

'''simple docstring'''






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 	_A  (					A__  ,       A__=10   ):






 """simple docstring"""



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


def 	_A  (					A__  ,       A__=10   ):






 """simple docstring"""



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

    __lowercase													=				torch.load(A__   )
    scheduler.load_state_dict(A__   )
 return lrs






@require_torch
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]    ):
   self.assertEqual(len(lowercase__    )      ,len(lowercase__    )    )
   for a, b in zip(lowercase__      ,lowercase__    ):
    self.assertAlmostEqual(lowercase__      ,lowercase__      ,delta=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				torch.tensor([0.1, -0.2, -0.1]      ,requires_grad=lowercase__    )
   __lowercase													=				torch.tensor([0.4, 0.2, -0.5]    )
   __lowercase													=				nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   __lowercase													=				AdamW(params=[w]      ,lr=2e-1      ,weight_decay=0.0    )
   for _ in range(1_0_0    ):
    __lowercase													=				criterion(lowercase__      ,lowercase__    )
    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       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				torch.tensor([0.1, -0.2, -0.1]      ,requires_grad=lowercase__    )
   __lowercase													=				torch.tensor([0.4, 0.2, -0.5]    )
   __lowercase													=				nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   __lowercase													=				Adafactor(
       params=[w]      ,lr=1e-2      ,eps=(1e-3_0, 1e-3)      ,clip_threshold=1.0      ,decay_rate=-0.8      ,betaa=lowercase__      ,weight_decay=0.0      ,relative_step=lowercase__      ,scale_parameter=lowercase__      ,warmup_init=lowercase__      ,)
   for _ in range(1_0_0_0    ):
    __lowercase													=				criterion(lowercase__      ,lowercase__    )
    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     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						List[str]           =    nn.Linear(5_0       , 5_0   ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE					:						List[str]           =    AdamW(m.parameters()       , lr=10.0   ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE					:						Tuple           =    1_0






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  Tuple      ,lowercase__     :  int      ,lowercase__     :  List[Any]      ,lowercase__     :  Optional[int]=None    ):
   self.assertEqual(len(lowercase__    )      ,len(lowercase__    )    )
   for a, b in zip(lowercase__      ,lowercase__    ):
    self.assertAlmostEqual(lowercase__      ,lowercase__      ,delta=lowercase__      ,msg=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				{'''num_warmup_steps''': 2, '''num_training_steps''': 1_0}
   # schedulers doct format
   # function: (sched_args_dict, expected_learning_rates)
   __lowercase													=				{
       get_constant_schedule: ({}, [1_0.0] * self.num_steps),
       get_constant_schedule_with_warmup: (
           {'''num_warmup_steps''': 4},
           [0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
       ),
       get_linear_schedule_with_warmup: (
           {**common_kwargs},
           [0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
       ),
       get_cosine_schedule_with_warmup: (
           {**common_kwargs},
           [0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
       ),
       get_cosine_with_hard_restarts_schedule_with_warmup: (
           {**common_kwargs, '''num_cycles''': 2},
           [0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
       ),
       get_polynomial_decay_schedule_with_warmup: (
           {**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
           [0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
       ),
       get_inverse_sqrt_schedule: (
           {'''num_warmup_steps''': 2},
           [0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
       ),
   }

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

    __lowercase													=				scheduler_func(self.optimizer      ,**lowercase__    )
    self.assertEqual(len([scheduler.get_lr()[0]]    )      ,1    )
    __lowercase													=				unwrap_schedule(lowercase__      ,self.num_steps    )
    self.assertListAlmostEqual(
        lowercase__      ,lowercase__      ,tol=1e-2      ,msg=F"failed for {scheduler_func} in normal scheduler"      ,)

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






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Tuple      ,lowercase__     :  Optional[Any]    ):
   __lowercase													=				fn






  def __call__(   self     :  Union[str, Any]      ,*lowercase__     :  Union[str, Any]      ,**lowercase__     :  List[Any]    ):
   return self.fn(*lowercase__      ,**lowercase__    )






  @classmethod
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Dict    ):
   __lowercase													=				list(map(self      ,scheduler.lr_lambdas    )    )




'''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  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



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

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

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


if __name__ == "__main__":
 lowerCAmelCase__      		=			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.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

