from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase    :      int	 =						{
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase    :      Optional[Any]	 =						['''DebertaV2TokenizerFast''']

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase    :      Optional[int]	 =						[
							    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFDebertaV2ForMaskedLM''',
							    '''TFDebertaV2ForQuestionAnswering''',
							    '''TFDebertaV2ForSequenceClassification''',
							    '''TFDebertaV2ForTokenClassification''',
							    '''TFDebertaV2Model''',
							    '''TFDebertaV2PreTrainedModel''',
							]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase    :      Optional[int]	 =						[
							    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DebertaV2ForMaskedLM''',
							    '''DebertaV2ForMultipleChoice''',
							    '''DebertaV2ForQuestionAnswering''',
							    '''DebertaV2ForSequenceClassification''',
							    '''DebertaV2ForTokenClassification''',
							    '''DebertaV2Model''',
							    '''DebertaV2PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_deberta_va import (
							    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DebertaVaConfig,
							    DebertaVaOnnxConfig,
							)
							from .tokenization_deberta_va import DebertaVaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta_va import (
														    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaVaForMaskedLM,
														    TFDebertaVaForQuestionAnswering,
														    TFDebertaVaForSequenceClassification,
														    TFDebertaVaForTokenClassification,
														    TFDebertaVaModel,
														    TFDebertaVaPreTrainedModel,
														)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta_va import (
														    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaVaForMaskedLM,
														    DebertaVaForMultipleChoice,
														    DebertaVaForQuestionAnswering,
														    DebertaVaForSequenceClassification,
														    DebertaVaForTokenClassification,
														    DebertaVaModel,
														    DebertaVaPreTrainedModel,
														)

else:
							import sys

							_lowerCamelCase    :      List[str]	 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_lowerCamelCase    :      int	 =						10_00_00_00_00_00
_lowerCamelCase    :      Union[str, Any]	 =						1_00_00

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





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							int     ):
					SCREAMING_SNAKE_CASE				=				pa.Table.from_pydict({"col": [0] * chunk_size}     )
					SCREAMING_SNAKE_CASE				=				pa.concat_tables([table] * (num_examples // chunk_size)     )
					return datasets.Dataset(UpperCAmelCase__					,       fingerprint="table_100B"     )


@dataclass
class   lowercase			:
							lowercase__  :				int
							lowercase__  :				int
							lowercase__  :				int
							lowercase__  :				int




							def 		__snake_case(  self      :    Tuple					)    -> Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				np.random.default_rng(self.seed					)
												SCREAMING_SNAKE_CASE				=				rng.integers(low=self.low		,							high=self.high		,							size=self.size					).tolist()




							def __iter__(  self      :    Union[str, Any]					)    -> Dict:



												'''simple docstring'''

												return iter(self._sampled_values					)





							def __len__(  self      :    Any					)    -> int:



												'''simple docstring'''

												return self.size





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[0]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[-1]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[range(len(UpperCAmelCase__     ) // 2					,       len(UpperCAmelCase__     ) // 2 + 1_0_2_4     )]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[RandIter(0					,       len(UpperCAmelCase__     )					,       1_0_2_4					,       seed=4_2     )]





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				{"num examples": SPEED_TEST_N_EXAMPLES}
					SCREAMING_SNAKE_CASE				=				(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print("generating dataset"     )
					SCREAMING_SNAKE_CASE				=				generate_100B_dataset(num_examples=UpperCAmelCase__					,       chunk_size=UpperCAmelCase__     )
					print("Functions"     )
					for func in functions:
										print(func.__name__     )
										SCREAMING_SNAKE_CASE				=				func(UpperCAmelCase__     )

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


if __name__ == "__main__":  # useful to run the profiler
							benchmark_table_100B()




