"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


__lowercase		:		Optional[int]														=							[
    "openmmlab/upernet-convnext-tiny",
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
__lowercase		:		Dict														=							"UperNetConfig"



class      _A	(						nn.Module					):




    """simple docstring"""




    def __init__(		self							:     Optional[Any]	,      A_							:     int	,      A_							:     int	,      A_							:     Union[int, Tuple[int, int]]	,      A_							:     Union[int, Tuple[int, int], str] = 0	,      A_							:     bool = False	,      A_							:     Union[int, Tuple[int, int]] = 1	,      )    ->       None:
         super().__init__()
         __snake_case				=							nn.Convad(
             in_channels=A_	,      out_channels=A_	,      kernel_size=A_	,      padding=A_	,      bias=A_	,      dilation=A_	,      )
         __snake_case				=							nn.BatchNormad(A_    )
         __snake_case				=							nn.ReLU()







    def  lowercase					(		self							:     Tuple	,      A_							:     torch.Tensor    )    ->       torch.Tensor:
         __snake_case				=							self.conv(A_    )
         __snake_case				=							self.batch_norm(A_    )
         __snake_case				=							self.activation(A_    )

         return output






class      _A	(						nn.Module					):




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]	,      A_							:     int	,      A_							:     int	,      A_							:     int    )    ->       None:
         super().__init__()
         __snake_case				=							[
             nn.AdaptiveAvgPoolad(A_    ),
             UperNetConvModule(A_	,      A_	,      kernel_size=1    ),
         ]
         for i, layer in enumerate(self.layers    ):
              self.add_module(str(A_    )	,      A_    )







    def  lowercase					(		self							:     Dict	,      A_							:     torch.Tensor    )    ->       torch.Tensor:
         __snake_case				=							input
         for layer in self.layers:
              __snake_case				=							layer(A_    )
         return hidden_state






class      _A	(						nn.Module					):




    """simple docstring"""




    def __init__(		self							:     List[str]	,      A_							:     Tuple[int, ...]	,      A_							:     int	,      A_							:     int	,      A_							:     bool    )    ->       None:
         super().__init__()
         __snake_case				=							pool_scales
         __snake_case				=							align_corners
         __snake_case				=							in_channels
         __snake_case				=							channels
         __snake_case				=							[]
         for i, pool_scale in enumerate(A_    ):
              __snake_case				=							UperNetPyramidPoolingBlock(pool_scale=A_	,      in_channels=A_	,      channels=A_    )
              self.blocks.append(A_    )
              self.add_module(str(A_    )	,      A_    )







    def  lowercase					(		self							:     Dict	,      A_							:     torch.Tensor    )    ->       List[torch.Tensor]:
         __snake_case				=							[]
         for ppm in self.blocks:
              __snake_case				=							ppm(A_    )
              __snake_case				=							nn.functional.interpolate(
                  A_	,      size=x.size()[2:]	,      mode='''bilinear'''	,      align_corners=self.align_corners    )
              ppm_outs.append(A_    )
         return ppm_outs






class      _A	(						nn.Module					):




    """simple docstring"""




    def __init__(		self							:     Optional[int]	,      A_							:     Dict	,      A_							:     List[Any]    )    ->       List[str]:
         super().__init__()

         __snake_case				=							config
         __snake_case				=							config.pool_scales  # e.g. (1, 2, 3, 6)
         __snake_case				=							in_channels
         __snake_case				=							config.hidden_size
         __snake_case				=							False
         __snake_case				=							nn.Convad(self.channels	,      config.num_labels	,      kernel_size=1    )

