

'''simple docstring'''
import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def 	UpperCamelCase   (       lowercase_						:    Any     ,					lowercase_						:    int	)		->	Dict:




  '''simple docstring'''



  lowercase									=torch.load(lowercase_     ,					map_location='''cpu'''	)

  lowercase									=chkpt['''model''']

  # We have the base model one level deeper than the original XLM repository
  lowercase									={}
  for k, v in state_dict.items():
    if "pred_layer" in k:
      lowercase									=v
    else:
      lowercase									=v

  lowercase									=chkpt['''params''']
  lowercase									={n: v for n, v in config.items() if not isinstance(lowercase_     ,					(torch.FloatTensor, numpy.ndarray)	)}

  lowercase									=chkpt['''dico_word2id''']
  lowercase									={s + '''</w>''' if s.find('''@@'''	) == -1 and i > 1_3 else s.replace('''@@'''     ,					''''''	): i for s, i in vocab.items()}

  # Save pytorch-model
  lowercase									=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
  lowercase									=pytorch_dump_folder_path + '''/''' + CONFIG_NAME
  lowercase									=pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

  print(f'Save PyTorch model to {pytorch_weights_dump_path}'	)
  torch.save(lowercase_     ,					lowercase_	)

  print(f'Save configuration file to {pytorch_config_dump_path}'	)
  with open(lowercase_     ,					'''w'''     ,					encoding='''utf-8'''	) as f:
    f.write(json.dumps(lowercase_     ,					indent=2	) + '''\n'''	)

  print(f'Save vocab file to {pytorch_config_dump_path}'	)
  with open(lowercase_     ,					'''w'''     ,					encoding='''utf-8'''	) as f:
    f.write(json.dumps(lowercase_     ,					indent=2	) + '''\n'''	)


if __name__ == "__main__":
     _UpperCAmelCase  :		Tuple  =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     _UpperCAmelCase  :		List[Any]  =		parser.parse_args()
     convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)


'''simple docstring'''




import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
      from transformers.models.maskformer import MaskFormerSwinModel





class 					A							:





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=13		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=[1, 2, 1]		,     SCREAMING_SNAKE_CASE=[2, 2, 4]		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=2.0		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1e-5		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=10		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=["stage1", "stage2", "stage3"]		,     SCREAMING_SNAKE_CASE=[1, 2, 3]		,     )					->							Optional[int]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






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

         A			:  Union[str, Any]   =		None
         if self.use_labels:
             A			:  int   =		ids_tensor([self.batch_size]		,     self.type_sequence_label_size      )

         A			:  str   =		self.get_config()

         return config, pixel_values, labels





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         return MaskFormerSwinConfig(
             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		,     encoder_stride=self.encoder_stride		,     out_features=self.out_features		,     out_indices=self.out_indices		,     )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Dict:






         """simple docstring"""






         A			:  List[Any]   =		MaskFormerSwinModel(config=SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()
         A			:  Optional[int]   =		model(SCREAMING_SNAKE_CASE      )

         A			:  Tuple   =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
         A			:  Optional[int]   =		int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

         self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, expected_seq_len, expected_dim)      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Any:






         """simple docstring"""






         A			:  Tuple   =		MaskFormerSwinBackbone(config=SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()
         A			:  Union[str, Any]   =		model(SCREAMING_SNAKE_CASE      )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps      )		,     len(config.out_features      )      )
         self.parent.assertListEqual(list(result.feature_maps[0].shape      )		,     [13, 16, 16, 16]      )

         # verify channels
         self.parent.assertEqual(len(model.channels      )		,     len(config.out_features      )      )
         self.parent.assertListEqual(model.channels		,     [16, 32, 64]      )

         # verify ValueError
         with self.parent.assertRaises(SCREAMING_SNAKE_CASE      ):
             A			:  str   =		['''stem''']
             A			:  Dict   =		MaskFormerSwinBackbone(config=SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A			:  str   =		self.prepare_config_and_inputs()
         A,    A,    A			:  int   =		config_and_inputs
         A			:  List[str]   =		{'''pixel_values''': pixel_values}
         return config, inputs_dict





@require_torch
class 					A							(				__snake_case       ,     __snake_case       ,     unittest.TestCase		):
     __magic_name__          =						(
         (
             MaskFormerSwinModel,
             MaskFormerSwinBackbone,
         )
         if is_torch_available()
         else ()
     )
     __magic_name__          =						{'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False





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






         """simple docstring"""






         A			:  Tuple   =		MaskFormerSwinModelTester(self      )
         A			:  int   =		ConfigTester(self		,     config_class=SCREAMING_SNAKE_CASE		,     embed_dim=37      )





     @require_torch_multi_gpu
     @unittest.skip(
         reason=(
             '''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
             ''' `nn.DataParallel`'''
         )      )
     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """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 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         return





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A			:  Optional[Any]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Optional[Any]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE      )





     @unittest.skip('''Swin does not use inputs_embeds'''      )
     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         pass





     @unittest.skip('''Swin does not support feedforward chunking'''      )
     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A,    A			:  List[str]   =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A			:  Optional[int]   =		model_class(SCREAMING_SNAKE_CASE      )
             self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module)      )
             A			:  int   =		model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE		,     nn.Linear      )      )





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A,    A			:  List[str]   =		self.model_tester.prepare_config_and_inputs_for_common()

