




"""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


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






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Dict:






      '''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 		_snake_case	(   _snake_case				:   Optional[int]							,    _snake_case				:   int				)       ->   List[Any]:






      '''simple docstring'''


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






def 		_snake_case	(   _snake_case				:   Tuple							,    _snake_case				:   List[str]				)       ->   int:






      '''simple docstring'''


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






def 		_snake_case	(   )       ->   List[Any]:






      '''simple docstring'''


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

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

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

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

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

      parser.add_argument('--log_interval'							,    type=_snake_case							,    default=5_00							,    help='Tensorboard logging interval.'				)
      parser.add_argument('--checkpoint_interval'							,    type=_snake_case							,    default=40_00							,    help='Checkpoint interval.'				)
      _A     =  parser.parse_args()
      sanity_checks(_snake_case				)

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

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

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

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

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

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

      _A     =  LmSeqsDataset(params=_snake_case							,    data=_snake_case				)
      logger.info('Data loader created.'				)

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

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

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

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

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

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


if __name__ == "__main__":
       main()




"""simple docstring"""

import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional

import yaml

from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool


# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
_UpperCamelCase		     =    sys.version_info >= (3, 1_0)



def       SCREAMING_SNAKE_CASE							( lowercase__=None					,					lowercase__=None   )			->						List[Any]:
 return field(default_factory=lambda: default					,					metadata=lowercase__   )






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      int
       __UpperCamelCase					:      float
       __UpperCamelCase					:      str
       __UpperCamelCase					:      bool






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      int    =						42
       __UpperCamelCase					:      str    =						field(default='toto'       ,	metadata={'help': 'help message'}       )






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      bool    =						False
       __UpperCamelCase					:      bool    =						True
       __UpperCamelCase					:      Optional[bool]    =						None






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[str]    =						'titi'
       __UpperCamelCase					:      Tuple    =						'toto'






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Tuple    =						'titi'
       __UpperCamelCase					:      List[Any]    =						'toto'
       __UpperCamelCase					:      List[str]    =						42






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      BasicEnum    =						"toto"






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				BasicEnum(self.foo   )






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      MixedTypeEnum    =						"toto"






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				MixedTypeEnum(self.foo   )






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      Optional[int]    =						None
       __UpperCamelCase					:      Optional[float]    =						field(default=__magic_name__       ,	metadata={'help': 'help message'}       )
       __UpperCamelCase					:      Optional[str]    =						None
       __UpperCamelCase					:      Optional[List[str]]    =						list_field(default=[]       )
       __UpperCamelCase					:      Optional[List[int]]    =						list_field(default=[]       )






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      List[int]    =						list_field(default=[]       )
       __UpperCamelCase					:      List[int]    =						list_field(default=[1, 2, 3]       )
       __UpperCamelCase					:      List[str]    =						list_field(default=['Hallo', 'Bonjour', 'Hello']       )
       __UpperCamelCase					:      List[float]    =						list_field(default=[0.1, 0.2, 0.3]       )






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      List[int]    =						field()
       __UpperCamelCase					:      str    =						field()
       __UpperCamelCase					:      BasicEnum    =						field()






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				BasicEnum(self.required_enum   )






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      int
       __UpperCamelCase					:      "BasicEnum"    =						field()
       __UpperCamelCase					:      "Optional[bool]"    =						None
       __UpperCamelCase					:      "str"    =						field(default='toto'       ,	metadata={'help': 'help message'}       )
       __UpperCamelCase					:      "List[str]"    =						list_field(default=['Hallo', 'Bonjour', 'Hello']       )


if is_python_no_less_than_3_10:






    @dataclass
    class 			__a					:





           """simple docstring"""


           __UpperCamelCase					:      bool    =						False
           __UpperCamelCase					:      bool    =						True
           __UpperCamelCase					:      bool | None    =						None






    @dataclass
    class 			__a					:





           """simple docstring"""


