
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


__lowerCAmelCase					=			trt.Logger(trt.Logger.WARNING)
__lowerCAmelCase					=			absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__lowerCAmelCase					=			logging.getLogger(__name__)

__lowerCAmelCase					=			argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '--onnx_model_path',
    default=None,
    type=str,
    required=True,
    help='Path to ONNX model: ',
)

parser.add_argument(
    '--output_dir',
    default=None,
    type=str,
    required=True,
    help='The output directory where the model checkpoints and predictions will be written.',
)

# Other parameters

parser.add_argument(
    '--tokenizer_name',
    default='',
    type=str,
    required=True,
    help='Pretrained tokenizer name or path if not the same as model_name',
)

parser.add_argument(
    '--version_2_with_negative',
    action='store_true',
    help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
    '--null_score_diff_threshold',
    type=float,
    default=0.0,
    help='If null_score - best_non_null is greater than the threshold predict null.',
)

parser.add_argument(
    '--max_seq_length',
    default=384,
    type=int,
    help=(
        'The maximum total input sequence length after WordPiece tokenization. Sequences '
        'longer than this will be truncated, and sequences shorter than this will be padded.'
    ),
)
parser.add_argument(
    '--doc_stride',
    default=128,
    type=int,
    help='When splitting up a long document into chunks, how much stride to take between chunks.',
)

parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')

parser.add_argument(
    '--n_best_size',
    default=20,
    type=int,
    help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
    '--max_answer_length',
    default=30,
    type=int,
    help=(
        'The maximum length of an answer that can be generated. This is needed because the start '
        'and end predictions are not conditioned on one another.'
    ),
)

parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')

parser.add_argument(
    '--dataset_name',
    type=str,
    default=None,
    required=True,
    help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--dataset_config_name',
    type=str,
    default=None,
    help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
    '--fp16',
    action='store_true',
    help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
    '--int8',
    action='store_true',
    help='Whether to use INT8',
)

__lowerCAmelCase					=			parser.parse_args()

if args.tokenizer_name:
				__lowerCAmelCase					=			AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
				raise ValueError(
				    'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
				    'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
				)

logger.info('Training/evaluation parameters %s', args)

__lowerCAmelCase					=			args.per_device_eval_batch_size

__lowerCAmelCase					=			(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__lowerCAmelCase					=			True

__lowerCAmelCase					=			'temp_engine/bert-fp32.engine'
if args.fpaa:
				__lowerCAmelCase					=			'temp_engine/bert-fp16.engine'
if args.inta:
				__lowerCAmelCase					=			'temp_engine/bert-int8.engine'

# import ONNX file
if not os.path.exists('temp_engine'):
				os.makedirs('temp_engine')

__lowerCAmelCase					=			1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
				with open(args.onnx_model_path, 'rb') as model:
								if not parser.parse(model.read()):
												for error in range(parser.num_errors):
																print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
				__lowerCAmelCase					=			[network.get_input(i) for i in range(network.num_inputs)]
				__lowerCAmelCase					=			[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

				with builder.create_builder_config() as config:
								__lowerCAmelCase					=			1 << 50
								if STRICT_TYPES:
												config.set_flag(trt.BuilderFlag.STRICT_TYPES)
								if args.fpaa:
												config.set_flag(trt.BuilderFlag.FPaa)
								if args.inta:
												config.set_flag(trt.BuilderFlag.INTa)
								__lowerCAmelCase					=			builder.create_optimization_profile()
								config.add_optimization_profile(profile)
								for i in range(len(input_names)):
												profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
								__lowerCAmelCase					=			builder.build_engine(network, config)

								# serialize_engine and store in file (can be directly loaded and deserialized):
								with open(engine_name, 'wb') as f:
												f.write(engine.serialize())






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
							_snake_case  =    np.asarray(inputs["""input_ids"""]				,   dtype=np.intaa )
							_snake_case  =    np.asarray(inputs["""attention_mask"""]				,   dtype=np.intaa )
							_snake_case  =    np.asarray(inputs["""token_type_ids"""]				,   dtype=np.intaa )

