






'''simple docstring'''






import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map
def 				lowerCamelCase__	(						_A    ):
  a						: Optional[int]					       =					[]
  if isinstance(__UpperCAmelCase			,      __UpperCAmelCase    ):
    for v in tree.values():
      shapes.extend(_fetch_dims(__UpperCAmelCase    )    )
  elif isinstance(__UpperCAmelCase			,      (list, tuple)    ):
    for t in tree:
      shapes.extend(_fetch_dims(__UpperCAmelCase    )    )
  elif isinstance(__UpperCAmelCase			,      torch.Tensor    ):
    shapes.append(tree.shape    )
  else:
    raise ValueError('Not supported'    )

  return shapes
@torch.jit.ignore
def 				lowerCamelCase__	(						_A			,      _A    ):
  a						: str					       =					[]
  for d in reversed(__UpperCAmelCase    ):
    idx.append(flat_idx % d    )
    a						: Optional[Any]					       =					flat_idx // d

  return tuple(reversed(__UpperCAmelCase    )    )
@torch.jit.ignore
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A = None			,      _A = None			,      ):

  # start_edges and end_edges both indicate whether, starting from any given
  # dimension, the start/end index is at the top/bottom edge of the
  # corresponding tensor, modeled as a tree
  def reduce_edge_list(_A    ) -> None:
    a						: Tuple					       =					True
    for i in range(len(__UpperCAmelCase    )    ):
      a						: Optional[Any]					       =					-1 * (i + 1)
      l[reversed_idx] &= tally
      a						: Dict					       =					l[reversed_idx]

  if start_edges is None:
    a						: Optional[int]					       =					[s == 0 for s in start]
    reduce_edge_list(__UpperCAmelCase    )
  if end_edges is None:
    a						: Optional[int]					       =					[e == (d - 1) for e, d in zip(__UpperCAmelCase			,      __UpperCAmelCase    )]
    reduce_edge_list(__UpperCAmelCase    )

  # Base cases. Either start/end are empty and we're done, or the final,
  # one-dimensional tensor can be simply sliced
  if len(__UpperCAmelCase    ) == 0:
    return [()]
  elif len(__UpperCAmelCase    ) == 1:
    return [(slice(start[0]			,      end[0] + 1    ),)]

  a						: List[Tuple[slice, ...]]					       =					[]
  a						: List[slice]					       =					[]

  # Dimensions common to start and end can be selected directly
  for s, e in zip(__UpperCAmelCase			,      __UpperCAmelCase    ):
    if s == e:
      path_list.append(slice(__UpperCAmelCase			,      s + 1    )    )
    else:
      break

  a						: Tuple[slice, ...]					       =					tuple(__UpperCAmelCase    )
  a						: int					       =					len(__UpperCAmelCase    )

  # start == end, and we're done
  if divergence_idx == len(__UpperCAmelCase    ):
    return [path]

  def upper() -> Tuple[Tuple[slice, ...], ...]:
    assert start_edges is not None
    assert end_edges is not None

    a						: int					       =					start[divergence_idx]
    return tuple(
        path + (slice(__UpperCAmelCase			,      sdi + 1    ),) + s
        for s in _get_minimal_slice_set(
            start[divergence_idx + 1 :]			,      [d - 1 for d in dims[divergence_idx + 1 :]]			,      dims[divergence_idx + 1 :]			,      start_edges=start_edges[divergence_idx + 1 :]			,      end_edges=[True for _ in end_edges[divergence_idx + 1 :]]			,      )    )

  def lower() -> Tuple[Tuple[slice, ...], ...]:
    assert start_edges is not None
    assert end_edges is not None

    a						: Dict					       =					end[divergence_idx]
    return tuple(
        path + (slice(__UpperCAmelCase			,      edi + 1    ),) + s
        for s in _get_minimal_slice_set(
            [0 for _ in start[divergence_idx + 1 :]]			,      end[divergence_idx + 1 :]			,      dims[divergence_idx + 1 :]			,      start_edges=[True for _ in start_edges[divergence_idx + 1 :]]			,      end_edges=end_edges[divergence_idx + 1 :]			,      )    )