           __UpperCamelCase					:      int | None    =						None
           __UpperCamelCase					:      float | None    =						field(default=__magic_name__       ,	metadata={'help': 'help message'}       )
           __UpperCamelCase					:      str | None    =						None
           __UpperCamelCase					:      list[str] | None    =						list_field(default=[]       )
           __UpperCamelCase					:      list[int] | None    =						list_field(default=[]       )






class 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case   ):





        """simple docstring"""

        self.assertEqual(len(a._actions   )			,					len(b._actions   )   )
        for x, y in zip(a._actions			,					b._actions   ):
         lowerCAmelCase__   :				Union[str, Any] 			=				{k: v for k, v in vars(snake_case   ).items() if k != "container"}
         lowerCAmelCase__   :				Union[str, Any] 			=				{k: v for k, v in vars(snake_case   ).items() if k != "container"}

         # Choices with mixed type have custom function as "type"
         # So we need to compare results directly for equality
         if xx.get("choices"			,					snake_case   ) and yy.get("choices"			,					snake_case   ):
          for expected_choice in yy["choices"] + xx["choices"]:
           self.assertEqual(xx["type"](snake_case   )			,					yy["type"](snake_case   )   )
          del xx["type"], yy["type"]

         self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				Optional[int] 			=				argparse.ArgumentParser()
        expected.add_argument("--foo"			,					type=snake_case			,					required=snake_case   )
        expected.add_argument("--bar"			,					type=snake_case			,					required=snake_case   )
        expected.add_argument("--baz"			,					type=snake_case			,					required=snake_case   )
        expected.add_argument("--flag"			,					type=snake_case			,					default=snake_case			,					const=snake_case			,					nargs="?"   )
        self.argparsersEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				List[Any] 			=				["--foo", "1", "--baz", "quux", "--bar", "0.5"]
        ((lowerCAmelCase__)			,						)   :				Dict 			=				parser.parse_args_into_dataclasses(snake_case			,					look_for_args_file=snake_case   )
        self.assertFalse(example.flag   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				Optional[int] 			=				argparse.ArgumentParser()
        expected.add_argument("--foo"			,					default=42			,					type=snake_case   )
        expected.add_argument("--baz"			,					default="toto"			,					type=snake_case			,					help="help message"   )
        self.argparsersEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				argparse.ArgumentParser()
        expected.add_argument("--foo"			,					type=snake_case			,					default=snake_case			,					const=snake_case			,					nargs="?"   )
        expected.add_argument("--baz"			,					type=snake_case			,					default=snake_case			,					const=snake_case			,					nargs="?"   )
        # A boolean no_* argument always has to come after its "default: True" regular counter-part
        # and its default must be set to False
        expected.add_argument("--no_baz"			,					action="store_false"			,					default=snake_case			,					dest="baz"   )
        expected.add_argument("--opt"			,					type=snake_case			,					default=snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				[WithDefaultBoolExample]
        if is_python_no_less_than_3_10:
         dataclass_types.append(snake_case   )

        for dataclass_type in dataclass_types:
         lowerCAmelCase__   :				Optional[Any] 			=				HfArgumentParser(snake_case   )
         self.argparsersEqual(snake_case			,					snake_case   )

         lowerCAmelCase__   :				Union[str, Any] 			=				parser.parse_args([]   )
         self.assertEqual(snake_case			,					Namespace(foo=snake_case			,					baz=snake_case			,					opt=snake_case   )   )

         lowerCAmelCase__   :				List[str] 			=				parser.parse_args(["--foo", "--no_baz"]   )
         self.assertEqual(snake_case			,					Namespace(foo=snake_case			,					baz=snake_case			,					opt=snake_case   )   )

         lowerCAmelCase__   :				Optional[Any] 			=				parser.parse_args(["--foo", "--baz"]   )
         self.assertEqual(snake_case			,					Namespace(foo=snake_case			,					baz=snake_case			,					opt=snake_case   )   )

         lowerCAmelCase__   :				List[Any] 			=				parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"]   )
         self.assertEqual(snake_case			,					Namespace(foo=snake_case			,					baz=snake_case			,					opt=snake_case   )   )

