

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

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	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   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	if args.student_type == "roberta":
		lowerCAmelCase__	:						Optional[Any]  				=	False
	elif args.student_type == "gpt2":
		lowerCAmelCase__	:						List[str]  				=	False

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	if args.student_type == "roberta":
		lowerCAmelCase__	:						Optional[int]  				=	False

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						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=5_00						,     help='''Tensorboard logging interval.''' )
	parser.add_argument('''--checkpoint_interval'''						,     type=A_						,     default=40_00						,     help='''Checkpoint interval.''' )
	lowerCAmelCase__	:						Optional[Any]  				=	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 )

	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	MODEL_CLASSES[args.student_type]
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	MODEL_CLASSES[args.teacher_type]

	# TOKENIZER #
	lowerCAmelCase__	:						Tuple  				=	teacher_tokenizer_class.from_pretrained(args.teacher_name )
	lowerCAmelCase__	:						Dict  				=	{}
	for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
		lowerCAmelCase__	:						Tuple  				=	tokenizer.all_special_tokens.index(A_ )
		lowerCAmelCase__	:						Tuple  				=	tokenizer.all_special_ids[idx]
	logger.info(f'Special tokens {special_tok_ids}' )
	lowerCAmelCase__	:						Optional[int]  				=	special_tok_ids
	lowerCAmelCase__	:						int  				=	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:
		lowerCAmelCase__	:						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:
			lowerCAmelCase__	:						Any  				=	pickle.load(A_ )

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

	lowerCAmelCase__	:						List[str]  				=	LmSeqsDataset(params=A_						,     data=A_ )
	logger.info('''Data loader created.''' )

	# STUDENT #
	logger.info(f'Loading student config from {args.student_config}' )
	lowerCAmelCase__	:						Dict  				=	student_config_class.from_pretrained(args.student_config )
	lowerCAmelCase__	:						Any  				=	True

	if args.student_pretrained_weights is not None:
		logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}' )
		lowerCAmelCase__	:						Union[str, Any]  				=	student_model_class.from_pretrained(args.student_pretrained_weights						,     config=A_ )
	else:
		lowerCAmelCase__	:						int  				=	student_model_class(A_ )

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

	# TEACHER #
	lowerCAmelCase__	:						List[str]  				=	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()
	lowerCAmelCase__	:						Optional[Any]  				=	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 __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
  from transformers import (
      TFAutoModel,
      TFAutoModelForCausalLM,
      TFAutoModelForMaskedLM,
      TFAutoModelForPreTraining,
      TFAutoModelForQuestionAnswering,
      TFAutoModelForSeqaSeqLM,
      TFAutoModelForSequenceClassification,
      TFAutoModelForTableQuestionAnswering,
      TFAutoModelForTokenClassification,
      TFAutoModelWithLMHead,
      TFBertForMaskedLM,
      TFBertForPreTraining,
      TFBertForQuestionAnswering,
      TFBertForSequenceClassification,
      TFBertModel,
      TFFunnelBaseModel,
      TFFunnelModel,
      TFGPTaLMHeadModel,
      TFRobertaForMaskedLM,
      TFTaForConditionalGeneration,
      TFTapasForQuestionAnswering,
  )
  from transformers.models.auto.modeling_tf_auto import (
      TF_MODEL_FOR_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_MASKED_LM_MAPPING,
      TF_MODEL_FOR_PRETRAINING_MAPPING,
      TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      TF_MODEL_MAPPING,
  )
  from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
  from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
  from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
  from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      int							=  '''new-model'''


if is_tf_available():






  class 					_SCREAMING_SNAKE_CASE(   A  ):
         SCREAMING_SNAKE_CASE_   :      Union[str, Any]							=  NewModelConfig






@require_tf
class 					_SCREAMING_SNAKE_CASE(   unittest.TestCase  ):




       @slow
       def     _UpperCamelCase     (						self							)			->      List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  '''bert-base-cased'''
             __SCREAMING_SNAKE_CASE   :int						=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__							)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
             self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :List[str]						=  TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
             self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       @slow
       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  '''bert-base-cased'''
             __SCREAMING_SNAKE_CASE   :List[str]						=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__							)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
             self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  TFAutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE__							)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
             self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       @slow
       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __SCREAMING_SNAKE_CASE   :List[str]						=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  TFAutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Dict						=  TFAutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__		,output_loading_info=SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       @slow
       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __SCREAMING_SNAKE_CASE   :List[str]						=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

                   __SCREAMING_SNAKE_CASE   :Any						=  TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       @slow
       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






             for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

                   __SCREAMING_SNAKE_CASE   :Tuple						=  TFAutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Tuple						=  TFAutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE__		,output_loading_info=SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       @slow
       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

                   __SCREAMING_SNAKE_CASE   :str						=  TFAutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Optional[int]						=  TFAutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__		,output_loading_info=SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       @slow
       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             for model_name in ["bert-base-uncased"]:
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

