"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFRoFormerForCausalLM,
					    TFRoFormerForMaskedLM,
					    TFRoFormerForMultipleChoice,
					    TFRoFormerForQuestionAnswering,
					    TFRoFormerForSequenceClassification,
					    TFRoFormerForTokenClassification,
					    TFRoFormerModel,
					)
					from transformers.models.roformer.modeling_tf_roformer import (
					    TFRoFormerSelfAttention,
					    TFRoFormerSinusoidalPositionalEmbedding,
					)



class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=13		,    lowerCAmelCase=7		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=99		,    lowerCAmelCase=32		,    lowerCAmelCase=2		,    lowerCAmelCase=4		,    lowerCAmelCase=37		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=16		,    lowerCAmelCase=2		,    lowerCAmelCase=0.02		,    lowerCAmelCase=3		,    lowerCAmelCase=4		,    lowerCAmelCase=None		,    ):






										"""simple docstring"""

										snake_case								=			parent
										snake_case								=			13
										snake_case								=			7
										snake_case								=			True
										snake_case								=			True
										snake_case								=			True
										snake_case								=			True
										snake_case								=			99
										snake_case								=			32
										snake_case								=			2
										snake_case								=			4
										snake_case								=			37
										snake_case								=			'gelu'
										snake_case								=			0.1
										snake_case								=			0.1
										snake_case								=			5_12
										snake_case								=			16
										snake_case								=			2
										snake_case								=			0.02
										snake_case								=			3
										snake_case								=			4
										snake_case								=			None

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size    )

										snake_case								=			None
										if self.use_input_mask:
																snake_case								=			random_attention_mask([self.batch_size, self.seq_length]    )

										snake_case								=			None
										if self.use_token_type_ids:
																snake_case								=			ids_tensor([self.batch_size, self.seq_length]		,    self.type_vocab_size    )

										snake_case								=			None
										snake_case								=			None
										snake_case								=			None
										if self.use_labels:
																snake_case								=			ids_tensor([self.batch_size]		,    self.type_sequence_label_size    )
																snake_case								=			ids_tensor([self.batch_size, self.seq_length]		,    self.num_labels    )
																snake_case								=			ids_tensor([self.batch_size]		,    self.num_choices    )

										snake_case								=			RoFormerConfig(
										    vocab_size=self.vocab_size		,    hidden_size=self.hidden_size		,    num_hidden_layers=self.num_hidden_layers		,    num_attention_heads=self.num_attention_heads		,    intermediate_size=self.intermediate_size		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    max_position_embeddings=self.max_position_embeddings		,    type_vocab_size=self.type_vocab_size		,    initializer_range=self.initializer_range		,    return_dict=lowerCAmelCase		,    )

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			TFRoFormerModel(config=lowerCAmelCase    )
										snake_case								=			{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

										snake_case								=			[input_ids, input_mask]
										snake_case								=			model(lowerCAmelCase    )

										snake_case								=			model(lowerCAmelCase    )

										self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			True
										snake_case								=			TFRoFormerForCausalLM(config=lowerCAmelCase    )
										snake_case								=			{
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}
										snake_case								=			model(lowerCAmelCase    )['logits']
										self.parent.assertListEqual(
										    list(prediction_scores.numpy().shape    )		,    [self.batch_size, self.seq_length, self.vocab_size]    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			TFRoFormerForMaskedLM(config=lowerCAmelCase    )
										snake_case								=			{
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}
										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.vocab_size)    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.num_labels
										snake_case								=			TFRoFormerForSequenceClassification(config=lowerCAmelCase    )
										snake_case								=			{
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}

										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.num_choices
										snake_case								=			TFRoFormerForMultipleChoice(config=lowerCAmelCase    )
										snake_case								=			tf.tile(tf.expand_dims(lowerCAmelCase		,    1    )		,    (1, self.num_choices, 1)    )
										snake_case								=			tf.tile(tf.expand_dims(lowerCAmelCase		,    1    )		,    (1, self.num_choices, 1)    )
										snake_case								=			tf.tile(tf.expand_dims(lowerCAmelCase		,    1    )		,    (1, self.num_choices, 1)    )
										snake_case								=			{
										    'input_ids': multiple_choice_inputs_ids,
										    'attention_mask': multiple_choice_input_mask,
										    'token_type_ids': multiple_choice_token_type_ids,
										}
										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_choices)    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.num_labels
										snake_case								=			TFRoFormerForTokenClassification(config=lowerCAmelCase    )
										snake_case								=			{
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}
										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.num_labels)    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			TFRoFormerForQuestionAnswering(config=lowerCAmelCase    )
										snake_case								=			{
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}

										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.start_logits.shape		,    (self.batch_size, self.seq_length)    )
										self.parent.assertEqual(result.end_logits.shape		,    (self.batch_size, self.seq_length)    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.prepare_config_and_inputs()
										(
										    (
										    snake_case
										)					,(
										    snake_case
										)					,(
										    snake_case
										)					,(
										    snake_case
										)					,(
										    snake_case
										)					,(
										    snake_case
										)					,(
										    snake_case
										)					,
										)								=			config_and_inputs
										snake_case								=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
										return config, inputs_dict