         lowerCAmelCase__   :				Optional[int] 			=				parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"]   )
         self.assertEqual(snake_case			,					Namespace(foo=snake_case			,					baz=snake_case			,					opt=snake_case   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				argparse.ArgumentParser()
        expected.add_argument(
            "--foo"			,					default="toto"			,					choices=["titi", "toto", 42]			,					type=make_choice_type_function(["titi", "toto", 42]   )			,					)
        self.argparsersEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				parser.parse_args([]   )
        self.assertEqual(args.foo			,					"toto"   )
        lowerCAmelCase__   :				List[str] 			=				parser.parse_args_into_dataclasses([]   )[0]
        self.assertEqual(enum_ex.foo			,					MixedTypeEnum.toto   )

        lowerCAmelCase__   :				List[Any] 			=				parser.parse_args(["--foo", "titi"]   )
        self.assertEqual(args.foo			,					"titi"   )
        lowerCAmelCase__   :				Any 			=				parser.parse_args_into_dataclasses(["--foo", "titi"]   )[0]
        self.assertEqual(enum_ex.foo			,					MixedTypeEnum.titi   )

        lowerCAmelCase__   :				Tuple 			=				parser.parse_args(["--foo", "42"]   )
        self.assertEqual(args.foo			,					42   )
        lowerCAmelCase__   :				Tuple 			=				parser.parse_args_into_dataclasses(["--foo", "42"]   )[0]
        self.assertEqual(enum_ex.foo			,					MixedTypeEnum.fourtytwo   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        @dataclass
        class 			__a					:





               """simple docstring"""


               __UpperCamelCase					:      Literal["titi", "toto", 42]    =						"toto"




        lowerCAmelCase__   :				Dict 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				Dict 			=				argparse.ArgumentParser()
        expected.add_argument(
            "--foo"			,					default="toto"			,					choices=("titi", "toto", 42)			,					type=make_choice_type_function(["titi", "toto", 42]   )			,					)
        self.argparsersEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				int 			=				parser.parse_args([]   )
        self.assertEqual(args.foo			,					"toto"   )

        lowerCAmelCase__   :				Optional[int] 			=				parser.parse_args(["--foo", "titi"]   )
        self.assertEqual(args.foo			,					"titi"   )

        lowerCAmelCase__   :				int 			=				parser.parse_args(["--foo", "42"]   )
        self.assertEqual(args.foo			,					42   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				argparse.ArgumentParser()
        expected.add_argument("--foo_int"			,					nargs="+"			,					default=[]			,					type=snake_case   )
        expected.add_argument("--bar_int"			,					nargs="+"			,					default=[1, 2, 3]			,					type=snake_case   )
        expected.add_argument("--foo_str"			,					nargs="+"			,					default=["Hallo", "Bonjour", "Hello"]			,					type=snake_case   )
        expected.add_argument("--foo_float"			,					nargs="+"			,					default=[0.1, 0.2, 0.3]			,					type=snake_case   )

        self.argparsersEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				Tuple 			=				parser.parse_args([]   )
        self.assertEqual(
            snake_case			,					Namespace(foo_int=[]			,					bar_int=[1, 2, 3]			,					foo_str=["Hallo", "Bonjour", "Hello"]			,					foo_float=[0.1, 0.2, 0.3]   )			,					)

        lowerCAmelCase__   :				List[Any] 			=				parser.parse_args("--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7".split()   )
        self.assertEqual(snake_case			,					Namespace(foo_int=[1]			,					bar_int=[2, 3]			,					foo_str=["a", "b", "c"]			,					foo_float=[0.1, 0.7]   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				argparse.ArgumentParser()
        expected.add_argument("--foo"			,					default=snake_case			,					type=snake_case   )
        expected.add_argument("--bar"			,					default=snake_case			,					type=snake_case			,					help="help message"   )
        expected.add_argument("--baz"			,					default=snake_case			,					type=snake_case   )
        expected.add_argument("--ces"			,					nargs="+"			,					default=[]			,					type=snake_case   )
        expected.add_argument("--des"			,					nargs="+"			,					default=[]			,					type=snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				[OptionalExample]
        if is_python_no_less_than_3_10:
         dataclass_types.append(snake_case   )

