
'''simple docstring'''





import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


UpperCAmelCase    					=   get_logger(__name__)


UpperCAmelCase    					=   r"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"

class 						a   :




				@add_start_docstrings(SCREAMING_SNAKE_CASE_      )
				def __call__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray      ):
										raise NotImplementedError(
										    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""      )




class 						a   :




				@add_start_docstrings(SCREAMING_SNAKE_CASE_      )
				def __call__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray      ):
										raise NotImplementedError(
										    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""      )




class 						a   (				__magic_name__    ):




				@add_start_docstrings(SCREAMING_SNAKE_CASE_      )
				def __call__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int,    **SCREAMING_SNAKE_CASE_	:				Any      ):
										for processor in self:
																snake_case       :     List[Any] =    inspect.signature(processor.__call__      ).parameters
																if len(SCREAMING_SNAKE_CASE_      ) > 3:
																						if not all(arg in kwargs for arg in list(function_args.keys()      )[2:]      ):
																												raise ValueError(
																												    F"""Make sure that all the required parameters: {list(function_args.keys()      )} for """
																												    F"""{processor.__class__} are passed to the logits processor."""      )
																						snake_case       :     List[Any] =    processor(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )
																else:
																						snake_case       :     Dict =    processor(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				float      ):
										if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) or not (temperature > 0):
																raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}"""      )

										snake_case       :     int =    temperature




				def __call__(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     List[Any] =    scores / self.temperature
										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				float,    SCREAMING_SNAKE_CASE_	:				float = -float('''Inf'''      ),    SCREAMING_SNAKE_CASE_	:				int = 1      ):
										if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) or (top_p < 0 or top_p > 1.0):
																raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}"""      )
										if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) or (min_tokens_to_keep < 1):
																raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"""      )

										snake_case       :     Any =    top_p
										snake_case       :     List[str] =    filter_value
										snake_case       :     str =    min_tokens_to_keep




				def __call__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case,					snake_case       :     Dict =    lax.top_k(SCREAMING_SNAKE_CASE_,    scores.shape[-1]      )

										snake_case       :     Optional[int] =    jnp.full_like(SCREAMING_SNAKE_CASE_,    self.filter_value      )
										snake_case       :     Tuple =    jax.nn.softmax(SCREAMING_SNAKE_CASE_,    axis=-1      ).cumsum(axis=-1      )
										snake_case       :     str =    cumulative_probs < self.top_p

										# include the token that is higher than top_p as well
										snake_case       :     str =    jnp.roll(SCREAMING_SNAKE_CASE_,    1      )
										score_mask |= score_mask.at[:, 0].set(SCREAMING_SNAKE_CASE_      )

										# min tokens to keep
										snake_case       :     Optional[Any] =    score_mask.at[:, : self.min_tokens_to_keep].set(SCREAMING_SNAKE_CASE_      )

										snake_case       :     Union[str, Any] =    jnp.where(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    jax.lax.sort_key_val(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )[-1]

										return next_scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				float = -float('''Inf'''      ),    SCREAMING_SNAKE_CASE_	:				int = 1      ):
										if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) or top_k <= 0:
																raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}"""      )

										snake_case       :     Dict =    max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    filter_value




				def __call__(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case,					snake_case       :     int =    scores.shape
										snake_case       :     int =    jnp.full(batch_size * vocab_size,    self.filter_value      )

										snake_case       :     Dict =    min(self.top_k,    scores.shape[-1]      )  # Safety check
										snake_case,					snake_case       :     Optional[Any] =    lax.top_k(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    jnp.broadcast_to((jnp.arange(SCREAMING_SNAKE_CASE_      ) * vocab_size)[:, None],    (batch_size, topk)      ).flatten()
										snake_case       :     Dict =    topk_scores.flatten()
										snake_case       :     Optional[Any] =    topk_indices.flatten() + shift

										snake_case       :     Tuple =    next_scores_flat.at[topk_indices_flat].set(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    next_scores_flat.reshape(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										return next_scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     List[Any] =    bos_token_id




				def __call__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     int =    jnp.full(scores.shape,    -float('''inf'''      )      )

										snake_case       :     List[Any] =    1 - jnp.bool_(cur_len - 1      )

										snake_case       :     Tuple =    jnp.where(SCREAMING_SNAKE_CASE_,    new_scores.at[:, self.bos_token_id].set(0      ),    SCREAMING_SNAKE_CASE_      )