							# Copy inputs
							cuda.memcpy_htod_async(d_inputs[0]				,   input_ids.ravel()				,   __a )
							cuda.memcpy_htod_async(d_inputs[1]				,   attention_mask.ravel()				,   __a )
							cuda.memcpy_htod_async(d_inputs[2]				,   token_type_ids.ravel()				,   __a )
							# start time
							_snake_case  =    time.time()
							# Run inference
							context.execute_async(
							    bindings=[int(__a ) for d_inp in d_inputs] + [int(__a ), int(__a )]				,   stream_handle=stream.handle )
							# Transfer predictions back from GPU
							cuda.memcpy_dtoh_async(__a				,   __a				,   __a )
							cuda.memcpy_dtoh_async(__a				,   __a				,   __a )
							# Synchronize the stream and take time
							stream.synchronize()
							# end time
							_snake_case  =    time.time()
							_snake_case  =    end_time - start_time
							_snake_case  =    (h_outputa, h_outputa)
							# print(outputs)
							return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__lowerCAmelCase					=			Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
				datasets.utils.logging.set_verbosity_warning()
				transformers.utils.logging.set_verbosity_info()
else:
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
				set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
				# Downloading and loading a dataset from the hub.
				__lowerCAmelCase					=			load_dataset(args.dataset_name, args.dataset_config_name)
else:
				raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

__lowerCAmelCase					=			raw_datasets['validation'].column_names

__lowerCAmelCase					=			'question' if 'question' in column_names else column_names[0]
__lowerCAmelCase					=			'context' if 'context' in column_names else column_names[1]
__lowerCAmelCase					=			'answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__lowerCAmelCase					=			tokenizer.padding_side == 'right'

if args.max_seq_length > tokenizer.model_max_length:
				logger.warning(
				    f'''The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'''
				    f'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''
				)

__lowerCAmelCase					=			min(args.max_seq_length, tokenizer.model_max_length)






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							# Some of the questions have lots of whitespace on the left, which is not useful and will make the
							# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
							# left whitespace
							_snake_case  =    [q.lstrip() for q in examples[question_column_name]]

							# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
							# in one example possible giving several features when a context is long, each of those features having a
							# context that overlaps a bit the context of the previous feature.
							_snake_case  =    tokenizer(
							    examples[question_column_name if pad_on_right else context_column_name]				,   examples[context_column_name if pad_on_right else question_column_name]				,   truncation="""only_second""" if pad_on_right else """only_first"""				,   max_length=__a				,   stride=args.doc_stride				,   return_overflowing_tokens=__a				,   return_offsets_mapping=__a				,   padding="""max_length"""				,   )

							# Since one example might give us several features if it has a long context, we need a map from a feature to
							# its corresponding example. This key gives us just that.
							_snake_case  =    tokenized_examples.pop("""overflow_to_sample_mapping""" )

							# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
							# corresponding example_id and we will store the offset mappings.
							_snake_case  =    []

							for i in range(len(tokenized_examples["""input_ids"""] ) ):
														# Grab the sequence corresponding to that example (to know what is the context and what is the question).
														_snake_case  =    tokenized_examples.sequence_ids(__a )
														_snake_case  =    1 if pad_on_right else 0

														# One example can give several spans, this is the index of the example containing this span of text.
														_snake_case  =    sample_mapping[i]
														tokenized_examples["example_id"].append(examples["""id"""][sample_index] )

														# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
														# position is part of the context or not.
														_snake_case  =    [
														    (o if sequence_ids[k] == context_index else None)
														    for k, o in enumerate(tokenized_examples["""offset_mapping"""][i] )
														]

							return tokenized_examples


__lowerCAmelCase					=			raw_datasets['validation']
# Validation Feature Creation
__lowerCAmelCase					=			eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='Running tokenizer on validation dataset',
)

