"""simple docstring"""
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE__   =					50_000
SCREAMING_SNAKE_CASE__   =					5_000

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





@get_duration
def 				lowerCAmelCase__      (     _UpperCamelCase    :			datasets.Dataset	,     _UpperCamelCase    :			Optional[Any]   )   ->			int:





						"""simple docstring"""






						for i in range(_UpperCamelCase   ):
												snake_case        =     dataset[i]





@get_duration
def 				lowerCAmelCase__      (     _UpperCamelCase    :			datasets.Dataset	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			int   )   ->			Optional[Any]:





						"""simple docstring"""






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





@get_duration
def 				lowerCAmelCase__      (     _UpperCamelCase    :			datasets.Dataset	,     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			str   )   ->			List[Any]:





						"""simple docstring"""






						with dataset.formatted_as(type=_UpperCamelCase   ):
												for i in range(_UpperCamelCase   ):
																		snake_case        =     dataset[i]





@get_duration
def 				lowerCAmelCase__      (     _UpperCamelCase    :			datasets.Dataset	,     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			str   )   ->			Optional[Any]:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     )   ->			List[Any]:





						"""simple docstring"""






						snake_case        =     {'num examples': SPEED_TEST_N_EXAMPLES}
						snake_case        =     [
						    (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}),
						]

						snake_case        =     [
						    (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'   )
												snake_case        =     datasets.Features(
												    {'list': datasets.Sequence(datasets.Value('float32'   )   ), 'numbers': datasets.Value('float32'   )}   )
												snake_case        =     generate_example_dataset(
												    os.path.join(_UpperCamelCase	,     'dataset.arrow'   )	,     _UpperCamelCase	,     num_examples=_UpperCamelCase	,     seq_shapes={'list': (1_0_0,)}	,     )
												print('first set of iterations'   )
												for func, kwargs in functions:
																		print(func.__name__	,     str(_UpperCamelCase   )   )
																		snake_case        =     func(_UpperCamelCase	,     **_UpperCamelCase   )

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



"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

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


