






"""simple docstring"""





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      a						(					tf.keras.optimizers.schedules.LearningRateSchedule		):






    def __init__(       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							List[str] = 1.0       ,						__SCREAMING_SNAKE_CASE     :							str = None       ,						)     ->		List[Any]:
      super().__init__()
      lowerCamelCase_         =						initial_learning_rate
      lowerCamelCase_         =						warmup_steps
      lowerCamelCase_         =						power
      lowerCamelCase_         =						decay_schedule_fn
      lowerCamelCase_         =						name






    def __call__(       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		List[str]:
      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`.
        lowerCamelCase_         =						tf.cast(__SCREAMING_SNAKE_CASE       ,						tf.floataa					)
        lowerCamelCase_         =						tf.cast(self.warmup_steps       ,						tf.floataa					)
        lowerCamelCase_         =						global_step_float / warmup_steps_float
        lowerCamelCase_         =						self.initial_learning_rate * tf.math.pow(__SCREAMING_SNAKE_CASE       ,						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=__SCREAMING_SNAKE_CASE       ,						)






    def    UpperCamelCase       (       self     :							int					)     ->		int:
      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      lowerCamelCase__       (							_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		Union[str, Any]		,				_lowerCamelCase       :		Dict		,				_lowerCamelCase       :		Optional[int] = 0.0		,				_lowerCamelCase       :		Tuple = 0.9		,				_lowerCamelCase       :		List[str] = 0.9_99		,				_lowerCamelCase       :		Any = 1e-8		,				_lowerCamelCase       :		str = None		,				_lowerCamelCase       :		Optional[int] = None		,				_lowerCamelCase       :		Dict = 0.0		,				_lowerCamelCase       :		str = 1.0		,				_lowerCamelCase       :		Tuple = None		,				)      ->				Optional[int]:
  lowerCamelCase_         =						tf.keras.optimizers.schedules.PolynomialDecay(
      initial_learning_rate=lowercase__		,				decay_steps=num_train_steps - num_warmup_steps		,				end_learning_rate=init_lr * min_lr_ratio		,				power=lowercase__		,				)
  if num_warmup_steps:
    lowerCamelCase_         =						WarmUp(
        initial_learning_rate=lowercase__		,				decay_schedule_fn=lowercase__		,				warmup_steps=lowercase__		,				)
  if weight_decay_rate > 0.0:
    lowerCamelCase_         =						AdamWeightDecay(
        learning_rate=lowercase__		,				weight_decay_rate=lowercase__		,				beta_a=lowercase__		,				beta_a=lowercase__		,				epsilon=lowercase__		,				clipnorm=lowercase__		,				global_clipnorm=lowercase__		,				exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']		,				include_in_weight_decay=lowercase__		,				)
  else:
    lowerCamelCase_         =						tf.keras.optimizers.Adam(
        learning_rate=lowercase__		,				beta_a=lowercase__		,				beta_a=lowercase__		,				epsilon=lowercase__		,				clipnorm=lowercase__		,				global_clipnorm=lowercase__		,				)
  # 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      a						(					__snake_case		):






    def __init__(       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							List[str] = 0.001       ,						__SCREAMING_SNAKE_CASE     :							List[Any] = 0.9       ,						__SCREAMING_SNAKE_CASE     :							Tuple = 0.999       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any] = 1e-7       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = False       ,						__SCREAMING_SNAKE_CASE     :							Tuple = 0.0       ,						__SCREAMING_SNAKE_CASE     :							Tuple = None       ,						__SCREAMING_SNAKE_CASE     :							List[Any] = None       ,						__SCREAMING_SNAKE_CASE     :							Any = "AdamWeightDecay"       ,						**__SCREAMING_SNAKE_CASE     :							Dict       ,						)     ->		int:
      super().__init__(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						weight_decay_rate
      lowerCamelCase_         =						include_in_weight_decay
      lowerCamelCase_         =						exclude_from_weight_decay






