





import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
      from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
      from tensorflow.keras.optimizers import Adam

class 		UpperCamelCase__			(				tf.keras.optimizers.schedules.LearningRateSchedule       ):



     '''simple docstring'''

     def __init__(							self						:   str						,lowerCamelCase__						:   float						,lowerCamelCase__						:   Callable						,lowerCamelCase__						:   int						,lowerCamelCase__						:   float = 1.0						,lowerCamelCase__						:   str = None						,)					->		Tuple:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						initial_learning_rate
           SCREAMING_SNAKE_CASE		=						warmup_steps
           SCREAMING_SNAKE_CASE		=						power
           SCREAMING_SNAKE_CASE		=						decay_schedule_fn
           SCREAMING_SNAKE_CASE		=						name






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




           '''simple docstring'''


           with tf.name_scope(self.name or """WarmUp"""	) as name:
                 # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
                 # learning rate will be `global_step/num_warmup_steps * init_lr`.
                 SCREAMING_SNAKE_CASE		=						tf.cast(__UpperCamelCase						,tf.floataa	)
                 SCREAMING_SNAKE_CASE		=						tf.cast(self.warmup_steps						,tf.floataa	)
                 SCREAMING_SNAKE_CASE		=						global_step_float / warmup_steps_float
                 SCREAMING_SNAKE_CASE		=						self.initial_learning_rate * tf.math.pow(__UpperCamelCase						,self.power	)
                 return tf.cond(
                     global_step_float < warmup_steps_float						,lambda: warmup_learning_rate						,lambda: self.decay_schedule_fn(step - self.warmup_steps	)						,name=__UpperCamelCase						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		Dict:




           '''simple docstring'''


           return {
               "initial_learning_rate": self.initial_learning_rate,
               "decay_schedule_fn": self.decay_schedule_fn,
               "warmup_steps": self.warmup_steps,
               "power": self.power,
               "name": self.name,
           }



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = 0.0			,						_SCREAMING_SNAKE_CASE = 0.9			,						_SCREAMING_SNAKE_CASE = 0.999			,						_SCREAMING_SNAKE_CASE = 1E-8			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = 0.0			,						_SCREAMING_SNAKE_CASE = 1.0			,						_SCREAMING_SNAKE_CASE = None			,						)			->       Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						tf.keras.optimizers.schedules.PolynomialDecay(
          initial_learning_rate=a__			,						decay_steps=num_train_steps - num_warmup_steps			,						end_learning_rate=init_lr * min_lr_ratio			,						power=a__			,						)
      if num_warmup_steps:
            SCREAMING_SNAKE_CASE		=						WarmUp(
                initial_learning_rate=a__			,						decay_schedule_fn=a__			,						warmup_steps=a__			,						)
      if weight_decay_rate > 0.0:
            SCREAMING_SNAKE_CASE		=						AdamWeightDecay(
                learning_rate=a__			,						weight_decay_rate=a__			,						beta_a=a__			,						beta_a=a__			,						epsilon=a__			,						clipnorm=a__			,						global_clipnorm=a__			,						exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]			,						include_in_weight_decay=a__			,						)
      else:
            SCREAMING_SNAKE_CASE		=						tf.keras.optimizers.Adam(
                learning_rate=a__			,						beta_a=a__			,						beta_a=a__			,						epsilon=a__			,						clipnorm=a__			,						global_clipnorm=a__			,						)
      # We return the optimizer and the LR scheduler in order to better track the
      # evolution of the LR independently of the optimizer.
      return optimizer, lr_schedule

class 		UpperCamelCase__			(				_lowercase       ):



     '''simple docstring'''

     def __init__(							self						:   str						,lowerCamelCase__						:   Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001						,lowerCamelCase__						:   float = 0.9						,lowerCamelCase__						:   float = 0.999						,lowerCamelCase__						:   float = 1e-7						,lowerCamelCase__						:   bool = False						,lowerCamelCase__						:   float = 0.0						,lowerCamelCase__						:   Optional[List[str]] = None						,lowerCamelCase__						:   Optional[List[str]] = None						,lowerCamelCase__						:   str = "AdamWeightDecay"						,**lowerCamelCase__						:   Union[str, Any]						,)					->		Optional[int]:




