





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							(    lowerCAmelCase_	)				->						List[Tuple[int, ...]]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple[int, ...]:
    SCREAMING_SNAKE_CASE__											=			[]
    for d in reversed(lowerCAmelCase_	):
        idx.append(flat_idx % d	)
        SCREAMING_SNAKE_CASE__											=			flat_idx // d

    return tuple(reversed(lowerCAmelCase_	)	)


@torch.jit.ignore
def 							__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:
        SCREAMING_SNAKE_CASE__											=			True
        for i in range(len(lowerCAmelCase_	)	):
            SCREAMING_SNAKE_CASE__											=			-1 * (i + 1)
            l[reversed_idx] &= tally
            SCREAMING_SNAKE_CASE__											=			l[reversed_idx]

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

    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			[]

    # 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

    SCREAMING_SNAKE_CASE__											=			tuple(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			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

        SCREAMING_SNAKE_CASE__											=			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

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

    # Get an ordered list of slices to perform
    SCREAMING_SNAKE_CASE__											=			_get_minimal_slice_set(
        lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							)

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

    SCREAMING_SNAKE_CASE__											=			[shape[:no_batch_dims] for shape in _fetch_dims(lowerCAmelCase_	)]
    SCREAMING_SNAKE_CASE__											=			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:
                SCREAMING_SNAKE_CASE__											=			t.expand(orig_batch_dims + t.shape[no_batch_dims:]	)
            SCREAMING_SNAKE_CASE__											=			t.reshape(-1 ,							*t.shape[no_batch_dims:]	)
        else:
            SCREAMING_SNAKE_CASE__											=			t.expand(orig_batch_dims + t.shape[no_batch_dims:]	)
        return t

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

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

    SCREAMING_SNAKE_CASE__											=			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

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

        SCREAMING_SNAKE_CASE__											=			tensor_tree_map(lowerCAmelCase_ ,							lowerCAmelCase_	)

        # Run the layer on the chunk
        SCREAMING_SNAKE_CASE__											=			layer(**lowerCAmelCase_	)

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

        i += chunk_size

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

    return out



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_ = 5_12   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			max_chunk_size
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

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

        if min_chunk_size >= self.max_chunk_size:
            return min_chunk_size

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

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

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

        return candidates[min_viable_chunk_size_index]



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			True
        for aa, aa in zip(A_   ,				A_     ):
            assert type(A_     ) == type(A_     )
            if isinstance(A_   ,				(list, tuple)     ):
                consistent &= self._compare_arg_caches(A_   ,				A_     )
            elif isinstance(A_   ,				A_     ):
                SCREAMING_SNAKE_CASE__											=			[v for _, v in sorted(aa.items()   ,				key=lambda A_     : x[0]     )]
                SCREAMING_SNAKE_CASE__											=			[v for _, v in sorted(aa.items()   ,				key=lambda A_     : x[0]     )]
                consistent &= self._compare_arg_caches(A_   ,				A_     )
            else:
                consistent &= aa == aa

        return consistent




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			tree_map(lambda A_     : a.shape if isinstance(A_   ,				torch.Tensor     ) else a   ,				A_   ,				A_     )
        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(A_     )
            SCREAMING_SNAKE_CASE__											=			self._compare_arg_caches(self.cached_arg_data   ,				A_     )
        else:
            # Otherwise, we can reuse the precomputed value
            SCREAMING_SNAKE_CASE__											=			False

        if not consistent:
            SCREAMING_SNAKE_CASE__											=			self._determine_favorable_chunk_size(
                A_   ,				A_   ,				A_   ,				)
            SCREAMING_SNAKE_CASE__											=			arg_data

        assert self.cached_chunk_size is not None

        return self.cached_chunk_size


import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
   import torch

   from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
   from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor






class     UpperCAmelCase						:



   def __init__(self						:   int       ,		snake_case__						:   Optional[int]       ,		snake_case__						:   Optional[int]=13       ,		snake_case__						:   Tuple=32       ,		snake_case__						:   Optional[Any]=3       ,		snake_case__						:   Tuple=4       ,		snake_case__						:   List[Any]=[10, 20, 30, 40]       ,		snake_case__						:   Dict=[2, 2, 3, 2]       ,		snake_case__						:   Union[str, Any]=True       ,		snake_case__						:   Union[str, Any]=True       ,		snake_case__						:   List[str]=37       ,		snake_case__						:   List[Any]="gelu"       ,		snake_case__						:   Union[str, Any]=10       ,		snake_case__						:   int=0.02       ,		snake_case__						:   str=["stage2", "stage3", "stage4"]       ,		snake_case__						:   int=[2, 3, 4]       ,		snake_case__						:   List[Any]=None       ,		)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= parent
          snake_case			:  Optional[int]     							= batch_size
          snake_case			:  Optional[Any]     							= image_size
          snake_case			:  Union[str, Any]     							= num_channels
          snake_case			:  Tuple     							= num_stages
          snake_case			:  Union[str, Any]     							= hidden_sizes
          snake_case			:  List[Any]     							= depths
          snake_case			:  Dict     							= is_training
          snake_case			:  List[Any]     							= use_labels
          snake_case			:  str     							= intermediate_size
          snake_case			:  Dict     							= hidden_act
          snake_case			:  Optional[int]     							= num_labels
          snake_case			:  Dict     							= initializer_range
          snake_case			:  List[Any]     							= out_features
          snake_case			:  int     							= out_indices
          snake_case			:  List[Any]     							= scope