                   __SCREAMING_SNAKE_CASE   :List[str]						=  TFAutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       @slow
       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             for model_name in ["bert-base-uncased"]:
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  TFAutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       @slow
       @require_tensorflow_probability
       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
                   __SCREAMING_SNAKE_CASE   :int						=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  TFAutoModelForTableQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Optional[int]						=  TFAutoModelForTableQuestionAnswering.from_pretrained(
                       SCREAMING_SNAKE_CASE__		,output_loading_info=SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__							)
             self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             self.assertEqual(model.num_parameters()		,1_44_10							)
             self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE__							)		,1_44_10							)




       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__							)
             self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             self.assertEqual(model.num_parameters()		,1_44_10							)
             self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE__							)		,1_44_10							)




       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''							)
             self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  copy.deepcopy(model.config							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  ['''FunnelBaseModel''']
             __SCREAMING_SNAKE_CASE   :int						=  TFAutoModel.from_config(SCREAMING_SNAKE_CASE__							)
             self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             with tempfile.TemporaryDirectory() as tmp_dir:
                   model.save_pretrained(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self							)			->      Optional[Any]:





             """simple docstring"""






             try:
                   AutoConfig.register('''new-model'''		,SCREAMING_SNAKE_CASE__							)

                   __SCREAMING_SNAKE_CASE   :Tuple						=  [
                       TFAutoModel,
                       TFAutoModelForCausalLM,
                       TFAutoModelForMaskedLM,
                       TFAutoModelForPreTraining,
                       TFAutoModelForQuestionAnswering,
                       TFAutoModelForSequenceClassification,
                       TFAutoModelForTokenClassification,
                   ]

                   for auto_class in auto_classes:
                         with self.subTest(auto_class.__name__							):
                               # Wrong config class will raise an error
                               with self.assertRaises(SCREAMING_SNAKE_CASE__							):
                                     auto_class.register(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
                               auto_class.register(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
                               # Trying to register something existing in the Transformers library will raise an error
                               with self.assertRaises(SCREAMING_SNAKE_CASE__							):
                                     auto_class.register(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

                               # Now that the config is registered, it can be used as any other config with the auto-API
                               __SCREAMING_SNAKE_CASE   :Any						=  BertModelTester(self							).get_config()
                               __SCREAMING_SNAKE_CASE   :Dict						=  NewModelConfig(**tiny_config.to_dict()							)
                               __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  auto_class.from_config(SCREAMING_SNAKE_CASE__							)
                               self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

                               with tempfile.TemporaryDirectory() as tmp_dir:
                                     model.save_pretrained(SCREAMING_SNAKE_CASE__							)
                                     __SCREAMING_SNAKE_CASE   :str						=  auto_class.from_pretrained(SCREAMING_SNAKE_CASE__							)
                                     self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             finally:
                   if "new-model" in CONFIG_MAPPING._extra_content:
                         del CONFIG_MAPPING._extra_content["new-model"]
                   for mapping in (
                       TF_MODEL_MAPPING,
                       TF_MODEL_FOR_PRETRAINING_MAPPING,
                       TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                       TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                       TF_MODEL_FOR_CAUSAL_LM_MAPPING,
                       TF_MODEL_FOR_MASKED_LM_MAPPING,
                   ):
                         if NewModelConfig in mapping._extra_content:
                               del mapping._extra_content[NewModelConfig]




       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             with self.assertRaisesRegex(
                 SCREAMING_SNAKE_CASE__		,'''bert-base is not a local folder and is not a valid model identifier'''							):
                   __SCREAMING_SNAKE_CASE   :int						=  TFAutoModel.from_pretrained('''bert-base'''							)




       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






             with self.assertRaisesRegex(
                 SCREAMING_SNAKE_CASE__		,R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''							):
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE__		,revision='''aaaaaa'''							)




       def     _UpperCamelCase     (						self							)			->      List[str]:





             """simple docstring"""






             with self.assertRaisesRegex(
                 SCREAMING_SNAKE_CASE__		,'''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''		,):
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''							)




       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             with self.assertRaisesRegex(SCREAMING_SNAKE_CASE__		,'''Use `from_pt=True` to load this model'''							):
                   __SCREAMING_SNAKE_CASE   :List[str]						=  TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''							)




       def     _UpperCamelCase     (						self							)			->      List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''							)
             with RequestCounter() as counter:
                   __SCREAMING_SNAKE_CASE   :int						=  TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''							)
                   self.assertEqual(counter.get_request_count		,0							)
                   self.assertEqual(counter.head_request_count		,1							)
                   self.assertEqual(counter.other_request_count		,0							)

             # With a sharded checkpoint
             __SCREAMING_SNAKE_CASE   :Dict						=  TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''							)
             with RequestCounter() as counter:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''							)
                   self.assertEqual(counter.get_request_count		,0							)
                   self.assertEqual(counter.head_request_count		,1							)
                   self.assertEqual(counter.other_request_count		,0							)