         # PSP Module
         __snake_case				=							UperNetPyramidPoolingModule(
             self.pool_scales	,      self.in_channels[-1]	,      self.channels	,      align_corners=self.align_corners	,      )
         __snake_case				=							UperNetConvModule(
             self.in_channels[-1] + len(self.pool_scales    ) * self.channels	,      self.channels	,      kernel_size=3	,      padding=1	,      )
         # FPN Module
         __snake_case				=							nn.ModuleList()
         __snake_case				=							nn.ModuleList()
         for in_channels in self.in_channels[:-1]:  # skip the top layer
              __snake_case				=							UperNetConvModule(A_	,      self.channels	,      kernel_size=1    )
              __snake_case				=							UperNetConvModule(self.channels	,      self.channels	,      kernel_size=3	,      padding=1    )
              self.lateral_convs.append(A_    )
              self.fpn_convs.append(A_    )

         __snake_case				=							UperNetConvModule(
             len(self.in_channels    ) * self.channels	,      self.channels	,      kernel_size=3	,      padding=1	,      )
    def  lowercase					(		self							:     List[Any]    )    ->       Optional[Any]:
         self.apply(self._init_weights    )
    def  lowercase					(		self							:     int	,      A_							:     List[str]    )    ->       List[str]:
         if isinstance(A_	,      nn.Convad    ):
              module.weight.data.normal_(mean=0.0	,      std=self.config.initializer_range    )
              if module.bias is not None:
                   module.bias.data.zero_()
    def  lowercase					(		self							:     str	,      A_							:     Any    )    ->       str:
         __snake_case				=							inputs[-1]
         __snake_case				=							[x]
         psp_outs.extend(self.psp_modules(A_    )    )
         __snake_case				=							torch.cat(A_	,      dim=1    )
         __snake_case				=							self.bottleneck(A_    )

         return output







    def  lowercase					(		self							:     Tuple	,      A_							:     torch.Tensor    )    ->       torch.Tensor:
         # build laterals
         __snake_case				=							[lateral_conv(encoder_hidden_states[i]    ) for i, lateral_conv in enumerate(self.lateral_convs    )]

         laterals.append(self.psp_forward(A_    )    )

         # build top-down path
         __snake_case				=							len(A_    )
         for i in range(used_backbone_levels - 1	,      0	,      -1    ):
              __snake_case				=							laterals[i - 1].shape[2:]
              __snake_case				=							laterals[i - 1] + nn.functional.interpolate(
                  laterals[i]	,      size=A_	,      mode='''bilinear'''	,      align_corners=self.align_corners    )

         # build outputs
         __snake_case				=							[self.fpn_convs[i](laterals[i]    ) for i in range(used_backbone_levels - 1    )]
         # append psp feature
         fpn_outs.append(laterals[-1]    )

         for i in range(used_backbone_levels - 1	,      0	,      -1    ):
              __snake_case				=							nn.functional.interpolate(
                  fpn_outs[i]	,      size=fpn_outs[0].shape[2:]	,      mode='''bilinear'''	,      align_corners=self.align_corners    )
         __snake_case				=							torch.cat(A_	,      dim=1    )
         __snake_case				=							self.fpn_bottleneck(A_    )
         __snake_case				=							self.classifier(A_    )

         return output






class      _A	(						nn.Module					):




    """simple docstring"""




    def __init__(		self							:     Any	,      A_							:     List[Any]	,      A_							:     int = 2	,      A_							:     int = 3	,      A_							:     Union[int, Tuple[int, int]] = 1    )    ->       None:
         super().__init__()

         __snake_case				=							config
         __snake_case				=							config.auxiliary_in_channels
         __snake_case				=							config.auxiliary_channels
         __snake_case				=							config.auxiliary_num_convs
         __snake_case				=							config.auxiliary_concat_input
         __snake_case				=							in_index

         __snake_case				=							(kernel_size // 2) * dilation
         __snake_case				=							[]
         convs.append(
             UperNetConvModule(
                 self.in_channels	,      self.channels	,      kernel_size=A_	,      padding=A_	,      dilation=A_    )    )
         for i in range(self.num_convs - 1    ):
              convs.append(
                  UperNetConvModule(
                      self.channels	,      self.channels	,      kernel_size=A_	,      padding=A_	,      dilation=A_    )    )
         if self.num_convs == 0:
              __snake_case				=							nn.Identity()
         else:
              __snake_case				=							nn.Sequential(*A_    )
         if self.concat_input:
              __snake_case				=							UperNetConvModule(
                  self.in_channels + self.channels	,      self.channels	,      kernel_size=A_	,      padding=kernel_size // 2    )

