



"""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       _lowerCamelCase				(							UpperCAmelCase_ :       int     )	-> List[Any]:






	"""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       _lowerCamelCase				(							UpperCAmelCase_ :       List[str],	UpperCAmelCase_ :       str     )	-> List[Any]:






	"""simple docstring"""





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

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






	"""simple docstring"""





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

def       _lowerCamelCase				(							)	-> Any:






	"""simple docstring"""





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

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

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

	parser.add_argument("--temperature",	default=2.0,	type=UpperCAmelCase_,	help="Temperature for the softmax temperature."     )
	parser.add_argument(
	    "--alpha_ce",	default=0.5,	type=UpperCAmelCase_,	help="Linear weight for the distillation loss. Must be >=0."     )
	parser.add_argument(
	    "--alpha_mlm",	default=0.0,	type=UpperCAmelCase_,	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=UpperCAmelCase_,	help="Linear weight for the CLM loss. Must be >=0."     )
	parser.add_argument("--alpha_mse",	default=0.0,	type=UpperCAmelCase_,	help="Linear weight of the MSE loss. Must be >=0."     )
	parser.add_argument(
	    "--alpha_cos",	default=0.0,	type=UpperCAmelCase_,	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=UpperCAmelCase_,	help="Proportion of tokens for which we need to make a prediction.",	)
	parser.add_argument("--word_mask",	default=0.8,	type=UpperCAmelCase_,	help="Proportion of tokens to mask out."     )
	parser.add_argument("--word_keep",	default=0.1,	type=UpperCAmelCase_,	help="Proportion of tokens to keep."     )
	parser.add_argument("--word_rand",	default=0.1,	type=UpperCAmelCase_,	help="Proportion of tokens to randomly replace."     )
	parser.add_argument(
	    "--mlm_smoothing",	default=0.7,	type=UpperCAmelCase_,	help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).",	)
	parser.add_argument("--token_counts",	type=UpperCAmelCase_,	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=UpperCAmelCase_,	default=3,	help="Number of pass on the whole dataset."     )
	parser.add_argument("--batch_size",	type=UpperCAmelCase_,	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=UpperCAmelCase_,	default=50,	help="Gradient accumulation for larger training batches.",	)
	parser.add_argument("--warmup_prop",	default=0.05,	type=UpperCAmelCase_,	help="Linear warmup proportion."     )
	parser.add_argument("--weight_decay",	default=0.0,	type=UpperCAmelCase_,	help="Weight decay if we apply some."     )
	parser.add_argument("--learning_rate",	default=5e-4,	type=UpperCAmelCase_,	help="The initial learning rate for Adam."     )
	parser.add_argument("--adam_epsilon",	default=1e-6,	type=UpperCAmelCase_,	help="Epsilon for Adam optimizer."     )
	parser.add_argument("--max_grad_norm",	default=5.0,	type=UpperCAmelCase_,	help="Max gradient norm."     )
	parser.add_argument("--initializer_range",	default=0.02,	type=UpperCAmelCase_,	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=UpperCAmelCase_,	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=UpperCAmelCase_,	default=1,	help="Number of GPUs in the node."     )
	parser.add_argument("--local_rank",	type=UpperCAmelCase_,	default=-1,	help="Distributed training - Local rank"     )
	parser.add_argument("--seed",	type=UpperCAmelCase_,	default=56,	help="Random seed"     )

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

	# ARGS #
	init_gpu_params(UpperCAmelCase_     )
	set_seed(UpperCAmelCase_     )
	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(UpperCAmelCase_     ),	UpperCAmelCase_,	indent=4     )
		git_log(args.dump_path     )

	A__							,							A__							,							A__											=					MODEL_CLASSES[args.student_type]
	A__							,							A__							,							A__											=					MODEL_CLASSES[args.teacher_type]

	# TOKENIZER #
	A__											=					teacher_tokenizer_class.from_pretrained(args.teacher_name     )
	A__											=					{}
	for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
		A__											=					tokenizer.all_special_tokens.index(UpperCAmelCase_     )
		A__											=					tokenizer.all_special_ids[idx]
	logger.info(F"""Special tokens {special_tok_ids}"""     )
	A__											=					special_tok_ids
	A__											=					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:
		A__											=					pickle.load(UpperCAmelCase_     )

