

'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case      =    logging.get_logger(__name__)







def    _A						(      snake_case	)     ->      List[Any]:
   _lowercase     : List[Any]     	=  MobileViTConfig()

   # size of the architecture
   if "mobilevit_s" in mobilevit_name:
      _lowercase     : Any     	=  [1_44, 1_92, 2_40]
      _lowercase     : Dict     	=  [16, 32, 64, 96, 1_28, 1_60, 6_40]
   elif "mobilevit_xs" in mobilevit_name:
      _lowercase     : Optional[int]     	=  [96, 1_20, 1_44]
      _lowercase     : Union[str, Any]     	=  [16, 32, 48, 64, 80, 96, 3_84]
   elif "mobilevit_xxs" in mobilevit_name:
      _lowercase     : Optional[Any]     	=  [64, 80, 96]
      _lowercase     : str     	=  [16, 16, 24, 48, 64, 80, 3_20]
      _lowercase     : Dict     	=  0.05
      _lowercase     : List[Any]     	=  2.0

   if mobilevit_name.startswith("deeplabv3_"	):
      _lowercase     : int     	=  5_12
      _lowercase     : str     	=  16
      _lowercase     : Union[str, Any]     	=  21
      _lowercase     : Optional[Any]     	=  "pascal-voc-id2label.json"
   else:
      _lowercase     : Optional[int]     	=  10_00
      _lowercase     : str     	=  "imagenet-1k-id2label.json"

   _lowercase     : str     	=  "huggingface/label-files"
   _lowercase     : str     	=  json.load(open(hf_hub_download(__UpperCAmelCase						,				__UpperCAmelCase						,				repo_type="dataset"	)						,				"r"	)	)
   _lowercase     : Any     	=  {int(__UpperCAmelCase	): v for k, v in idalabel.items()}
   _lowercase     : Optional[Any]     	=  idalabel
   _lowercase     : List[str]     	=  {v: k for k, v in idalabel.items()}

   return config







def    _A						(      snake_case						,				snake_case=False	)     ->      str:







   for i in range(1						,				6	):
      if F'''layer_{i}.''' in name:
         _lowercase     : Tuple     	=  name.replace(F'''layer_{i}.'''						,				F'''encoder.layer.{i - 1}.'''	)

   if "conv_1." in name:
      _lowercase     : List[str]     	=  name.replace("conv_1."						,				"conv_stem."	)
   if ".block." in name:
      _lowercase     : List[str]     	=  name.replace(".block."						,				"."	)

   if "exp_1x1" in name:
      _lowercase     : str     	=  name.replace("exp_1x1"						,				"expand_1x1"	)
   if "red_1x1" in name:
      _lowercase     : List[Any]     	=  name.replace("red_1x1"						,				"reduce_1x1"	)
   if ".local_rep.conv_3x3." in name:
      _lowercase     : Union[str, Any]     	=  name.replace(".local_rep.conv_3x3."						,				".conv_kxk."	)
   if ".local_rep.conv_1x1." in name:
      _lowercase     : int     	=  name.replace(".local_rep.conv_1x1."						,				".conv_1x1."	)
   if ".norm." in name:
      _lowercase     : Dict     	=  name.replace(".norm."						,				".normalization."	)
   if ".conv." in name:
      _lowercase     : int     	=  name.replace(".conv."						,				".convolution."	)
   if ".conv_proj." in name:
      _lowercase     : Optional[int]     	=  name.replace(".conv_proj."						,				".conv_projection."	)

   for i in range(0						,				2	):
      for j in range(0						,				4	):
         if F'''.{i}.{j}.''' in name:
            _lowercase     : Tuple     	=  name.replace(F'''.{i}.{j}.'''						,				F'''.{i}.layer.{j}.'''	)

   for i in range(2						,				6	):
      for j in range(0						,				4	):
         if F'''.{i}.{j}.''' in name:
            _lowercase     : List[Any]     	=  name.replace(F'''.{i}.{j}.'''						,				F'''.{i}.'''	)
            if "expand_1x1" in name:
               _lowercase     : Any     	=  name.replace("expand_1x1"						,				"downsampling_layer.expand_1x1"	)
            if "conv_3x3" in name:
               _lowercase     : Tuple     	=  name.replace("conv_3x3"						,				"downsampling_layer.conv_3x3"	)
            if "reduce_1x1" in name:
               _lowercase     : Tuple     	=  name.replace("reduce_1x1"						,				"downsampling_layer.reduce_1x1"	)

