



import gc
import threading
import time

import psutil
import torch






class SCREAMING_SNAKE_CASE__     :




    '''simple docstring'''






    def __init__(       self		):
        A :       Any          =   psutil.Process()
        A :       List[Any]          =   False






    def 			_lowerCAmelCase					(       self		):
        A :       int          =   -1

        while True:
            A :       Optional[int]          =   max(self.process.memory_info().rss,		self.cpu_memory_peak		)

            # can't sleep or will not catch the peak right (this comment is here on purpose)
            if not self.peak_monitoring:
                break






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   True
        A :       Dict          =   threading.Thread(target=self.peak_monitor		)
        A :       List[str]          =   True
        self.thread.start()






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   False
        self.thread.join()
        return self.cpu_memory_peak


SCREAMING_SNAKE_CASE_:Any             =						PeakCPUMemory()




def 	__UpperCamelCase	(  )       ->  Union[str, Any]:



    """simple docstring"""






    A :       Optional[int]          =   {"""time""": time.time()}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    A :       Any          =   psutil.Process().memory_info().rss
    cpu_peak_tracker.start()

    # GPU mem
    for i in range(torch.cuda.device_count()		):
        A :       Any          =   torch.cuda.memory_allocated(_lowerCAmelCase		)
    torch.cuda.reset_peak_memory_stats()

    return measures




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  str:



    """simple docstring"""






    A :       str          =   {"""time""": time.time() - start_measures["""time"""]}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    A :       Dict          =   (psutil.Process().memory_info().rss - start_measures["""cpu"""]) / 2**20
    A :       str          =   (cpu_peak_tracker.stop() - start_measures["""cpu"""]) / 2**20

    # GPU mem
    for i in range(torch.cuda.device_count()		):
        A :       Optional[int]          =   (torch.cuda.memory_allocated(_lowerCAmelCase		) - start_measures[str(_lowerCAmelCase		)]) / 2**20
        A :       Dict          =   (torch.cuda.max_memory_allocated(_lowerCAmelCase		) - start_measures[str(_lowerCAmelCase		)]) / 2**20

    return measures




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Optional[Any]:



    """simple docstring"""






    print(f'''{description}:'''		)
    print(f'''- Time: {measures["time"]:.2f}s'''		)
    for i in range(torch.cuda.device_count()		):
        print(f'''- GPU {i} allocated: {measures[str(_lowerCAmelCase		)]:.2f}MiB'''		)
        A :       Union[str, Any]          =   measures[f'''{i}-peak''']
        print(f'''- GPU {i} peak: {peak:.2f}MiB'''		)
    print(f'''- CPU RAM allocated: {measures["cpu"]:.2f}MiB'''		)
    print(f'''- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB'''		)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:Optional[Any]             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      "camembert"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       List[Any]          =   vocab_size
        A :       Dict          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[Any]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Tuple          =   intermediate_size
        A :       Tuple          =   hidden_dropout_prob
        A :       List[Any]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Tuple          =   position_embedding_type
        A :       str          =   use_cache
        A :       Any          =   classifier_dropout






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       int          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       Tuple          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


