




import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST



class       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      Any ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Optional[int]=13 ,   __snake_case       :      str=7 ,   __snake_case       :      Optional[Any]=True ,   __snake_case       :      List[Any]=True ,   __snake_case       :      Dict=True ,   __snake_case       :      Optional[int]=True ,   __snake_case       :      str=99 ,   __snake_case       :      Optional[Any]=32 ,   __snake_case       :      str=5 ,   __snake_case       :      List[Any]=4 ,   __snake_case       :      str=37 ,   __snake_case       :      Union[str, Any]="gelu" ,   __snake_case       :      Dict=0.1 ,   __snake_case       :      Optional[Any]=0.1 ,   __snake_case       :      int=5_12 ,   __snake_case       :      Tuple=16 ,   __snake_case       :      List[str]=2 ,   __snake_case       :      Optional[int]=0.02 ,   __snake_case       :      str=False ,   __snake_case       :      Tuple=True ,   __snake_case       :      Optional[Any]="None" ,   __snake_case       :      Union[str, Any]=3 ,   __snake_case       :      Union[str, Any]=4 ,   __snake_case       :      Optional[int]=None ,   ):
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					seq_length
         UpperCAmelCase_										=					is_training
         UpperCAmelCase_										=					use_input_mask
         UpperCAmelCase_										=					use_token_type_ids
         UpperCAmelCase_										=					use_labels
         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					hidden_size
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					intermediate_size
         UpperCAmelCase_										=					hidden_act
         UpperCAmelCase_										=					hidden_dropout_prob
         UpperCAmelCase_										=					attention_probs_dropout_prob
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					type_vocab_size
         UpperCAmelCase_										=					type_sequence_label_size
         UpperCAmelCase_										=					initializer_range
         UpperCAmelCase_										=					num_labels
         UpperCAmelCase_										=					num_choices
         UpperCAmelCase_										=					relative_attention
         UpperCAmelCase_										=					position_biased_input
         UpperCAmelCase_										=					pos_att_type
         UpperCAmelCase_										=					scope
     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size    )

         UpperCAmelCase_										=					None
         if self.use_input_mask:
             UpperCAmelCase_										=					ids_tensor([self.batch_size, self.seq_length] ,   vocab_size=2    )

         UpperCAmelCase_										=					None
         if self.use_token_type_ids:
             UpperCAmelCase_										=					ids_tensor([self.batch_size, self.seq_length] ,   self.type_vocab_size    )

         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					None
         if self.use_labels:
             UpperCAmelCase_										=					ids_tensor([self.batch_size] ,   self.type_sequence_label_size    )
             UpperCAmelCase_										=					ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels    )
             UpperCAmelCase_										=					ids_tensor([self.batch_size] ,   self.num_choices    )

         UpperCAmelCase_										=					self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         return DebertaConfig(
             vocab_size=self.vocab_size ,   hidden_size=self.hidden_size ,   num_hidden_layers=self.num_hidden_layers ,   num_attention_heads=self.num_attention_heads ,   intermediate_size=self.intermediate_size ,   hidden_act=self.hidden_act ,   hidden_dropout_prob=self.hidden_dropout_prob ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob ,   max_position_embeddings=self.max_position_embeddings ,   type_vocab_size=self.type_vocab_size ,   initializer_range=self.initializer_range ,   relative_attention=self.relative_attention ,   position_biased_input=self.position_biased_input ,   pos_att_type=self.pos_att_type ,   )
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					self.get_config()
         UpperCAmelCase_										=					3_00
         return config
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      str    ):
         self.parent.assertListEqual(list(result.loss.size()    ) ,   []    )
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      Optional[Any] ,   __snake_case       :      Tuple ,   __snake_case       :      Any ,   __snake_case       :      Tuple ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Tuple ,   __snake_case       :      Optional[int]    ):
         UpperCAmelCase_										=					DebertaModel(config=__snake_case    )
         model.to(__snake_case    )
         model.eval()
         UpperCAmelCase_										=					model(__snake_case ,   attention_mask=__snake_case ,   token_type_ids=__snake_case    )[0]
         UpperCAmelCase_										=					model(__snake_case ,   token_type_ids=__snake_case    )[0]
         UpperCAmelCase_										=					model(__snake_case    )[0]

         self.parent.assertListEqual(list(sequence_output.size()    ) ,   [self.batch_size, self.seq_length, self.hidden_size]    )
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      Any ,   __snake_case       :      Tuple ,   __snake_case       :      Optional[int] ,   __snake_case       :      List[Any] ,   __snake_case       :      Optional[Any] ,   __snake_case       :      Any ,   __snake_case       :      Optional[int]    ):
         UpperCAmelCase_										=					DebertaForMaskedLM(config=__snake_case    )
         model.to(__snake_case    )
         model.eval()
         UpperCAmelCase_										=					model(__snake_case ,   attention_mask=__snake_case ,   token_type_ids=__snake_case ,   labels=__snake_case    )

