


import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
  import tensorflow as tf

  tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
  import torch

if is_datasets_available():
  from datasets import load_dataset


UpperCamelCase__				       =			5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Dict:




       '''simple docstring'''






       return PTtoTFCommand(
           args.model_name    ,     args.local_dir    ,     args.max_error    ,     args.new_weights    ,     args.no_pr    ,     args.push    ,     args.extra_commit_description    ,     args.override_model_class    ,     )







class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''




     @staticmethod
     def   lowerCAmelCase_   ( UpperCamelCase       :       ArgumentParser		):





            """simple docstring"""



            _lowercase					:				Any           =			parser.add_parser(
                '''pt-to-tf'''	,      help=(
                    '''CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'''
                    ''' Can also be used to validate existing weights without opening PRs, with --no-pr.'''
                )	,      )
            train_parser.add_argument(
                '''--model-name'''	,      type=UpperCamelCase	,      required=UpperCamelCase	,      help='''The model name, including owner/organization, as seen on the hub.'''	,      )
            train_parser.add_argument(
                '''--local-dir'''	,      type=UpperCamelCase	,      default=''''''	,      help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''	,      )
            train_parser.add_argument(
                '''--max-error'''	,      type=UpperCamelCase	,      default=UpperCamelCase	,      help=(
                    F'Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk.'
                )	,      )
            train_parser.add_argument(
                '''--new-weights'''	,      action='''store_true'''	,      help='''Optional flag to create new TensorFlow weights, even if they already exist.'''	,      )
            train_parser.add_argument(
                '''--no-pr'''	,      action='''store_true'''	,      help='''Optional flag to NOT open a PR with converted weights.'''		)
            train_parser.add_argument(
                '''--push'''	,      action='''store_true'''	,      help='''Optional flag to push the weights directly to `main` (requires permissions)'''	,      )
            train_parser.add_argument(
                '''--extra-commit-description'''	,      type=UpperCamelCase	,      default=''''''	,      help='''Optional additional commit description to use when opening a PR (e.g. to tag the owner).'''	,      )
            train_parser.add_argument(
                '''--override-model-class'''	,      type=UpperCamelCase	,      default=UpperCamelCase	,      help='''If you think you know better than the auto-detector, you can specify the model class here. '''
                '''Can be either an AutoModel class or a specific model class like BertForSequenceClassification.'''	,      )
            train_parser.set_defaults(func=UpperCamelCase		)




     @staticmethod
     def   lowerCAmelCase_   ( UpperCamelCase       :       int	,      UpperCamelCase       :       str		):





            """simple docstring"""



            _lowercase					:				Dict           =			set(pt_outputs.keys()		)
            _lowercase					:				str           =			set(tf_outputs.keys()		)
            if pt_out_attrs != tf_out_attrs:
                   raise ValueError(
                       F'The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:'
                       F' {tf_out_attrs})'		)

            # 2. For each output attribute, computes the difference
            def _find_pt_tf_differences(UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       str	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       str=""		):
                   # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
                   # recursivelly, keeping the name of the attribute.
                   if isinstance(UpperCamelCase	,      torch.Tensor		):
                          _lowercase					:				str           =			np.max(np.abs(pt_out.numpy() - tf_out.numpy()		)		)
                          _lowercase					:				Dict           =			tensor_difference
                   else:
                          _lowercase					:				Tuple           =			attr_name
                          for i, pt_item in enumerate(UpperCamelCase		):
                                 # If it is a named attribute, we keep the name. Otherwise, just its index.
                                 if isinstance(UpperCamelCase	,      UpperCamelCase		):
                                        _lowercase					:				List[str]           =			root_name + pt_item
                                        _lowercase					:				Dict           =			tf_out[pt_item]
                                        _lowercase					:				Tuple           =			pt_out[pt_item]
                                 else:
                                        _lowercase					:				Dict           =			root_name + F'[{i}]'
                                        _lowercase					:				List[Any]           =			tf_out[i]
                                 _lowercase					:				Any           =			_find_pt_tf_differences(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)

                   return differences

            return _find_pt_tf_differences(UpperCamelCase	,      UpperCamelCase	,      {}		)




     def __init__( self       :       Tuple	,      UpperCamelCase       :       str	,      UpperCamelCase       :       str	,      UpperCamelCase       :       float	,      UpperCamelCase       :       bool	,      UpperCamelCase       :       bool	,      UpperCamelCase       :       bool	,      UpperCamelCase       :       str	,      UpperCamelCase       :       str	,      *UpperCamelCase       :       Dict	,      ):