   for i in range(2						,				5	):
      if F'''.global_rep.{i}.weight''' in name:
         _lowercase     : str     	=  name.replace(F'''.global_rep.{i}.weight'''						,				".layernorm.weight"	)
      if F'''.global_rep.{i}.bias''' in name:
         _lowercase     : Dict     	=  name.replace(F'''.global_rep.{i}.bias'''						,				".layernorm.bias"	)

   if ".global_rep." in name:
      _lowercase     : Dict     	=  name.replace(".global_rep."						,				".transformer."	)
   if ".pre_norm_mha.0." in name:
      _lowercase     : Dict     	=  name.replace(".pre_norm_mha.0."						,				".layernorm_before."	)
   if ".pre_norm_mha.1.out_proj." in name:
      _lowercase     : Optional[int]     	=  name.replace(".pre_norm_mha.1.out_proj."						,				".attention.output.dense."	)
   if ".pre_norm_ffn.0." in name:
      _lowercase     : Union[str, Any]     	=  name.replace(".pre_norm_ffn.0."						,				".layernorm_after."	)
   if ".pre_norm_ffn.1." in name:
      _lowercase     : List[Any]     	=  name.replace(".pre_norm_ffn.1."						,				".intermediate.dense."	)
   if ".pre_norm_ffn.4." in name:
      _lowercase     : int     	=  name.replace(".pre_norm_ffn.4."						,				".output.dense."	)
   if ".transformer." in name:
      _lowercase     : List[str]     	=  name.replace(".transformer."						,				".transformer.layer."	)

   if ".aspp_layer." in name:
      _lowercase     : Optional[int]     	=  name.replace(".aspp_layer."						,				"."	)
   if ".aspp_pool." in name:
      _lowercase     : Optional[Any]     	=  name.replace(".aspp_pool."						,				"."	)
   if "seg_head." in name:
      _lowercase     : Union[str, Any]     	=  name.replace("seg_head."						,				"segmentation_head."	)
   if "segmentation_head.classifier.classifier." in name:
      _lowercase     : Optional[Any]     	=  name.replace("segmentation_head.classifier.classifier."						,				"segmentation_head.classifier."	)

   if "classifier.fc." in name:
      _lowercase     : Any     	=  name.replace("classifier.fc."						,				"classifier."	)
   elif (not base_model) and ("segmentation_head." not in name):
      _lowercase     : List[str]     	=  "mobilevit." + name

   return name







def    _A						(      snake_case						,				snake_case						,				snake_case=False	)     ->      Optional[int]:







   if base_model:
      _lowercase     : List[str]     	=  ""
   else:
      _lowercase     : str     	=  "mobilevit."

   for key in orig_state_dict.copy().keys():
      _lowercase     : int     	=  orig_state_dict.pop(__UpperCAmelCase	)

      if key[:8] == "encoder.":
         _lowercase     : List[str]     	=  key[8:]

      if "qkv" in key:
         _lowercase     : List[Any]     	=  key.split("."	)
         _lowercase     : str     	=  int(key_split[0][6:]	) - 1
         _lowercase     : Union[str, Any]     	=  int(key_split[3]	)
         _lowercase     : Union[str, Any]     	=  model.get_submodule(F'''{model_prefix}encoder.layer.{layer_num}'''	)
         _lowercase     : Dict     	=  layer.transformer.layer[transformer_num].attention.attention.all_head_size
         _lowercase     : List[str]     	=  (
             F'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
         )
         if "weight" in key:
            _lowercase     : Union[str, Any]     	=  val[:dim, :]
            _lowercase     : List[Any]     	=  val[dim : dim * 2, :]
            _lowercase     : Optional[int]     	=  val[-dim:, :]
         else:
            _lowercase     : Any     	=  val[:dim]
            _lowercase     : str     	=  val[dim : dim * 2]
            _lowercase     : List[Any]     	=  val[-dim:]
      else:
         _lowercase     : Optional[Any]     	=  val

   return orig_state_dict







def    _A						(      )     ->      int:
   _lowercase     : Optional[Any]     	=  "http://images.cocodataset.org/val2017/000000039769.jpg"
   _lowercase     : str     	=  Image.open(requests.get(__UpperCAmelCase						,				stream=__UpperCAmelCase	).raw	)
   return im