           '''simple docstring'''


           super().__init__(__UpperCamelCase						,__UpperCamelCase						,__UpperCamelCase						,__UpperCamelCase						,__UpperCamelCase						,__UpperCamelCase						,**__UpperCamelCase	)
           SCREAMING_SNAKE_CASE		=						weight_decay_rate
           SCREAMING_SNAKE_CASE		=						include_in_weight_decay
           SCREAMING_SNAKE_CASE		=						exclude_from_weight_decay






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Optional[Any]						,lowerCamelCase__						:   Union[str, Any]	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{"""WarmUp""": WarmUp}
           return super(__UpperCamelCase						,cls	).from_config(__UpperCamelCase						,custom_objects=__UpperCamelCase	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple						,lowerCamelCase__						:   str						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Any	)					->		List[str]:




           '''simple docstring'''


           super(__UpperCamelCase						,self	)._prepare_local(__UpperCamelCase						,__UpperCamelCase						,__UpperCamelCase	)
           SCREAMING_SNAKE_CASE		=						tf.constant(
               self.weight_decay_rate						,name="""adam_weight_decay_rate"""	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   str	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._do_use_weight_decay(var.name	)
           if do_decay:
                 return var.assign_sub(
                     learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["""weight_decay_rate"""]						,use_locking=self._use_locking						,)
           return tf.no_op()






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[str]=None						,**lowerCamelCase__						:   List[Any]	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						list(zip(*__UpperCamelCase	)	)
           return super(__UpperCamelCase						,self	).apply_gradients(zip(__UpperCamelCase						,__UpperCamelCase	)						,name=__UpperCamelCase						,**__UpperCamelCase	)






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




           '''simple docstring'''


           if apply_state is None:
                 return self._decayed_lr_t[var_dtype], {}

           SCREAMING_SNAKE_CASE		=						apply_state or {}
           SCREAMING_SNAKE_CASE		=						apply_state.get((var_device, var_dtype)	)
           if coefficients is None:
                 SCREAMING_SNAKE_CASE		=						self._fallback_apply_state(__UpperCamelCase						,__UpperCamelCase	)
                 SCREAMING_SNAKE_CASE		=						coefficients

           return coefficients["lr_t"], {"apply_state": apply_state}






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   str						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Optional[int]=None	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self._get_lr(var.device						,var.dtype.base_dtype						,__UpperCamelCase	)
           SCREAMING_SNAKE_CASE		=						self._decay_weights_op(__UpperCamelCase						,__UpperCamelCase						,__UpperCamelCase	)
           with tf.control_dependencies([decay]	):
                 return super(__UpperCamelCase						,self	)._resource_apply_dense(__UpperCamelCase						,__UpperCamelCase						,**__UpperCamelCase	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   Any=None	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self._get_lr(var.device						,var.dtype.base_dtype						,__UpperCamelCase	)
           SCREAMING_SNAKE_CASE		=						self._decay_weights_op(__UpperCamelCase						,__UpperCamelCase						,__UpperCamelCase	)
           with tf.control_dependencies([decay]	):
                 return super(__UpperCamelCase						,self	)._resource_apply_sparse(__UpperCamelCase						,__UpperCamelCase						,__UpperCamelCase						,**__UpperCamelCase	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						super().get_config()
           config.update({"""weight_decay_rate""": self.weight_decay_rate}	)
           return config






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Any	)					->		Dict:




           '''simple docstring'''


           if self.weight_decay_rate == 0:
                 return False

           if self._include_in_weight_decay:
                 for r in self._include_in_weight_decay:
                       if re.search(__UpperCamelCase						,__UpperCamelCase	) is not None:
                             return True

           if self._exclude_from_weight_decay:
                 for r in self._exclude_from_weight_decay:
                       if re.search(__UpperCamelCase						,__UpperCamelCase	) is not None:
                             return False
           return True

class 		UpperCamelCase__			(				_lowercase       ):