            """simple docstring"""



            _lowercase					:				Optional[Any]           =			logging.get_logger('''transformers-cli/pt_to_tf'''		)
            _lowercase					:				Optional[Any]           =			model_name
            _lowercase					:				int           =			local_dir if local_dir else os.path.join('''/tmp'''	,      UpperCamelCase		)
            _lowercase					:				Optional[Any]           =			max_error
            _lowercase					:				List[Any]           =			new_weights
            _lowercase					:				Union[str, Any]           =			no_pr
            _lowercase					:				Tuple           =			push
            _lowercase					:				Tuple           =			extra_commit_description
            _lowercase					:				Optional[int]           =			override_model_class




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





            """simple docstring"""



            def _get_audio_input():
                   _lowercase					:				str           =			load_dataset('''hf-internal-testing/librispeech_asr_dummy'''	,      '''clean'''	,      split='''validation'''		)
                   _lowercase					:				Union[str, Any]           =			ds.sort('''id'''		).select(range(2		)		)[:2]['''audio''']
                   _lowercase					:				List[Any]           =			[x['''array'''] for x in speech_samples]
                   return raw_samples

            _lowercase					:				List[str]           =			type(pt_model.config		)
            if model_config_class in PROCESSOR_MAPPING:
                   _lowercase					:				str           =			AutoProcessor.from_pretrained(self._local_dir		)
                   if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
                          _lowercase					:				str           =			processor.tokenizer.eos_token
            elif model_config_class in IMAGE_PROCESSOR_MAPPING:
                   _lowercase					:				List[Any]           =			AutoImageProcessor.from_pretrained(self._local_dir		)
            elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
                   _lowercase					:				Union[str, Any]           =			AutoFeatureExtractor.from_pretrained(self._local_dir		)
            elif model_config_class in TOKENIZER_MAPPING:
                   _lowercase					:				Any           =			AutoTokenizer.from_pretrained(self._local_dir		)
                   if processor.pad_token is None:
                          _lowercase					:				Tuple           =			processor.eos_token
            else:
                   raise ValueError(F'Unknown data processing type (model config type: {model_config_class})'		)

            _lowercase					:				int           =			set(inspect.signature(pt_model.forward		).parameters.keys()		)
            _lowercase					:				Optional[int]           =			{}
            if "input_ids" in model_forward_signature:
                   processor_inputs.update(
                       {
                           '''text''': ['''Hi there!''', '''I am a batch with more than one row and different input lengths.'''],
                           '''padding''': True,
                           '''truncation''': True,
                       }		)
            if "pixel_values" in model_forward_signature:
                   _lowercase					:				List[Any]           =			load_dataset('''cifar10'''	,      '''plain_text'''	,      split='''test'''		)[:2]['''img''']
                   processor_inputs.update({'''images''': sample_images}		)
            if "input_features" in model_forward_signature:
                   _lowercase					:				Any           =			inspect.signature(processor.feature_extractor		).parameters
                   # Pad to the largest input length by default but take feature extractor default
                   # padding value if it exists e.g. "max_length" and is not False or None
                   if "padding" in feature_extractor_signature:
                          _lowercase					:				str           =			feature_extractor_signature['''padding'''].default
                          if default_strategy is not False and default_strategy is not None:
                                 _lowercase					:				Dict           =			default_strategy
                          else:
                                 _lowercase					:				int           =			True
                   else:
                          _lowercase					:				Any           =			True
                   processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': padding_strategy}		)
            if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
                   processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': True}		)
            _lowercase					:				Optional[Any]           =			processor(**UpperCamelCase	,      return_tensors='''pt'''		)
            _lowercase					:				Optional[int]           =			processor(**UpperCamelCase	,      return_tensors='''tf'''		)

            # Extra input requirements, in addition to the input modality
            if (
                config.is_encoder_decoder
                or (hasattr(UpperCamelCase	,      '''encoder'''		) and hasattr(UpperCamelCase	,      '''decoder'''		))
                or "decoder_input_ids" in tf_dummy_inputs
            ):
                   _lowercase					:				Any           =			np.asarray([[1], [1]]	,      dtype=UpperCamelCase		) * (pt_model.config.decoder_start_token_id or 0)
                   pt_input.update({'''decoder_input_ids''': torch.tensor(UpperCamelCase		)}		)
                   tf_input.update({'''decoder_input_ids''': tf.convert_to_tensor(UpperCamelCase		)}		)

            return pt_input, tf_input




     def   lowerCAmelCase_   ( self       :       Optional[Any]		):





