

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[Tuple[int, ...]]:
 __lowerCamelCase    :    List[Any] 	=			[]
 if isinstance(lowerCamelCase__     ,			lowerCamelCase__      ):
  for v in tree.values():
   shapes.extend(_fetch_dims(lowerCamelCase__      )      )
 elif isinstance(lowerCamelCase__     ,			(list, tuple)      ):
  for t in tree:
   shapes.extend(_fetch_dims(lowerCamelCase__      )      )
 elif isinstance(lowerCamelCase__     ,			torch.Tensor      ):
  shapes.append(tree.shape      )
 else:
  raise ValueError('Not supported'      )

 return shapes






@torch.jit.ignore
def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Tuple[int, ...]:
 __lowerCamelCase    :    Optional[Any] 	=			[]
 for d in reversed(lowerCamelCase__      ):
  idx.append(flat_idx % d      )
  __lowerCamelCase    :    List[str] 	=			flat_idx // d

 return tuple(reversed(lowerCamelCase__      )      )






@torch.jit.ignore
def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ = None     ,			lowerCamelCase__ = None     ,			)  ->					List[Tuple[slice, ...]]:

 # 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(lowerCamelCase__      ) -> None:
  __lowerCamelCase    :    List[Any] 	=			True
  for i in range(len(lowerCamelCase__      )      ):
   __lowerCamelCase    :    Dict 	=			-1 * (i + 1)
   l[reversed_idx] &= tally
   __lowerCamelCase    :    int 	=			l[reversed_idx]

 if start_edges is None:
  __lowerCamelCase    :    int 	=			[s == 0 for s in start]
  reduce_edge_list(lowerCamelCase__      )
 if end_edges is None:
  __lowerCamelCase    :    List[Any] 	=			[e == (d - 1) for e, d in zip(lowerCamelCase__     ,			lowerCamelCase__      )]
  reduce_edge_list(lowerCamelCase__      )

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

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

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

 __lowerCamelCase    :    Tuple[slice, ...] 	=			tuple(lowerCamelCase__      )
 __lowerCamelCase    :    Dict 	=			len(lowerCamelCase__      )

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

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

  __lowerCamelCase    :    str 	=			start[divergence_idx]
  return tuple(
      path + (slice(lowerCamelCase__     ,			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

  __lowerCamelCase    :    Optional[Any] 	=			end[divergence_idx]
  return tuple(
      path + (slice(lowerCamelCase__     ,			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()      )
  __lowerCamelCase    :    List[Any] 	=			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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					torch.Tensor:
 __lowerCamelCase    :    str 	=			t.shape[:no_batch_dims]
 __lowerCamelCase    :    Optional[int] 	=			list(_flat_idx_to_idx(lowerCamelCase__     ,			lowerCamelCase__      )      )
 # _get_minimal_slice_set is inclusive
 __lowerCamelCase    :    int 	=			list(_flat_idx_to_idx(flat_end - 1     ,			lowerCamelCase__      )      )

 # Get an ordered list of slices to perform
 __lowerCamelCase    :    Optional[int] 	=			_get_minimal_slice_set(
     lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			)

 __lowerCamelCase    :    Optional[Any] 	=			[t[s] for s in slices]

 return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]      ) for s in sliced_tensors]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ = False     ,			lowerCamelCase__ = None     ,			lowerCamelCase__ = False     ,			)  ->					Any:
 if not (len(lowerCamelCase__      ) > 0):
  raise ValueError('Must provide at least one input'      )

 __lowerCamelCase    :    Tuple 	=			[shape[:no_batch_dims] for shape in _fetch_dims(lowerCamelCase__      )]
 __lowerCamelCase    :    Optional[Any] 	=			tuple([max(lowerCamelCase__      ) for s in zip(*lowerCamelCase__      )]      )

 def _prep_inputs(lowerCamelCase__      ) -> torch.Tensor:
  if not low_mem:
   if not sum(t.shape[:no_batch_dims]      ) == no_batch_dims:
    __lowerCamelCase    :    int 	=			t.expand(orig_batch_dims + t.shape[no_batch_dims:]      )
   __lowerCamelCase    :    Optional[int] 	=			t.reshape(-1     ,			*t.shape[no_batch_dims:]      )
  else:
   __lowerCamelCase    :    List[Any] 	=			t.expand(orig_batch_dims + t.shape[no_batch_dims:]      )
  return t

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

 __lowerCamelCase    :    int 	=			1
 for d in orig_batch_dims:
  flat_batch_dim *= d

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

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

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

  __lowerCamelCase    :    Dict[str, Any] 	=			tensor_tree_map(lowerCamelCase__     ,			lowerCamelCase__      )

  # Run the layer on the chunk
  __lowerCamelCase    :    str 	=			layer(**lowerCamelCase__      )

  # Allocate space for the output
  if out is None:
   __lowerCamelCase    :    str 	=			tensor_tree_map(lambda lowerCamelCase__      : t.new_zeros((flat_batch_dim,) + t.shape[1:]      )     ,			lowerCamelCase__      )

