
"""simple docstring"""


import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  torch.exp(lowerCAmelCase__						)
			lowerCamelCase_					=  torch.sum(lowerCAmelCase__	,dim=1						)  # sum of exp(x_i)
			lowerCamelCase_					=  torch.sum(x * exp_x	,dim=1						)  # sum of x_i * exp(x_i)
			return torch.log(lowerCAmelCase__						) - B / A




class  __lowerCamelCase  ( nn.Module			):




				def __init__(				self							,						UpperCAmelCase   ):
							super().__init__()
							lowerCamelCase_					=  config.output_attentions
							lowerCamelCase_					=  config.output_hidden_states
							lowerCamelCase_					=  nn.ModuleList([BertLayer(UpperCAmelCase   ) for _ in range(config.num_hidden_layers   )]   )
							lowerCamelCase_					=  nn.ModuleList([BertHighway(UpperCAmelCase   ) for _ in range(config.num_hidden_layers   )]   )

							lowerCamelCase_					=  [-1 for _ in range(config.num_hidden_layers   )]




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							if (type(UpperCAmelCase   ) is float) or (type(UpperCAmelCase   ) is int):
										for i in range(len(self.early_exit_entropy   )   ):
													lowerCamelCase_					=  x
							else:
										lowerCamelCase_					=  x




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  pooler.state_dict()
							for highway in self.highway:
										for name, param in highway.pooler.state_dict().items():
													param.copy_(loaded_model[name]   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						):
							lowerCamelCase_					=  ()
							lowerCamelCase_					=  ()
							lowerCamelCase_					=  ()
							for i, layer_module in enumerate(self.layer   ):
										if self.output_hidden_states:
													lowerCamelCase_					=  all_hidden_states + (hidden_states,)

										lowerCamelCase_					=  layer_module(
										    UpperCAmelCase							,						UpperCAmelCase							,						head_mask[i]							,						UpperCAmelCase							,						UpperCAmelCase   )
										lowerCamelCase_					=  layer_outputs[0]

										if self.output_attentions:
													lowerCamelCase_					=  all_attentions + (layer_outputs[1],)

										lowerCamelCase_					=  (hidden_states,)
										if self.output_hidden_states:
													lowerCamelCase_					=  current_outputs + (all_hidden_states,)
										if self.output_attentions:
													lowerCamelCase_					=  current_outputs + (all_attentions,)

										lowerCamelCase_					=  self.highway[i](UpperCAmelCase   )
										# logits, pooled_output

										if not self.training:
													lowerCamelCase_					=  highway_exit[0]
													lowerCamelCase_					=  entropy(UpperCAmelCase   )
													lowerCamelCase_					=  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
													lowerCamelCase_					=  all_highway_exits + (highway_exit,)

													if highway_entropy < self.early_exit_entropy[i]:
																lowerCamelCase_					=  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																raise HighwayException(UpperCAmelCase							,						i + 1   )
										else:
													lowerCamelCase_					=  all_highway_exits + (highway_exit,)

        # Add last layer
							if self.output_hidden_states:
										lowerCamelCase_					=  all_hidden_states + (hidden_states,)

							lowerCamelCase_					=  (hidden_states,)
							if self.output_hidden_states:
										lowerCamelCase_					=  outputs + (all_hidden_states,)
							if self.output_attentions:
										lowerCamelCase_					=  outputs + (all_attentions,)

							lowerCamelCase_					=  outputs + (all_highway_exits,)
							return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits




@add_start_docstrings(
    'The Bert Model transformer with early exiting (DeeBERT). '						,	lowerCAmelCase						,	)
class  __lowerCamelCase  ( lowerCAmelCase			):




				def __init__(				self							,						UpperCAmelCase   ):
							super().__init__(UpperCAmelCase   )
							lowerCamelCase_					=  config

							lowerCamelCase_					=  BertEmbeddings(UpperCAmelCase   )
							lowerCamelCase_					=  DeeBertEncoder(UpperCAmelCase   )
							lowerCamelCase_					=  BertPooler(UpperCAmelCase   )

							self.init_weights()




				def        UpperCAmelCase__						(				self   ):
							self.encoder.init_highway_pooler(self.pooler   )




				def        UpperCAmelCase__						(				self   ):
							return self.embeddings.word_embeddings




