

import gc
import threading
import time

import psutil
import torch






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Optional[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							psutil.Process()
				SCREAMING_SNAKE_CASE :   int					  =							False
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							-1

				while True:
					SCREAMING_SNAKE_CASE :   Tuple					  =							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 				__UpperCamelCase			(      self				:			Dict						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							True
				SCREAMING_SNAKE_CASE :   Tuple					  =							threading.Thread(target=self.peak_monitor						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							True
				self.thread.start()






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							False
				self.thread.join()
				return self.cpu_memory_peak

a_      =     PeakCPUMemory()

def 			lowerCamelCase__			(       ):
	# Time
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							{"time": time.time()}

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

	# CPU mem
	SCREAMING_SNAKE_CASE :   List[Any]					  =							psutil.Process().memory_info().rss
	cpu_peak_tracker.start()

	# GPU mem
	for i in range(torch.cuda.device_count()):
		SCREAMING_SNAKE_CASE :   Dict					  =							torch.cuda.memory_allocated(_a)
	torch.cuda.reset_peak_memory_stats()

	return measures

def 			lowerCamelCase__			(       _a):
	# Time
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							{"time": time.time() - start_measures["time"]}

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

	# CPU mem
	SCREAMING_SNAKE_CASE :   Tuple					  =							(psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

	# GPU mem
	for i in range(torch.cuda.device_count()):
		SCREAMING_SNAKE_CASE :   Tuple					  =							(torch.cuda.memory_allocated(_a) - start_measures[str(_a)]) / 2**20
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(torch.cuda.max_memory_allocated(_a) - start_measures[str(_a)]) / 2**20

	return measures


def 			lowerCamelCase__			(       _a				,      _a):
	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(_a)]:.2f}MiB")
		SCREAMING_SNAKE_CASE :   Tuple					  =							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


a_      =     logging.get_logger(__name__)

a_      =     {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='roformer'
			def __init__(      self				:			Dict   ,   a				:			Any=5_0000   ,   a				:			List[Any]=None   ,   a				:			str=768   ,   a				:			str=12   ,   a				:			Tuple=12   ,   a				:			Optional[Any]=3072   ,   a				:			List[str]="gelu"   ,   a				:			List[Any]=0.1   ,   a				:			Union[str, Any]=0.1   ,   a				:			Tuple=1536   ,   a				:			List[str]=2   ,   a				:			Tuple=0.02   ,   a				:			Any=1e-12   ,   a				:			Optional[int]=0   ,   a				:			Union[str, Any]=False   ,   a				:			int=True   ,   **a				:			str   ,   )				->							int:

				"""simple docstring"""

				super().__init__(pad_token_id=a   ,   **a						)

				SCREAMING_SNAKE_CASE :   str					  =							vocab_size
				SCREAMING_SNAKE_CASE :   int					  =							hidden_size if embedding_size is None else embedding_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   int					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   int					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Any					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   List[str]					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   List[str]					  =							rotary_value
				SCREAMING_SNAKE_CASE :   int					  =							use_cache






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task == "multiple-choice":
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{0: "batch", 1: "choice", 2: "sequence"}
				else:
					SCREAMING_SNAKE_CASE :   str					  =							{0: "batch", 1: "sequence"}
				SCREAMING_SNAKE_CASE :   List[Any]					  =							{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]						)