with open(SCREAMING_SNAKE_CASE_			,  'r'    ) as f:
          for line in f:
               lowercase__					:     Dict   = line.strip()
               if len(SCREAMING_SNAKE_CASE_    ) > 0:
                    lines.append(SCREAMING_SNAKE_CASE_    )
     return lines

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[Any]			,  SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					int    ):



     '''simple docstring'''





     lowercase__					:     Optional[Any]   = []
     lowercase__					:     Tuple   = []
     for example in examples:
          for name in class_names:
               premises.append(SCREAMING_SNAKE_CASE_    )
               hypotheses.append(hypothesis_template.format(SCREAMING_SNAKE_CASE_    )    )
     return premises, hypotheses

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple    ):



     '''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 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					float			,  SCREAMING_SNAKE_CASE_	:					bool			,  SCREAMING_SNAKE_CASE_	:					bool			,  SCREAMING_SNAKE_CASE_	:					bool			,  SCREAMING_SNAKE_CASE_	:					bool			,  ):



     '''simple docstring'''





     lowercase__					:     Union[str, Any]   = AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_    )
     lowercase__					:     Optional[Any]   = model.config
     if not no_cuda and torch.cuda.is_available():
          lowercase__					:     str   = nn.DataParallel(model.cuda()    )
          batch_size *= len(model.device_ids    )
     lowercase__					:     List[str]   = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			,  use_fast=SCREAMING_SNAKE_CASE_    )

     lowercase__   ,				lowercase__					:     List[str]   = get_premise_hypothesis_pairs(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
     lowercase__					:     Any   = []

     for i in tqdm(range(0			,  len(SCREAMING_SNAKE_CASE_    )			,  SCREAMING_SNAKE_CASE_    )    ):
          lowercase__					:     Dict   = premises[i : i + batch_size]
          lowercase__					:     Union[str, Any]   = hypotheses[i : i + batch_size]

          lowercase__					:     List[str]   = tokenizer(
              SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  padding=SCREAMING_SNAKE_CASE_			,  truncation='only_first'			,  return_tensors='pt'			,  )

          with torch.cuda.amp.autocast(enabled=SCREAMING_SNAKE_CASE_    ):
               with torch.no_grad():
                    lowercase__					:     Optional[Any]   = model(**SCREAMING_SNAKE_CASE_    )
          logits.append(outputs.logits.detach().cpu().float()    )

     lowercase__					:     str   = get_entailment_id(SCREAMING_SNAKE_CASE_    )
     lowercase__					:     Optional[Any]   = -1 if entail_id == 0 else 0
     lowercase__					:     int   = torch.cat(SCREAMING_SNAKE_CASE_			,  dim=0    )  # N*K x 3
     lowercase__					:     str   = logits.reshape(len(SCREAMING_SNAKE_CASE_    )			,  len(SCREAMING_SNAKE_CASE_    )			,  -1    )[..., [contr_id, entail_id]]  # N x K x 2

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

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

def 			snake_case__    (					):



     '''simple docstring'''





     lowercase__					:     int   = HfArgumentParser(
         (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)			,  description=SCREAMING_SNAKE_CASE_			,  )

     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__					:     List[Any]   = parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1]    )    )
     else:
          lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:     List[Any]   = parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     lowercase__					:     List[str]   = None
     if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
          lowercase__					:     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__					:     Optional[Any]   = read_lines(data_args.data_file    )
     lowercase__					:     List[Any]   = 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__					:     Tuple   = get_teacher_predictions(
         teacher_args.teacher_name_or_path			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  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__					:     int   = Dataset.from_dict(
         {
             'text': examples,
             'labels': teacher_soft_preds,
         }    )

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

     # 4. train student on teacher predictions
     lowercase__					:     Any   = dataset.map(SCREAMING_SNAKE_CASE_			,  input_columns='text'    )
     dataset.set_format('torch'    )

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

     lowercase__					:     int   = DistillationTrainer(
         model=SCREAMING_SNAKE_CASE_			,  tokenizer=SCREAMING_SNAKE_CASE_			,  args=SCREAMING_SNAKE_CASE_			,  train_dataset=SCREAMING_SNAKE_CASE_			,  compute_metrics=SCREAMING_SNAKE_CASE_			,  )

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

     if training_args.do_eval:
          lowercase__					:     str   = trainer.evaluate(eval_dataset=SCREAMING_SNAKE_CASE_    )['eval_agreement']
          logger.info(f"""Agreement of student and teacher predictions: {agreement * 100:0.2f}%"""    )

     trainer.save_model()


if __name__ == "__main__":
       main()