     '''simple docstring'''

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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[]
           SCREAMING_SNAKE_CASE		=						None






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Dict:




           '''simple docstring'''


           if self._accum_steps is None:
                 SCREAMING_SNAKE_CASE		=						tf.Variable(
                     tf.constant(0						,dtype=tf.intaa	)						,trainable=__UpperCamelCase						,synchronization=tf.VariableSynchronization.ON_READ						,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA						,)

           return self._accum_steps.value()






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		List[str]:




           '''simple docstring'''


           if not self._gradients:
                 raise ValueError("""The accumulator should be called first to initialize the gradients"""	)
           return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






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




           '''simple docstring'''


           if not self._gradients:
                 SCREAMING_SNAKE_CASE		=						self.step  # Create the step variable.
                 self._gradients.extend(
                     [
                         tf.Variable(
                             tf.zeros_like(__UpperCamelCase	)						,trainable=__UpperCamelCase						,synchronization=tf.VariableSynchronization.ON_READ						,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA						,)
                         if gradient is not None
                         else gradient
                         for gradient in gradients
                     ]	)
           if len(__UpperCamelCase	) != len(self._gradients	):
                 raise ValueError(F"""Expected {len(self._gradients	)} gradients, but got {len(__UpperCamelCase	)}"""	)

           for accum_gradient, gradient in zip(self._gradients						,__UpperCamelCase	):
                 if accum_gradient is not None and gradient is not None:
                       accum_gradient.assign_add(__UpperCamelCase	)

           self._accum_steps.assign_add(1	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Optional[Any]:




           '''simple docstring'''


           if not self._gradients:
                 return
           self._accum_steps.assign(0	)
           for gradient in self._gradients:
                 if gradient is not None:
                       gradient.assign(tf.zeros_like(__UpperCamelCase	)	)









import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


SCREAMING_SNAKE_CASE_						       =	"""\
    Text data.
    Second line of data."""

SCREAMING_SNAKE_CASE_						       =	"""file"""



@pytest.fixture(scope="""session"""     )
def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						tmp_path_factory.mktemp("""data"""     ) / (FILE_PATH + """.zstd""")
      SCREAMING_SNAKE_CASE		=						bytes(_SCREAMING_SNAKE_CASE			,						"""utf-8"""     )
      with zstd.open(_SCREAMING_SNAKE_CASE			,						"""wb"""     ) as f:
            f.write(_SCREAMING_SNAKE_CASE     )
      return path



@pytest.fixture
def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       List[Any]:


      '''simple docstring'''


      with open(os.path.join(tmpfs.local_root_dir			,						_SCREAMING_SNAKE_CASE     )			,						"""w"""     ) as f:
            f.write(_SCREAMING_SNAKE_CASE     )
      return FILE_PATH



@pytest.mark.parametrize("""compression_format"""			,						["""gzip""", """xz""", """zstd"""]     )
def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						{"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
      SCREAMING_SNAKE_CASE		=						input_paths[compression_format]
      SCREAMING_SNAKE_CASE		=						tmp_path / """cache"""
      SCREAMING_SNAKE_CASE		=						DownloadConfig(cache_dir=_SCREAMING_SNAKE_CASE			,						extract_compressed_file=_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						cached_path(_SCREAMING_SNAKE_CASE			,						download_config=_SCREAMING_SNAKE_CASE     )
      with open(_SCREAMING_SNAKE_CASE     ) as f:
            SCREAMING_SNAKE_CASE		=						f.read()
      with open(_SCREAMING_SNAKE_CASE     ) as f:
            SCREAMING_SNAKE_CASE		=						f.read()
      assert extracted_file_content == expected_file_content



@pytest.mark.parametrize("""default_extracted"""			,						[True, False]     )
@pytest.mark.parametrize("""default_cache_dir"""			,						[True, False]     )
def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						"""custom_cache"""
      SCREAMING_SNAKE_CASE		=						"""custom_extracted_dir"""
      SCREAMING_SNAKE_CASE		=						tmp_path / """custom_extracted_path"""
      if default_extracted:
            SCREAMING_SNAKE_CASE		=						("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
      else:
            monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR"""			,						_SCREAMING_SNAKE_CASE     )
            monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""			,						str(_SCREAMING_SNAKE_CASE     )     )
            SCREAMING_SNAKE_CASE		=						custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

