



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      __snake_case	(			_UpperCAmelCase				):
    __a						       =				[]
    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      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				[]
    for d in reversed(_UpperCAmelCase				):
        idx.append(flat_idx % d				)
        __a						       =				flat_idx // d

    return tuple(reversed(_UpperCAmelCase				)				)





@torch.jit.ignore
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase = None   , _UpperCAmelCase = 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(_UpperCAmelCase				) -> None:
        __a						       =				True
        for i in range(len(_UpperCAmelCase				)				):
            __a						       =				-1 * (i + 1)
            l[reversed_idx] &= tally
            __a						       =				l[reversed_idx]

    if start_edges is None:
        __a						       =				[s == 0 for s in start]
        reduce_edge_list(_UpperCAmelCase				)
    if end_edges is None:
        __a						       =				[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						       =				[]
    __a						       =				[]

    # 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(_UpperCAmelCase				)
    __a						       =				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						       =				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						       =				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						       =				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      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				t.shape[:no_batch_dims]
    __a						       =				list(_flat_idx_to_idx(_UpperCAmelCase   , _UpperCAmelCase				)				)
    # _get_minimal_slice_set is inclusive
    __a						       =				list(_flat_idx_to_idx(flat_end - 1   , _UpperCAmelCase				)				)

    # Get an ordered list of slices to perform
    __a						       =				_get_minimal_slice_set(
        _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , )

    __a						       =				[t[s] for s in slices]

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





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase = False   , _UpperCAmelCase = None   , _UpperCAmelCase = False   , ):
    if not (len(_UpperCAmelCase				) > 0):
        raise ValueError('''Must provide at least one input'''				)

    __a						       =				[shape[:no_batch_dims] for shape in _fetch_dims(_UpperCAmelCase				)]
    __a						       =				tuple([max(_UpperCAmelCase				) for s in zip(*_UpperCAmelCase				)]				)

    def _prep_inputs(_UpperCAmelCase				) -> torch.Tensor:
        if not low_mem:
            if not sum(t.shape[:no_batch_dims]				) == no_batch_dims:
                __a						       =				t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
            __a						       =				t.reshape(-1   , *t.shape[no_batch_dims:]				)
        else:
            __a						       =				t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
        return t

    __a						       =				tensor_tree_map(_prep_inputs   , _UpperCAmelCase				)
    __a						       =				None
    if _out is not None:
        __a						       =				tensor_tree_map(lambda _UpperCAmelCase				: t.view([-1] + list(t.shape[no_batch_dims:]				)				)   , _out				)

    __a						       =				1
    for d in orig_batch_dims:
        flat_batch_dim *= d

    __a						       =				flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

    __a						       =				0
    __a						       =				prepped_outputs
    for _ in range(_UpperCAmelCase				):
        # Chunk the input
        if not low_mem:
            __a						       =				_select_chunk
        else:
            __a						       =				partial(
                _chunk_slice   , flat_start=_UpperCAmelCase   , flat_end=min(_UpperCAmelCase   , i + chunk_size				)   , no_batch_dims=len(_UpperCAmelCase				)   , )

        __a						       =				tensor_tree_map(_UpperCAmelCase   , _UpperCAmelCase				)

        # Run the layer on the chunk
        __a						       =				layer(**_UpperCAmelCase				)

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

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

            def assign(_UpperCAmelCase   , _UpperCAmelCase				) -> 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						       =				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						       =				xa
        elif isinstance(_UpperCAmelCase   , torch.Tensor				):
            if _add_into_out:
                out[i : i + chunk_size] += output_chunk
            else:
                __a						       =				output_chunk
        else:
            raise ValueError('''Not supported'''				)

        i += chunk_size

    __a						       =				tensor_tree_map(lambda _UpperCAmelCase				: t.view(orig_batch_dims + t.shape[1:]				)   , _UpperCAmelCase				)

    return out

class     _A       :



       def __init__(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     int = 512     ,		):


           '''simple docstring'''

           __a						       =				max_chunk_size
           __a						       =				None
           __a						       =				None



       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Callable     ,		__SCREAMING_SNAKE_CASE							:     tuple     ,		__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           logging.info('''Tuning chunk size...''')

