'''simple docstring'''





import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase    :			int						     =  logging.get_logger()



@dataclass
class    UpperCamelCase :







    """simple docstring"""





    A			:  int            =       42
    A			:  int            =       field(default_factory=a_    )
    A			:  Optional[Any]            =       field(default_factory=a_    )
    A			:  int            =       field(default_factory=a_    )





    def 					SCREAMING_SNAKE_CASE_			( self			:		Dict				,				UpperCAmelCase_			:		Optional[int]				,				UpperCAmelCase_			:		Any				,				UpperCAmelCase_			:		Tuple				,				UpperCAmelCase_			:		Any):


         """simple docstring"""







         a :				str				=      len(list(m.modules())) == 1 or isinstance(__lowercase				,				nn.Convad) or isinstance(__lowercase				,				nn.BatchNormad)
         if has_not_submodules:
              self.traced.append(__lowercase)
              a :				Tuple				=      m





    def __call__( self			:		Optional[int]				,				UpperCAmelCase_			:		Optional[Any]):


         """simple docstring"""







         for name, m in self.module.named_modules():
              self.handles.append(m.register_forward_hook(partial(self._forward_hook				,				name=__lowercase)))
         self.module(__lowercase)
         [x.remove() for x in self.handles]
         return self







    @property
    def 					SCREAMING_SNAKE_CASE_			( self			:		Any):


         """simple docstring"""







         return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys())) > 0}






class    UpperCamelCase (    nn.Module    ):







    """simple docstring"""





    def __init__( self			:		List[Any]				,				UpperCAmelCase_			:		int):


         """simple docstring"""







         super().__init__()

         a :				List[Tuple[str, nn.Module]]				=      []
         # - get the stem
         feature_blocks.append(('conv1', model.stem))
         # - get all the feature blocks
         for k, v in model.trunk_output.named_children():
              assert k.startswith('block'), f"""Unexpected layer name {k}"""
              a :				str				=      len(__lowercase) + 1
              feature_blocks.append((f"""res{block_index}""", v))

         a :				List[str]				=      nn.ModuleDict(__lowercase)







    def 					SCREAMING_SNAKE_CASE_			( self			:		int				,				UpperCAmelCase_			:		List[str]):


         """simple docstring"""







         return get_trunk_forward_outputs(
             __lowercase				,				out_feat_keys=__lowercase				,				feature_blocks=self._feature_blocks				,				)






class    UpperCamelCase (    a_    ):







    """simple docstring"""





    def 					SCREAMING_SNAKE_CASE_			( self			:		int):


         """simple docstring"""







         return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]






def SCREAMING_SNAKE_CASE__   (							snake_case    :							Optional[int]   )					->							Dict[str, str]:







     """simple docstring"""
     a :				Any				=      RegNetConfig(depths=[2, 7, 17, 1]							,     hidden_sizes=[8, 8, 8, 8]							,     groups_width=8   )
     if "in1k" in model_name:
          a :				List[Any]				=      RegNetForImageClassification(snake_case   )
     else:
          a :				Any				=      RegNetModel(snake_case   )
     # create from model (with small weights)
     a :				Dict				=      FakeRegNetVisslWrapper(
         RegNet(FakeRegNetParams(depth=27							,     group_width=1_010							,     w_a=1_744							,     w_a=6_20.83							,     w_m=2.52   )   )   )

     with torch.no_grad():
          a :				str				=      from_model.eval()
          a :				Tuple				=      our_model.eval()

          a :				Any				=      torch.randn((1, 3, 32, 32)   )
          # trace both
          a :				str				=      Tracker(snake_case   )
          a :				int				=      dest_tracker(snake_case   ).parametrized

          pprint(dest_tracker.nameamodule   )
          a :				List[str]				=      Tracker(snake_case   )
          a :				List[str]				=      src_tracker(snake_case   ).parametrized