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  value




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							for layer, heads in heads_to_prune.items():
										self.encoder.layer[layer].attention.prune_heads(UpperCAmelCase   )





				@add_start_docstrings_to_model_forward(UpperCAmelCase   )
				def        UpperCAmelCase__						(				self							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						):
							if input_ids is not None and inputs_embeds is not None:
										raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''   )
							elif input_ids is not None:
										lowerCamelCase_					=  input_ids.size()
							elif inputs_embeds is not None:
										lowerCamelCase_					=  inputs_embeds.size()[:-1]
							else:
										raise ValueError('''You have to specify either input_ids or inputs_embeds'''   )

							lowerCamelCase_					=  input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
										lowerCamelCase_					=  torch.ones(UpperCAmelCase							,						device=UpperCAmelCase   )
							if encoder_attention_mask is None:
										lowerCamelCase_					=  torch.ones(UpperCAmelCase							,						device=UpperCAmelCase   )
							if token_type_ids is None:
										lowerCamelCase_					=  torch.zeros(UpperCAmelCase							,						dtype=torch.long							,						device=UpperCAmelCase   )

							# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
							# ourselves in which case we just need to make it broadcastable to all heads.
							lowerCamelCase_					=  self.get_extended_attention_mask(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )

							# If a 2D ou 3D attention mask is provided for the cross-attention
							# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
							if encoder_attention_mask.dim() == 3:
										lowerCamelCase_					=  encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
										lowerCamelCase_					=  encoder_attention_mask[:, None, None, :]

							lowerCamelCase_					=  encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()   ).dtype   )  # fp16 compatibility
							lowerCamelCase_					=  (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

							# Prepare head mask if needed
							# 1.0 in head_mask indicate we keep the head
							# attention_probs has shape bsz x n_heads x N x N
							# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
							# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
							lowerCamelCase_					=  self.get_head_mask(UpperCAmelCase							,						self.config.num_hidden_layers   )

							lowerCamelCase_					=  self.embeddings(
							    input_ids=UpperCAmelCase							,						position_ids=UpperCAmelCase							,						token_type_ids=UpperCAmelCase							,						inputs_embeds=UpperCAmelCase   )
							lowerCamelCase_					=  self.encoder(
							    UpperCAmelCase							,						attention_mask=UpperCAmelCase							,						head_mask=UpperCAmelCase							,						encoder_hidden_states=UpperCAmelCase							,						encoder_attention_mask=UpperCAmelCase							,						)
							lowerCamelCase_					=  encoder_outputs[0]
							lowerCamelCase_					=  self.pooler(UpperCAmelCase   )

							lowerCamelCase_					=  (
							    sequence_output,
							    pooled_output,
							) + encoder_outputs[
							    1:
							]  # add hidden_states and attentions if they are here
							return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits




class  __lowerCamelCase  ( lowerCAmelCase			):




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  message
							lowerCamelCase_					=  exit_layer  # start from 1!




class  __lowerCamelCase  ( nn.Module			):




				def __init__(				self							,						UpperCAmelCase   ):
							super().__init__()
							lowerCamelCase_					=  BertPooler(UpperCAmelCase   )
							lowerCamelCase_					=  nn.Dropout(config.hidden_dropout_prob   )
							lowerCamelCase_					=  nn.Linear(config.hidden_size							,						config.num_labels   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							# Pooler
							lowerCamelCase_					=  encoder_outputs[0]
							lowerCamelCase_					=  self.pooler(UpperCAmelCase   )
							# "return" pooler_output