         self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size)    )
     def   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      Dict ,   __snake_case       :      str ,   __snake_case       :      Any ,   __snake_case       :      Optional[Any] ,   __snake_case       :      str ,   __snake_case       :      List[str] ,   __snake_case       :      str    ):
         UpperCAmelCase_										=					self.num_labels
         UpperCAmelCase_										=					DebertaForSequenceClassification(__snake_case    )
         model.to(__snake_case    )
         model.eval()
         UpperCAmelCase_										=					model(__snake_case ,   attention_mask=__snake_case ,   token_type_ids=__snake_case ,   labels=__snake_case    )
         self.parent.assertListEqual(list(result.logits.size()    ) ,   [self.batch_size, self.num_labels]    )
         self.check_loss_output(__snake_case    )
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      Optional[int] ,   __snake_case       :      Dict ,   __snake_case       :      List[Any] ,   __snake_case       :      Dict ,   __snake_case       :      Any ,   __snake_case       :      str ,   __snake_case       :      Optional[int]    ):
         UpperCAmelCase_										=					self.num_labels
         UpperCAmelCase_										=					DebertaForTokenClassification(config=__snake_case    )
         model.to(__snake_case    )
         model.eval()
         UpperCAmelCase_										=					model(__snake_case ,   attention_mask=__snake_case ,   token_type_ids=__snake_case ,   labels=__snake_case    )
         self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels)    )
     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      Optional[int] ,   __snake_case       :      List[Any] ,   __snake_case       :      Tuple ,   __snake_case       :      Any ,   __snake_case       :      Dict ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Tuple    ):
         UpperCAmelCase_										=					DebertaForQuestionAnswering(config=__snake_case    )
         model.to(__snake_case    )
         model.eval()
         UpperCAmelCase_										=					model(
             __snake_case ,   attention_mask=__snake_case ,   token_type_ids=__snake_case ,   start_positions=__snake_case ,   end_positions=__snake_case ,   )
         self.parent.assertEqual(result.start_logits.shape ,   (self.batch_size, self.seq_length)    )
         self.parent.assertEqual(result.end_logits.shape ,   (self.batch_size, self.seq_length)    )




     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_							
         )			,							(
             UpperCAmelCase_							
         )			,							(
             UpperCAmelCase_							
         )			,							(
             UpperCAmelCase_							
         )			,							(
             UpperCAmelCase_							
         )			,							(
             UpperCAmelCase_							
         )			,							(
             UpperCAmelCase_							
         )			,														
         )										=					config_and_inputs
         UpperCAmelCase_										=					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict



@require_torch
class       a					(       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  (
         (
             DebertaModel,
             DebertaForMaskedLM,
             DebertaForSequenceClassification,
             DebertaForTokenClassification,
             DebertaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     lowerCAmelCase    :	List[Any]       		=  (
         {
             'feature-extraction': DebertaModel,
             'fill-mask': DebertaForMaskedLM,
             'question-answering': DebertaForQuestionAnswering,
             'text-classification': DebertaForSequenceClassification,
             'token-classification': DebertaForTokenClassification,
             'zero-shot': DebertaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )

     lowerCAmelCase    :	Optional[int]       		=  True
     lowerCAmelCase    :	str       		=  False
     lowerCAmelCase    :	str       		=  False
     lowerCAmelCase    :	Any       		=  False
     lowerCAmelCase    :	int       		=  False
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					DebertaModelTester(self    )
         UpperCAmelCase_										=					ConfigTester(self ,   config_class=__snake_case ,   hidden_size=37    )
     def   lowerCamelCase_       (     self       :      Tuple    ):
         self.config_tester.run_common_tests()
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_model(*__snake_case    )
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_sequence_classification(*__snake_case    )
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_masked_lm(*__snake_case    )
     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_question_answering(*__snake_case    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_token_classification(*__snake_case    )




     @slow
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase_										=					DebertaModel.from_pretrained(__snake_case    )
             self.assertIsNotNone(__snake_case    )



@require_torch
@require_sentencepiece
@require_tokenizers
class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     @unittest.skip(reason='''Model not available yet'''    )
     def   lowerCamelCase_       (     self       :      Tuple    ):
         pass




     @slow
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					DebertaModel.from_pretrained('''microsoft/deberta-base'''    )

         UpperCAmelCase_										=					torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]    )
         UpperCAmelCase_										=					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
         with torch.no_grad():
             UpperCAmelCase_										=					model(__snake_case ,   attention_mask=__snake_case    )[0]
         # compare the actual values for a slice.
         UpperCAmelCase_										=					torch.tensor(
             [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]    )
         self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,   __snake_case ,   atol=1E-4    ) ,   F'{output[:, 1:4, 1:4]}'    )











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     (	__UpperCamelCase      :						Union[dict, list, tuple, torch.Tensor]				)  ->							List[Tuple[int, ...]]:
    UpperCAmelCase_										=					[]
    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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						Tuple[int, ...]				)  ->							Tuple[int, ...]:
    UpperCAmelCase_										=					[]
    for d in reversed(__UpperCamelCase				):
        idx.append(flat_idx % d				)
        UpperCAmelCase_										=					flat_idx // d

    return tuple(reversed(__UpperCamelCase				)				)