     # convert the keys -> module dict to keys -> params
     def to_params_dict(snake_case    :							Dict   ):
          a :				List[Any]				=      OrderedDict()
          for name, module in dict_with_modules.items():
               for param_name, param in module.state_dict().items():
                    a :				List[Any]				=      param
          return params_dict

     a :				Optional[Any]				=      {}

     a :				str				=      to_params_dict(snake_case   )
     a :				List[str]				=      to_params_dict(snake_case   )

     for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()							,     dst_state_dict.items()   ):
          a :				List[str]				=      dest_key
          logger.info(F"""{src_key} -> {dest_key}"""   )
     # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
     if "in1k" in model_name:
          a :				Tuple				=      'classifier.1.weight'
          a :				str				=      'classifier.1.bias'

     return from_to_ours_keys






def SCREAMING_SNAKE_CASE__   (							snake_case    :							int							,     snake_case    :							Union[str, Any] = None							,     snake_case    :							List[str] = True   )					->							str:







     """simple docstring"""
     a :				str				=      'imagenet-1k-id2label.json'
     a :				List[Any]				=      1_000

     a :				List[Any]				=      'huggingface/label-files'
     a :				List[str]				=      num_labels
     a :				str				=      json.load(open(cached_download(hf_hub_url(snake_case							,     snake_case							,     repo_type='dataset'   )   )							,     'r'   )   )
     a :				Tuple				=      {int(snake_case   ): v for k, v in idalabel.items()}

     a :				str				=      idalabel
     a :				Tuple				=      {v: k for k, v in idalabel.items()}

     a :				int				=      partial(snake_case							,     num_labels=snake_case							,     idalabel=snake_case							,     labelaid=snake_case   )

     a :				Any				=      {
         'regnet-y-10b-seer': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]							,     hidden_sizes=[2_020, 4_040, 11_110, 28_280]							,     groups_width=1_010   ),
         # finetuned on imagenet
         'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]							,     hidden_sizes=[2_020, 4_040, 11_110, 28_280]							,     groups_width=1_010   ),
     }

     # add seer weights logic
     def load_using_classy_vision(snake_case    :							Any   ) -> Tuple[Dict, Dict]:
          a :				Dict				=      torch.hub.load_state_dict_from_url(snake_case							,     model_dir=str(snake_case   )							,     map_location='cpu'   )
          # check if we have a head, if yes add it
          a :				List[Any]				=      files['classy_state_dict']['base_model']['model']
          return model_state_dict["trunk"], model_state_dict["heads"]

     a :				Union[str, Any]				=      {
         'regnet-y-10b-seer': partial(
             snake_case							,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'							,     ),
         'regnet-y-10b-seer-in1k': partial(
             snake_case							,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'							,     ),
     }

     a :				Optional[int]				=      get_from_to_our_keys(snake_case   )

     if not (save_directory / F"""{model_name}.pth""").exists():
          logger.info('Loading original state_dict.'   )
          a :				Tuple				=      names_to_from_model[model_name]()
          a :				Dict				=      from_state_dict_trunk
          if "in1k" in model_name:
               # add the head
               a :				Tuple				=      {**from_state_dict_trunk, **from_state_dict_head}
          logger.info('Done!'   )

          a :				Dict				=      {}

          a :				Optional[Any]				=      list(from_state_dict.keys()   )
          a :				List[str]				=      R'\.block.-part.'
          # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
          for key in from_state_dict.keys():
               # remove the weird "block[0,1]-part" from the key
               a :				Dict				=      re.sub(snake_case							,     ''							,     snake_case   )
               # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
               a :				str				=      from_to_ours_keys[src_key]
               # store the parameter with our key
               a :				int				=      from_state_dict[key]
               not_used_keys.remove(snake_case   )
          # check that all keys have been updated
          assert len(snake_case   ) == 0, F"""Some keys where not used {','.join(snake_case   )}"""

          logger.info(F"""The following keys were not used: {','.join(snake_case   )}"""   )