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

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

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

  i += chunk_size

 __lowerCamelCase    :    str 	=			tensor_tree_map(lambda lowerCamelCase__      : t.view(orig_batch_dims + t.shape[1:]      )     ,			lowerCamelCase__      )

 return out






class 	A_		:
       def __init__(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      int = 5_1_2       ,):
        __lowerCamelCase    :    Dict 	=			max_chunk_size
        __lowerCamelCase    :    Optional[int] 	=			None
        __lowerCamelCase    :    Optional[tuple] 	=			None
       def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Callable       ,SCREAMING_SNAKE_CASE__		:      tuple       ,SCREAMING_SNAKE_CASE__		:      int):
        logging.info('Tuning chunk size...')

        if min_chunk_size >= self.max_chunk_size:
         return min_chunk_size

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

        def test_chunk_size(SCREAMING_SNAKE_CASE__		:      int) -> bool:
         try:
          with torch.no_grad():
           fn(*SCREAMING_SNAKE_CASE__       ,chunk_size=SCREAMING_SNAKE_CASE__)
          return True
         except RuntimeError:
          return False

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

        return candidates[min_viable_chunk_size_index]
       def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Iterable       ,SCREAMING_SNAKE_CASE__		:      Iterable):
        __lowerCamelCase    :    List[Any] 	=			True
        for aa, aa in zip(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__):
         assert type(SCREAMING_SNAKE_CASE__) == type(SCREAMING_SNAKE_CASE__)
         if isinstance(SCREAMING_SNAKE_CASE__       ,(list, tuple)):
          consistent &= self._compare_arg_caches(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
         elif isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__):
          __lowerCamelCase    :    int 	=			[v for _, v in sorted(aa.items()       ,key=lambda SCREAMING_SNAKE_CASE__: x[0])]
          __lowerCamelCase    :    str 	=			[v for _, v in sorted(aa.items()       ,key=lambda SCREAMING_SNAKE_CASE__: x[0])]
          consistent &= self._compare_arg_caches(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
         else:
          consistent &= aa == aa

        return consistent


       def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Callable       ,SCREAMING_SNAKE_CASE__		:      tuple       ,SCREAMING_SNAKE_CASE__		:      int       ,):
        __lowerCamelCase    :    int 	=			True
        __lowerCamelCase    :    tuple 	=			tree_map(lambda SCREAMING_SNAKE_CASE__: a.shape if isinstance(SCREAMING_SNAKE_CASE__       ,torch.Tensor) else a       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
        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(SCREAMING_SNAKE_CASE__)
         __lowerCamelCase    :    Optional[int] 	=			self._compare_arg_caches(self.cached_arg_data       ,SCREAMING_SNAKE_CASE__)
        else:
         # Otherwise, we can reuse the precomputed value
         __lowerCamelCase    :    int 	=			False

        if not consistent:
         __lowerCamelCase    :    List[str] 	=			self._determine_favorable_chunk_size(
             SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,)
         __lowerCamelCase    :    Optional[int] 	=			arg_data

        assert self.cached_chunk_size is not None

        return self.cached_chunk_size







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a		={
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""RoFormerTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RoFormerForCausalLM""",
					    """RoFormerForMaskedLM""",
					    """RoFormerForMultipleChoice""",
					    """RoFormerForQuestionAnswering""",
					    """RoFormerForSequenceClassification""",
					    """RoFormerForTokenClassification""",
					    """RoFormerLayer""",
					    """RoFormerModel""",
					    """RoFormerPreTrainedModel""",
					    """load_tf_weights_in_roformer""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRoFormerForCausalLM""",
					    """TFRoFormerForMaskedLM""",
					    """TFRoFormerForMultipleChoice""",
					    """TFRoFormerForQuestionAnswering""",
					    """TFRoFormerForSequenceClassification""",
					    """TFRoFormerForTokenClassification""",
					    """TFRoFormerLayer""",
					    """TFRoFormerModel""",
					    """TFRoFormerPreTrainedModel""",
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaxRoFormerForMaskedLM""",
					    """FlaxRoFormerForMultipleChoice""",
					    """FlaxRoFormerForQuestionAnswering""",
					    """FlaxRoFormerForSequenceClassification""",
					    """FlaxRoFormerForTokenClassification""",
					    """FlaxRoFormerModel""",
					    """FlaxRoFormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
					from .tokenization_roformer import RoFormerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roformer_fast import RoFormerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roformer import (
										    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RoFormerForCausalLM,
										    RoFormerForMaskedLM,
										    RoFormerForMultipleChoice,
										    RoFormerForQuestionAnswering,
										    RoFormerForSequenceClassification,
										    RoFormerForTokenClassification,
										    RoFormerLayer,
										    RoFormerModel,
										    RoFormerPreTrainedModel,
										    load_tf_weights_in_roformer,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roformer import (
										    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRoFormerForCausalLM,
										    TFRoFormerForMaskedLM,
										    TFRoFormerForMultipleChoice,
										    TFRoFormerForQuestionAnswering,
										    TFRoFormerForSequenceClassification,
										    TFRoFormerForTokenClassification,
										    TFRoFormerLayer,
										    TFRoFormerModel,
										    TFRoFormerPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roformer import (
										    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxRoFormerForMaskedLM,
										    FlaxRoFormerForMultipleChoice,
										    FlaxRoFormerForQuestionAnswering,
										    FlaxRoFormerForSequenceClassification,
										    FlaxRoFormerForTokenClassification,
										    FlaxRoFormerModel,
										    FlaxRoFormerPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