  # If both start and end are at the edges of the subtree rooted at
  # divergence_idx, we can just select the whole subtree at once
  if start_edges[divergence_idx] and end_edges[divergence_idx]:
    slices.append(path + (slice(start[divergence_idx]			,      end[divergence_idx] + 1    ),)    )
  # If just start is at the edge, we can grab almost all of the subtree,
  # treating only the ragged bottom edge as an edge case
  elif start_edges[divergence_idx]:
    slices.append(path + (slice(start[divergence_idx]			,      end[divergence_idx]    ),)    )
    slices.extend(lower()    )
  # Analogous to the previous case, but the top is ragged this time
  elif end_edges[divergence_idx]:
    slices.extend(upper()    )
    slices.append(path + (slice(start[divergence_idx] + 1			,      end[divergence_idx] + 1    ),)    )
  # If both sides of the range are ragged, we need to handle both sides
  # separately. If there's contiguous meat in between them, we can index it
  # in one big chunk
  else:
    slices.extend(upper()    )
    a						: Optional[int]					       =					end[divergence_idx] - start[divergence_idx]
    if middle_ground > 1:
      slices.append(path + (slice(start[divergence_idx] + 1			,      end[divergence_idx]    ),)    )
    slices.extend(lower()    )

  return slices
@torch.jit.ignore
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A    ):
  a						: List[Any]					       =					t.shape[:no_batch_dims]
  a						: Optional[Any]					       =					list(_flat_idx_to_idx(__UpperCAmelCase			,      __UpperCAmelCase    )    )
  # _get_minimal_slice_set is inclusive
  a						: Dict					       =					list(_flat_idx_to_idx(flat_end - 1			,      __UpperCAmelCase    )    )

  # Get an ordered list of slices to perform
  a						: int					       =					_get_minimal_slice_set(
      __UpperCAmelCase			,      __UpperCAmelCase			,      __UpperCAmelCase			,      )

  a						: Tuple					       =					[t[s] for s in slices]

  return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]    ) for s in sliced_tensors]    )
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A			,      _A = False			,      _A = None			,      _A = False			,      ):
  if not (len(__UpperCAmelCase    ) > 0):
    raise ValueError('Must provide at least one input'    )

  a						: List[Any]					       =					[shape[:no_batch_dims] for shape in _fetch_dims(__UpperCAmelCase    )]
  a						: int					       =					tuple([max(__UpperCAmelCase    ) for s in zip(*__UpperCAmelCase    )]    )

  def _prep_inputs(_A    ) -> torch.Tensor:
    if not low_mem:
      if not sum(t.shape[:no_batch_dims]    ) == no_batch_dims:
        a						: Any					       =					t.expand(orig_batch_dims + t.shape[no_batch_dims:]    )
      a						: List[str]					       =					t.reshape(-1			,      *t.shape[no_batch_dims:]    )
    else:
      a						: Union[str, Any]					       =					t.expand(orig_batch_dims + t.shape[no_batch_dims:]    )
    return t

  a						: Dict[str, Any]					       =					tensor_tree_map(_prep_inputs			,      __UpperCAmelCase    )
  a						: int					       =					None
  if _out is not None:
    a						: str					       =					tensor_tree_map(lambda _A    : t.view([-1] + list(t.shape[no_batch_dims:]    )    )			,      _out    )

  a						: Optional[Any]					       =					1
  for d in orig_batch_dims:
    flat_batch_dim *= d

  a						: Any					       =					flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

  def _select_chunk(_A    ) -> torch.Tensor:
    return t[i : i + chunk_size] if t.shape[0] != 1 else t

  a						: Optional[Any]					       =					0
  a						: int					       =					prepped_outputs
  for _ in range(__UpperCAmelCase    ):
    # Chunk the input
    if not low_mem:
      a						: int					       =					_select_chunk
    else:
      a						: Optional[Any]					       =					partial(
          _chunk_slice			,      flat_start=__UpperCAmelCase			,      flat_end=min(__UpperCAmelCase			,      i + chunk_size    )			,      no_batch_dims=len(__UpperCAmelCase    )			,      )

    a						: Dict[str, Any]					       =					tensor_tree_map(__UpperCAmelCase			,      __UpperCAmelCase    )

    # Run the layer on the chunk
    a						: Optional[Any]					       =					layer(**__UpperCAmelCase    )

    # Allocate space for the output
    if out is None:
      a						: Any					       =					tensor_tree_map(lambda _A    : t.new_zeros((flat_batch_dim,) + t.shape[1:]    )			,      __UpperCAmelCase    )

    # Put the chunk in its pre-allocated space
    if isinstance(__UpperCAmelCase			,      __UpperCAmelCase    ):

      def assign(_A			,      _A    ) -> None:
        for k, v in da.items():
          if isinstance(__UpperCAmelCase			,      __UpperCAmelCase    ):
            assign(__UpperCAmelCase			,      da[k]    )
          else:
            if _add_into_out:
              v[i : i + chunk_size] += da[k]
            else:
              a						: Optional[int]					       =					da[k]

      assign(__UpperCAmelCase			,      __UpperCAmelCase    )
    elif isinstance(__UpperCAmelCase			,      __UpperCAmelCase    ):
      for xa, xa in zip(__UpperCAmelCase			,      __UpperCAmelCase    ):
        if _add_into_out:
          xa[i : i + chunk_size] += xa
        else:
          a						: Optional[int]					       =					xa
    elif isinstance(__UpperCAmelCase			,      torch.Tensor    ):
      if _add_into_out:
        out[i : i + chunk_size] += output_chunk
      else:
        a						: Union[str, Any]					       =					output_chunk
    else:
      raise ValueError('Not supported'    )