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

          snake_case			:  List[Any]     							= None
          if self.use_labels:
                 snake_case			:  List[Any]     							= ids_tensor([self.batch_size]       ,		self.num_labels					)

          snake_case			:  str     							= self.get_config()

          return config, pixel_values, labels



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any					)						->      Dict:


          '''simple docstring'''


          return ConvNextVaConfig(
              num_channels=self.num_channels       ,		hidden_sizes=self.hidden_sizes       ,		depths=self.depths       ,		num_stages=self.num_stages       ,		hidden_act=self.hidden_act       ,		is_decoder=snake_case__       ,		initializer_range=self.initializer_range       ,		out_features=self.out_features       ,		out_indices=self.out_indices       ,		num_labels=self.num_labels       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   str       ,		snake_case__						:   int       ,		snake_case__						:   List[Any]					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  List[str]     							= ConvNextVaModel(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  Any     							= model(snake_case__					)
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape       ,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   str       ,		snake_case__						:   List[Any]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  str     							= ConvNextVaForImageClassification(snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  Tuple     							= model(snake_case__       ,		labels=snake_case__					)
          self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   Dict       ,		snake_case__						:   int       ,		snake_case__						:   Optional[int]					)						->      int:


          '''simple docstring'''


          snake_case			:  int     							= ConvNextVaBackbone(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  Union[str, Any]     							= model(snake_case__					)

          # verify hidden states
          self.parent.assertEqual(len(result.feature_maps					)       ,		len(config.out_features					)					)
          self.parent.assertListEqual(list(result.feature_maps[0].shape					)       ,		[self.batch_size, self.hidden_sizes[1], 4, 4]					)

          # verify channels
          self.parent.assertEqual(len(model.channels					)       ,		len(config.out_features					)					)
          self.parent.assertListEqual(model.channels       ,		config.hidden_sizes[1:]					)

          # verify backbone works with out_features=None
          snake_case			:  Dict     							= None
          snake_case			:  Tuple     							= ConvNextVaBackbone(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  int     							= model(snake_case__					)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps					)       ,		1					)
          self.parent.assertListEqual(list(result.feature_maps[0].shape					)       ,		[self.batch_size, self.hidden_sizes[-1], 1, 1]					)

          # verify channels
          self.parent.assertEqual(len(model.channels					)       ,		1					)
          self.parent.assertListEqual(model.channels       ,		[config.hidden_sizes[-1]]					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  str     							= self.prepare_config_and_inputs()
          snake_case							,   snake_case							,   snake_case			:  Union[str, Any]     							= config_and_inputs
          snake_case			:  int     							= {"pixel_values": pixel_values}
          return config, inputs_dict



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= self.prepare_config_and_inputs()
          snake_case							,   snake_case							,   snake_case			:  str     							= config_and_inputs
          snake_case			:  Optional[int]     							= {"pixel_values": pixel_values, "labels": labels}
          return config, inputs_dict






@require_torch
class     UpperCAmelCase						(		A_			,A_			,unittest.TestCase					):
   A__   :						Tuple			=	(
       (
           ConvNextVaModel,
           ConvNextVaForImageClassification,
           ConvNextVaBackbone,
       )
       if is_torch_available()
       else ()
   )
   A__   :						int			=	(
       {"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
       if is_torch_available()
       else {}
   )

   A__   :						str			=	False
   A__   :						Dict			=	False
   A__   :						Optional[int]			=	False
   A__   :						List[Any]			=	False
   A__   :						str			=	False



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= ConvNextVaModelTester(self					)
          snake_case			:  int     							= ConfigTester(self       ,		config_class=snake_case__       ,		has_text_modality=snake_case__       ,		hidden_size=37					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Tuple:


          '''simple docstring'''


          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Dict:


          '''simple docstring'''


          return



   @unittest.skip(reason="ConvNextV2 does not use inputs_embeds"					)
   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Optional[int]:


