






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase								=		{
    "configuration_squeezebert": [
        "SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SqueezeBertConfig",
        "SqueezeBertOnnxConfig",
    ],
    "tokenization_squeezebert": ["SqueezeBertTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase								=		["SqueezeBertTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase								=		[
							    "SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "SqueezeBertForMaskedLM",
							    "SqueezeBertForMultipleChoice",
							    "SqueezeBertForQuestionAnswering",
							    "SqueezeBertForSequenceClassification",
							    "SqueezeBertForTokenClassification",
							    "SqueezeBertModel",
							    "SqueezeBertModule",
							    "SqueezeBertPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_squeezebert import (
							    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SqueezeBertConfig,
							    SqueezeBertOnnxConfig,
							)
							from .tokenization_squeezebert import SqueezeBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_squeezebert import (
														    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SqueezeBertForMaskedLM,
														    SqueezeBertForMultipleChoice,
														    SqueezeBertForQuestionAnswering,
														    SqueezeBertForSequenceClassification,
														    SqueezeBertForTokenClassification,
														    SqueezeBertModel,
														    SqueezeBertModule,
														    SqueezeBertPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowercase		:  List[str]			       =5_0000
_lowercase		:  str			       =5000

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


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(0							,len(lowerCAmelCase__						)							,lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i : i + batch_size]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(lowerCAmelCase__						):
						lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(0							,lowerCAmelCase__							,lowerCAmelCase__						):
						lowerCamelCase_	:     Optional[int] 						=      dataset[i : i + batch_size]


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Any 						=      {'num examples': SPEED_TEST_N_EXAMPLES}
		lowerCamelCase_	:     Optional[Any] 						=      [
		    (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': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (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': 10_00}),
		]

		lowerCamelCase_	:     Optional[Any] 						=      [
		    (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': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (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': 10_00}),
		]
		with tempfile.TemporaryDirectory() as tmp_dir:
				print('generating dataset'						)
				lowerCamelCase_	:     List[Any] 						=      datasets.Features(
				    {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
				lowerCamelCase_	:     Union[str, Any] 						=      generate_example_dataset(
				    os.path.join(lowerCAmelCase__							,'dataset.arrow'						)							,lowerCAmelCase__							,num_examples=lowerCAmelCase__							,seq_shapes={'list': (1_00,)}							,)
				print('first set of iterations'						)
				for func, kwargs in functions:
						print(func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[int] 						=      func(lowerCAmelCase__							,**lowerCAmelCase__						)

				print('shuffling dataset'						)
				lowerCamelCase_	:     int 						=      dataset.shuffle()
				print('Second set of iterations (after shuffling'						)
				for func, kwargs in functions_shuffled:
						print('shuffled '							,func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[Any] 						=      func(
						    lowerCAmelCase__							,**lowerCAmelCase__						)

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


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