    i += chunk_size

  a						: Any					       =					tensor_tree_map(lambda _A    : t.view(orig_batch_dims + t.shape[1:]    )			,      __UpperCAmelCase    )

  return out

class 							a__:







    def __init__(				self			:  Optional[Any]						,      __snake_case			:  Tuple = 5_12						,      ):
      a						: int					       =					max_chunk_size
      a						: Optional[int]					       =					None
      a						: Optional[tuple]					       =					None







    def       lowercase_	(				self			:  int						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  List[Any]						,      __snake_case			:  Tuple     ):
      logging.info('Tuning chunk size...'     )

      if min_chunk_size >= self.max_chunk_size:
        return min_chunk_size

      a						: List[int]					       =					[2**l for l in range(int(math.log(self.max_chunk_size						,      2     )     ) + 1     )]
      a						: List[Any]					       =					[c for c in candidates if c > min_chunk_size]
      a						: str					       =					[min_chunk_size] + candidates
      candidates[-1] += 4

      def test_chunk_size(__snake_case			:  List[str]     ) -> bool:
        try:
          with torch.no_grad():
            fn(*_UpperCAmelCase						,      chunk_size=_UpperCAmelCase     )
          return True
        except RuntimeError:
          return False

      a						: Any					       =					0
      a						: Any					       =					len(_UpperCAmelCase     ) - 1
      while i > min_viable_chunk_size_index:
        a						: List[str]					       =					test_chunk_size(candidates[i]     )
        if not viable:
          a						: Optional[int]					       =					(min_viable_chunk_size_index + i) // 2
        else:
          a						: Dict					       =					i
          a						: Union[str, Any]					       =					(i + len(_UpperCAmelCase     ) - 1) // 2

      return candidates[min_viable_chunk_size_index]







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  Any						,      __snake_case			:  Union[str, Any]     ):
      a						: Tuple					       =					True
      for aa, aa in zip(_UpperCAmelCase						,      _UpperCAmelCase     ):
        assert type(_UpperCAmelCase     ) == type(_UpperCAmelCase     )
        if isinstance(_UpperCAmelCase						,      (list, tuple)     ):
          consistent &= self._compare_arg_caches(_UpperCAmelCase						,      _UpperCAmelCase     )
        elif isinstance(_UpperCAmelCase						,      _UpperCAmelCase     ):
          a						: Union[str, Any]					       =					[v for _, v in sorted(aa.items()						,      key=lambda __snake_case     : x[0]     )]
          a						: Union[str, Any]					       =					[v for _, v in sorted(aa.items()						,      key=lambda __snake_case     : x[0]     )]
          consistent &= self._compare_arg_caches(_UpperCAmelCase						,      _UpperCAmelCase     )
        else:
          consistent &= aa == aa

      return consistent







    def       lowercase_	(				self			:  str						,      __snake_case			:  Optional[Any]						,      __snake_case			:  int						,      __snake_case			:  Any						,      ):
      a						: str					       =					True
      a						: tuple					       =					tree_map(lambda __snake_case     : a.shape if isinstance(_UpperCAmelCase						,      torch.Tensor     ) else a						,      _UpperCAmelCase						,      _UpperCAmelCase     )
      if self.cached_arg_data is not None:
        # If args have changed shape/value, we need to re-tune
        assert len(self.cached_arg_data     ) == len(_UpperCAmelCase     )
        a						: str					       =					self._compare_arg_caches(self.cached_arg_data						,      _UpperCAmelCase     )
      else:
        # Otherwise, we can reuse the precomputed value
        a						: Optional[int]					       =					False

      if not consistent:
        a						: List[Any]					       =					self._determine_favorable_chunk_size(
            _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      )
        a						: str					       =					arg_data

      assert self.cached_chunk_size is not None

      return self.cached_chunk_size




'''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			:  Optional[Any]						,      __snake_case			:  float						,      __snake_case			:  Callable						,      __snake_case			:  int						,      __snake_case			:  float = 1.0						,      __snake_case			:  str = None						,      ):
      super().__init__()
      a						: Dict					       =					initial_learning_rate
      a						: Optional[int]					       =					warmup_steps
      a						: Tuple					       =					power
      a						: str					       =					decay_schedule_fn
      a						: Optional[int]					       =					name







    def __call__(				self			:  List[Any]						,      __snake_case			:  int     ):
      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`.
        a						: Optional[int]					       =					tf.cast(__snake_case						,      tf.floataa     )
        a						: Tuple					       =					tf.cast(self.warmup_steps						,      tf.floataa     )
        a						: str					       =					global_step_float / warmup_steps_float
        a						: Union[str, Any]					       =					self.initial_learning_rate * tf.math.pow(__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=__snake_case						,      )







