






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE__      :			Dict							=		5_00_00
SCREAMING_SNAKE_CASE__      :			List[str]							=		50_00

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

@get_duration
def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				for i in range(snake_case ):
								__magic_name__		:Optional[Any]        =      dataset[i]

@get_duration
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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

@get_duration
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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

@get_duration
def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      {'''num examples''': SPEED_TEST_N_EXAMPLES}
				__magic_name__		:List[str]        =      [
				    (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__		:Optional[Any]        =      [
				    (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__		:str        =      datasets.Features(
								    {'''list''': datasets.Sequence(datasets.Value('''float32''' ) ), '''numbers''': datasets.Value('''float32''' )} )
								__magic_name__		:List[str]        =      generate_example_dataset(
								    os.path.join(snake_case,  '''dataset.arrow''' ),  snake_case,  num_examples=snake_case,  seq_shapes={'''list''': (1_0_0,)},  )
								print('''first set of iterations''' )
								for func, kwargs in functions:
												print(func.__name__,  str(snake_case ) )
												__magic_name__		:int        =      func(snake_case,  **snake_case )

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

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


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














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__      :			Tuple							=		{
    """configuration_table_transformer""": [
        """TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TableTransformerConfig""",
        """TableTransformerOnnxConfig""",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      :			List[str]							=		[
					    """TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TableTransformerForObjectDetection""",
					    """TableTransformerModel""",
					    """TableTransformerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_table_transformer import (
					    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    TableTransformerConfig,
					    TableTransformerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_table_transformer import (
										    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TableTransformerForObjectDetection,
										    TableTransformerModel,
										    TableTransformerPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