      SCREAMING_SNAKE_CASE		=						xz_file
      SCREAMING_SNAKE_CASE		=						(
          DownloadConfig(extract_compressed_file=_SCREAMING_SNAKE_CASE     )
          if default_cache_dir
          else DownloadConfig(cache_dir=tmp_path / custom_cache_dir			,						extract_compressed_file=_SCREAMING_SNAKE_CASE     )
      )
      SCREAMING_SNAKE_CASE		=						cached_path(_SCREAMING_SNAKE_CASE			,						download_config=_SCREAMING_SNAKE_CASE     )
      assert Path(_SCREAMING_SNAKE_CASE     ).parent.parts[-2:] == expected



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						str(Path(_SCREAMING_SNAKE_CASE     ).resolve()     )
      assert cached_path(_SCREAMING_SNAKE_CASE     ) == text_file
      # relative path
      SCREAMING_SNAKE_CASE		=						str(Path(_SCREAMING_SNAKE_CASE     ).resolve().relative_to(Path(os.getcwd()     )     )     )
      assert cached_path(_SCREAMING_SNAKE_CASE     ) == text_file



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						str(tmp_path.resolve() / """__missing_file__.txt"""     )
      with pytest.raises(_SCREAMING_SNAKE_CASE     ):
            cached_path(_SCREAMING_SNAKE_CASE     )
      # relative path
      SCREAMING_SNAKE_CASE		=						"""./__missing_file__.txt"""
      with pytest.raises(_SCREAMING_SNAKE_CASE     ):
            cached_path(_SCREAMING_SNAKE_CASE     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						get_from_cache(F"""tmp://{tmpfs_file}"""     )
      with open(_SCREAMING_SNAKE_CASE     ) as f:
            SCREAMING_SNAKE_CASE		=						f.read()
      assert output_file_content == FILE_CONTENT



@patch("""datasets.config.HF_DATASETS_OFFLINE"""			,						_SCREAMING_SNAKE_CASE     )
def        __lowercase       (				)			->       Dict:


      '''simple docstring'''


      with pytest.raises(_SCREAMING_SNAKE_CASE     ):
            cached_path("""https://huggingface.co"""     )



@patch("""datasets.config.HF_DATASETS_OFFLINE"""			,						_SCREAMING_SNAKE_CASE     )
def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						tmp_path_factory.mktemp("""data"""     ) / """file.html"""
      with pytest.raises(_SCREAMING_SNAKE_CASE     ):
            http_get("""https://huggingface.co"""			,						temp_file=_SCREAMING_SNAKE_CASE     )
      with pytest.raises(_SCREAMING_SNAKE_CASE     ):
            http_head("""https://huggingface.co"""     )



@patch("""datasets.config.HF_DATASETS_OFFLINE"""			,						_SCREAMING_SNAKE_CASE     )
def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						tmp_path_factory.mktemp("""data"""     ) / """file.html"""
      with pytest.raises(_SCREAMING_SNAKE_CASE     ):
            ftp_get("""ftp://huggingface.co"""			,						temp_file=_SCREAMING_SNAKE_CASE     )
      with pytest.raises(_SCREAMING_SNAKE_CASE     ):
            ftp_head("""ftp://huggingface.co"""     )



@patch("""datasets.config.HF_DATASETS_OFFLINE"""			,						_SCREAMING_SNAKE_CASE     )
def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						tmp_path_factory.mktemp("""data"""     ) / """file.html"""
      with pytest.raises(_SCREAMING_SNAKE_CASE     ):
            fsspec_get("""s3://huggingface.co"""			,						temp_file=_SCREAMING_SNAKE_CASE     )
      with pytest.raises(_SCREAMING_SNAKE_CASE     ):
            fsspec_head("""s3://huggingface.co"""     )