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

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

	A__											=					LmSeqsDataset(params=UpperCAmelCase_,	data=UpperCAmelCase_     )
	logger.info("Data loader created."     )

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

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

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

	# TEACHER #
	A__											=					teacher_model_class.from_pretrained(args.teacher_name,	output_hidden_states=UpperCAmelCase_     )
	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(UpperCAmelCase_,	UpperCAmelCase_     )
	if args.freeze_token_type_embds:
		freeze_token_type_embeddings(UpperCAmelCase_,	UpperCAmelCase_     )

	# 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()
	A__											=					Distiller(
	    params=UpperCAmelCase_,	dataset=UpperCAmelCase_,	token_probs=UpperCAmelCase_,	student=UpperCAmelCase_,	teacher=UpperCAmelCase_     )
	distiller.train()
	logger.info("Let's go get some drinks."     )


if __name__ == "__main__":
			main()









"""simple docstring"""







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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Optional[int]							  =     "perceiver"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=1280						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=26						,	SCREAMING_SNAKE_CASE__=8						,	SCREAMING_SNAKE_CASE__=8						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__="kv"						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-12						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=262						,	SCREAMING_SNAKE_CASE__=2048						,	SCREAMING_SNAKE_CASE__=56						,	SCREAMING_SNAKE_CASE__=[368, 496]						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=1920						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=[1, 16, 224, 224]						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Optional[int]:
				super().__init__(**SCREAMING_SNAKE_CASE__		)

				A__											=					num_latents
				A__											=					d_latents
				A__											=					d_model
				A__											=					num_blocks
				A__											=					num_self_attends_per_block
				A__											=					num_self_attention_heads
				A__											=					num_cross_attention_heads
				A__											=					qk_channels
				A__											=					v_channels
				A__											=					cross_attention_shape_for_attention
				A__											=					self_attention_widening_factor
				A__											=					cross_attention_widening_factor
				A__											=					hidden_act
				A__											=					attention_probs_dropout_prob
				A__											=					initializer_range
				A__											=					layer_norm_eps
				A__											=					use_query_residual
				# masked language modeling attributes
				A__											=					vocab_size
				A__											=					max_position_embeddings
				# image classification attributes
				A__											=					image_size
				# flow attributes
				A__											=					train_size
				# multimodal autoencoding attributes
				A__											=					num_frames
				A__											=					audio_samples_per_frame
				A__											=					samples_per_patch
				A__											=					output_shape






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			@property
			def   snake_case__	(      self		)      ->	Mapping[str, Mapping[int, str]]:
				if self.task == "multiple-choice":
					A__											=					{0: "batch", 1: "choice", 2: "sequence"}
				else:
					A__											=					{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("inputs", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				    ]		)


			@property
			def   snake_case__	(      self		)      ->	float:
				return 1e-4







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 3						,	SCREAMING_SNAKE_CASE__ = 40						,	SCREAMING_SNAKE_CASE__ = 40						,	)      ->	Mapping[str, Any]:
				# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

				if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__											=					compute_effective_axis_dimension(
					    SCREAMING_SNAKE_CASE__						,	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
					A__											=					preprocessor.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__		)
					A__											=					compute_effective_axis_dimension(
					    SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_sequence						,	num_token_to_add=SCREAMING_SNAKE_CASE__		)
					# Generate dummy inputs according to compute batch and sequence
					A__											=					[" ".join(["a"]		) * seq_length] * batch_size
					A__											=					dict(preprocessor(SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__		)		)
					A__											=					inputs.pop("input_ids"		)
					return inputs
				elif isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		) and preprocessor.model_input_names[0] == "pixel_values":
					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__											=					compute_effective_axis_dimension(SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_batch		)
					A__											=					self._generate_dummy_images(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					A__											=					dict(preprocessor(images=SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__		)		)
					A__											=					inputs.pop("pixel_values"		)
					return inputs
				else:
					raise ValueError(
					    "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."		)