__lowerCAmelCase					=			default_data_collator

__lowerCAmelCase					=			eval_dataset.remove_columns(['example_id', 'offset_mapping'])
__lowerCAmelCase					=			DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE="eval" ):
							# Post-processing: we match the start logits and end logits to answers in the original context.
							_snake_case  =    postprocess_qa_predictions(
							    examples=__a				,   features=__a				,   predictions=__a				,   version_2_with_negative=args.version_2_with_negative				,   n_best_size=args.n_best_size				,   max_answer_length=args.max_answer_length				,   null_score_diff_threshold=args.null_score_diff_threshold				,   output_dir=args.output_dir				,   prefix=__a				,   )
							# Format the result to the format the metric expects.
							if args.version_2_with_negative:
														_snake_case  =    [
														    {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
														]
							else:
														_snake_case  =    [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

							_snake_case  =    [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
							return EvalPrediction(predictions=__a				,   label_ids=__a )


__lowerCAmelCase					=			load_metric('squad_v2' if args.version_2_with_negative else 'squad')

# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
				# setup for TRT inferrence
				for i in range(len(input_names)):
								context.set_binding_shape(i, INPUT_SHAPE)
				assert context.all_binding_shapes_specified






				def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
											return trt.volume(engine.get_binding_shape(__a ) ) * engine.get_binding_dtype(__a ).itemsize

				# Allocate device memory for inputs and outputs.
				__lowerCAmelCase					=			[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

				# Allocate output buffer
				__lowerCAmelCase					=			cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
				__lowerCAmelCase					=			cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
				__lowerCAmelCase					=			cuda.mem_alloc(h_outputa.nbytes)
				__lowerCAmelCase					=			cuda.mem_alloc(h_outputa.nbytes)

				# Create a stream in which to copy inputs/outputs and run inference.
				__lowerCAmelCase					=			cuda.Stream()

				# Evaluation
				logger.info('***** Running Evaluation *****')
				logger.info(f'''  Num examples = {len(eval_dataset)}''')
				logger.info(f'''  Batch size = {args.per_device_eval_batch_size}''')

				__lowerCAmelCase					=			0.0
				__lowerCAmelCase					=			0
				__lowerCAmelCase					=			timeit.default_timer()

				__lowerCAmelCase					=			None
				for step, batch in enumerate(eval_dataloader):
								__lowerCAmelCase					=			model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
								total_time += infer_time
								niter += 1

								__lowerCAmelCase					=			outputs
								__lowerCAmelCase					=			torch.tensor(start_logits)
								__lowerCAmelCase					=			torch.tensor(end_logits)

								# necessary to pad predictions and labels for being gathered
								__lowerCAmelCase					=			accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
								__lowerCAmelCase					=			accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

								__lowerCAmelCase					=			(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
								__lowerCAmelCase					=			logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

				if all_preds is not None:
								__lowerCAmelCase					=			nested_truncate(all_preds, len(eval_dataset))

				__lowerCAmelCase					=			timeit.default_timer() - start_time
				logger.info('  Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
				# Inference time from TRT
				logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1_000 / niter))
				logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1_000))

				logger.info('Total Number of Inference =  %d', niter)

__lowerCAmelCase					=			post_processing_function(eval_examples, eval_dataset, all_preds)
__lowerCAmelCase					=			metric.compute(predictions=prediction.predictions, references=prediction.label_ids)

logger.info(f'''Evaluation metrics: {eval_metric}''')



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
_lowerCAmelCase:						Any   				=       sys.version_info >= (3, 10)






def   _lowercase(					__a				:						int=None    ,     __a				:						Any=None     ):
							return field(default_factory=lambda: default    ,     metadata=__a     )




@dataclass
class       lowercase_					:
				snake_case	    =42
				snake_case	    =42
				snake_case	    =42
				snake_case	    =42







@dataclass
class       lowercase_					:
				snake_case	    =42
				snake_case	    =field(default='toto'      ,					metadata={'help': 'help message'}   )







@dataclass
class       lowercase_					:
				snake_case	    =False
				snake_case	    =True
				snake_case	    =None







class       lowercase_					(lowercase__   ):
				snake_case	    ='titi'
				snake_case	    ='toto'







class       lowercase_					(lowercase__   ):
				snake_case	    ='titi'
				snake_case	    ='toto'
				snake_case	    =42