    @classmethod
    def    UpperCamelCase       (       cls     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]					)     ->		Optional[int]:
      lowerCamelCase_         =						{'WarmUp': WarmUp}
      return super(__SCREAMING_SNAKE_CASE       ,						cls					).from_config(__SCREAMING_SNAKE_CASE       ,						custom_objects=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		List[Any]:
      super(__SCREAMING_SNAKE_CASE       ,						self					)._prepare_local(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						tf.constant(
          self.weight_decay_rate       ,						name='adam_weight_decay_rate'					)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							str					)     ->		Tuple:
      lowerCamelCase_         =						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    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=None       ,						**__SCREAMING_SNAKE_CASE     :							int					)     ->		Tuple:
      lowerCamelCase_         =						list(zip(*__SCREAMING_SNAKE_CASE					)					)
      return super(__SCREAMING_SNAKE_CASE       ,						self					).apply_gradients(zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)       ,						name=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							str					)     ->		Tuple:
      if apply_state is None:
        return self._decayed_lr_t[var_dtype], {}

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

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






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=None					)     ->		str:
      lowerCamelCase_         =						self._get_lr(var.device       ,						var.dtype.base_dtype       ,						__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self._decay_weights_op(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      with tf.control_dependencies([decay]					):
        return super(__SCREAMING_SNAKE_CASE       ,						self					)._resource_apply_dense(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]=None					)     ->		Optional[int]:
      lowerCamelCase_         =						self._get_lr(var.device       ,						var.dtype.base_dtype       ,						__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self._decay_weights_op(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      with tf.control_dependencies([decay]					):
        return super(__SCREAMING_SNAKE_CASE       ,						self					)._resource_apply_sparse(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		Any:
      lowerCamelCase_         =						super().get_config()
      config.update({'weight_decay_rate': self.weight_decay_rate}					)
      return config






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Dict					)     ->		Tuple:
      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(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					) is not None:
            return True

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






class      a						(					__snake_case		):






    def __init__(       self     :							Union[str, Any]					)     ->		List[Any]:
      lowerCamelCase_         =						[]
      lowerCamelCase_         =						None






    @property
    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		List[str]:
      if self._accum_steps is None:
        lowerCamelCase_         =						tf.Variable(
            tf.constant(0       ,						dtype=tf.intaa					)       ,						trainable=__SCREAMING_SNAKE_CASE       ,						synchronization=tf.VariableSynchronization.ON_READ       ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,						)

      return self._accum_steps.value()






    @property
    def    UpperCamelCase       (       self     :							Tuple					)     ->		Optional[int]:
      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     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]					)     ->		int:
      if not self._gradients:
        lowerCamelCase_         =						self.step  # Create the step variable.
        self._gradients.extend(
            [
                tf.Variable(
                    tf.zeros_like(__SCREAMING_SNAKE_CASE					)       ,						trainable=__SCREAMING_SNAKE_CASE       ,						synchronization=tf.VariableSynchronization.ON_READ       ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,						)
                if gradient is not None
                else gradient
                for gradient in gradients
            ]					)
      if len(__SCREAMING_SNAKE_CASE					) != len(self._gradients					):
        raise ValueError(F'''Expected {len(self._gradients					)} gradients, but got {len(__SCREAMING_SNAKE_CASE					)}'''					)

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

      self._accum_steps.assign_add(1					)






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










"""simple docstring"""






# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


lowercase__     						=						get_logger(__name__)






class lowerCAmelCase__	:






			'''simple docstring'''



			lowerCamelCase__      =			"""dummy_data"""
			lowerCamelCase__      =			"""datasets"""
			lowerCamelCase__      =			False
			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase = None  ,      lowercase = False  ,      lowercase = True  ,      lowercase = None  ,      ):
							_lowerCamelCase     :			Optional[Any]    =    0
							_lowerCamelCase     :			Dict    =    dataset_name
							_lowerCamelCase     :			Union[str, Any]    =    cache_dir
							_lowerCamelCase     :			Dict    =    use_local_dummy_data
							_lowerCamelCase     :			Tuple    =    config
							# download_callbacks take a single url as input
							_lowerCamelCase     :			List[Callable]    =    download_callbacks or []
							# if False, it doesn't load existing files and it returns the paths of the dummy files relative
							# to the dummy_data zip file root
							_lowerCamelCase     :			Any    =    load_existing_dummy_data