@torch.no_grad()
def    _A						(      snake_case						,				snake_case						,				snake_case						,				snake_case=False	)     ->      List[str]:
   _lowercase     : Optional[int]     	=  get_mobilevit_config(__UpperCAmelCase	)

   # load original state_dict
   _lowercase     : List[str]     	=  torch.load(__UpperCAmelCase						,				map_location="cpu"	)

   # load 🤗 model
   if mobilevit_name.startswith("deeplabv3_"	):
      _lowercase     : Dict     	=  MobileViTForSemanticSegmentation(__UpperCAmelCase	).eval()
   else:
      _lowercase     : Tuple     	=  MobileViTForImageClassification(__UpperCAmelCase	).eval()

   _lowercase     : Any     	=  convert_state_dict(__UpperCAmelCase						,				__UpperCAmelCase	)
   model.load_state_dict(__UpperCAmelCase	)

   # Check outputs on an image, prepared by MobileViTImageProcessor
   _lowercase     : Any     	=  MobileViTImageProcessor(crop_size=config.image_size						,				size=config.image_size + 32	)
   _lowercase     : List[str]     	=  image_processor(images=prepare_img()						,				return_tensors="pt"	)
   _lowercase     : Tuple     	=  model(**__UpperCAmelCase	)
   _lowercase     : str     	=  outputs.logits

   if mobilevit_name.startswith("deeplabv3_"	):
      assert logits.shape == (1, 21, 32, 32)

      if mobilevit_name == "deeplabv3_mobilevit_s":
         _lowercase     : Union[str, Any]     	=  torch.tensor(
             [
                 [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
                 [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
                 [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
             ]	)
      elif mobilevit_name == "deeplabv3_mobilevit_xs":
         _lowercase     : Union[str, Any]     	=  torch.tensor(
             [
                 [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
                 [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
                 [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
             ]	)
      elif mobilevit_name == "deeplabv3_mobilevit_xxs":
         _lowercase     : str     	=  torch.tensor(
             [
                 [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                 [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.8624, -9.5964], [-10.88_40, -10.81_58, -10.66_59]],
                 [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
             ]	)
      else:
         raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''	)

      assert torch.allclose(logits[0, :3, :3, :3]						,				__UpperCAmelCase						,				atol=1E-4	)
   else:
      assert logits.shape == (1, 10_00)

      if mobilevit_name == "mobilevit_s":
         _lowercase     : Union[str, Any]     	=  torch.tensor([-0.9866, 0.2392, -1.1241]	)
      elif mobilevit_name == "mobilevit_xs":
         _lowercase     : Dict     	=  torch.tensor([-2.4761, -0.9399, -1.9587]	)
      elif mobilevit_name == "mobilevit_xxs":
         _lowercase     : Tuple     	=  torch.tensor([-1.9364, -1.2327, -0.4653]	)
      else:
         raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''	)

      assert torch.allclose(logits[0, :3]						,				__UpperCAmelCase						,				atol=1E-4	)

   Path(__UpperCAmelCase	).mkdir(exist_ok=__UpperCAmelCase	)
   print(F'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}'''	)
   model.save_pretrained(__UpperCAmelCase	)
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''	)
   image_processor.save_pretrained(__UpperCAmelCase	)

   if push_to_hub:
      _lowercase     : Union[str, Any]     	=  {
          "mobilevit_s": "mobilevit-small",
          "mobilevit_xs": "mobilevit-x-small",
          "mobilevit_xxs": "mobilevit-xx-small",
          "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
          "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
          "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
      }

      print("Pushing to the hub..."	)
      _lowercase     : List[Any]     	=  model_mapping[mobilevit_name]
      image_processor.push_to_hub(__UpperCAmelCase						,				organization="apple"	)
      model.push_to_hub(__UpperCAmelCase						,				organization="apple"	)


if __name__ == "__main__":
    _snake_case      =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--mobilevit_name',
        default='mobilevit_s',
        type=str,
        help=(
            'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
            ' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
        ),
    )
    parser.add_argument(
        '--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )

    _snake_case      =    parser.parse_args()
    convert_movilevit_checkpoint(
        args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )













'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
    from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class __a			:





   def __init__(       self					:      Any					,lowerCamelCase					:      Any					,lowerCamelCase					:      int=3					,lowerCamelCase					:      Optional[int]=32					,lowerCamelCase					:      Any=3					,lowerCamelCase					:      Dict=10					,lowerCamelCase					:      Union[str, Any]=[10, 20, 30, 40]					,lowerCamelCase					:      Any=[1, 1, 2, 1]					,lowerCamelCase					:      int=True					,lowerCamelCase					:      Optional[Any]=True					,lowerCamelCase					:      Dict="relu"					,lowerCamelCase					:      str=3					,lowerCamelCase					:      Union[str, Any]=None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							image_size
      __SCREAMING_SNAKE_CASE   				=							num_channels
      __SCREAMING_SNAKE_CASE   				=							embeddings_size
      __SCREAMING_SNAKE_CASE   				=							hidden_sizes
      __SCREAMING_SNAKE_CASE   				=							depths
      __SCREAMING_SNAKE_CASE   				=							is_training
      __SCREAMING_SNAKE_CASE   				=							use_labels
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							num_labels
      __SCREAMING_SNAKE_CASE   				=							scope
      __SCREAMING_SNAKE_CASE   				=							len(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_labels:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.num_labels     )

      __SCREAMING_SNAKE_CASE   				=							self.get_config()

      return config, pixel_values, labels





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return RegNetConfig(
          num_channels=self.num_channels					,embeddings_size=self.embeddings_size					,hidden_sizes=self.hidden_sizes					,depths=self.depths					,hidden_act=self.hidden_act					,num_labels=self.num_labels					,)





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      int					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							RegNetModel(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      # 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   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.num_labels
      __SCREAMING_SNAKE_CASE   				=							RegNetForImageClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,labels=lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

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







@require_torch
class __a			( _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			List[str]						   =						(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
   __UpperCamelCase :			Optional[int]						   =						(
       {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
       if is_torch_available()
       else {}
   )

   __UpperCamelCase :			Dict						   =						False
   __UpperCamelCase :			str						   =						False
   __UpperCamelCase :			Any						   =						False
   __UpperCamelCase :			Tuple						   =						False





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							RegNetModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(self					,config_class=lowerCamelCase					,has_text_modality=lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''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   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return





   @unittest.skip(reason="""RegNet does not use inputs_embeds"""     )
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      pass





   @unittest.skip(reason="""RegNet does not support input and output embeddings"""     )
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      pass





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

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

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





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE   				=							model_class(config=lowerCamelCase     )
         for name, module in model.named_modules():
            if isinstance(lowerCamelCase					,(nn.BatchNormad, nn.GroupNorm)     ):
               self.assertTrue(
                   torch.all(module.weight == 1     )					,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""					,)
               self.assertTrue(
                   torch.all(module.bias == 0     )					,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""					,)





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      def check_hidden_states_output(lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Optional[int]     ):
         __SCREAMING_SNAKE_CASE   				=							model_class(lowerCamelCase     )
         model.to(lowerCamelCase     )
         model.eval()

         with torch.no_grad():
            __SCREAMING_SNAKE_CASE   				=							model(**self._prepare_for_class(lowerCamelCase					,lowerCamelCase     )     )

         __SCREAMING_SNAKE_CASE   				=							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         __SCREAMING_SNAKE_CASE   				=							self.model_tester.num_stages
         self.assertEqual(len(lowerCamelCase     )					,expected_num_stages + 1     )

         # RegNet'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 // 2, self.model_tester.image_size // 2]					,)

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							["""basic""", """bottleneck"""]
      for model_class in self.all_model_classes:
         for layer_type in layers_type:
            __SCREAMING_SNAKE_CASE   				=							layer_type
            __SCREAMING_SNAKE_CASE   				=							True
            check_hidden_states_output(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __SCREAMING_SNAKE_CASE   				=							True

            check_hidden_states_output(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCamelCase     )





   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __SCREAMING_SNAKE_CASE   				=							RegNetModel.from_pretrained(lowerCamelCase     )
         self.assertIsNotNone(lowerCamelCase     )





def     __magic_name__							(				)    ->       List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
   return image







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





   @cached_property
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return (
          AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
          if is_vision_available()
          else None
      )





   @slow
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(lowerCamelCase     )

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

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

      # verify the logits
      __SCREAMING_SNAKE_CASE   				=							torch.Size((1, 1000)     )
      self.assertEqual(outputs.logits.shape					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-0.4_180, -1.5_051, -3.4_836]     ).to(lowerCamelCase     )

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