							# BertModel
							lowerCamelCase_					=  (pooler_input, pooler_output) + encoder_outputs[1:]
							# "return" bmodel_output

							# Dropout and classification
							lowerCamelCase_					=  bmodel_output[1]

							lowerCamelCase_					=  self.dropout(UpperCAmelCase   )
							lowerCamelCase_					=  self.classifier(UpperCAmelCase   )

							return logits, pooled_output




@add_start_docstrings(
    'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '						,	lowerCAmelCase						,	)
class  __lowerCamelCase  ( lowerCAmelCase			):




				def __init__(				self							,						UpperCAmelCase   ):
							super().__init__(UpperCAmelCase   )
							lowerCamelCase_					=  config.num_labels
							lowerCamelCase_					=  config.num_hidden_layers

							lowerCamelCase_					=  DeeBertModel(UpperCAmelCase   )
							lowerCamelCase_					=  nn.Dropout(config.hidden_dropout_prob   )
							lowerCamelCase_					=  nn.Linear(config.hidden_size							,						self.config.num_labels   )

							self.init_weights()





				@add_start_docstrings_to_model_forward(UpperCAmelCase   )
				def        UpperCAmelCase__						(				self							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=-1							,						UpperCAmelCase=False							,						):
							lowerCamelCase_					=  self.num_layers
							try:
										lowerCamelCase_					=  self.bert(
										    UpperCAmelCase							,						attention_mask=UpperCAmelCase							,						token_type_ids=UpperCAmelCase							,						position_ids=UpperCAmelCase							,						head_mask=UpperCAmelCase							,						inputs_embeds=UpperCAmelCase							,						)
										# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

										lowerCamelCase_					=  outputs[1]

										lowerCamelCase_					=  self.dropout(UpperCAmelCase   )
										lowerCamelCase_					=  self.classifier(UpperCAmelCase   )
										lowerCamelCase_					=  (logits,) + outputs[2:]  # add hidden states and attention if they are here
							except HighwayException as e:
										lowerCamelCase_					=  e.message
										lowerCamelCase_					=  e.exit_layer
										lowerCamelCase_					=  outputs[0]

							if not self.training:
										lowerCamelCase_					=  entropy(UpperCAmelCase   )
										lowerCamelCase_					=  []
										lowerCamelCase_					=  []
							if labels is not None:
										if self.num_labels == 1:
													#  We are doing regression
													lowerCamelCase_					=  MSELoss()
													lowerCamelCase_					=  loss_fct(logits.view(-1   )							,						labels.view(-1   )   )
										else:
													lowerCamelCase_					=  CrossEntropyLoss()
													lowerCamelCase_					=  loss_fct(logits.view(-1							,						self.num_labels   )							,						labels.view(-1   )   )

										# work with highway exits
										lowerCamelCase_					=  []
										for highway_exit in outputs[-1]:
													lowerCamelCase_					=  highway_exit[0]
													if not self.training:
																highway_logits_all.append(UpperCAmelCase   )
																highway_entropy.append(highway_exit[2]   )
													if self.num_labels == 1:
																#  We are doing regression
																lowerCamelCase_					=  MSELoss()
																lowerCamelCase_					=  loss_fct(highway_logits.view(-1   )							,						labels.view(-1   )   )
													else:
																lowerCamelCase_					=  CrossEntropyLoss()
																lowerCamelCase_					=  loss_fct(highway_logits.view(-1							,						self.num_labels   )							,						labels.view(-1   )   )
													highway_losses.append(UpperCAmelCase   )

										if train_highway:
													lowerCamelCase_					=  (sum(highway_losses[:-1]   ),) + outputs
													# exclude the final highway, of course
										else:
													lowerCamelCase_					=  (loss,) + outputs
							if not self.training:
										lowerCamelCase_					=  outputs + ((original_entropy, highway_entropy), exit_layer)
										if output_layer >= 0:
													lowerCamelCase_					=  (
													    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
													)  # use the highway of the last layer

							return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)






"""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   )






