






"""simple docstring"""







import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


_UpperCamelCase					      =  {
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}

def _a			(		_snake_case							):







						"""simple docstring"""






						assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
						assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
						if args.mlm:
												assert os.path.isfile(args.token_counts							)
												assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
						else:
												assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

						assert args.teacher_type == args.student_type or (
						    args.student_type == "distilbert" and args.teacher_type == "bert"
						)
						assert os.path.isfile(args.student_config							)
						if args.student_pretrained_weights is not None:
												assert os.path.isfile(args.student_pretrained_weights							)

						if args.freeze_token_type_embds:
												assert args.student_type in ["roberta"]

						assert args.alpha_ce >= 0.0
						assert args.alpha_mlm >= 0.0
						assert args.alpha_clm >= 0.0
						assert args.alpha_mse >= 0.0
						assert args.alpha_cos >= 0.0
						assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0

def _a			(		_snake_case				,	_snake_case							):







						"""simple docstring"""






						if args.student_type == "roberta":
												UpperCAmelCase          =	False
						elif args.student_type == "gpt2":
												UpperCAmelCase          =	False

def _a			(		_snake_case				,	_snake_case							):







						"""simple docstring"""






						if args.student_type == "roberta":
												UpperCAmelCase          =	False

def _a			(		):







						"""simple docstring"""






						UpperCAmelCase          =	argparse.ArgumentParser(description="""Training"""							)
						parser.add_argument("""--force"""				,	action="""store_true"""				,	help="""Overwrite dump_path if it already exists."""							)

						parser.add_argument(
						    """--dump_path"""				,	type=A_				,	required=A_				,	help="""The output directory (log, checkpoints, parameters, etc.)"""							)
						parser.add_argument(
						    """--data_file"""				,	type=A_				,	required=A_				,	help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""				,	)

						parser.add_argument(
						    """--student_type"""				,	type=A_				,	choices=["""distilbert""", """roberta""", """gpt2"""]				,	required=A_				,	help="""The student type (DistilBERT, RoBERTa)."""				,	)
						parser.add_argument("""--student_config"""				,	type=A_				,	required=A_				,	help="""Path to the student configuration."""							)
						parser.add_argument(
						    """--student_pretrained_weights"""				,	default=A_				,	type=A_				,	help="""Load student initialization checkpoint."""							)

						parser.add_argument(
						    """--teacher_type"""				,	choices=["""bert""", """roberta""", """gpt2"""]				,	required=A_				,	help="""Teacher type (BERT, RoBERTa)."""							)
						parser.add_argument("""--teacher_name"""				,	type=A_				,	required=A_				,	help="""The teacher model."""							)

						parser.add_argument("""--temperature"""				,	default=2.0				,	type=A_				,	help="""Temperature for the softmax temperature."""							)
						parser.add_argument(
						    """--alpha_ce"""				,	default=0.5				,	type=A_				,	help="""Linear weight for the distillation loss. Must be >=0."""							)
						parser.add_argument(
						    """--alpha_mlm"""				,	default=0.0				,	type=A_				,	help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."""				,	)
						parser.add_argument("""--alpha_clm"""				,	default=0.5				,	type=A_				,	help="""Linear weight for the CLM loss. Must be >=0."""							)
						parser.add_argument("""--alpha_mse"""				,	default=0.0				,	type=A_				,	help="""Linear weight of the MSE loss. Must be >=0."""							)
						parser.add_argument(
						    """--alpha_cos"""				,	default=0.0				,	type=A_				,	help="""Linear weight of the cosine embedding loss. Must be >=0."""							)

						parser.add_argument(
						    """--mlm"""				,	action="""store_true"""				,	help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""							)
						parser.add_argument(
						    """--mlm_mask_prop"""				,	default=0.15				,	type=A_				,	help="""Proportion of tokens for which we need to make a prediction."""				,	)
						parser.add_argument("""--word_mask"""				,	default=0.8				,	type=A_				,	help="""Proportion of tokens to mask out."""							)
						parser.add_argument("""--word_keep"""				,	default=0.1				,	type=A_				,	help="""Proportion of tokens to keep."""							)
						parser.add_argument("""--word_rand"""				,	default=0.1				,	type=A_				,	help="""Proportion of tokens to randomly replace."""							)
						parser.add_argument(
						    """--mlm_smoothing"""				,	default=0.7				,	type=A_				,	help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."""				,	)
						parser.add_argument("""--token_counts"""				,	type=A_				,	help="""The token counts in the data_file for MLM."""							)

						parser.add_argument(
						    """--restrict_ce_to_mask"""				,	action="""store_true"""				,	help="""If true, compute the distillation loss only the [MLM] prediction distribution."""				,	)
						parser.add_argument(
						    """--freeze_pos_embs"""				,	action="""store_true"""				,	help="""Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only."""				,	)
						parser.add_argument(
						    """--freeze_token_type_embds"""				,	action="""store_true"""				,	help="""Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only."""				,	)