          # save our state dict to disk
          torch.save(snake_case							,     save_directory / F"""{model_name}.pth"""   )

          del converted_state_dict
     else:
          logger.info('The state_dict was already stored on disk.'   )
     if push_to_hub:
          logger.info(F"""Token is {os.environ['HF_TOKEN']}"""   )
          logger.info('Loading our model.'   )
          # create our model
          a :				Union[str, Any]				=      names_to_config[model_name]
          a :				Union[str, Any]				=      RegNetModel
          if "in1k" in model_name:
               a :				Optional[int]				=      RegNetForImageClassification
          a :				Dict				=      our_model_func(snake_case   )
          # place our model to the meta device (so remove all the weights)
          our_model.to(torch.device('meta'   )   )
          logger.info('Loading state_dict in our model.'   )
          # load state dict
          a :				Dict				=      our_model.state_dict().keys()
          PreTrainedModel._load_pretrained_model_low_mem(
              snake_case							,     snake_case							,     [save_directory / F"""{model_name}.pth"""]   )
          logger.info('Finally, pushing!'   )
          # push it to hub
          our_model.push_to_hub(
              repo_path_or_name=save_directory / model_name							,     commit_message='Add model'							,     output_dir=save_directory / model_name							,     )
          a :				Optional[int]				=      384
          # we can use the convnext one
          a :				int				=      AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'							,     size=snake_case   )
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / model_name							,     commit_message='Add image processor'							,     output_dir=save_directory / model_name							,     )


if __name__ == "__main__":
      UpperCamelCase    :			List[Any]						     =  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default=None,
          type=str,
          help=(
              """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
              """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=Path,
          required=True,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument(
          """--push_to_hub""",
          default=True,
          type=bool,
          required=False,
          help="""If True, push model and image processor to the hub.""",
      )

      UpperCamelCase    :			Union[str, Any]						     =  parser.parse_args()

      UpperCamelCase    :			int						     =  args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
							import torch
							from torch import nn

							from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
							from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import SwinaSRImageProcessor


class 				__SCREAMING_SNAKE_CASE							:

					def __init__(				self ,       __lowercase ,       __lowercase=13 ,       __lowercase=32 ,       __lowercase=1 ,       __lowercase=3 ,       __lowercase=16 ,       __lowercase=[1, 2, 1] ,       __lowercase=[2, 2, 4] ,       __lowercase=2 ,       __lowercase=2.0 ,       __lowercase=True ,       __lowercase=0.0 ,       __lowercase=0.0 ,       __lowercase=0.1 ,       __lowercase="gelu" ,       __lowercase=False ,       __lowercase=True ,       __lowercase=0.02 ,       __lowercase=1E-5 ,       __lowercase=True ,       __lowercase=None ,       __lowercase=False ,       __lowercase=2 ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							   =							parent
						lowerCamelCase_							:		List[str]							   =							batch_size
						lowerCamelCase_							:		Dict							   =							image_size
						lowerCamelCase_							:		Any							   =							patch_size
						lowerCamelCase_							:		Any							   =							num_channels
						lowerCamelCase_							:		str							   =							embed_dim
						lowerCamelCase_							:		Tuple							   =							depths
						lowerCamelCase_							:		Union[str, Any]							   =							num_heads
						lowerCamelCase_							:		int							   =							window_size
						lowerCamelCase_							:		Tuple							   =							mlp_ratio
						lowerCamelCase_							:		int							   =							qkv_bias
						lowerCamelCase_							:		Optional[Any]							   =							hidden_dropout_prob
						lowerCamelCase_							:		Optional[int]							   =							attention_probs_dropout_prob
						lowerCamelCase_							:		Optional[int]							   =							drop_path_rate
						lowerCamelCase_							:		Union[str, Any]							   =							hidden_act
						lowerCamelCase_							:		Optional[Any]							   =							use_absolute_embeddings
						lowerCamelCase_							:		Union[str, Any]							   =							patch_norm
						lowerCamelCase_							:		Tuple							   =							layer_norm_eps
						lowerCamelCase_							:		Dict							   =							initializer_range
						lowerCamelCase_							:		Dict							   =							is_training
						lowerCamelCase_							:		Union[str, Any]							   =							scope
						lowerCamelCase_							:		Optional[Any]							   =							use_labels
						lowerCamelCase_							:		Any							   =							upscale