            """simple docstring"""



            if version.parse(huggingface_hub.__version__		) < version.parse('''0.9.0'''		):
                   raise ImportError(
                       '''The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'''
                       ''' installation.'''		)
            else:
                   from huggingface_hub import Repository, create_commit
                   from huggingface_hub._commit_api import CommitOperationAdd

            # Fetch remote data
            _lowercase					:				Optional[int]           =			Repository(local_dir=self._local_dir	,      clone_from=self._model_name		)

            # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
            _lowercase					:				List[str]           =			AutoConfig.from_pretrained(self._local_dir		)
            _lowercase					:				Tuple           =			config.architectures
            if self._override_model_class is not None:
                   if self._override_model_class.startswith('''TF'''		):
                          _lowercase					:				int           =			[self._override_model_class[2:]]
                   else:
                          _lowercase					:				Union[str, Any]           =			[self._override_model_class]
                   try:
                          _lowercase					:				str           =			getattr(import_module('''transformers'''		)	,      architectures[0]		)
                   except AttributeError:
                          raise ValueError(F'Model class {self._override_model_class} not found in transformers.'		)
                   try:
                          _lowercase					:				str           =			getattr(import_module('''transformers'''		)	,      '''TF''' + architectures[0]		)
                   except AttributeError:
                          raise ValueError(F'TF model class TF{self._override_model_class} not found in transformers.'		)
            elif architectures is None:  # No architecture defined -- use auto classes
                   _lowercase					:				Tuple           =			getattr(import_module('''transformers'''		)	,      '''AutoModel'''		)
                   _lowercase					:				Any           =			getattr(import_module('''transformers'''		)	,      '''TFAutoModel'''		)
                   self._logger.warning('''No detected architecture, using AutoModel/TFAutoModel'''		)
            else:  # Architecture defined -- use it
                   if len(UpperCamelCase		) > 1:
                          raise ValueError(F'More than one architecture was found, aborting. (architectures = {architectures})'		)
                   self._logger.warning(F'Detected architecture: {architectures[0]}'		)
                   _lowercase					:				Optional[int]           =			getattr(import_module('''transformers'''		)	,      architectures[0]		)
                   try:
                          _lowercase					:				Dict           =			getattr(import_module('''transformers'''		)	,      '''TF''' + architectures[0]		)
                   except AttributeError:
                          raise AttributeError(F'The TensorFlow equivalent of {architectures[0]} doesn\'t exist in transformers.'		)

        # Check the TF dummy inputs to see what keys we need in the forward pass
            _lowercase					:				List[str]           =			tf_class.from_config(UpperCamelCase		)
            _lowercase					:				Optional[Any]           =			tf_from_pt_model.dummy_inputs

            del tf_from_pt_model  # Try to keep only one model in memory at a time

            # Load the model and get some basic inputs
            _lowercase					:				List[Any]           =			pt_class.from_pretrained(self._local_dir		)
            pt_model.eval()

            _lowercase					:				Union[str, Any]           =			self.get_inputs(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)

            with torch.no_grad():
                   _lowercase					:				Tuple           =			pt_model(**UpperCamelCase	,      output_hidden_states=UpperCamelCase		)
            del pt_model  # will no longer be used, and may have a large memory footprint

            _lowercase					:				Dict           =			tf_class.from_pretrained(self._local_dir	,      from_pt=UpperCamelCase		)
            _lowercase					:				List[Any]           =			tf_from_pt_model(**UpperCamelCase	,      output_hidden_states=UpperCamelCase	,      training=UpperCamelCase		)

            # Confirms that cross loading PT weights into TF worked.
            _lowercase					:				Optional[int]           =			self.find_pt_tf_differences(UpperCamelCase	,      UpperCamelCase		)
            _lowercase					:				Any           =			{k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
            _lowercase					:				List[str]           =			{k: v for k, v in crossload_differences.items() if '''hidden''' in k}
            if len(UpperCamelCase		) == 0 and architectures is not None:
                   raise ValueError(
                       F'Something went wrong -- the config file has architectures ({architectures}), but no model head'
                       ''' output was found. All outputs start with \'hidden\''''		)
            _lowercase					:				Union[str, Any]           =			max(output_differences.values()		) if output_differences else 0.0
            _lowercase					:				Tuple           =			max(hidden_differences.values()		)
            if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
                   raise ValueError(
                       '''The cross-loaded TensorFlow model has different outputs, something went wrong!\n'''
                       + F'\nList of maximum output differences above the threshold ({self._max_error}):\n'
                       + '''\n'''.join([F'{k}: {v:.3e}' for k, v in output_differences.items() if v > self._max_error]		)
                       + F'\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'
                       + '''\n'''.join([F'{k}: {v:.3e}' for k, v in hidden_differences.items() if v > self._max_error]		)		)

