
import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase_  :	Union[str, Any] 				=					logging.get_logger(__name__)


lowerCamelCase_  :	Dict 				=					{name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Dict:
				if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
								raise ValueError(F'''Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()  )}.'''  )

				if tokenizer_name is None:
								UpperCamelCase_:							Tuple		=  TOKENIZER_CLASSES
				else:
								UpperCamelCase_:							Optional[Any]		=  {tokenizer_name: getattr(lowerCamelCase       ,      tokenizer_name + """Fast"""  )}

				logger.info(F'''Loading tokenizer classes: {tokenizer_names}'''  )

				for tokenizer_name in tokenizer_names:
								UpperCamelCase_:							List[str]		=  TOKENIZER_CLASSES[tokenizer_name]

								UpperCamelCase_:							Optional[Any]		=  True
								if checkpoint_name is None:
												UpperCamelCase_:							Any		=  list(tokenizer_class.max_model_input_sizes.keys()  )
								else:
												UpperCamelCase_:							Tuple		=  [checkpoint_name]

								logger.info(F'''For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'''  )

								for checkpoint in checkpoint_names:
												logger.info(F'''Loading {tokenizer_class.__class__.__name__} {checkpoint}'''  )

												# Load tokenizer
												UpperCamelCase_:							int		=  tokenizer_class.from_pretrained(lowerCamelCase       ,      force_download=lowerCamelCase  )

												# Save fast tokenizer
												logger.info(F'''Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'''  )

												# For organization names we create sub-directories
												if "/" in checkpoint:
																UpperCamelCase_,				UpperCamelCase_:							List[str]		=  checkpoint.split("""/"""  )
																UpperCamelCase_:							Tuple		=  os.path.join(lowerCamelCase       ,      lowerCamelCase  )
												elif add_prefix:
																UpperCamelCase_:							Optional[int]		=  checkpoint
																UpperCamelCase_:							List[str]		=  dump_path
												else:
																UpperCamelCase_:							List[str]		=  None
																UpperCamelCase_:							Optional[Any]		=  dump_path

												logger.info(F'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''  )

												if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()  )[0]:
																UpperCamelCase_:							List[str]		=  list(tokenizer.pretrained_vocab_files_map.values()  )[0][checkpoint]
																UpperCamelCase_:							Any		=  file_path.split(lowerCamelCase  )[-1][0]
																if next_char == "/":
																				UpperCamelCase_:							int		=  os.path.join(lowerCamelCase       ,      lowerCamelCase  )
																				UpperCamelCase_:							str		=  None

																logger.info(F'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''  )

												UpperCamelCase_:							str		=  tokenizer.save_pretrained(
												    lowerCamelCase       ,      legacy_format=lowerCamelCase       ,      filename_prefix=lowerCamelCase  )
												logger.info(F'''=> File names {file_names}'''  )

												for file_name in file_names:
																if not file_name.endswith("""tokenizer.json"""  ):
																				os.remove(lowerCamelCase  )
																				logger.info(F'''=> removing {file_name}'''  )


if __name__ == "__main__":
							lowerCamelCase_  :	List[str] 				=					argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--dump_path""", default=None, type=str, required=True, help="""Path to output generated fast tokenizer files."""
							)
							parser.add_argument(
							    """--tokenizer_name""",
							    default=None,
							    type=str,
							    help=(
							        F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
							        """download and convert all the checkpoints from AWS."""
							    ),
							)
							parser.add_argument(
							    """--checkpoint_name""",
							    default=None,
							    type=str,
							    help="""Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.""",
							)
							parser.add_argument(
							    """--force_download""",
							    action="""store_true""",
							    help="""Re-download checkpoints.""",
							)
							lowerCamelCase_  :	Any 				=					parser.parse_args()

							convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)








import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase_  :	Any 				=					5_00_00
lowerCamelCase_  :	Any 				=					50_00

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







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(lowerCamelCase  ):
								UpperCamelCase_:							Dict		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(0       ,      len(lowerCamelCase  )       ,      lowerCamelCase  ):
								UpperCamelCase_:							List[Any]		=  dataset[i : i + batch_size]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Any:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(lowerCamelCase  ):
												UpperCamelCase_:							List[str]		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(0       ,      lowerCamelCase       ,      lowerCamelCase  ):
												UpperCamelCase_:							Union[str, Any]		=  dataset[i : i + batch_size]







def     A__   (  )					->		Tuple:
				UpperCamelCase_:							int		=  {"""num examples""": SPEED_TEST_N_EXAMPLES}
				UpperCamelCase_:							Union[str, 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}),
				]

				UpperCamelCase_:							Tuple		=  [
				    (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"""  )
								UpperCamelCase_:							int		=  datasets.Features(
								    {"""list""": datasets.Sequence(datasets.Value("""float32"""  )  ), """numbers""": datasets.Value("""float32"""  )}  )
								UpperCamelCase_:							Optional[int]		=  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  )  )
												UpperCamelCase_:							List[Any]		=  func(lowerCamelCase       ,      **lowerCamelCase  )

								print("""shuffling dataset"""  )
								UpperCamelCase_:							Dict		=  dataset.shuffle()
								print("""Second set of iterations (after shuffling"""  )
								for func, kwargs in functions_shuffled:
												print("""shuffled """       ,      func.__name__       ,      str(lowerCamelCase  )  )
												UpperCamelCase_:							List[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()






