




import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     Adafactor,
     AdamW,
     get_constant_schedule,
     get_constant_schedule_with_warmup,
     get_cosine_schedule_with_warmup,
     get_cosine_with_hard_restarts_schedule_with_warmup,
     get_inverse_sqrt_schedule,
     get_linear_schedule_with_warmup,
     get_polynomial_decay_schedule_with_warmup,
 )

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict     ,							lowercase     :	List[str]=10					):


      '''simple docstring'''



      lowerCamelCase_							=   []
      for _ in range(lowercase					):
            lrs.append(scheduler.get_lr()[0]					)
            scheduler.step()
      return lrs

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Tuple     ,							lowercase     :	Any=10					):


      '''simple docstring'''



      lowerCamelCase_							=   []
      for step in range(lowercase					):
            lrs.append(scheduler.get_lr()[0]					)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        lowerCamelCase_							=   os.path.join(lowercase     ,							'schedule.bin'					)
                        torch.save(scheduler.state_dict()     ,							lowercase					)

                        lowerCamelCase_							=   torch.load(lowercase					)
                        scheduler.load_state_dict(lowercase					)
      return lrs







@require_torch
class   A(						unittest.TestCase       ):



 '''simple docstring'''


 def     a__			(    self :  Dict						,							A_ :  Tuple						,							A_ :  str						,							A_ :  Tuple  )       ->     List[Any]:

       """simple docstring"""






       self.assertEqual(len(A_  )						,							len(A_  )  )
       for a, b in zip(A_						,							A_  ):
             self.assertAlmostEqual(A_						,							A_						,							delta=A_  )

 def     a__			(    self :  Dict  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   torch.tensor([0.1, -0.2, -0.1]						,							requires_grad=A_  )
       lowerCamelCase_							=   torch.tensor([0.4, 0.2, -0.5]  )
       lowerCamelCase_							=   nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       lowerCamelCase_							=   AdamW(params=[w]						,							lr=2E-1						,							weight_decay=0.0  )
       for _ in range(100  ):
             lowerCamelCase_							=   criterion(A_						,							A_  )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()						,							[0.4, 0.2, -0.5]						,							tol=1E-2  )





 def     a__			(    self :  str  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   torch.tensor([0.1, -0.2, -0.1]						,							requires_grad=A_  )
       lowerCamelCase_							=   torch.tensor([0.4, 0.2, -0.5]  )
       lowerCamelCase_							=   nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       lowerCamelCase_							=   Adafactor(
           params=[w]						,							lr=1E-2						,							eps=(1E-30, 1E-3)						,							clip_threshold=1.0						,							decay_rate=-0.8						,							betaa=A_						,							weight_decay=0.0						,							relative_step=A_						,							scale_parameter=A_						,							warmup_init=A_						,							)
       for _ in range(1000  ):
             lowerCamelCase_							=   criterion(A_						,							A_  )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()						,							[0.4, 0.2, -0.5]						,							tol=1E-2  )







@require_torch
class   A(						unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						nn.Linear(50							,				50       ) if is_torch_available() else None
 UpperCamelCase						       =						AdamW(m.parameters()							,				lr=10.0       ) if is_torch_available() else None
 UpperCamelCase						       =						10

 def     a__			(    self :  Dict						,							A_ :  str						,							A_ :  Tuple						,							A_ :  Any						,							A_ :  List[Any]=None  )       ->     Dict:

       """simple docstring"""






       self.assertEqual(len(A_  )						,							len(A_  )  )
       for a, b in zip(A_						,							A_  ):
             self.assertAlmostEqual(A_						,							A_						,							delta=A_						,							msg=A_  )





 def     a__			(    self :  List[str]  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_							=   {'num_warmup_steps': 2, 'num_training_steps': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       lowerCamelCase_							=   {
           get_constant_schedule: ({}, [10.0] * self.num_steps),
           get_constant_schedule_with_warmup: (
               {'num_warmup_steps': 4},
               [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
           ),
           get_linear_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
           ),
           get_cosine_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
           ),
           get_cosine_with_hard_restarts_schedule_with_warmup: (
               {**common_kwargs, 'num_cycles': 2},
               [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
           ),
           get_polynomial_decay_schedule_with_warmup: (
               {**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
               [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {'num_warmup_steps': 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
             lowerCamelCase_					,    lowerCamelCase_							=   data

             lowerCamelCase_							=   scheduler_func(self.optimizer						,							**A_  )
             self.assertEqual(len([scheduler.get_lr()[0]]  )						,							1  )
             lowerCamelCase_							=   unwrap_schedule(A_						,							self.num_steps  )
             self.assertListAlmostEqual(
                 A_						,							A_						,							tol=1E-2						,							msg=f"""failed for {scheduler_func} in normal scheduler"""						,							)

             lowerCamelCase_							=   scheduler_func(self.optimizer						,							**A_  )
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(A_  )  # wrap to test picklability of the schedule
             lowerCamelCase_							=   unwrap_and_save_reload_schedule(A_						,							self.num_steps  )
             self.assertListEqual(A_						,							A_						,							msg=f"""failed for {scheduler_func} in save and reload"""  )







class   A:



 '''simple docstring'''


 def __init__(    self :  Optional[Any]						,							A_ :  int  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   fn

 def __call__(    self :  Any						,							*A_ :  List[str]						,							**A_ :  List[str]  )       ->     int:

       """simple docstring"""






       return self.fn(*A_						,							**A_  )





 @classmethod
 def     a__			(    self :  Union[str, Any]						,							A_ :  List[Any]  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   list(map(self						,							scheduler.lr_lambdas  )  )









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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	str     ,							lowercase     :	int     ,							lowercase     :	List[Any]					):


      '''simple docstring'''



      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:
            lowerCamelCase_							=   TOKENIZER_CLASSES
      else:
            lowerCamelCase_							=   {tokenizer_name: getattr(lowercase     ,							tokenizer_name + 'Fast'					)}

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

      for tokenizer_name in tokenizer_names:
            lowerCamelCase_							=   TOKENIZER_CLASSES[tokenizer_name]

            lowerCamelCase_							=   True
            if checkpoint_name is None:
                  lowerCamelCase_							=   list(tokenizer_class.max_model_input_sizes.keys()					)
            else:
                  lowerCamelCase_							=   [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
                  lowerCamelCase_							=   tokenizer_class.from_pretrained(lowercase     ,							force_download=lowercase					)

                  # 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:
                        lowerCamelCase_					,    lowerCamelCase_							=   checkpoint.split('/'					)
                        lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
                  elif add_prefix:
                        lowerCamelCase_							=   checkpoint
                        lowerCamelCase_							=   dump_path
                  else:
                        lowerCamelCase_							=   None
                        lowerCamelCase_							=   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]:
                        lowerCamelCase_							=   list(tokenizer.pretrained_vocab_files_map.values()					)[0][checkpoint]
                        lowerCamelCase_							=   file_path.split(lowercase					)[-1][0]
                        if next_char == "/":
                              lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
                              lowerCamelCase_							=   None

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

                  lowerCamelCase_							=   tokenizer.save_pretrained(
                      lowercase     ,							legacy_format=lowercase     ,							filename_prefix=lowercase					)
                  logger.info(f"""=> File names {file_names}"""					)

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


if __name__ == "__main__":
 lowerCamelCase    :		Tuple			   =       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    :		Union[str, Any]			   =       parser.parse_args()

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



