





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__A				:    Dict       =   50_000
__A				:    str       =   5_000

__A    , __A				:    List[Any]       =   os.path.split(__file__)
__A				:    str       =   os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))





@get_duration
def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

			for i in range(UpperCamelCase__ ):
						UpperCAmelCase   		=				dataset[i]





@get_duration
def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				List[Any]:







			'''simple docstring'''

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





@get_duration
def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				Any:







			'''simple docstring'''

			with dataset.formatted_as(type=UpperCamelCase__ ):
						for i in range(UpperCamelCase__ ):
									UpperCAmelCase   		=				dataset[i]





@get_duration
def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (				)  ->				Tuple:







			'''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''': 1000}),
			    (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''': 1000}),
			]

			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''': 1000}),
			    (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''': 1000}),
			]
			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(UpperCamelCase__			,			'''dataset.arrow''' )			,			UpperCamelCase__			,			num_examples=UpperCamelCase__			,			seq_shapes={'''list''': (100,)}			,			)
						print('''first set of iterations''' )
						for func, kwargs in functions:
									print(func.__name__			,			str(UpperCamelCase__ ) )
									UpperCAmelCase   		=				func(UpperCamelCase__			,			**UpperCamelCase__ )

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

			with open(UpperCamelCase__			,			'''wb''' ) as f:
						f.write(json.dumps(UpperCamelCase__ ).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				:    int       =   logging.get_logger(__name__)

__A				:    Tuple       =   {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 	A_       (a_			):
	UpperCAmelCase__					=			'''big_bird'''
	def __init__(    self							,					_A=5_0_3_5_8							,					_A=7_6_8							,					_A=1_2							,					_A=1_2							,					_A=3_0_7_2							,					_A="gelu_new"							,					_A=0.1							,					_A=0.1							,					_A=4_0_9_6							,					_A=2							,					_A=0.02							,					_A=1E-12							,					_A=True							,					_A=0							,					_A=1							,					_A=2							,					_A=6_6							,					_A="block_sparse"							,					_A=True							,					_A=False							,					_A=6_4							,					_A=3							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(
				    pad_token_id=_A							,					bos_token_id=_A							,					eos_token_id=_A							,					sep_token_id=_A							,					**_A							,					)

				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				max_position_embeddings
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				intermediate_size
				UpperCAmelCase   		=				hidden_act
				UpperCAmelCase   		=				hidden_dropout_prob
				UpperCAmelCase   		=				attention_probs_dropout_prob
				UpperCAmelCase   		=				initializer_range
				UpperCAmelCase   		=				type_vocab_size
				UpperCAmelCase   		=				layer_norm_eps
				UpperCAmelCase   		=				use_cache

				UpperCAmelCase   		=				rescale_embeddings
				UpperCAmelCase   		=				attention_type
				UpperCAmelCase   		=				use_bias
				UpperCAmelCase   		=				block_size
				UpperCAmelCase   		=				num_random_blocks
				UpperCAmelCase   		=				classifier_dropout

class 	A_       (a_			):
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







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