										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     List[str] =    max_length
										snake_case       :     Tuple =    eos_token_id




				def __call__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Any =    jnp.full(scores.shape,    -float('''inf'''      )      )

										snake_case       :     Dict =    1 - jnp.bool_(cur_len - self.max_length + 1      )

										snake_case       :     int =    jnp.where(SCREAMING_SNAKE_CASE_,    new_scores.at[:, self.eos_token_id].set(0      ),    SCREAMING_SNAKE_CASE_      )

										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				int      ):
										if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) or min_length < 0:
																raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}"""      )

										if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) or eos_token_id < 0:
																raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}"""      )

										snake_case       :     Optional[int] =    min_length
										snake_case       :     str =    eos_token_id




				def __call__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										# create boolean flag to decide if min length penalty should be applied
										snake_case       :     int =    1 - jnp.clip(cur_len - self.min_length,    0,    1      )

										snake_case       :     Optional[int] =    jnp.where(SCREAMING_SNAKE_CASE_,    scores.at[:, self.eos_token_id].set(-float('''inf'''      )      ),    SCREAMING_SNAKE_CASE_      )

										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Dict      ):
										snake_case       :     Union[str, Any] =    list(SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[Any] =    begin_index




				def __call__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Optional[int] =    1 - jnp.bool_(cur_len - self.begin_index      )

										snake_case       :     int =    jnp.where(SCREAMING_SNAKE_CASE_,    scores.at[:, self.begin_suppress_tokens].set(-float('''inf'''      )      ),    SCREAMING_SNAKE_CASE_      )

										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				list      ):
										snake_case       :     Optional[int] =    list(SCREAMING_SNAKE_CASE_      )




				def __call__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     List[Any] =    scores.at[..., self.suppress_tokens].set(-float('''inf'''      )      )

										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Dict =    dict(SCREAMING_SNAKE_CASE_      )
										# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
										# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
										# Indexes without forced tokens will have a negative value.
										snake_case       :     Tuple =    jnp.ones((max(force_token_map.keys()      ) + 1),    dtype=jnp.intaa      ) * -1
										for index, token in force_token_map.items():
																if token is not None:
																						snake_case       :     Any =    force_token_array.at[index].set(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Any =    jnp.intaa(SCREAMING_SNAKE_CASE_      )




				def __call__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				jnp.ndarray,    SCREAMING_SNAKE_CASE_	:				int      ):
										def _force_token(SCREAMING_SNAKE_CASE_	:				Dict      ):
																snake_case       :     Union[str, Any] =    scores.shape[0]
																snake_case       :     Optional[Any] =    self.force_token_array[generation_idx]

																snake_case       :     List[str] =    jnp.ones_like(SCREAMING_SNAKE_CASE_,    dtype=scores.dtype      ) * -float('''inf'''      )
																snake_case       :     Optional[Any] =    jnp.zeros((batch_size, 1),    dtype=scores.dtype      )
																snake_case       :     List[str] =    lax.dynamic_update_slice(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    (0, current_token)      )
																return new_scores

										snake_case       :     List[Any] =    lax.cond(
										    cur_len >= self.force_token_array.shape[0],    lambda: scores,    lambda: lax.cond(
										        self.force_token_array[cur_len] >= 0,    lambda: _force_token(SCREAMING_SNAKE_CASE_      ),    lambda: scores,    ),    )
										return scores




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				List[str]      ):
										snake_case       :     int =    generate_config.eos_token_id
										snake_case       :     Any =    generate_config.no_timestamps_token_id
										snake_case       :     Tuple =    generate_config.no_timestamps_token_id + 1

										snake_case       :     int =    decoder_input_length + 1

										if generate_config.is_multilingual:
																# room for language token and task token
																self.begin_index += 2
										if hasattr(SCREAMING_SNAKE_CASE_,    '''max_initial_timestamp_index'''      ):
																snake_case       :     int =    generate_config.max_initial_timestamp_index
										else:
																snake_case       :     Optional[int] =    model_config.vocab_size
										if self.max_initial_timestamp_index is None:
																snake_case       :     str =    model_config.vocab_size




				def __call__(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				int      ):
										# suppress <|notimestamps|> which is handled by without_timestamps
										snake_case       :     Optional[Any] =    scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''      )      )

										def handle_pairs(SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
																snake_case       :     int =    jnp.where((cur_len - self.begin_index) >= 1,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
																snake_case       :     Dict =    jnp.where(
																    input_ids_k[cur_len - 1] >= self.timestamp_begin,    True and last_was_timestamp,    SCREAMING_SNAKE_CASE_,    )

