



'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


snake_case_ :	Union[str, Any]         =  {
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}






class 							lowercase__	(							_a   ):
	lowercase__  				=			"""albert"""





	def __init__( self					:				int ,lowerCamelCase__					:				Optional[int]=30000 ,lowerCamelCase__					:				Union[str, Any]=128 ,lowerCamelCase__					:				int=4096 ,lowerCamelCase__					:				Dict=12 ,lowerCamelCase__					:				Dict=1 ,lowerCamelCase__					:				List[Any]=64 ,lowerCamelCase__					:				Any=16384 ,lowerCamelCase__					:				Dict=1 ,lowerCamelCase__					:				str="gelu_new" ,lowerCamelCase__					:				str=0 ,lowerCamelCase__					:				Union[str, Any]=0 ,lowerCamelCase__					:				Optional[int]=512 ,lowerCamelCase__					:				Any=2 ,lowerCamelCase__					:				Union[str, Any]=0.0_2 ,lowerCamelCase__					:				Tuple=1E-12 ,lowerCamelCase__					:				Dict=0.1 ,lowerCamelCase__					:				int="absolute" ,lowerCamelCase__					:				Optional[Any]=0 ,lowerCamelCase__					:				List[Any]=2 ,lowerCamelCase__					:				str=3 ,**lowerCamelCase__					:				List[str] ,):







							'''simple docstring'''
							super().__init__(pad_token_id=UpperCamelCase__ ,bos_token_id=UpperCamelCase__ ,eos_token_id=UpperCamelCase__ ,**UpperCamelCase__ )

							_UpperCamelCase					:		List[str] 				=   vocab_size
							_UpperCamelCase					:		Union[str, Any] 				=   embedding_size
							_UpperCamelCase					:		Optional[int] 				=   hidden_size
							_UpperCamelCase					:		Any 				=   num_hidden_layers
							_UpperCamelCase					:		List[Any] 				=   num_hidden_groups
							_UpperCamelCase					:		Optional[int] 				=   num_attention_heads
							_UpperCamelCase					:		str 				=   inner_group_num
							_UpperCamelCase					:		List[Any] 				=   hidden_act
							_UpperCamelCase					:		Dict 				=   intermediate_size
							_UpperCamelCase					:		Any 				=   hidden_dropout_prob
							_UpperCamelCase					:		Union[str, Any] 				=   attention_probs_dropout_prob
							_UpperCamelCase					:		Dict 				=   max_position_embeddings
							_UpperCamelCase					:		List[Any] 				=   type_vocab_size
							_UpperCamelCase					:		List[Any] 				=   initializer_range
							_UpperCamelCase					:		Tuple 				=   layer_norm_eps
							_UpperCamelCase					:		Optional[int] 				=   classifier_dropout_prob
							_UpperCamelCase					:		Optional[Any] 				=   position_embedding_type






class 							lowercase__	(							_a   ):





	@property
	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''







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







'''simple docstring'''


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase	:       List[str]      	=	5_0000
_lowerCamelCase	:       Optional[int]      	=	5000

_lowerCamelCase	,_lowerCamelCase	:       int      	=	os.path.split(__file__)
_lowerCamelCase	:       str      	=	os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   for i in range(A__    ):
      UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   for i in range(0    ,						len(A__    )    ,						A__    ):
      UpperCamelCase					=  dataset[i : i + batch_size]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	List[Any]:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(A__    ):
         UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(0    ,						A__    ,						A__    ):
         UpperCamelCase					=  dataset[i : i + batch_size]







def 				__lowerCamelCase				(   )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  {'num examples': SPEED_TEST_N_EXAMPLES}
   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]

   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print('generating dataset'    )
      UpperCamelCase					=  datasets.Features(
          {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
      UpperCamelCase					=  generate_example_dataset(
          os.path.join(A__    ,						'dataset.arrow'    )    ,						A__    ,						num_examples=A__    ,						seq_shapes={'list': (100,)}    ,						)
      print('first set of iterations'    )
      for func, kwargs in functions:
         print(func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(A__    ,						**A__    )

      print('shuffling dataset'    )
      UpperCamelCase					=  dataset.shuffle()
      print('Second set of iterations (after shuffling'    )
      for func, kwargs in functions_shuffled:
         print('shuffled '    ,						func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(
             A__    ,						**A__    )

   with open(A__    ,						'wb'    ) as f:
      f.write(json.dumps(A__    ).encode('utf-8'    )    )


if __name__ == "__main__":  # useful to run the profiler
   benchmark_iterating()