						# here we set some attributes to make tests pass
						lowerCamelCase_							:		int							   =							len(__lowercase					)
						lowerCamelCase_							:		Optional[int]							   =							embed_dim
						lowerCamelCase_							:		Tuple							   =							(image_size // patch_size) ** 2

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

						lowerCamelCase_							:		List[Any]							   =							None
						if self.use_labels:
							lowerCamelCase_							:		Dict							   =							ids_tensor([self.batch_size] ,       self.type_sequence_label_size					)

						lowerCamelCase_							:		int							   =							self.get_config()

						return config, pixel_values, labels

					def 	lowercase_							(				self					):







						'''simple docstring'''




						return SwinaSRConfig(
						    image_size=self.image_size ,       patch_size=self.patch_size ,       num_channels=self.num_channels ,       embed_dim=self.embed_dim ,       depths=self.depths ,       num_heads=self.num_heads ,       window_size=self.window_size ,       mlp_ratio=self.mlp_ratio ,       qkv_bias=self.qkv_bias ,       hidden_dropout_prob=self.hidden_dropout_prob ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob ,       drop_path_rate=self.drop_path_rate ,       hidden_act=self.hidden_act ,       use_absolute_embeddings=self.use_absolute_embeddings ,       path_norm=self.patch_norm ,       layer_norm_eps=self.layer_norm_eps ,       initializer_range=self.initializer_range ,       upscale=self.upscale ,       )

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Any							   =							SwinaSRModel(config=__lowercase					)
						model.to(__lowercase					)
						model.eval()
						lowerCamelCase_							:		int							   =							model(__lowercase					)

						self.parent.assertEqual(
						    result.last_hidden_state.shape ,       (self.batch_size, self.embed_dim, self.image_size, self.image_size)					)

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							SwinaSRForImageSuperResolution(__lowercase					)
						model.to(__lowercase					)
						model.eval()
						lowerCamelCase_							:		Any							   =							model(__lowercase					)

						lowerCamelCase_							:		int							   =							self.image_size * self.upscale
						self.parent.assertEqual(
						    result.reconstruction.shape ,       (self.batch_size, self.num_channels, expected_image_size, expected_image_size)					)





					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.prepare_config_and_inputs()
						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Union[str, Any]							   =							config_and_inputs
						lowerCamelCase_							:		Any							   =							{'pixel_values': pixel_values}
						return config, inputs_dict


@require_torch
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
					_lowerCamelCase		  = {"""feature-extraction""": SwinaSRModel} if is_torch_available() else {}

					_lowerCamelCase		  = False
					_lowerCamelCase		  = False
					_lowerCamelCase		  = False
					_lowerCamelCase		  = False
					_lowerCamelCase		  = False

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							SwinaSRModelTester(self					)
						lowerCamelCase_							:		Optional[Any]							   =							ConfigTester(self ,       config_class=__lowercase ,       embed_dim=37					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						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 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_super_resolution(*__lowercase					)

					@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'					)
					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass

					@unittest.skip(reason='Swin2SR does not use inputs_embeds'					)
					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass

					@unittest.skip(reason='Swin2SR does not support training yet'					)
					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass

					@unittest.skip(reason='Swin2SR does not support training yet'					)
					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_   ,			lowerCamelCase_							:		Dict							   =							self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							lowerCamelCase_							:		Optional[Any]							   =							model_class(__lowercase					)
							self.assertIsInstance(model.get_input_embeddings() ,       (nn.Module)					)
							lowerCamelCase_							:		Union[str, Any]							   =							model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__lowercase ,       nn.Linear					)					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_   ,			lowerCamelCase_							:		Optional[int]							   =							self.model_tester.prepare_config_and_inputs_for_common()

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

							lowerCamelCase_							:		Tuple							   =							['pixel_values']
							self.assertListEqual(arg_names[:1] ,       __lowercase					)