						parser.add_argument("""--n_epoch"""				,	type=A_				,	default=3				,	help="""Number of pass on the whole dataset."""							)
						parser.add_argument("""--batch_size"""				,	type=A_				,	default=5				,	help="""Batch size (for each process)."""							)
						parser.add_argument(
						    """--group_by_size"""				,	action="""store_false"""				,	help="""If true, group sequences that have similar length into the same batch. Default is true."""				,	)

						parser.add_argument(
						    """--gradient_accumulation_steps"""				,	type=A_				,	default=50				,	help="""Gradient accumulation for larger training batches."""				,	)
						parser.add_argument("""--warmup_prop"""				,	default=0.05				,	type=A_				,	help="""Linear warmup proportion."""							)
						parser.add_argument("""--weight_decay"""				,	default=0.0				,	type=A_				,	help="""Weight decay if we apply some."""							)
						parser.add_argument("""--learning_rate"""				,	default=5E-4				,	type=A_				,	help="""The initial learning rate for Adam."""							)
						parser.add_argument("""--adam_epsilon"""				,	default=1E-6				,	type=A_				,	help="""Epsilon for Adam optimizer."""							)
						parser.add_argument("""--max_grad_norm"""				,	default=5.0				,	type=A_				,	help="""Max gradient norm."""							)
						parser.add_argument("""--initializer_range"""				,	default=0.02				,	type=A_				,	help="""Random initialization range."""							)

						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(
						    """--fp16_opt_level"""				,	type=A_				,	default="""O1"""				,	help=(
						        """For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."""
						        """See details at https://nvidia.github.io/apex/amp.html"""
						    )				,	)
						parser.add_argument("""--n_gpu"""				,	type=A_				,	default=1				,	help="""Number of GPUs in the node."""							)
						parser.add_argument("""--local_rank"""				,	type=A_				,	default=-1				,	help="""Distributed training - Local rank"""							)
						parser.add_argument("""--seed"""				,	type=A_				,	default=56				,	help="""Random seed"""							)

						parser.add_argument("""--log_interval"""				,	type=A_				,	default=500				,	help="""Tensorboard logging interval."""							)
						parser.add_argument("""--checkpoint_interval"""				,	type=A_				,	default=4000				,	help="""Checkpoint interval."""							)
						UpperCAmelCase          =	parser.parse_args()
						sanity_checks(A_							)

						# ARGS #
						init_gpu_params(A_							)
						set_seed(A_							)
						if args.is_master:
												if os.path.exists(args.dump_path							):
																		if not args.force:
																								raise ValueError(
																								    F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
																								    """ itUse `--force` if you want to overwrite it"""							)
																		else:
																								shutil.rmtree(args.dump_path							)

												if not os.path.exists(args.dump_path							):
																		os.makedirs(args.dump_path							)
												logger.info(F'''Experiment will be dumped and logged in {args.dump_path}'''							)

												# SAVE PARAMS #
												logger.info(F'''Param: {args}'''							)
												with open(os.path.join(args.dump_path				,	"""parameters.json"""							)				,	"""w"""							) as f:
																		json.dump(vars(A_							)				,	A_				,	indent=4							)
												git_log(args.dump_path							)

						UpperCAmelCase          =	MODEL_CLASSES[args.student_type]
						UpperCAmelCase          =	MODEL_CLASSES[args.teacher_type]

						# TOKENIZER #
						UpperCAmelCase          =	teacher_tokenizer_class.from_pretrained(args.teacher_name							)
						UpperCAmelCase          =	{}
						for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
												UpperCAmelCase          =	tokenizer.all_special_tokens.index(A_							)
												UpperCAmelCase          =	tokenizer.all_special_ids[idx]
						logger.info(F'''Special tokens {special_tok_ids}'''							)
						UpperCAmelCase          =	special_tok_ids
						UpperCAmelCase          =	tokenizer.max_model_input_sizes[args.teacher_name]

						# DATA LOADER #
						logger.info(F'''Loading data from {args.data_file}'''							)
						with open(args.data_file				,	"""rb"""							) as fp:
												UpperCAmelCase          =	pickle.load(A_							)

						if args.mlm:
												logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)'''							)
												with open(args.token_counts				,	"""rb"""							) as fp:
																		UpperCAmelCase          =	pickle.load(A_							)

												UpperCAmelCase          =	np.maximum(A_				,	1							) ** -args.mlm_smoothing
												for idx in special_tok_ids.values():
																		UpperCAmelCase          =	0.0  # do not predict special tokens
												UpperCAmelCase          =	torch.from_numpy(A_							)
						else:
												UpperCAmelCase          =	None

						UpperCAmelCase          =	LmSeqsDataset(params=A_				,	data=A_							)
						logger.info("""Data loader created."""							)