@require_tf
class   lowerCAmelCase_    (			lowerCAmelCase     ,    lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Tuple			  =						(
				    (
				        TFRoFormerModel,
				        TFRoFormerForCausalLM,
				        TFRoFormerForMaskedLM,
				        TFRoFormerForQuestionAnswering,
				        TFRoFormerForSequenceClassification,
				        TFRoFormerForTokenClassification,
				        TFRoFormerForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowerCAmelCase     :    List[str]			  =						(
				    {
				        """feature-extraction""": TFRoFormerModel,
				        """fill-mask""": TFRoFormerForMaskedLM,
				        """question-answering""": TFRoFormerForQuestionAnswering,
				        """text-classification""": TFRoFormerForSequenceClassification,
				        """text-generation""": TFRoFormerForCausalLM,
				        """token-classification""": TFRoFormerForTokenClassification,
				        """zero-shot""": TFRoFormerForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				_lowerCAmelCase     :    List[str]			  =						False
				_lowerCAmelCase     :    Optional[int]			  =						False

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										if pipeline_test_casse_name == "TextGenerationPipelineTests":
																return True

										return False

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			TFRoFormerModelTester(self    )
										snake_case								=			ConfigTester(self		,    config_class=lowerCAmelCase		,    hidden_size=37    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase    )

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'    )
										self.assertIsNotNone(lowerCAmelCase    )





@require_tf
class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'    )
										snake_case								=			tf.constant([[0, 1, 2, 3, 4, 5]]    )
										snake_case								=			model(lowerCAmelCase    )[0]

										# TODO Replace vocab size
										snake_case								=			5_00_00

										snake_case								=			[1, 6, vocab_size]
										self.assertEqual(output.shape		,    lowerCAmelCase    )

										print(output[:, :3, :3]    )

										# TODO Replace values below with what was printed above.
										snake_case								=			tf.constant(
										    [
										        [
										            [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
										            [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
										            [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
										        ]
										    ]    )
										tf.debugging.assert_near(output[:, :3, :3]		,    lowerCAmelCase		,    atol=1E-4    )





@require_tf
class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Tuple			  =						1e-4

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			tf.constant([[4, 10]]    )
										snake_case								=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=6		,    embedding_dim=6    )

										snake_case								=			emba(input_ids.shape    )
										snake_case								=			tf.constant(
										    [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]]    )

										tf.debugging.assert_near(lowerCAmelCase		,    lowerCAmelCase		,    atol=self.tolerance    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			tf.constant(
										    [
										        [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
										        [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
										        [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
										    ]    )
										snake_case								=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12		,    embedding_dim=5_12    )
										emba([2, 16, 5_12]    )
										snake_case								=			emba.weight[:3, :5]

										tf.debugging.assert_near(lowerCAmelCase		,    lowerCAmelCase		,    atol=self.tolerance    )





@require_tf
class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Optional[Any]			  =						1e-4

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			tf.reshape(tf.range(2 * 12 * 16 * 64		,    dtype=tf.floataa    )		,    shape=(2, 12, 16, 64)    ) / 1_00

										snake_case								=			-tf.reshape(tf.range(2 * 12 * 16 * 64		,    dtype=tf.floataa    )		,    shape=(2, 12, 16, 64)    ) / 1_00

										snake_case								=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=32		,    embedding_dim=64    )
										snake_case								=			embed_positions([2, 16, 7_68]    )[None, None, :, :]

										snake_case					,snake_case								=			TFRoFormerSelfAttention.apply_rotary_position_embeddings(
										    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

										snake_case								=			tf.constant(
										    [
										        [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
										        [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
										        [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
										        [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
										        [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
										        [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
										    ]    )
										snake_case								=			tf.constant(
										    [
										        [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
										        [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
										        [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
										        [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
										        [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
										        [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
										    ]    )

										tf.debugging.assert_near(query_layer[0, 0, :6, :8]		,    lowerCAmelCase		,    atol=self.tolerance    )
										tf.debugging.assert_near(key_layer[0, 0, :6, :8]		,    lowerCAmelCase		,    atol=self.tolerance    )



"""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


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





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				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       :				Optional[Any]					)					->				int:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Dict					)					->				Tuple:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			)					->				Optional[int]:





						"""simple docstring"""






						snake_case								=			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=5_0 ,			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=5_6 ,			help='Random seed'					)

						parser.add_argument('--log_interval' ,			type=_UpperCamelCase ,			default=5_0_0 ,			help='Tensorboard logging interval.'					)
						parser.add_argument('--checkpoint_interval' ,			type=_UpperCamelCase ,			default=4_0_0_0 ,			help='Checkpoint interval.'					)
						snake_case								=			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					)

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

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

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

						snake_case								=			LmSeqsDataset(params=_UpperCamelCase ,			data=_UpperCamelCase					)
						logger.info('Data loader created.'					)

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

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

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

						# TEACHER #
						snake_case								=			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()
						snake_case								=			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()


