





'''simple docstring'''

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowercase_					= {
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [32, 64],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

lowercase_					= {
    """sample_size""": 64,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [192, 192 * 2, 192 * 3, 192 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

lowercase_					= {
    """sample_size""": 256,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

lowercase_					= {
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}

lowercase_					= {
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}

lowercase_					= {
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}





def lowerCamelCase						(     __lowerCamelCase :    Dict    )						->Optional[int]:
    if isinstance(__lowerCamelCase					,       __lowerCamelCase    ):
        return v
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise argparse.ArgumentTypeError("""boolean value expected"""    )





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    str					,       __lowerCamelCase :    str					,       __lowerCamelCase :    List[str]					,       __lowerCamelCase :    Optional[int]=False    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.in_layers.0.weight']
    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.in_layers.0.bias']
    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.in_layers.2.weight']
    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.in_layers.2.bias']
    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.emb_layers.1.weight']
    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.emb_layers.1.bias']
    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.out_layers.0.weight']
    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.out_layers.0.bias']
    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.out_layers.3.weight']
    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.out_layers.3.bias']

    if has_skip:
        _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.skip_connection.weight']
        _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.skip_connection.bias']

    return new_checkpoint





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    str					,       __lowerCamelCase :    Union[str, Any]=None    )						->int:
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.qkv.weight'].chunk(3					,       dim=0    )
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.qkv.bias'].chunk(3					,       dim=0    )

    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.norm.weight']
    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.norm.bias']

    _SCREAMING_SNAKE_CASE									=			weight_q.squeeze(-1    ).squeeze(-1    )
    _SCREAMING_SNAKE_CASE									=			bias_q.squeeze(-1    ).squeeze(-1    )
    _SCREAMING_SNAKE_CASE									=			weight_k.squeeze(-1    ).squeeze(-1    )
    _SCREAMING_SNAKE_CASE									=			bias_k.squeeze(-1    ).squeeze(-1    )
    _SCREAMING_SNAKE_CASE									=			weight_v.squeeze(-1    ).squeeze(-1    )
    _SCREAMING_SNAKE_CASE									=			bias_v.squeeze(-1    ).squeeze(-1    )

    _SCREAMING_SNAKE_CASE									=			(
        checkpoint[F'{old_prefix}.proj_out.weight'].squeeze(-1    ).squeeze(-1    )
    )
    _SCREAMING_SNAKE_CASE									=			checkpoint[F'{old_prefix}.proj_out.bias'].squeeze(-1    ).squeeze(-1    )

    return new_checkpoint





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Tuple    )						->List[str]:
    _SCREAMING_SNAKE_CASE									=			torch.load(__lowerCamelCase					,       map_location="""cpu"""    )
    _SCREAMING_SNAKE_CASE									=			{}

    _SCREAMING_SNAKE_CASE									=			checkpoint["""time_embed.0.weight"""]
    _SCREAMING_SNAKE_CASE									=			checkpoint["""time_embed.0.bias"""]
    _SCREAMING_SNAKE_CASE									=			checkpoint["""time_embed.2.weight"""]
    _SCREAMING_SNAKE_CASE									=			checkpoint["""time_embed.2.bias"""]

    if unet_config["num_class_embeds"] is not None:
        _SCREAMING_SNAKE_CASE									=			checkpoint["""label_emb.weight"""]

    _SCREAMING_SNAKE_CASE									=			checkpoint["""input_blocks.0.0.weight"""]
    _SCREAMING_SNAKE_CASE									=			checkpoint["""input_blocks.0.0.bias"""]

    _SCREAMING_SNAKE_CASE									=			unet_config["""down_block_types"""]
    _SCREAMING_SNAKE_CASE									=			unet_config["""layers_per_block"""]
    _SCREAMING_SNAKE_CASE									=			unet_config["""attention_head_dim"""]
    _SCREAMING_SNAKE_CASE									=			unet_config["""block_out_channels"""]
    _SCREAMING_SNAKE_CASE									=			1
    _SCREAMING_SNAKE_CASE									=			channels_list[0]

    for i, layer_type in enumerate(__lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE									=			channels_list[i]
        _SCREAMING_SNAKE_CASE									=			current_channels != prev_channels
        if layer_type == "ResnetDownsampleBlock2D":
            for j in range(__lowerCamelCase    ):
                _SCREAMING_SNAKE_CASE									=			F'down_blocks.{i}.resnets.{j}'
                _SCREAMING_SNAKE_CASE									=			F'input_blocks.{current_layer}.0'
                _SCREAMING_SNAKE_CASE									=			True if j == 0 and downsample_block_has_skip else False
                _SCREAMING_SNAKE_CASE									=			convert_resnet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       has_skip=__lowerCamelCase    )
                current_layer += 1

