

'''simple docstring'''





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


A__						=							50000
A__						=							5000

A__		,       A__						=							os.path.split(__file__)
A__						=							os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))




@get_duration
def 					_lowercase     (				a_				:					datasets.Dataset					,a_				:					List[Any]				)				->	Any:




					'''simple docstring'''







					for i in range(a_				):
										__magic_name__							=    dataset[i]




@get_duration
def 					_lowercase     (				a_				:					datasets.Dataset					,a_				:					Tuple					,a_				:					Tuple				)				->	Optional[int]:




					'''simple docstring'''







					for i in range(0					,len(a_				)					,a_				):
										__magic_name__							=    dataset[i : i + batch_size]




@get_duration
def 					_lowercase     (				a_				:					datasets.Dataset					,a_				:					Dict					,a_				:					Union[str, Any]				)				->	List[str]:




					'''simple docstring'''







					with dataset.formatted_as(type=a_				):
										for i in range(a_				):
															__magic_name__							=    dataset[i]




@get_duration
def 					_lowercase     (				a_				:					datasets.Dataset					,a_				:					List[str]					,a_				:					int					,a_				:					List[str]				)				->	Union[str, Any]:




					'''simple docstring'''







					with dataset.formatted_as(type=a_				):
										for i in range(0					,a_					,a_				):
															__magic_name__							=    dataset[i : i + batch_size]




def 					_lowercase     (				)				->	Any:




					'''simple docstring'''







					__magic_name__							=    {'num examples': SPEED_TEST_N_EXAMPLES}
					__magic_name__							=    [
					    (read, {'length': SMALL_TEST}),
					    (read, {'length': SPEED_TEST_N_EXAMPLES}),
					    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_0}),
					    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_0_0}),
					    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_0_0_0}),
					    (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': 1_0}),
					    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_0_0_0}),
					]

					__magic_name__							=    [
					    (read, {'length': SMALL_TEST}),
					    (read, {'length': SPEED_TEST_N_EXAMPLES}),
					    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_0}),
					    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_0_0}),
					    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_0_0_0}),
					    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
					    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_0}),
					    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_0_0_0}),
					]
					with tempfile.TemporaryDirectory() as tmp_dir:
										print('generating dataset'				)
										__magic_name__							=    datasets.Features(
										    {'list': datasets.Sequence(datasets.Value('float32'				)				), 'numbers': datasets.Value('float32'				)}				)
										__magic_name__							=    generate_example_dataset(
										    os.path.join(a_					,'dataset.arrow'				)					,a_					,num_examples=a_					,seq_shapes={'list': (1_0_0,)}					,)
										print('first set of iterations'				)
										for func, kwargs in functions:
															print(func.__name__					,str(a_				)				)
															__magic_name__							=    func(a_					,**a_				)

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







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__						=							{
    "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 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[int]       				=  "camembert"







	def __init__(   self: Optional[Any]  , __UpperCamelCase: Any=3_05_22  , __UpperCamelCase: Tuple=7_68  , __UpperCamelCase: str=12  , __UpperCamelCase: Optional[int]=12  , __UpperCamelCase: List[str]=30_72  , __UpperCamelCase: Any="gelu"  , __UpperCamelCase: Optional[int]=0.1  , __UpperCamelCase: Any=0.1  , __UpperCamelCase: str=5_12  , __UpperCamelCase: Dict=2  , __UpperCamelCase: str=0.02  , __UpperCamelCase: List[Any]=1E-12  , __UpperCamelCase: List[Any]=1  , __UpperCamelCase: Any=0  , __UpperCamelCase: Union[str, Any]=2  , __UpperCamelCase: Dict="absolute"  , __UpperCamelCase: Any=True  , __UpperCamelCase: Any=None  , **__UpperCamelCase: Union[str, Any]  , ):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase  , bos_token_id=__UpperCamelCase  , eos_token_id=__UpperCamelCase  , **__UpperCamelCase      )

						__magic_name__							=    vocab_size
						__magic_name__							=    hidden_size
						__magic_name__							=    num_hidden_layers
						__magic_name__							=    num_attention_heads
						__magic_name__							=    hidden_act
						__magic_name__							=    intermediate_size
						__magic_name__							=    hidden_dropout_prob
						__magic_name__							=    attention_probs_dropout_prob
						__magic_name__							=    max_position_embeddings
						__magic_name__							=    type_vocab_size
						__magic_name__							=    initializer_range
						__magic_name__							=    layer_norm_eps
						__magic_name__							=    position_embedding_type
						__magic_name__							=    use_cache
						__magic_name__							=    classifier_dropout




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





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