           if min_chunk_size >= self.max_chunk_size:
               return min_chunk_size

           __a						       =				[2**l for l in range(int(math.log(self.max_chunk_size     ,		2)) + 1)]
           __a						       =				[c for c in candidates if c > min_chunk_size]
           __a						       =				[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

           __a						       =				0
           __a						       =				len(__SCREAMING_SNAKE_CASE) - 1
           while i > min_viable_chunk_size_index:
               __a						       =				test_chunk_size(candidates[i])
               if not viable:
                   __a						       =				(min_viable_chunk_size_index + i) // 2
               else:
                   __a						       =				i
                   __a						       =				(i + len(__SCREAMING_SNAKE_CASE) - 1) // 2

           return candidates[min_viable_chunk_size_index]



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Iterable     ,		__SCREAMING_SNAKE_CASE							:     Iterable):


           '''simple docstring'''

           __a						       =				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):
                   __a						       =				[v for _, v in sorted(aa.items()     ,		key=lambda __SCREAMING_SNAKE_CASE: x[0])]
                   __a						       =				[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							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Callable     ,		__SCREAMING_SNAKE_CASE							:     tuple     ,		__SCREAMING_SNAKE_CASE							:     int     ,		):


           '''simple docstring'''

           __a						       =				True
           __a						       =				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)
               __a						       =				self._compare_arg_caches(self.cached_arg_data     ,		__SCREAMING_SNAKE_CASE)
           else:
               # Otherwise, we can reuse the precomputed value
               __a						       =				False

           if not consistent:
               __a						       =				self._determine_favorable_chunk_size(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		)
               __a						       =				arg_data

           assert self.cached_chunk_size is not None

           return self.cached_chunk_size







"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_a		       =							{
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		"""albert"""







  def __init__(		self				,   lowercase_=3_0000				,   lowercase_=128				,   lowercase_=4096				,   lowercase_=12				,   lowercase_=1				,   lowercase_=64				,   lowercase_=1_6384				,   lowercase_=1				,   lowercase_="gelu_new"				,   lowercase_=0				,   lowercase_=0				,   lowercase_=512				,   lowercase_=2				,   lowercase_=0.02				,   lowercase_=1E-1_2				,   lowercase_=0.1				,   lowercase_="absolute"				,   lowercase_=0				,   lowercase_=2				,   lowercase_=3				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(pad_token_id=lowercase_				,   bos_token_id=lowercase_				,   eos_token_id=lowercase_				,   **lowercase_  )

     UpperCAmelCase_					:					int			     =    vocab_size
     UpperCAmelCase_					:					Optional[int]			     =    embedding_size
     UpperCAmelCase_					:					List[str]			     =    hidden_size
     UpperCAmelCase_					:					Optional[int]			     =    num_hidden_layers
     UpperCAmelCase_					:					Union[str, Any]			     =    num_hidden_groups
     UpperCAmelCase_					:					Dict			     =    num_attention_heads
     UpperCAmelCase_					:					Any			     =    inner_group_num
     UpperCAmelCase_					:					Union[str, Any]			     =    hidden_act
     UpperCAmelCase_					:					Union[str, Any]			     =    intermediate_size
     UpperCAmelCase_					:					List[str]			     =    hidden_dropout_prob
     UpperCAmelCase_					:					Union[str, Any]			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					Optional[Any]			     =    max_position_embeddings
     UpperCAmelCase_					:					Any			     =    type_vocab_size
     UpperCAmelCase_					:					List[str]			     =    initializer_range
     UpperCAmelCase_					:					Optional[int]			     =    layer_norm_eps
     UpperCAmelCase_					:					List[Any]			     =    classifier_dropout_prob
     UpperCAmelCase_					:					Tuple			     =    position_embedding_type




class A_	(lowercase__      ):




  '''simple docstring'''







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if self.task == "multiple-choice":
        UpperCAmelCase_					:					int			     =    {0: "batch", 1: "choice", 2: "sequence"}
     else:
        UpperCAmelCase_					:					Optional[Any]			     =    {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
             ("token_type_ids", dynamic_axis),
         ]  )


