



"""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      __magic_name__				(							lowercase							,			lowercase=10 ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=[]
   for _ in range(snake_case__ ):
      lrs.append(scheduler.get_lr()[0] )
      scheduler.step()
   return lrs





def      __magic_name__				(							lowercase							,			lowercase=10 ):
   SCREAMING_SNAKE_CASE_: Any     			=[]
   for step in range(snake_case__ ):
      lrs.append(scheduler.get_lr()[0] )
      scheduler.step()
      if step == num_steps // 2:
         with tempfile.TemporaryDirectory() as tmpdirname:
            SCREAMING_SNAKE_CASE_: Union[str, Any]     			=os.path.join(snake_case__							,			"""schedule.bin""" )
            torch.save(scheduler.state_dict()							,			snake_case__ )

            SCREAMING_SNAKE_CASE_: int     			=torch.load(snake_case__ )
            scheduler.load_state_dict(snake_case__ )
   return lrs
@require_torch
class 					a	(  unittest.TestCase       ):



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					Tuple       ,		lowerCAmelCase :					List[Any]		)     ->					List[str]:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.tensor([0.1, -0.2, -0.1]       ,		requires_grad=A_		)
     SCREAMING_SNAKE_CASE_: int     			=torch.tensor([0.4, 0.2, -0.5]		)
     SCREAMING_SNAKE_CASE_: Dict     			=nn.MSELoss()
     # No warmup, constant schedule, no gradient clipping
     SCREAMING_SNAKE_CASE_: List[str]     			=AdamW(params=[w]       ,		lr=2E-1       ,		weight_decay=0.0		)
     for _ in range(100		):
        SCREAMING_SNAKE_CASE_: Optional[int]     			=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 	lowerCamelCase__       (						self :					str		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=torch.tensor([0.1, -0.2, -0.1]       ,		requires_grad=A_		)
     SCREAMING_SNAKE_CASE_: Tuple     			=torch.tensor([0.4, 0.2, -0.5]		)
     SCREAMING_SNAKE_CASE_: List[str]     			=nn.MSELoss()
     # No warmup, constant schedule, no gradient clipping
     SCREAMING_SNAKE_CASE_: Optional[int]     			=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		):
        SCREAMING_SNAKE_CASE_: Dict     			=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       ):
  UpperCamelCase	:				Dict     						=					nn.Linear(5_0	,      5_0       ) if is_torch_available() else None
  UpperCamelCase	:				Dict     						=					AdamW(m.parameters()	,      lr=10.0       ) if is_torch_available() else None
  UpperCamelCase	:				Optional[int]     						=					1_0



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					List[Any]       ,		lowerCAmelCase :					Dict       ,		lowerCAmelCase :					Dict       ,		lowerCAmelCase :					List[Any]=None		)     ->					int:


     '''simple docstring'''




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





  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={"""num_warmup_steps""": 2, """num_training_steps""": 10}
     # schedulers doct format
     # function: (sched_args_dict, expected_learning_rates)
     SCREAMING_SNAKE_CASE_: int     			={
         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():
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Dict     			=data

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

        SCREAMING_SNAKE_CASE_: Dict     			=scheduler_func(self.optimizer       ,		**A_		)
        if scheduler_func.__name__ != "get_constant_schedule":
           LambdaScheduleWrapper.wrap_scheduler(A_		)  # wrap to test picklability of the schedule
        SCREAMING_SNAKE_CASE_: List[str]     			=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	:



  def __init__(						self :					Tuple       ,		lowerCAmelCase :					Dict		)     ->					Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=fn



  def __call__(						self :					Tuple       ,		*lowerCAmelCase :					List[Any]       ,		**lowerCAmelCase :					str		)     ->					Optional[Any]:


     '''simple docstring'''




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





  @classmethod
  def 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					Optional[Any]		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=list(map(self       ,		scheduler.lr_lambdas		)		)








"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class 					a	(  yaml.SafeLoader       ):