							# TODO(PVP, QL) might need to make this more general
							_lowerCamelCase     :			str    =    str(lowercase  )
							# to be downloaded
							_lowerCamelCase     :			Union[str, Any]    =    None
							_lowerCamelCase     :			int    =    None
			@property
			def 					A_    (  self  ):
							if self._dummy_file is None:
											_lowerCamelCase     :			Tuple    =    self.download_dummy_data()
							return self._dummy_file
			@property
			def 					A_    (  self  ):
							if self.config is not None:
											# structure is dummy / config_name / version_name
											return os.path.join('dummy'  ,      self.config.name  ,      self.version_name  )
							# structure is dummy / version_name
							return os.path.join('dummy'  ,      self.version_name  )
			@property
			def 					A_    (  self  ):
							return os.path.join(self.dummy_data_folder  ,      'dummy_data.zip'  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    (
							    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
							)

							_lowerCamelCase     :			int    =    cached_path(
							    lowercase  ,      cache_dir=self.cache_dir  ,      extract_compressed_file=lowercase  ,      force_extract=lowercase  )

							return os.path.join(lowercase  ,      self.dummy_file_name  )
			@property
			def 					A_    (  self  ):
							return os.path.join(self.datasets_scripts_dir  ,      self.dataset_name  ,      self.dummy_zip_file  )
			@property
			def 					A_    (  self  ):
							if self._bucket_url is None:
											_lowerCamelCase     :			List[Any]    =    hf_github_url(self.dataset_name  ,      self.dummy_zip_file.replace(os.sep  ,      '/'  )  )
							return self._bucket_url
			@property
			def 					A_    (  self  ):
							# return full path if its a dir
							if os.path.isdir(self.dummy_file  ):
											return self.dummy_file
							# else cut off path to file -> example `xsum`.
							return "/".join(self.dummy_file.replace(os.sep  ,      '/'  ).split('/'  )[:-1]  )
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ):
							if self.load_existing_dummy_data:
											# dummy data is downloaded and tested
											_lowerCamelCase     :			Union[str, Any]    =    self.dummy_file
							else:
											# dummy data cannot be downloaded and only the path to dummy file is returned
											_lowerCamelCase     :			Union[str, Any]    =    self.dummy_file_name

							# special case when data_url is a dict
							if isinstance(lowercase  ,      lowercase  ):
											return self.create_dummy_data_dict(lowercase  ,      lowercase  )
							elif isinstance(lowercase  ,      (list, tuple)  ):
											return self.create_dummy_data_list(lowercase  ,      lowercase  )
							else:
											return self.create_dummy_data_single(lowercase  ,      lowercase  )
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ):
							return self.download_and_extract(lowercase  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							return self.download_and_extract(lowercase  )
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ,      **lowercase  ):
							return path
			def 					A_    (  self  ):
							return {}
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[int]    =    {}
							for key, single_urls in data_url.items():
											for download_callback in self.download_callbacks:
															if isinstance(lowercase  ,      lowercase  ):
																			for single_url in single_urls:
																							download_callback(lowercase  )
															else:
																			_lowerCamelCase     :			List[Any]    =    single_urls
																			download_callback(lowercase  )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
											if isinstance(lowercase  ,      lowercase  ):
															_lowerCamelCase     :			List[Any]    =    [os.path.join(lowercase  ,      urllib.parse.quote_plus(Path(lowercase  ).name  )  ) for x in single_urls]
											else:
															_lowerCamelCase     :			Optional[int]    =    single_urls
															_lowerCamelCase     :			List[Any]    =    os.path.join(lowercase  ,      urllib.parse.quote_plus(Path(lowercase  ).name  )  )
											_lowerCamelCase     :			int    =    value