         __snake_case				=							nn.Convad(self.channels	,      config.num_labels	,      kernel_size=1    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         self.apply(self._init_weights    )
    def  lowercase					(		self							:     int	,      A_							:     Dict    )    ->       str:
         if isinstance(A_	,      nn.Convad    ):
              module.weight.data.normal_(mean=0.0	,      std=self.config.initializer_range    )
              if module.bias is not None:
                   module.bias.data.zero_()







    def  lowercase					(		self							:     Optional[int]	,      A_							:     torch.Tensor    )    ->       torch.Tensor:
         # just take the relevant feature maps
         __snake_case				=							encoder_hidden_states[self.in_index]
         __snake_case				=							self.convs(A_    )
         if self.concat_input:
              __snake_case				=							self.conv_cat(torch.cat([hidden_states, output]	,      dim=1    )    )
         __snake_case				=							self.classifier(A_    )
         return output






class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					UperNetConfig
    UpperCamelCase_	:					Optional[Any]							=					'''pixel_values'''
    UpperCamelCase_	:					int							=					True
    def  lowercase					(		self							:     int	,      A_							:     Union[str, Any]    )    ->       List[str]:
         if isinstance(A_	,      A_    ):
              module.backbone.init_weights()
              module.decode_head.init_weights()
              module.auxiliary_head.init_weights()
    def  lowercase					(		self							:     List[str]    )    ->       Union[str, Any]:
         self.backbone.init_weights()
         self.decode_head.init_weights()
         self.auxiliary_head.init_weights()







    def  lowercase					(		self							:     int	,      A_							:     Dict	,      A_							:     List[str]=False    )    ->       Dict:
         if isinstance(A_	,      A_    ):
              __snake_case				=							value






__lowercase		:		Tuple														=							r"\n    Parameters:\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

__lowercase		:		Optional[Any]														=							r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n            `attentions` under returned tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n            returned tensors for more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"



@add_start_docstrings(
    '''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.'''							, _UpperCAmelCase							, )
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    def __init__(		self							:     Optional[int]	,      A_							:     Optional[int]    )    ->       Dict:
         super().__init__(A_    )

         __snake_case				=							AutoBackbone.from_config(config.backbone_config    )

         # Semantic segmentation head(s)
         __snake_case				=							UperNetHead(A_	,      in_channels=self.backbone.channels    )
         __snake_case				=							UperNetFCNHead(A_    ) if config.use_auxiliary_head else None

         # Initialize weights and apply final processing
         self.post_init()







    @add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length'''    )    )
    @replace_return_docstrings(output_type=A_	,      config_class=_CONFIG_FOR_DOC    )
    def  lowercase					(		self							:     List[Any]	,      A_							:     Optional[torch.Tensor] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[torch.Tensor] = None	,      A_							:     Optional[bool] = None	,      )    ->       Union[tuple, SemanticSegmenterOutput]:
         __snake_case				=							return_dict if return_dict is not None else self.config.use_return_dict
         __snake_case				=							(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         __snake_case				=							output_attentions if output_attentions is not None else self.config.output_attentions

         __snake_case				=							self.backbone.forward_with_filtered_kwargs(
             A_	,      output_hidden_states=A_	,      output_attentions=A_    )
         __snake_case				=							outputs.feature_maps

         __snake_case				=							self.decode_head(A_    )
         __snake_case				=							nn.functional.interpolate(A_	,      size=pixel_values.shape[2:]	,      mode='''bilinear'''	,      align_corners=A_    )

         __snake_case				=							None
         if self.auxiliary_head is not None:
              __snake_case				=							self.auxiliary_head(A_    )
              __snake_case				=							nn.functional.interpolate(
                  A_	,      size=pixel_values.shape[2:]	,      mode='''bilinear'''	,      align_corners=A_    )