          '''simple docstring'''


          pass



   @unittest.skip(reason="ConvNextV2 does not support input and output embeddings"					)
   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Optional[Any]:


          '''simple docstring'''


          pass



   @unittest.skip(reason="ConvNextV2 does not use feedforward chunking"					)
   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      List[str]:


          '''simple docstring'''


          pass



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Optional[int]:


          '''simple docstring'''


          if not self.model_tester.is_training:
                 return

          for model_class in self.all_model_classes:
                 snake_case							,   snake_case			:  str     							= self.model_tester.prepare_config_and_inputs_with_labels()
                 snake_case			:  Tuple     							= True

                 if model_class.__name__ in [
                     *get_values(snake_case__					),
                     *get_values(snake_case__					),
                 ]:
                        continue

                 snake_case			:  Union[str, Any]     							= model_class(snake_case__					)
                 model.to(snake_case__					)
                 model.train()
                 snake_case			:  str     							= self._prepare_for_class(snake_case__       ,		snake_case__       ,		return_labels=snake_case__					)
                 snake_case			:  List[str]     							= model(**snake_case__					).loss
                 loss.backward()



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      List[str]:


          '''simple docstring'''


          if not self.model_tester.is_training:
                 return

          for model_class in self.all_model_classes:
                 snake_case							,   snake_case			:  List[str]     							= self.model_tester.prepare_config_and_inputs_with_labels()
                 snake_case			:  List[Any]     							= False
                 snake_case			:  Any     							= True

                 if (
                     model_class.__name__
                     in [*get_values(snake_case__					), *get_values(snake_case__					)]
                     or not model_class.supports_gradient_checkpointing
                 ):
                        continue

                 snake_case			:  Union[str, Any]     							= model_class(snake_case__					)
                 model.to(snake_case__					)
                 model.gradient_checkpointing_enable()
                 model.train()
                 snake_case			:  List[str]     							= self._prepare_for_class(snake_case__       ,		snake_case__       ,		return_labels=snake_case__					)
                 snake_case			:  Any     							= model(**snake_case__					).loss
                 loss.backward()



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      List[str]:


          '''simple docstring'''


          snake_case							,   snake_case			:  Optional[Any]     							= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 snake_case			:  int     							= model_class(snake_case__					)
                 snake_case			:  List[str]     							= inspect.signature(model.forward					)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 snake_case			:  str     							= [*signature.parameters.keys()]

                 snake_case			:  Any     							= ["pixel_values"]
                 self.assertListEqual(arg_names[:1]       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      str:


          '''simple docstring'''


          def check_hidden_states_output(snake_case__						:   Dict       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Dict					):
                 snake_case			:  List[str]     							= model_class(snake_case__					)
                 model.to(snake_case__					)
                 model.eval()

                 with torch.no_grad():
                        snake_case			:  Any     							= model(**self._prepare_for_class(snake_case__       ,		snake_case__					)					)

                 snake_case			:  List[Any]     							= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 snake_case			:  Dict     							= self.model_tester.num_stages
                 self.assertEqual(len(snake_case__					)       ,		expected_num_stages + 1					)

                 # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:]					)       ,		[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,		)

          snake_case							,   snake_case			:  int     							= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 snake_case			:  Dict     							= True
                 check_hidden_states_output(snake_case__       ,		snake_case__       ,		snake_case__					)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 snake_case			:  int     							= True

                 check_hidden_states_output(snake_case__       ,		snake_case__       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case__					)



   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      List[Any]:


          '''simple docstring'''


          for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case			:  Optional[int]     							= ConvNextVaModel.from_pretrained(snake_case__					)
                 self.assertIsNotNone(snake_case__					)



def      UpperCamelCase   (       ):
       snake_case			:  Dict     							= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
       return image






@require_torch
@require_vision
class     UpperCAmelCase						(		unittest.TestCase					):



   @cached_property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Any:


          '''simple docstring'''


          return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224"					) if is_vision_available() else None



   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      List[Any]:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224"					).to(snake_case__					)

          snake_case			:  List[str]     							= self.default_image_processor
          snake_case			:  str     							= prepare_img()
          snake_case			:  List[str]     							= preprocessor(images=snake_case__       ,		return_tensors="pt"					).to(snake_case__					)

          # forward pass
          with torch.no_grad():
                 snake_case			:  str     							= model(**snake_case__					)

          # verify the logits
          snake_case			:  Any     							= torch.Size((1, 10_00)					)
          self.assertEqual(outputs.logits.shape       ,		snake_case__					)

          snake_case			:  str     							= torch.tensor([0.9996, 0.1966, -0.4386]					).to(snake_case__					)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,		snake_case__       ,		atol=1e-4					)					)