							# make sure that values are unique
							if all(isinstance(lowercase  ,      lowercase  ) for i in dummy_data_dict.values()  ) and len(set(dummy_data_dict.values()  )  ) < len(
							    dummy_data_dict.values()  ):
											# append key to value to make its name unique
											_lowerCamelCase     :			List[Any]    =    {key: value + key for key, value in dummy_data_dict.items()}

							return dummy_data_dict
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[Any]    =    []
							# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
							_lowerCamelCase     :			List[str]    =    all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'  ,      lowercase  )  ) for url in data_url  )
							_lowerCamelCase     :			int    =    all(
							    url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'  ) for url in data_url  )
							if data_url and (is_tf_records or is_pubmed_records):
											_lowerCamelCase     :			List[str]    =    [data_url[0]] * len(lowercase  )
							for single_url in data_url:
											for download_callback in self.download_callbacks:
															download_callback(lowercase  )
											# we force the name of each key to be the last file / folder name of the url path
											# if the url has arguments, we need to encode them with urllib.parse.quote_plus
											_lowerCamelCase     :			str    =    os.path.join(lowercase  ,      urllib.parse.quote_plus(single_url.split('/'  )[-1]  )  )
											dummy_data_list.append(lowercase  )
							return dummy_data_list
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							for download_callback in self.download_callbacks:
											download_callback(lowercase  )
							# we force the name of each key to be the last file / folder name of the url path
							# if the url has arguments, we need to encode them with urllib.parse.quote_plus
							_lowerCamelCase     :			Tuple    =    os.path.join(lowercase  ,      urllib.parse.quote_plus(data_url.split('/'  )[-1]  )  )
							if os.path.exists(lowercase  ) or not self.load_existing_dummy_data:
											return value
							else:
											# Backward compatibility, maybe deprecate at one point.
											# For many datasets with single url calls to dl_manager.download_and_extract,
											# the dummy_data.zip file is actually the zipped downloaded file
											# while now we expected the dummy_data.zip file to be a directory containing
											# the downloaded file.
											return path_to_dummy_data
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ,      lowercase  ):
							def _iter_archive_members(lowercase  ):
											# this preserves the order of the members inside the ZIP archive
											_lowerCamelCase     :			str    =    Path(self.dummy_file  ).parent
											_lowerCamelCase     :			Union[str, Any]    =    path.relative_to(lowercase  )
											with ZipFile(self.local_path_to_dummy_data  ) as zip_file:
															_lowerCamelCase     :			List[str]    =    zip_file.namelist()
											for member in members:
															if member.startswith(relative_path.as_posix()  ):
																			yield dummy_parent_path.joinpath(lowercase  )

							_lowerCamelCase     :			Optional[int]    =    Path(lowercase  )
							_lowerCamelCase     :			Dict    =    _iter_archive_members(lowercase  ) if self.use_local_dummy_data else path.rglob('*'  )
							for file_path in file_paths:
											if file_path.is_file() and not file_path.name.startswith(('.', '__')  ):
															yield file_path.relative_to(lowercase  ).as_posix(), file_path.open('rb'  )






			def 					A_    (  self  ,      lowercase  ):
							if not isinstance(lowercase  ,      lowercase  ):
											_lowerCamelCase     :			List[str]    =    [paths]
							for path in paths:
											if os.path.isfile(lowercase  ):
															if os.path.basename(lowercase  ).startswith(('.', '__')  ):
																			return
															yield path
											else:
															for dirpath, dirnames, filenames in os.walk(lowercase  ):
																			if os.path.basename(lowercase  ).startswith(('.', '__')  ):
																							continue
																			dirnames.sort()
																			for filename in sorted(lowercase  ):
																							if filename.startswith(('.', '__')  ):
																											continue
																							yield os.path.join(lowercase  ,      lowercase  )