         __snake_case				=							None
         if labels is not None:
              if self.config.num_labels == 1:
                   raise ValueError('''The number of labels should be greater than one'''    )
              else:
                   # compute weighted loss
                   __snake_case				=							CrossEntropyLoss(ignore_index=self.config.loss_ignore_index    )
                   __snake_case				=							loss_fct(A_	,      A_    )
                   __snake_case				=							loss_fct(A_	,      A_    )
                   __snake_case				=							main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

         if not return_dict:
              if output_hidden_states:
                   __snake_case				=							(logits,) + outputs[1:]
              else:
                   __snake_case				=							(logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output

         return SemanticSegmenterOutput(
             loss=A_	,      logits=A_	,      hidden_states=outputs.hidden_states	,      attentions=outputs.attentions	,      )
"""simple docstring"""



import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
       import jax

       from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
       from PIL import Image

       from transformers import BeitImageProcessor



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]	,      A_							:     List[str]	,      A_							:     List[Any]=100	,      A_							:     Any=13	,      A_							:     Dict=30	,      A_							:     Optional[int]=2	,      A_							:     str=3	,      A_							:     Tuple=True	,      A_							:     str=True	,      A_							:     Union[str, Any]=32	,      A_							:     int=5	,      A_							:     List[Any]=4	,      A_							:     Optional[Any]=37	,      A_							:     Any="gelu"	,      A_							:     List[str]=0.1	,      A_							:     int=0.1	,      A_							:     Tuple=10	,      A_							:     int=0.02	,      A_							:     Tuple=3	,      )    ->       Optional[int]:
         __snake_case				=							parent
         __snake_case				=							vocab_size
         __snake_case				=							batch_size
         __snake_case				=							image_size
         __snake_case				=							patch_size
         __snake_case				=							num_channels
         __snake_case				=							is_training
         __snake_case				=							use_labels
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							intermediate_size
         __snake_case				=							hidden_act
         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							type_sequence_label_size
         __snake_case				=							initializer_range

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __snake_case				=							(image_size // patch_size) ** 2
         __snake_case				=							num_patches + 1
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[str]:
         __snake_case				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

         __snake_case				=							None
         if self.use_labels:
              __snake_case				=							ids_tensor([self.batch_size]	,      self.type_sequence_label_size    )

         __snake_case				=							BeitConfig(
             vocab_size=self.vocab_size	,      image_size=self.image_size	,      patch_size=self.patch_size	,      num_channels=self.num_channels	,      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	,      is_decoder=A_	,      initializer_range=self.initializer_range	,      )

         return config, pixel_values, labels
    def  lowercase					(		self							:     Optional[int]	,      A_							:     str	,      A_							:     str	,      A_							:     List[str]    )    ->       List[Any]:
         __snake_case				=							FlaxBeitModel(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)    )
    def  lowercase					(		self							:     List[str]	,      A_							:     Any	,      A_							:     Any	,      A_							:     str    )    ->       str:
         __snake_case				=							FlaxBeitForMaskedImageModeling(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length - 1, self.vocab_size)    )
    def  lowercase					(		self							:     str	,      A_							:     List[Any]	,      A_							:     Optional[int]	,      A_							:     List[str]    )    ->       str:
         __snake_case				=							self.type_sequence_label_size
         __snake_case				=							FlaxBeitForImageClassification(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)    )

         # test greyscale images
         __snake_case				=							1
         __snake_case				=							FlaxBeitForImageClassification(A_    )