@dataclass
class       lowercase_					:
				snake_case	    ="toto"





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =BasicEnum(self.foo)







@dataclass
class       lowercase_					:
				snake_case	    ="toto"





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =MixedTypeEnum(self.foo)







@dataclass
class       lowercase_					:
				snake_case	    =None
				snake_case	    =field(default=lowercase__      ,					metadata={'help': 'help message'}   )
				snake_case	    =None
				snake_case	    =list_field(default=[]   )
				snake_case	    =list_field(default=[]   )







@dataclass
class       lowercase_					:
				snake_case	    =list_field(default=[]   )
				snake_case	    =list_field(default=[1, 2, 3]   )
				snake_case	    =list_field(default=['Hallo', 'Bonjour', 'Hello']   )
				snake_case	    =list_field(default=[0.1, 0.2, 0.3]   )







@dataclass
class       lowercase_					:
				snake_case	    =field()
				snake_case	    =field()
				snake_case	    =field()





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =BasicEnum(self.required_enum)







@dataclass
class       lowercase_					:
				snake_case	    =42
				snake_case	    =field()
				snake_case	    =None
				snake_case	    =field(default='toto'      ,					metadata={'help': 'help message'}   )
				snake_case	    =list_field(default=['Hallo', 'Bonjour', 'Hello']   )


if is_python_no_less_than_3_10:




	@dataclass
	class       lowercase_					:
					snake_case	    =False
					snake_case	    =True
					snake_case	    =None







	@dataclass
	class       lowercase_					:
					snake_case	    =None
					snake_case	    =field(default=lowercase__      ,					metadata={'help': 'help message'}   )
					snake_case	    =None
					snake_case	    =list_field(default=[]   )
					snake_case	    =list_field(default=[]   )







class       lowercase_					(unittest.TestCase   ):





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> int:
											self.assertEqual(len(a._actions)       ,  len(b._actions))
											for x, y in zip(a._actions       ,  b._actions):
																		a__      ={k: v for k, v in vars(lowercase_).items() if k != 'container'}
																		a__      ={k: v for k, v in vars(lowercase_).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'       ,  lowercase_) and yy.get('choices'       ,  lowercase_):
																									for expected_choice in yy["choices"] + xx["choices"]:
																																self.assertEqual(xx['type'](lowercase_)       ,  yy['type'](lowercase_))
																									del xx["type"], yy["type"]

																		self.assertEqual(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =HfArgumentParser(lowercase_)

											a__      =argparse.ArgumentParser()
											expected.add_argument('--foo'       ,  type=lowercase_       ,  required=lowercase_)
											expected.add_argument('--bar'       ,  type=lowercase_       ,  required=lowercase_)
											expected.add_argument('--baz'       ,  type=lowercase_       ,  required=lowercase_)
											expected.add_argument('--flag'       ,  type=lowercase_       ,  default=lowercase_       ,  const=lowercase_       ,  nargs='?')
											self.argparsersEqual(lowercase_       ,  lowercase_)

											a__      =['--foo', '1', '--baz', 'quux', '--bar', '0.5']
											((a__)    ,     )      =parser.parse_args_into_dataclasses(lowercase_       ,  look_for_args_file=lowercase_)
											self.assertFalse(example.flag)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =HfArgumentParser(lowercase_)

											a__      =argparse.ArgumentParser()
											expected.add_argument('--foo'       ,  default=42       ,  type=lowercase_)
											expected.add_argument('--baz'       ,  default='toto'       ,  type=lowercase_       ,  help='help message')
											self.argparsersEqual(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =argparse.ArgumentParser()
											expected.add_argument('--foo'       ,  type=lowercase_       ,  default=lowercase_       ,  const=lowercase_       ,  nargs='?')
											expected.add_argument('--baz'       ,  type=lowercase_       ,  default=lowercase_       ,  const=lowercase_       ,  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=lowercase_       ,  dest='baz')
											expected.add_argument('--opt'       ,  type=lowercase_       ,  default=lowercase_)