        elif layer_type == "AttnDownBlock2D":
            for j in range(__lowerCamelCase    ):
                _SCREAMING_SNAKE_CASE									=			F'down_blocks.{i}.resnets.{j}'
                _SCREAMING_SNAKE_CASE									=			F'input_blocks.{current_layer}.0'
                _SCREAMING_SNAKE_CASE									=			True if j == 0 and downsample_block_has_skip else False
                _SCREAMING_SNAKE_CASE									=			convert_resnet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       has_skip=__lowerCamelCase    )
                _SCREAMING_SNAKE_CASE									=			F'down_blocks.{i}.attentions.{j}'
                _SCREAMING_SNAKE_CASE									=			F'input_blocks.{current_layer}.1'
                _SCREAMING_SNAKE_CASE									=			convert_attention(
                    __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
                current_layer += 1

        if i != len(__lowerCamelCase    ) - 1:
            _SCREAMING_SNAKE_CASE									=			F'down_blocks.{i}.downsamplers.0'
            _SCREAMING_SNAKE_CASE									=			F'input_blocks.{current_layer}.0'
            _SCREAMING_SNAKE_CASE									=			convert_resnet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
            current_layer += 1

        _SCREAMING_SNAKE_CASE									=			current_channels

    # hardcoded the mid-block for now
    _SCREAMING_SNAKE_CASE									=			"""mid_block.resnets.0"""
    _SCREAMING_SNAKE_CASE									=			"""middle_block.0"""
    _SCREAMING_SNAKE_CASE									=			convert_resnet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			"""mid_block.attentions.0"""
    _SCREAMING_SNAKE_CASE									=			"""middle_block.1"""
    _SCREAMING_SNAKE_CASE									=			convert_attention(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			"""mid_block.resnets.1"""
    _SCREAMING_SNAKE_CASE									=			"""middle_block.2"""
    _SCREAMING_SNAKE_CASE									=			convert_resnet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			0
    _SCREAMING_SNAKE_CASE									=			unet_config["""up_block_types"""]

    for i, layer_type in enumerate(__lowerCamelCase    ):
        if layer_type == "ResnetUpsampleBlock2D":
            for j in range(layers_per_block + 1    ):
                _SCREAMING_SNAKE_CASE									=			F'up_blocks.{i}.resnets.{j}'
                _SCREAMING_SNAKE_CASE									=			F'output_blocks.{current_layer}.0'
                _SCREAMING_SNAKE_CASE									=			convert_resnet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       has_skip=__lowerCamelCase    )
                current_layer += 1

            if i != len(__lowerCamelCase    ) - 1:
                _SCREAMING_SNAKE_CASE									=			F'up_blocks.{i}.upsamplers.0'
                _SCREAMING_SNAKE_CASE									=			F'output_blocks.{current_layer-1}.1'
                _SCREAMING_SNAKE_CASE									=			convert_resnet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
        elif layer_type == "AttnUpBlock2D":
            for j in range(layers_per_block + 1    ):
                _SCREAMING_SNAKE_CASE									=			F'up_blocks.{i}.resnets.{j}'
                _SCREAMING_SNAKE_CASE									=			F'output_blocks.{current_layer}.0'
                _SCREAMING_SNAKE_CASE									=			convert_resnet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       has_skip=__lowerCamelCase    )
                _SCREAMING_SNAKE_CASE									=			F'up_blocks.{i}.attentions.{j}'
                _SCREAMING_SNAKE_CASE									=			F'output_blocks.{current_layer}.1'
                _SCREAMING_SNAKE_CASE									=			convert_attention(
                    __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
                current_layer += 1

            if i != len(__lowerCamelCase    ) - 1:
                _SCREAMING_SNAKE_CASE									=			F'up_blocks.{i}.upsamplers.0'
                _SCREAMING_SNAKE_CASE									=			F'output_blocks.{current_layer-1}.2'
                _SCREAMING_SNAKE_CASE									=			convert_resnet(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			checkpoint["""out.0.weight"""]
    _SCREAMING_SNAKE_CASE									=			checkpoint["""out.0.bias"""]
    _SCREAMING_SNAKE_CASE									=			checkpoint["""out.2.weight"""]
    _SCREAMING_SNAKE_CASE									=			checkpoint["""out.2.bias"""]

    return new_checkpoint


if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser()

      parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
      parser.add_argument(
          """--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
      )
      parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")

      lowercase_					= parser.parse_args()
      lowercase_					= strabool(args.class_cond)

      lowercase_					= os.path.basename(args.unet_path)
      print(f"""Checkpoint: {ckpt_name}""")