         __snake_case				=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
         __snake_case				=							model(A_    )







    def  lowercase					(		self							:     Dict    )    ->       List[str]:
         __snake_case				=							self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )		,	(
             __snake_case
         )		,	(
             __snake_case
         )		,	
         )				=							config_and_inputs
         __snake_case				=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					(
        (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
    )
    def  lowercase					(		self							:     List[Any]    )    ->       None:
         __snake_case				=							FlaxBeitModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=A_	,      has_text_modality=A_	,      hidden_size=37    )
    def  lowercase					(		self							:     Tuple    )    ->       Union[str, Any]:
         self.config_tester.run_common_tests()
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __snake_case				=							model_class(A_    )
              __snake_case				=							inspect.signature(model.__call__    )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __snake_case				=							[*signature.parameters.keys()]

              __snake_case				=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]	,      A_    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       int:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__    ):
                   __snake_case				=							self._prepare_for_class(A_	,      A_    )
                   __snake_case				=							model_class(A_    )

                   @jax.jit
                   def model_jitted(A_							:     Union[str, Any]	,      **A_							:     Union[str, Any]    ):
                        return model(pixel_values=A_	,      **A_    )

                   with self.subTest('''JIT Enabled'''    ):
                        __snake_case				=							model_jitted(**A_    ).to_tuple()

                   with self.subTest('''JIT Disabled'''    ):
                        with jax.disable_jit():
                             __snake_case				=							model_jitted(**A_    ).to_tuple()

                   self.assertEqual(len(A_    )	,      len(A_    )    )
                   for jitted_output, output in zip(A_	,      A_    ):
                        self.assertEqual(jitted_output.shape	,      output.shape    )
    def  lowercase					(		self							:     str    )    ->       List[Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       Union[str, Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A_    )
    def  lowercase					(		self							:     str    )    ->       int:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A_    )







    @slow
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         for model_class_name in self.all_model_classes:
              __snake_case				=							model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''    )
              __snake_case				=							model(np.ones((1, 3, 224, 224)    )    )
              self.assertIsNotNone(A_    )






def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
     return image




@require_vision
@require_flax
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @cached_property
    def  lowercase					(		self							:     str    )    ->       str:
         return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''    ) if is_vision_available() else None
    @slow
    def  lowercase					(		self							:     Union[str, Any]    )    ->       List[str]:
         __snake_case				=							FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=A_	,      return_tensors='''np'''    ).pixel_values

         # prepare bool_masked_pos
         __snake_case				=							np.ones((1, 196)	,      dtype=A_    )

         # forward pass
         __snake_case				=							model(pixel_values=A_	,      bool_masked_pos=A_    )
         __snake_case				=							outputs.logits

         # verify the logits
         __snake_case				=							(1, 196, 8_192)
         self.assertEqual(logits.shape	,      A_    )

         __snake_case				=							np.array(
             [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]    )

         self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]	,      A_	,      atol=1E-2    )    )
    @slow
    def  lowercase					(		self							:     List[Any]    )    ->       List[str]:
         __snake_case				=							FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=A_	,      return_tensors='''np'''    )

         # forward pass
         __snake_case				=							model(**A_    )
         __snake_case				=							outputs.logits

         # verify the logits
         __snake_case				=							(1, 1_000)
         self.assertEqual(logits.shape	,      A_    )

         __snake_case				=							np.array([-1.23_85, -1.09_87, -1.01_08]    )

         self.assertTrue(np.allclose(logits[0, :3]	,      A_	,      atol=1E-4    )    )

         __snake_case				=							281
         self.assertEqual(logits.argmax(-1    ).item()	,      A_    )







    @slow
    def  lowercase					(		self							:     int    )    ->       str:
         __snake_case				=							FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=A_	,      return_tensors='''np'''    )

         # forward pass
         __snake_case				=							model(**A_    )
         __snake_case				=							outputs.logits

         # verify the logits
         __snake_case				=							(1, 21_841)
         self.assertEqual(logits.shape	,      A_    )

         __snake_case				=							np.array([1.68_81, -0.27_87, 0.59_01]    )

         self.assertTrue(np.allclose(logits[0, :3]	,      A_	,      atol=1E-4    )    )

         __snake_case				=							2_396
         self.assertEqual(logits.argmax(-1    ).item()	,      A_    )