																snake_case       :     Optional[int] =    jnp.where((cur_len - self.begin_index) < 2,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
																snake_case       :     Dict =    jnp.where(
																    input_ids_k[cur_len - 2] >= self.timestamp_begin,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    )

																return jnp.where(
																    SCREAMING_SNAKE_CASE_,    jnp.where(
																        penultimate_was_timestamp > 0,    scores_k.at[self.timestamp_begin :].set(-float('''inf'''      )      ),    scores_k.at[: self.eos_token_id].set(-float('''inf'''      )      ),    ),    SCREAMING_SNAKE_CASE_,    )

										snake_case       :     Optional[Any] =    jax.vmap(SCREAMING_SNAKE_CASE_      )(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )

										snake_case       :     Tuple =    jnp.where(cur_len == self.begin_index,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[int] =    jnp.where(
										    self.max_initial_timestamp_index is not None,    True and apply_max_initial_timestamp,    SCREAMING_SNAKE_CASE_,    )

										snake_case       :     int =    self.timestamp_begin + self.max_initial_timestamp_index

										snake_case       :     List[str] =    jnp.where(
										    SCREAMING_SNAKE_CASE_,    scores.at[:, last_allowed + 1 :].set(-float('''inf'''      )      ),    SCREAMING_SNAKE_CASE_,    )

										# if sum of probability over timestamps is above any other token, sample timestamp
										snake_case       :     Tuple =    jax.nn.log_softmax(SCREAMING_SNAKE_CASE_,    axis=-1      )

										def handle_cumulative_probs(SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
																snake_case       :     Union[str, Any] =    jax.nn.logsumexp(logprobs_k[self.timestamp_begin :],    axis=-1      )
																snake_case       :     Dict =    jnp.max(logprobs_k[: self.timestamp_begin]      )
																return jnp.where(
																    timestamp_logprob > max_text_token_logprob,    scores_k.at[: self.timestamp_begin].set(-float('''inf'''      )      ),    SCREAMING_SNAKE_CASE_,    )

										snake_case       :     Any =    jax.vmap(SCREAMING_SNAKE_CASE_      )(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )

										return scores



'''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 (       A_							:					List[str]       ):
						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 (       A_							:					Union[str, Any]    ,							A_							:					Any       ):
						if args.student_type == "roberta":
												snake_case       :     Union[str, Any] =    False
						elif args.student_type == "gpt2":
												snake_case       :     Union[str, Any] =    False

def      A (       A_							:					Dict    ,							A_							:					int       ):
						if args.student_type == "roberta":
												snake_case       :     str =    False

def      A (       ):
						snake_case       :     Optional[Any] =    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.'''       )
						snake_case       :     Tuple =    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       )

						snake_case,					snake_case,					snake_case       :     Optional[int] =    MODEL_CLASSES[args.student_type]
						snake_case,					snake_case,					snake_case       :     Dict =    MODEL_CLASSES[args.teacher_type]

						# TOKENIZER #
						snake_case       :     Optional[int] =    teacher_tokenizer_class.from_pretrained(args.teacher_name       )
						snake_case       :     int =    {}
						for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
												snake_case       :     Optional[int] =    tokenizer.all_special_tokens.index(A_       )
												snake_case       :     Union[str, Any] =    tokenizer.all_special_ids[idx]
						logger.info(F"""Special tokens {special_tok_ids}"""       )
						snake_case       :     List[Any] =    special_tok_ids
						snake_case       :     Tuple =    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:
												snake_case       :     Optional[int] =    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:
																		snake_case       :     int =    pickle.load(A_       )

												snake_case       :     Union[str, Any] =    np.maximum(A_    ,							1       ) ** -args.mlm_smoothing
												for idx in special_tok_ids.values():
																		snake_case       :     List[Any] =    0.0  # do not predict special tokens
												snake_case       :     Dict =    torch.from_numpy(A_       )
						else:
												snake_case       :     Any =    None

						snake_case       :     Optional[Any] =    LmSeqsDataset(params=A_    ,							data=A_       )
						logger.info('''Data loader created.'''       )

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

						if args.student_pretrained_weights is not None:
												logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""       )
												snake_case       :     List[Any] =    student_model_class.from_pretrained(args.student_pretrained_weights    ,							config=A_       )
						else:
												snake_case       :     int =    student_model_class(A_       )

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

						# TEACHER #
						snake_case       :     Tuple =    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()
						snake_case       :     Tuple =    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()