											a__      =[WithDefaultBoolExample]
											if is_python_no_less_than_3_10:
																		dataclass_types.append(lowercase_)

											for dataclass_type in dataclass_types:
																		a__      =HfArgumentParser(lowercase_)
																		self.argparsersEqual(lowercase_       ,  lowercase_)

																		a__      =parser.parse_args([])
																		self.assertEqual(lowercase_       ,  Namespace(foo=lowercase_       ,  baz=lowercase_       ,  opt=lowercase_))

																		a__      =parser.parse_args(['--foo', '--no_baz'])
																		self.assertEqual(lowercase_       ,  Namespace(foo=lowercase_       ,  baz=lowercase_       ,  opt=lowercase_))

																		a__      =parser.parse_args(['--foo', '--baz'])
																		self.assertEqual(lowercase_       ,  Namespace(foo=lowercase_       ,  baz=lowercase_       ,  opt=lowercase_))

																		a__      =parser.parse_args(['--foo', 'True', '--baz', 'True', '--opt', 'True'])
																		self.assertEqual(lowercase_       ,  Namespace(foo=lowercase_       ,  baz=lowercase_       ,  opt=lowercase_))

																		a__      =parser.parse_args(['--foo', 'False', '--baz', 'False', '--opt', 'False'])
																		self.assertEqual(lowercase_       ,  Namespace(foo=lowercase_       ,  baz=lowercase_       ,  opt=lowercase_))





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =HfArgumentParser(lowercase_)

											a__      =argparse.ArgumentParser()
											expected.add_argument(
											    '--foo'       ,  default='toto'       ,  choices=['titi', 'toto', 42]       ,  type=make_choice_type_function(['titi', 'toto', 42])       ,  )
											self.argparsersEqual(lowercase_       ,  lowercase_)

											a__      =parser.parse_args([])
											self.assertEqual(args.foo       ,  'toto')
											a__      =parser.parse_args_into_dataclasses([])[0]
											self.assertEqual(enum_ex.foo       ,  MixedTypeEnum.toto)

											a__      =parser.parse_args(['--foo', 'titi'])
											self.assertEqual(args.foo       ,  'titi')
											a__      =parser.parse_args_into_dataclasses(['--foo', 'titi'])[0]
											self.assertEqual(enum_ex.foo       ,  MixedTypeEnum.titi)

											a__      =parser.parse_args(['--foo', '42'])
											self.assertEqual(args.foo       ,  42)
											a__      =parser.parse_args_into_dataclasses(['--foo', '42'])[0]
											self.assertEqual(enum_ex.foo       ,  MixedTypeEnum.fourtytwo)





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




											@dataclass
											class       lowercase_					:
															snake_case	    ="toto"







											a__      =HfArgumentParser(lowercase_)

											a__      =argparse.ArgumentParser()
											expected.add_argument(
											    '--foo'       ,  default='toto'       ,  choices=('titi', 'toto', 42)       ,  type=make_choice_type_function(['titi', 'toto', 42])       ,  )
											self.argparsersEqual(lowercase_       ,  lowercase_)

											a__      =parser.parse_args([])
											self.assertEqual(args.foo       ,  'toto')

											a__      =parser.parse_args(['--foo', 'titi'])
											self.assertEqual(args.foo       ,  'titi')

											a__      =parser.parse_args(['--foo', '42'])
											self.assertEqual(args.foo       ,  42)





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =HfArgumentParser(lowercase_)

											a__      =argparse.ArgumentParser()
											expected.add_argument('--foo_int'       ,  nargs='+'       ,  default=[]       ,  type=lowercase_)
											expected.add_argument('--bar_int'       ,  nargs='+'       ,  default=[1, 2, 3]       ,  type=lowercase_)
											expected.add_argument('--foo_str'       ,  nargs='+'       ,  default=['Hallo', 'Bonjour', 'Hello']       ,  type=lowercase_)
											expected.add_argument('--foo_float'       ,  nargs='+'       ,  default=[0.1, 0.2, 0.3]       ,  type=lowercase_)