      # Get U-Net config
      if "imagenet64" in ckpt_name:
            lowercase_					= IMAGENET_64_UNET_CONFIG
      elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
            lowercase_					= LSUN_256_UNET_CONFIG
      elif "test" in ckpt_name:
            lowercase_					= TEST_UNET_CONFIG
      else:
            raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

      if not args.class_cond:
            lowercase_					= None

      lowercase_					= con_pt_to_diffuser(args.unet_path, unet_config)

      lowercase_					= UNetaDModel(**unet_config)
      image_unet.load_state_dict(converted_unet_ckpt)

      # Get scheduler config
      if "cd" in ckpt_name or "test" in ckpt_name:
            lowercase_					= CD_SCHEDULER_CONFIG
      elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
            lowercase_					= CT_IMAGENET_64_SCHEDULER_CONFIG
      elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
            lowercase_					= CT_LSUN_256_SCHEDULER_CONFIG
      else:
            raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

      lowercase_					= CMStochasticIterativeScheduler(**scheduler_config)

      lowercase_					= ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
      consistency_model.save_pretrained(args.dump_path)









"""simple docstring"""




import inspect
import unittest

from transformers import ViTMSNConfig
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 torch import nn

					from transformers import ViTMSNForImageClassification, ViTMSNModel
					from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor






class     __magic_name__							:






				'''simple docstring'''





				def __init__(       self		, _a		, _a=13		, _a=30		, _a=2		, _a=3		, _a=True		, _a=True		, _a=32		, _a=5		, _a=4		, _a=37		, _a="gelu"		, _a=0.1		, _a=0.1		, _a=10		, _a=0.02		, _a=None		, ):





							"""simple docstring"""
							lowerCamelCase			      =					parent
							lowerCamelCase			      =					batch_size
							lowerCamelCase			      =					image_size
							lowerCamelCase			      =					patch_size
							lowerCamelCase			      =					num_channels
							lowerCamelCase			      =					is_training
							lowerCamelCase			      =					use_labels
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					hidden_dropout_prob
							lowerCamelCase			      =					attention_probs_dropout_prob
							lowerCamelCase			      =					type_sequence_label_size
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					scope

							# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							lowerCamelCase			      =					(image_size // patch_size) ** 2
							lowerCamelCase			      =					num_patches + 1





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

							lowerCamelCase			      =					self.get_config()

							return config, pixel_values, labels





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return ViTMSNConfig(
							    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		, initializer_range=self.initializer_range		, )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					ViTMSNModel(config=_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a )
							self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.type_sequence_label_size
							lowerCamelCase			      =					ViTMSNForImageClassification(_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a		, labels=_a )
							print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}""" )
							print("""Labels: {labels}""" )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size) )

							# test greyscale images
							lowerCamelCase			      =					1
							lowerCamelCase			      =					ViTMSNForImageClassification(_a )
							model.to(_a )
							model.eval()

							lowerCamelCase			      =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
							lowerCamelCase			      =					model(_a )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size) )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.prepare_config_and_inputs()
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					config_and_inputs
							lowerCamelCase			      =					{"""pixel_values""": pixel_values}
							return config, inputs_dict






@require_torch
class     __magic_name__							(       UpperCAmelCase__						,					UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				__UpperCamelCase			  =						(
				    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					ViTMSNModelTester(self )
							lowerCamelCase			      =					ConfigTester(self		, config_class=_a		, has_text_modality=_a		, hidden_size=37 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							self.config_tester.run_common_tests()





				@unittest.skip(reason="""ViTMSN does not use inputs_embeds""" )
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							pass





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase      ,   lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowerCamelCase			      =					model_class(_a )
										self.assertIsInstance(model.get_input_embeddings()		, (nn.Module) )
										lowerCamelCase			      =					model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(_a		, nn.Linear ) )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase      ,   lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCamelCase			      =					["""pixel_values"""]
										self.assertListEqual(arg_names[:1]		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_a )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase			      =					ViTMSNModel.from_pretrained(_a )
										self.assertIsNotNone(_a )




def     a__ (				)						->					Any:
			lowerCamelCase			      =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
			return image






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






				'''simple docstring'''





				@cached_property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small""" ) if is_vision_available() else None





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							torch.manual_seed(2 )
							lowerCamelCase			      =					ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small""" ).to(_a )

							lowerCamelCase			      =					self.default_image_processor
							lowerCamelCase			      =					prepare_img()
							lowerCamelCase			      =					image_processor(images=_a		, return_tensors="""pt""" ).to(_a )

							# forward pass
							with torch.no_grad():
										lowerCamelCase			      =					model(**_a )

							# verify the logits
							lowerCamelCase			      =					torch.Size((1, 1_000) )
							self.assertEqual(outputs.logits.shape		, _a )

							lowerCamelCase			      =					torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(_a )

							self.assertTrue(torch.allclose(outputs.logits[0, :3]		, _a		, atol=1e-4 ) )