						# STUDENT #
						logger.info(F'''Loading student config from {args.student_config}'''							)
						UpperCAmelCase          =	student_config_class.from_pretrained(args.student_config							)
						UpperCAmelCase          =	True

						if args.student_pretrained_weights is not None:
												logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''							)
												UpperCAmelCase          =	student_model_class.from_pretrained(args.student_pretrained_weights				,	config=A_							)
						else:
												UpperCAmelCase          =	student_model_class(A_							)

						if args.n_gpu > 0:
												student.to(F'''cuda:{args.local_rank}'''							)
						logger.info("""Student loaded."""							)

						# TEACHER #
						UpperCAmelCase          =	teacher_model_class.from_pretrained(args.teacher_name				,	output_hidden_states=A_							)
						if args.n_gpu > 0:
												teacher.to(F'''cuda:{args.local_rank}'''							)
						logger.info(F'''Teacher loaded from {args.teacher_name}.'''							)

						# FREEZING #
						if args.freeze_pos_embs:
												freeze_pos_embeddings(A_				,	A_							)
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(A_				,	A_							)

						# SANITY CHECKS #
						assert student.config.vocab_size == teacher.config.vocab_size
						assert student.config.hidden_size == teacher.config.hidden_size
						assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
						if args.mlm:
												assert token_probs.size(0							) == stu_architecture_config.vocab_size

						# DISTILLER #
						torch.cuda.empty_cache()
						UpperCAmelCase          =	Distiller(
						    params=A_				,	dataset=A_				,	token_probs=A_				,	student=A_				,	teacher=A_							)
						distiller.train()
						logger.info("""Let\'s go get some drinks."""							)


if __name__ == "__main__":
		main()











"""simple docstring"""







from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image


_UpperCamelCase					      =  ["""text""", """image""", """audio"""]

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]

      for input_type in input_types:
            if input_type == "text":
                  inputs.append("""Text input"""							)
            elif input_type == "image":
                  inputs.append(
                      Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""							)							) / """000000039769.png"""							).resize((512, 512)							)							)
            elif input_type == "audio":
                  inputs.append(torch.ones(3000							)							)
            elif isinstance(_snake_case				,	_snake_case							):
                  inputs.append(create_inputs(_snake_case							)							)
            else:
                  raise ValueError(F'''Invalid type requested: {input_type}'''							)

      return inputs

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]

      for output in outputs:
            if isinstance(_snake_case				,	(str, AgentText)							):
                  output_types.append("""text"""							)
            elif isinstance(_snake_case				,	(Image.Image, AgentImage)							):
                  output_types.append("""image"""							)
            elif isinstance(_snake_case				,	(torch.Tensor, AgentAudio)							):
                  output_types.append("""audio"""							)
            else:
                  raise ValueError(F'''Invalid output: {output}'''							)

      return output_types







@is_tool_test
class  lowerCamelCase__ :






    def     _UpperCamelCase							(			self ):
          self.assertTrue(hasattr(self.tool     ,"""inputs""" ) )
          self.assertTrue(hasattr(self.tool     ,"""outputs""" ) )

          UpperCAmelCase          =	self.tool.inputs
          for _input in inputs:
                if isinstance(_input     ,A ):
                      for __input in _input:
                            self.assertTrue(__input in authorized_types )
                else:
                      self.assertTrue(_input in authorized_types )

          UpperCAmelCase          =	self.tool.outputs
          for _output in outputs:
                self.assertTrue(_output in authorized_types )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	create_inputs(self.tool.inputs )
          UpperCAmelCase          =	self.tool(*A )

          # There is a single output
          if len(self.tool.outputs ) == 1:
                UpperCAmelCase          =	[outputs]

          self.assertListEqual(output_types(A )     ,self.tool.outputs )






    def     _UpperCamelCase							(			self ):
          self.assertTrue(hasattr(self.tool     ,"""description""" ) )
          self.assertTrue(hasattr(self.tool     ,"""default_checkpoint""" ) )
          self.assertTrue(self.tool.description.startswith("""This is a tool that""" ) )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	create_inputs(self.tool.inputs )
          UpperCAmelCase          =	self.tool(*A )

          if not isinstance(A     ,A ):
                UpperCAmelCase          =	[outputs]

          self.assertEqual(len(A )     ,len(self.tool.outputs ) )

          for output, output_type in zip(A     ,self.tool.outputs ):
                UpperCAmelCase          =	AGENT_TYPE_MAPPING[output_type]
                self.assertTrue(isinstance(A     ,A ) )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	create_inputs(self.tool.inputs )

          UpperCAmelCase          =	[]

          for _input, input_type in zip(A     ,self.tool.inputs ):
                if isinstance(A     ,A ):
                      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
                else:
                      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )

        # Should not raise an error
          UpperCAmelCase          =	self.tool(*A )

          if not isinstance(A     ,A ):
                UpperCAmelCase          =	[outputs]

          self.assertEqual(len(A )     ,len(self.tool.outputs ) )