        for dataclass_type in dataclass_types:
         lowerCAmelCase__   :				Tuple 			=				HfArgumentParser(snake_case   )

         self.argparsersEqual(snake_case			,					snake_case   )

         lowerCAmelCase__   :				Any 			=				parser.parse_args([]   )
         self.assertEqual(snake_case			,					Namespace(foo=snake_case			,					bar=snake_case			,					baz=snake_case			,					ces=[]			,					des=[]   )   )

         lowerCAmelCase__   :				List[str] 			=				parser.parse_args("--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3".split()   )
         self.assertEqual(snake_case			,					Namespace(foo=12			,					bar=3.14			,					baz="42"			,					ces=["a", "b", "c"]			,					des=[1, 2, 3]   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				List[Any] 			=				argparse.ArgumentParser()
        expected.add_argument("--required_list"			,					nargs="+"			,					type=snake_case			,					required=snake_case   )
        expected.add_argument("--required_str"			,					type=snake_case			,					required=snake_case   )
        expected.add_argument(
            "--required_enum"			,					type=make_choice_type_function(["titi", "toto"]   )			,					choices=["titi", "toto"]			,					required=snake_case			,					)
        self.argparsersEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				argparse.ArgumentParser()
        expected.add_argument("--foo"			,					type=snake_case			,					required=snake_case   )
        expected.add_argument(
            "--required_enum"			,					type=make_choice_type_function(["titi", "toto"]   )			,					choices=["titi", "toto"]			,					required=snake_case			,					)
        expected.add_argument("--opt"			,					type=snake_case			,					default=snake_case   )
        expected.add_argument("--baz"			,					default="toto"			,					type=snake_case			,					help="help message"   )
        expected.add_argument("--foo_str"			,					nargs="+"			,					default=["Hallo", "Bonjour", "Hello"]			,					type=snake_case   )
        self.argparsersEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				List[Any] 			=				{
            "foo": 12,
            "bar": 3.14,
            "baz": "42",
            "flag": True,
        }

        lowerCAmelCase__   :				Tuple 			=				parser.parse_dict(snake_case   )[0]
        lowerCAmelCase__   :				Any 			=				BasicExample(**snake_case   )
        self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				List[Any] 			=				{
            "foo": 12,
            "bar": 3.14,
            "baz": "42",
            "flag": True,
            "extra": 42,
        }

        self.assertRaises(snake_case			,					parser.parse_dict			,					snake_case			,					allow_extra_keys=snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				{
            "foo": 12,
            "bar": 3.14,
            "baz": "42",
            "flag": True,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
         lowerCAmelCase__   :				int 			=				os.path.join(snake_case			,					"temp_json"   )
         os.mkdir(snake_case   )
         with open(temp_local_path + ".json"			,					"w+"   ) as f:
          json.dump(snake_case			,					snake_case   )
         lowerCAmelCase__   :				Any 			=				parser.parse_yaml_file(Path(temp_local_path + ".json"   )   )[0]

        lowerCAmelCase__   :				str 			=				BasicExample(**snake_case   )
        self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				int 			=				{
            "foo": 12,
            "bar": 3.14,
            "baz": "42",
            "flag": True,
        }
        with tempfile.TemporaryDirectory() as tmp_dir:
         lowerCAmelCase__   :				Optional[Any] 			=				os.path.join(snake_case			,					"temp_yaml"   )
         os.mkdir(snake_case   )
         with open(temp_local_path + ".yaml"			,					"w+"   ) as f:
          yaml.dump(snake_case			,					snake_case   )
         lowerCAmelCase__   :				Dict 			=				parser.parse_yaml_file(Path(temp_local_path + ".yaml"   )   )[0]
        lowerCAmelCase__   :				int 			=				BasicExample(**snake_case   )
        self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				HfArgumentParser(snake_case   )
        self.assertIsNotNone(snake_case   )