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCamelCase_							:		Tuple							   =							SwinaSRModel.from_pretrained(__lowercase					)
							self.assertIsNotNone(__lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_   ,			lowerCamelCase_							:		Union[str, Any]							   =							self.model_tester.prepare_config_and_inputs_for_common()

						lowerCamelCase_							:		Optional[Any]							   =							_config_zero_init(__lowercase					)
						for model_class in self.all_model_classes:
							lowerCamelCase_							:		List[str]							   =							model_class(config=__lowercase					)
							for name, param in model.named_parameters():
								if "logit_scale" in name:
									continue
								if param.requires_grad:
									self.assertIn(
									    ((param.data.mean() * 1E9).round() / 1E9).item() ,       [0.0, 1.0] ,       msg=f"Parameter {name} of model {model_class} seems not properly initialized" ,       )





					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_   ,			lowerCamelCase_							:		Union[str, Any]							   =							self.model_tester.prepare_config_and_inputs_for_common()
						lowerCamelCase_							:		Union[str, Any]							   =							True

						for model_class in self.all_model_classes:
							lowerCamelCase_							:		Dict							   =							True
							lowerCamelCase_							:		Optional[Any]							   =							False
							lowerCamelCase_							:		Optional[Any]							   =							True
							lowerCamelCase_							:		Dict							   =							model_class(__lowercase					)
							model.to(__lowercase					)
							model.eval()
							with torch.no_grad():
								lowerCamelCase_							:		Optional[Any]							   =							model(**self._prepare_for_class(__lowercase ,       __lowercase					)					)
							lowerCamelCase_							:		Optional[int]							   =							outputs.attentions
							lowerCamelCase_							:		Tuple							   =							len(self.model_tester.depths					)
							self.assertEqual(len(__lowercase					) ,       __lowercase					)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							lowerCamelCase_							:		Any							   =							True
							lowerCamelCase_							:		Optional[int]							   =							config.window_size**2
							lowerCamelCase_							:		str							   =							model_class(__lowercase					)
							model.to(__lowercase					)
							model.eval()
							with torch.no_grad():
								lowerCamelCase_							:		Tuple							   =							model(**self._prepare_for_class(__lowercase ,       __lowercase					)					)
							lowerCamelCase_							:		Dict							   =							outputs.attentions
							self.assertEqual(len(__lowercase					) ,       __lowercase					)

							self.assertListEqual(
							    list(attentions[0].shape[-3:]					) ,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,       )
							lowerCamelCase_							:		str							   =							len(__lowercase					)

							# Check attention is always last and order is fine
							lowerCamelCase_							:		Tuple							   =							True
							lowerCamelCase_							:		List[Any]							   =							True
							lowerCamelCase_							:		Tuple							   =							model_class(__lowercase					)
							model.to(__lowercase					)
							model.eval()
							with torch.no_grad():
								lowerCamelCase_							:		List[str]							   =							model(**self._prepare_for_class(__lowercase ,       __lowercase					)					)

							self.assertEqual(out_len + 1 ,       len(__lowercase					)					)

							lowerCamelCase_							:		str							   =							outputs.attentions

							self.assertEqual(len(__lowercase					) ,       __lowercase					)

							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]					) ,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,       )


@require_vision
@require_torch
@slow
class 				__SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							SwinaSRImageProcessor()
						lowerCamelCase_							:		Optional[int]							   =							SwinaSRForImageSuperResolution.from_pretrained('caidas/swin2SR-classical-sr-x2-64'					).to(__lowercase					)

						lowerCamelCase_							:		int							   =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
						lowerCamelCase_							:		Optional[int]							   =							processor(images=__lowercase ,       return_tensors='pt'					).to(__lowercase					)

						# forward pass
						with torch.no_grad():
							lowerCamelCase_							:		Optional[int]							   =							model(**__lowercase					)

						# verify the logits
						lowerCamelCase_							:		Dict							   =							torch.Size([1, 3, 976, 1_296]					)
						self.assertEqual(outputs.reconstruction.shape ,       __lowercase					)
						lowerCamelCase_							:		Union[str, Any]							   =							torch.tensor(
						    [[0.54_58, 0.55_46, 0.56_38], [0.55_26, 0.55_65, 0.56_51], [0.53_96, 0.54_26, 0.56_21]]					).to(__lowercase					)
						self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3] ,       __lowercase ,       atol=1E-4					)					)