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					List[str]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=[self.constructed_objects[key_node] for key_node, _ in node.value]
     SCREAMING_SNAKE_CASE_: Any     			=[tuple(lowerCAmelCase		) if isinstance(lowerCAmelCase       ,		lowerCAmelCase		) else key for key in keys]
     SCREAMING_SNAKE_CASE_: Dict     			=Counter(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=[key for key in counter if counter[key] > 1]
     if duplicate_keys:
        raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''		)





  def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					Optional[int]=False		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=super().construct_mapping(lowerCAmelCase       ,		deep=lowerCAmelCase		)
     self._check_no_duplicates_on_constructed_node(lowerCAmelCase		)
     return mapping







def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=list(readme_content.splitlines() )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=full_content[1:].index("""---""" ) + 1
      SCREAMING_SNAKE_CASE_: List[str]     			="""\n""".join(full_content[1:sep_idx] )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :] )

   return None, "\n".join(lowercase )
class 					a	(  UpperCAmelCase__       ):
  # class attributes
  UpperCamelCase	:				Tuple     						=					{'train_eval_index'}  # train-eval-index in the YAML metadata



  @classmethod
  def 	lowerCamelCase__       (						cls :					List[Any]       ,		lowerCAmelCase :					Path		)     ->					"DatasetMetadata":


     '''simple docstring'''




     with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Union[str, Any]     			=_split_yaml_from_readme(readme_file.read()		)
     if yaml_string is not None:
        return cls.from_yaml_string(lowerCAmelCase		)
     else:
        return cls()



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Path		)     ->					List[str]:


     '''simple docstring'''




     if path.exists():
        with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
           SCREAMING_SNAKE_CASE_: str     			=readme_file.read()
     else:
        SCREAMING_SNAKE_CASE_: str     			=None
     SCREAMING_SNAKE_CASE_: Tuple     			=self._to_readme(lowerCAmelCase		)
     with open(lowerCAmelCase       ,		"""w"""       ,		encoding="""utf-8"""		) as readme_file:
        readme_file.write(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Optional[str] = None		)     ->					str:


     '''simple docstring'''




     if readme_content is not None:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=_split_yaml_from_readme(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: List[str]     			="""---\n""" + self.to_yaml_string() + """---\n""" + content
     else:
        SCREAMING_SNAKE_CASE_: List[Any]     			="""---\n""" + self.to_yaml_string() + """---\n"""
     return full_content



  @classmethod
  def 	lowerCamelCase__       (						cls :					Optional[int]       ,		lowerCAmelCase :					str		)     ->					"DatasetMetadata":


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=yaml.load(lowerCAmelCase       ,		Loader=_NoDuplicateSafeLoader		) or {}

     # Convert the YAML keys to DatasetMetadata fields
     SCREAMING_SNAKE_CASE_: List[Any]     			={
         (key.replace("""-"""       ,		"""_"""		) if key.replace("""-"""       ,		"""_"""		) in cls._FIELDS_WITH_DASHES else key): value
         for key, value in metadata_dict.items()
     }
     return cls(**lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     return yaml.safe_dump(
         {
             (key.replace("""_"""       ,		"""-"""		) if key in self._FIELDS_WITH_DASHES else key): value
             for key, value in self.items()
         }       ,		sort_keys=lowerCAmelCase       ,		allow_unicode=lowerCAmelCase       ,		encoding="""utf-8"""       ,		).decode("""utf-8"""		)







_UpperCAmelCase     =			{
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
   from argparse import ArgumentParser

   _UpperCAmelCase     =			ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
   ap.add_argument("""readme_filepath""")
   _UpperCAmelCase     =			ap.parse_args()

   _UpperCAmelCase     =			Path(args.readme_filepath)
   _UpperCAmelCase     =			DatasetMetadata.from_readme(readme_filepath)
   print(dataset_metadata)
   dataset_metadata.to_readme(readme_filepath)