@torch.jit.ignore
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Sequence[int]	,					__UpperCamelCase      :						Sequence[int]	,					__UpperCamelCase      :						Sequence[int]	,					__UpperCamelCase      :						Optional[Sequence[bool]] = None	,					__UpperCamelCase      :						Optional[Sequence[bool]] = 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(__UpperCamelCase      :						List[bool]				) -> None:
        UpperCAmelCase_										=					True
        for i in range(len(__UpperCamelCase				)				):
            UpperCAmelCase_										=					-1 * (i + 1)
            l[reversed_idx] &= tally
            UpperCAmelCase_										=					l[reversed_idx]

    if start_edges is None:
        UpperCAmelCase_										=					[s == 0 for s in start]
        reduce_edge_list(__UpperCamelCase				)
    if end_edges is None:
        UpperCAmelCase_										=					[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				),)]

    UpperCAmelCase_										=					[]
    UpperCAmelCase_										=					[]

    # 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

    UpperCAmelCase_										=					tuple(__UpperCamelCase				)
    UpperCAmelCase_										=					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

        UpperCAmelCase_										=					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

        UpperCAmelCase_										=					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()				)
        UpperCAmelCase_										=					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     (	__UpperCamelCase      :						torch.Tensor	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						int				)  ->							torch.Tensor:
    UpperCAmelCase_										=					t.shape[:no_batch_dims]
    UpperCAmelCase_										=					list(_flat_idx_to_idx(__UpperCamelCase	,					__UpperCamelCase				)				)
    # _get_minimal_slice_set is inclusive
    UpperCAmelCase_										=					list(_flat_idx_to_idx(flat_end - 1	,					__UpperCamelCase				)				)

    # Get an ordered list of slices to perform
    UpperCAmelCase_										=					_get_minimal_slice_set(
        __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					)

    UpperCAmelCase_										=					[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     (	__UpperCamelCase      :						Callable	,					__UpperCamelCase      :						Dict[str, Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						bool = False	,					__UpperCamelCase      :						Any = None	,					__UpperCamelCase      :						bool = False	,					)  ->							Any:
    if not (len(__UpperCamelCase				) > 0):
        raise ValueError('''Must provide at least one input'''				)

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

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

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

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

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

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

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

        UpperCAmelCase_										=					tensor_tree_map(__UpperCamelCase	,					__UpperCamelCase				)

        # Run the layer on the chunk
        UpperCAmelCase_										=					layer(**__UpperCamelCase				)

        # Allocate space for the output
        if out is None:
            UpperCAmelCase_										=					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      :						dict	,					__UpperCamelCase      :						dict				) -> 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:
                            UpperCAmelCase_										=					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:
                    UpperCAmelCase_										=					xa
        elif isinstance(__UpperCamelCase	,					torch.Tensor				):
            if _add_into_out:
                out[i : i + chunk_size] += output_chunk
            else:
                UpperCAmelCase_										=					output_chunk
        else:
            raise ValueError('''Not supported'''				)

        i += chunk_size

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

    return out



class       a					:

     '''simple docstring'''





     def __init__(     self       :      List[Any] ,   __snake_case       :      int = 5_12 ,   ):
         UpperCAmelCase_										=					max_chunk_size
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					None
     def   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      Callable ,   __snake_case       :      tuple ,   __snake_case       :      int    ):
         logging.info('''Tuning chunk size...'''    )

         if min_chunk_size >= self.max_chunk_size:
             return min_chunk_size

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

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

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

         return candidates[min_viable_chunk_size_index]
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      Iterable ,   __snake_case       :      Iterable    ):
         UpperCAmelCase_										=					True
         for aa, aa in zip(__snake_case ,   __snake_case    ):
             assert type(__snake_case    ) == type(__snake_case    )
             if isinstance(__snake_case ,   (list, tuple)    ):
                 consistent &= self._compare_arg_caches(__snake_case ,   __snake_case    )
             elif isinstance(__snake_case ,   __snake_case    ):
                 UpperCAmelCase_										=					[v for _, v in sorted(aa.items() ,   key=lambda __snake_case    : x[0]    )]
                 UpperCAmelCase_										=					[v for _, v in sorted(aa.items() ,   key=lambda __snake_case    : x[0]    )]
                 consistent &= self._compare_arg_caches(__snake_case ,   __snake_case    )
             else:
                 consistent &= aa == aa

         return consistent




     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      Callable ,   __snake_case       :      tuple ,   __snake_case       :      int ,   ):
         UpperCAmelCase_										=					True
         UpperCAmelCase_										=					tree_map(lambda __snake_case    : a.shape if isinstance(__snake_case ,   torch.Tensor    ) else a ,   __snake_case ,   __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(__snake_case    )
             UpperCAmelCase_										=					self._compare_arg_caches(self.cached_arg_data ,   __snake_case    )
         else:
             # Otherwise, we can reuse the precomputed value
             UpperCAmelCase_										=					False

         if not consistent:
             UpperCAmelCase_										=					self._determine_favorable_chunk_size(
                 __snake_case ,   __snake_case ,   __snake_case ,   )
             UpperCAmelCase_										=					arg_data

         assert self.cached_chunk_size is not None

         return self.cached_chunk_size