            # Save the weights in a TF format (if needed) and confirms that the results are still good
            _lowercase					:				Any           =			os.path.join(self._local_dir	,      UpperCamelCase		)
            _lowercase					:				int           =			os.path.join(self._local_dir	,      UpperCamelCase		)
            if (not os.path.exists(UpperCamelCase		) and not os.path.exists(UpperCamelCase		)) or self._new_weights:
                   tf_from_pt_model.save_pretrained(self._local_dir		)
            del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

            _lowercase					:				int           =			tf_class.from_pretrained(self._local_dir		)
            _lowercase					:				Optional[int]           =			tf_model(**UpperCamelCase	,      output_hidden_states=UpperCamelCase		)

            _lowercase					:				int           =			self.find_pt_tf_differences(UpperCamelCase	,      UpperCamelCase		)
            _lowercase					:				Optional[Any]           =			{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
            _lowercase					:				int           =			{k: v for k, v in conversion_differences.items() if '''hidden''' in k}
            if len(UpperCamelCase		) == 0 and architectures is not None:
                   raise ValueError(
                       F'Something went wrong -- the config file has architectures ({architectures}), but no model head'
                       ''' output was found. All outputs start with \'hidden\''''		)
            _lowercase					:				Optional[int]           =			max(output_differences.values()		) if output_differences else 0.0
            _lowercase					:				int           =			max(hidden_differences.values()		)
            if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
                   raise ValueError(
                       '''The converted TensorFlow model has different outputs, something went wrong!\n'''
                       + F'\nList of maximum output differences above the threshold ({self._max_error}):\n'
                       + '''\n'''.join([F'{k}: {v:.3e}' for k, v in output_differences.items() if v > self._max_error]		)
                       + F'\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'
                       + '''\n'''.join([F'{k}: {v:.3e}' for k, v in hidden_differences.items() if v > self._max_error]		)		)

            _lowercase					:				Tuple           =			'''Update TF weights''' if self._new_weights else '''Add TF weights'''
            if self._push:
                   repo.git_add(auto_lfs_track=UpperCamelCase		)
                   repo.git_commit(UpperCamelCase		)
                   repo.git_push(blocking=UpperCamelCase		)  # this prints a progress bar with the upload
                   self._logger.warning(F'TF weights pushed into {self._model_name}'		)
            elif not self._no_pr:
                   self._logger.warning('''Uploading the weights into a new PR...'''		)
                   _lowercase					:				Any           =			(
                       '''Model converted by the [`transformers`\' `pt_to_tf`'''
                       ''' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '''
                       '''All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'''
                       F'Maximum crossload output difference={max_crossload_output_diff:.3e}; '
                       F'Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n'
                       F'Maximum conversion output difference={max_conversion_output_diff:.3e}; '
                       F'Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n'
                   )
                   if self._max_error > MAX_ERROR:
                          commit_descrition += (
                              F'\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!'
                          )
                   if self._extra_commit_description:
                          commit_descrition += "\n\n" + self._extra_commit_description

                   # sharded model -> adds all related files (index and .h5 shards)
                   if os.path.exists(UpperCamelCase		):
                          _lowercase					:				List[str]           =			[
                              CommitOperationAdd(path_in_repo=UpperCamelCase	,      path_or_fileobj=UpperCamelCase		)
                          ]
                          for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''		):
                                 operations += [
                                     CommitOperationAdd(path_in_repo=os.path.basename(UpperCamelCase		)	,      path_or_fileobj=UpperCamelCase		)
                                 ]
                   else:
                          _lowercase					:				Any           =			[CommitOperationAdd(path_in_repo=UpperCamelCase	,      path_or_fileobj=UpperCamelCase		)]

                   _lowercase					:				List[str]           =			create_commit(
                       repo_id=self._model_name	,      operations=UpperCamelCase	,      commit_message=UpperCamelCase	,      commit_description=UpperCamelCase	,      repo_type='''model'''	,      create_pr=UpperCamelCase	,      ).pr_url
                   self._logger.warning(F'PR open in {hub_pr_url}'		)



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()