											self.argparsersEqual(lowercase_       ,  lowercase_)

											a__      =parser.parse_args([])
											self.assertEqual(
											    lowercase_       ,  Namespace(foo_int=[]       ,  bar_int=[1, 2, 3]       ,  foo_str=['Hallo', 'Bonjour', 'Hello']       ,  foo_float=[0.1, 0.2, 0.3])       ,  )

											a__      =parser.parse_args('--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'.split())
											self.assertEqual(lowercase_       ,  Namespace(foo_int=[1]       ,  bar_int=[2, 3]       ,  foo_str=['a', 'b', 'c']       ,  foo_float=[0.1, 0.7]))





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =argparse.ArgumentParser()
											expected.add_argument('--foo'       ,  default=lowercase_       ,  type=lowercase_)
											expected.add_argument('--bar'       ,  default=lowercase_       ,  type=lowercase_       ,  help='help message')
											expected.add_argument('--baz'       ,  default=lowercase_       ,  type=lowercase_)
											expected.add_argument('--ces'       ,  nargs='+'       ,  default=[]       ,  type=lowercase_)
											expected.add_argument('--des'       ,  nargs='+'       ,  default=[]       ,  type=lowercase_)

											a__      =[OptionalExample]
											if is_python_no_less_than_3_10:
																		dataclass_types.append(lowercase_)

											for dataclass_type in dataclass_types:
																		a__      =HfArgumentParser(lowercase_)

																		self.argparsersEqual(lowercase_       ,  lowercase_)

																		a__      =parser.parse_args([])
																		self.assertEqual(lowercase_       ,  Namespace(foo=lowercase_       ,  bar=lowercase_       ,  baz=lowercase_       ,  ces=[]       ,  des=[]))

																		a__      =parser.parse_args('--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'.split())
																		self.assertEqual(lowercase_       ,  Namespace(foo=12       ,  bar=3.14       ,  baz='42'       ,  ces=['a', 'b', 'c']       ,  des=[1, 2, 3]))





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =HfArgumentParser(lowercase_)

											a__      =argparse.ArgumentParser()
											expected.add_argument('--required_list'       ,  nargs='+'       ,  type=lowercase_       ,  required=lowercase_)
											expected.add_argument('--required_str'       ,  type=lowercase_       ,  required=lowercase_)
											expected.add_argument(
											    '--required_enum'       ,  type=make_choice_type_function(['titi', 'toto'])       ,  choices=['titi', 'toto']       ,  required=lowercase_       ,  )
											self.argparsersEqual(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =HfArgumentParser(lowercase_)

											a__      =argparse.ArgumentParser()
											expected.add_argument('--foo'       ,  type=lowercase_       ,  required=lowercase_)
											expected.add_argument(
											    '--required_enum'       ,  type=make_choice_type_function(['titi', 'toto'])       ,  choices=['titi', 'toto']       ,  required=lowercase_       ,  )
											expected.add_argument('--opt'       ,  type=lowercase_       ,  default=lowercase_)
											expected.add_argument('--baz'       ,  default='toto'       ,  type=lowercase_       ,  help='help message')
											expected.add_argument('--foo_str'       ,  nargs='+'       ,  default=['Hallo', 'Bonjour', 'Hello']       ,  type=lowercase_)
											self.argparsersEqual(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =HfArgumentParser(lowercase_)

											a__      ={
											    'foo': 12,
											    'bar': 3.14,
											    'baz': '42',
											    'flag': True,
											}

											a__      =parser.parse_dict(lowercase_)[0]
											a__      =BasicExample(**lowercase_)
											self.assertEqual(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =HfArgumentParser(lowercase_)

											a__      ={
											    'foo': 12,
											    'bar': 3.14,
											    'baz': '42',
											    'flag': True,
											    'extra': 42,
											}

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





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =HfArgumentParser(lowercase_)

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

											a__      =BasicExample(**lowercase_)
											self.assertEqual(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =HfArgumentParser(lowercase_)

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





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =HfArgumentParser(lowercase_)
											self.assertIsNotNone(lowercase_)

