
'''simple docstring'''

import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCAmelCase__   : int					=		10_00_00_00_00_00
UpperCAmelCase__   : Optional[Any]					=		1_00_00

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







def   A     (      UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Tuple      )					->       datasets.Dataset:







			'''simple docstring'''



			lowerCAmelCase__								=							pa.Table.from_pydict({"col": [0] * chunk_size}      )
			lowerCAmelCase__								=							pa.concat_tables([table] * (num_examples // chunk_size)      )
			return datasets.Dataset(SCREAMING_SNAKE_CASE__						,	fingerprint="table_100B"      )





@dataclass
class    A     :
	snake_case__      :Optional[Any]        =     42
	snake_case__      :Union[str, Any]        =     42
	snake_case__      :Optional[Any]        =     42
	snake_case__      :Tuple        =     42





	def 							__SCREAMING_SNAKE_CASE  (					self			:       str							):


				"""simple docstring"""

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





	def __iter__(					self			:       str							):


				"""simple docstring"""

				return iter(self._sampled_values							)







	def __len__(					self			:       Union[str, Any]							):


				"""simple docstring"""

				return self.size







@get_duration
def   A     (      UpperCamelCase_					:     Any      )					->       List[Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							dataset[0]







@get_duration
def   A     (      UpperCamelCase_					:     Any      )					->       List[str]:







			'''simple docstring'''



			lowerCAmelCase__								=							dataset[-1]







@get_duration
def   A     (      UpperCamelCase_					:     str      )					->       Tuple:







			'''simple docstring'''



			lowerCAmelCase__								=							dataset[range(len(SCREAMING_SNAKE_CASE__      ) // 2						,	len(SCREAMING_SNAKE_CASE__      ) // 2 + 10_24      )]







@get_duration
def   A     (      UpperCamelCase_					:     Optional[Any]      )					->       Tuple:







			'''simple docstring'''



			lowerCAmelCase__								=							dataset[RandIter(0						,	len(SCREAMING_SNAKE_CASE__      )						,	10_24						,	seed=42      )]







def   A     (      )					->       Dict:







			'''simple docstring'''



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

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


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







'''simple docstring'''

import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Optional[Any]      )					->       List[str]:







			'''simple docstring'''



			lowerCAmelCase__								=							TaConfig.from_json_file(UpperCamelCase_      )
			print(F"""Building PyTorch model from configuration: {config}"""      )
			lowerCAmelCase__								=							TaForConditionalGeneration(UpperCamelCase_      )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )

			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
			model.save_pretrained(UpperCamelCase_      )


if __name__ == "__main__":
		UpperCAmelCase__   : List[str]					=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		UpperCAmelCase__   : Tuple					=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





