





import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	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:
		snake_case_		:			int											=	TOKENIZER_CLASSES
	else:
		snake_case_		:			Any											=	{tokenizer_name: getattr(__a   , tokenizer_name + 'Fast'   )}

	logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""   )

	for tokenizer_name in tokenizer_names:
		snake_case_		:			str											=	TOKENIZER_CLASSES[tokenizer_name]

		snake_case_		:			Dict											=	True
		if checkpoint_name is None:
			snake_case_		:			List[Any]											=	list(tokenizer_class.max_model_input_sizes.keys()   )
		else:
			snake_case_		:			Union[str, Any]											=	[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
			snake_case_		:			Union[str, Any]											=	tokenizer_class.from_pretrained(__a   , force_download=__a   )

			# 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:
				snake_case_						,snake_case_		:			Tuple											=	checkpoint.split('/'   )
				snake_case_		:			Optional[int]											=	os.path.join(__a   , __a   )
			elif add_prefix:
				snake_case_		:			List[str]											=	checkpoint
				snake_case_		:			Optional[Any]											=	dump_path
			else:
				snake_case_		:			Optional[Any]											=	None
				snake_case_		:			List[str]											=	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]:
				snake_case_		:			Tuple											=	list(tokenizer.pretrained_vocab_files_map.values()   )[0][checkpoint]
				snake_case_		:			str											=	file_path.split(__a   )[-1][0]
				if next_char == "/":
					snake_case_		:			Tuple											=	os.path.join(__a   , __a   )
					snake_case_		:			Optional[Any]											=	None

				logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""   )

			snake_case_		:			List[str]											=	tokenizer.save_pretrained(
			    __a   , legacy_format=__a   , filename_prefix=__a   )
			logger.info(f"""=> File names {file_names}"""   )

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


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		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.""",
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()

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








# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


_SCREAMING_SNAKE_CASE      	=		Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

_SCREAMING_SNAKE_CASE      	=		{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

_SCREAMING_SNAKE_CASE      	=		"""zero2"""
_SCREAMING_SNAKE_CASE      	=		"""zero3"""
_SCREAMING_SNAKE_CASE      	=		[ZEROa, ZEROa]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	# customize the test name generator function as we want both params to appear in the sub-test
	# name, as by default it shows only the first param
	snake_case_		:			Tuple											=	parameterized.to_safe_name('_'.join(str(__a   ) for x in param.args   )   )
	return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
_SCREAMING_SNAKE_CASE      	=		list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[Any]		,   _A      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Any		,   _A      :      int	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[str]		,   _A      :      str	)							->    Tuple:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      int		,   _A      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      Union[str, Any]	)							->    Any:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      bool = True		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Any:







							"""simple docstring"""


							snake_case_		:			Dict											=	models[model]

							snake_case_		:			str											=	self.run_trainer(
							    stage=_A		,   model_name=_A		,   eval_steps=_A		,   num_train_epochs=1		,   distributed=_A		,   fpaa=_A		,   )

							self.do_checks(_A	)

							return output_dir






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      int = 1		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Dict:







							"""simple docstring"""


							snake_case_		:			str											=	self.get_auto_remove_tmp_dir('./xxx'		,   after=_A	)
							snake_case_		:			Tuple											=	F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_A	)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

							if fpaa:
								args.extend(['--fp16']	)

							# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
							# hence the separate config files
							snake_case_		:			Any											=	F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
							snake_case_		:			str											=	[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
							snake_case_		:			int											=	self.get_launcher(_A	)

							snake_case_		:			List[str]											=	launcher + script + args + ds_args
							# keep for quick debug
							# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
							execute_subprocess_async(_A		,   env=self.get_env()	)

							return output_dir






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Optional[Any]=False	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			str											=	min(2		,   get_gpu_count()	) if distributed else 1
							return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()