    def       lowercase_	(				self			:  Dict     ):
      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__	(						_A			,      _A			,      _A			,      _A = 0.0			,      _A = 0.9			,      _A = 0.999			,      _A = 1E-8			,      _A = None			,      _A = None			,      _A = 0.0			,      _A = 1.0			,      _A = None			,      ):
  a						: Optional[int]					       =					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:
    a						: Tuple					       =					WarmUp(
        initial_learning_rate=_A			,      decay_schedule_fn=_A			,      warmup_steps=_A			,      )
  if weight_decay_rate > 0.0:
    a						: List[Any]					       =					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:
    a						: Tuple					       =					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 							a__( lowerCamelCase__     ):







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







    @classmethod
    def       lowercase_	(				cls			:  Any						,      __snake_case			:  Optional[Any]     ):
      a						: Any					       =					{'WarmUp': WarmUp}
      return super(__snake_case						,      cls     ).from_config(__snake_case						,      custom_objects=__snake_case     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Optional[int]						,      __snake_case			:  Dict						,      __snake_case			:  Optional[int]     ):
      super(__snake_case						,      self     )._prepare_local(__snake_case						,      __snake_case						,      __snake_case     )
      a						: List[str]					       =					tf.constant(
          self.weight_decay_rate						,      name='adam_weight_decay_rate'     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  Dict						,      __snake_case			:  List[Any]						,      __snake_case			:  str     ):
      a						: Optional[Any]					       =					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       lowercase_	(				self			:  List[str]						,      __snake_case			:  List[str]						,      __snake_case			:  Any=None						,      **__snake_case			:  Tuple     ):
      a       ,     a						: Dict					       =					list(zip(*__snake_case     )     )
      return super(__snake_case						,      self     ).apply_gradients(zip(__snake_case						,      __snake_case     )						,      name=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  List[str]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Optional[int]     ):
      if apply_state is None:
        return self._decayed_lr_t[var_dtype], {}

      a						: List[Any]					       =					apply_state or {}
      a						: Optional[Any]					       =					apply_state.get((var_device, var_dtype)     )
      if coefficients is None:
        a						: int					       =					self._fallback_apply_state(__snake_case						,      __snake_case     )
        a						: Any					       =					coefficients

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







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Dict						,      __snake_case			:  Tuple=None     ):
      a       ,     a						: List[Any]					       =					self._get_lr(var.device						,      var.dtype.base_dtype						,      __snake_case     )
      a						: List[Any]					       =					self._decay_weights_op(__snake_case						,      __snake_case						,      __snake_case     )
      with tf.control_dependencies([decay]     ):
        return super(__snake_case						,      self     )._resource_apply_dense(__snake_case						,      __snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  Any						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  List[str]						,      __snake_case			:  int=None     ):
      a       ,     a						: Tuple					       =					self._get_lr(var.device						,      var.dtype.base_dtype						,      __snake_case     )
      a						: int					       =					self._decay_weights_op(__snake_case						,      __snake_case						,      __snake_case     )
      with tf.control_dependencies([decay]     ):
        return super(__snake_case						,      self     )._resource_apply_sparse(__snake_case						,      __snake_case						,      __snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Union[str, Any]					       =					super().get_config()
      config.update({'weight_decay_rate': self.weight_decay_rate}     )
      return config







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Dict     ):
      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(__snake_case						,      __snake_case     ) is not None:
            return True

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

class 							a__( lowerCamelCase__     ):







    def __init__(				self			:  List[str]     ):
      a						: List[Any]					       =					[]
      a						: Optional[int]					       =					None







    @property
    def       lowercase_	(				self			:  Any     ):
      if self._accum_steps is None:
        a						: List[Any]					       =					tf.Variable(
            tf.constant(0						,      dtype=tf.intaa     )						,      trainable=__snake_case						,      synchronization=tf.VariableSynchronization.ON_READ						,      aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA						,      )

      return self._accum_steps.value()







    @property
    def       lowercase_	(				self			:  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			:  List[Any]						,      __snake_case			:  Any     ):
      if not self._gradients:
        a						: Union[str, Any]					       =					self.step  # Create the step variable.
        self._gradients.extend(
            [
                tf.Variable(
                    tf.zeros_like(__snake_case     )						,      trainable=__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(__snake_case     ) != len(self._gradients     ):
        raise ValueError(F"""Expected {len(self._gradients     )} gradients, but got {len(__snake_case     )}"""     )

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

      self._accum_steps.assign_add(1     )







    def       lowercase_	(				self			:  Tuple     ):
      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(__snake_case     )     )