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

             A			:  Union[str, Any]   =		['''pixel_values''']
             self.assertListEqual(arg_names[:1]		,     SCREAMING_SNAKE_CASE      )





     @unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'''      )
     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         pass





     @unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''      )
     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Optional[int]:






         """simple docstring"""






         A			:  Tuple   =		model_class(SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()

         with torch.no_grad():
             A			:  Any   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )

         A			:  List[str]   =		outputs.hidden_states

         A			:  str   =		getattr(
             self.model_tester		,     '''expected_num_hidden_layers'''		,     len(self.model_tester.depths      ) + 1      )
         self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     SCREAMING_SNAKE_CASE      )

         # Swin has a different seq_length
         A			:  List[str]   =		(
             config.patch_size
             if isinstance(config.patch_size		,     collections.abc.Iterable      )
             else (config.patch_size, config.patch_size)
         )

         A			:  Optional[Any]   =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]      )		,     [num_patches, self.model_tester.embed_dim]		,     )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






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

         A			:  Union[str, Any]   =		(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size		,     collections.abc.Iterable      )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
             A			:  str   =		True
             self.check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             A			:  Optional[Any]   =		True

             self.check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A,    A			:  Any   =		self.model_tester.prepare_config_and_inputs_for_common()
         A			:  Union[str, Any]   =		3

         A			:  List[str]   =		(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size		,     collections.abc.Iterable      )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         A			:  Dict   =		(
             config.patch_size
             if isinstance(config.patch_size		,     collections.abc.Iterable      )
             else (config.patch_size, config.patch_size)
         )

         A			:  Any   =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         A			:  Any   =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
             A			:  Optional[Any]   =		True
             self.check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     (padded_height, padded_width)      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             A			:  Optional[Any]   =		True
             self.check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     (padded_height, padded_width)      )





     @unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''      )
     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         pass





     @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         pass





     @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A,    A			:  str   =		self.model_tester.prepare_config_and_inputs_for_common()

         def set_nan_tensor_to_zero(SCREAMING_SNAKE_CASE      ):
             A			:  List[str]   =		0
             return t

         def check_equivalence(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE={}      ):
             with torch.no_grad():
                 A			:  List[Any]   =		model(**SCREAMING_SNAKE_CASE		,     return_dict=SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      )
                 A			:  str   =		model(**SCREAMING_SNAKE_CASE		,     return_dict=SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      ).to_tuple()

                 def recursive_check(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                     if isinstance(SCREAMING_SNAKE_CASE		,     (List, Tuple)      ):
                         for tuple_iterable_value, dict_iterable_value in zip(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                             recursive_check(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
                     elif isinstance(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                         for tuple_iterable_value, dict_iterable_value in zip(
                             tuple_object.values()		,     dict_object.values()      ):
                             recursive_check(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
                     elif tuple_object is None:
                         return
                     else:
                         self.assertTrue(
                             torch.allclose(
                                 set_nan_tensor_to_zero(SCREAMING_SNAKE_CASE      )		,     set_nan_tensor_to_zero(SCREAMING_SNAKE_CASE      )		,     atol=1e-5      )		,     msg=(
                                 '''Tuple and dict output are not equal. Difference:'''
                                 F' {torch.max(torch.abs(tuple_object - dict_object      )      )}. Tuple has `nan`:'
                                 F' {torch.isnan(SCREAMING_SNAKE_CASE      ).any()} and `inf`: {torch.isinf(SCREAMING_SNAKE_CASE      )}. Dict has'
                                 F' `nan`: {torch.isnan(SCREAMING_SNAKE_CASE      ).any()} and `inf`: {torch.isinf(SCREAMING_SNAKE_CASE      )}.'
                             )		,     )

                 recursive_check(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

         for model_class in self.all_model_classes:
             A			:  Tuple   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()

             A			:  List[str]   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
             A			:  int   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
             check_equivalence(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

             A			:  Union[str, Any]   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )
             A			:  int   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )
             check_equivalence(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

             A			:  Tuple   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
             A			:  Any   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
             check_equivalence(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     {'''output_hidden_states''': True}      )

             A			:  Optional[int]   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )
             A			:  Dict   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )
             check_equivalence(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     {'''output_hidden_states''': True}      )





@require_torch
class 					A							(				unittest.TestCase       ,     __snake_case		):
     __magic_name__          =						(MaskFormerSwinBackbone,) if is_torch_available() else ()
     __magic_name__          =						MaskFormerSwinConfig





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Dict   =		MaskFormerSwinModelTester(self      )





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A,    A			:  int   =		self.model_tester.prepare_config_and_inputs_for_common()
         A			:  str   =		inputs_dict['''pixel_values'''].shape[0]

         for backbone_class in self.all_model_classes:
             A			:  Any   =		backbone_class(SCREAMING_SNAKE_CASE      )
             backbone.to(SCREAMING_SNAKE_CASE      )
             backbone.eval()

             A			:  Dict   =		backbone(**SCREAMING_SNAKE_CASE      )

             # Test default outputs and verify feature maps
             self.assertIsInstance(outputs.feature_maps		,     SCREAMING_SNAKE_CASE      )
             self.assertTrue(len(outputs.feature_maps      ) == len(backbone.channels      )      )
             for feature_map, n_channels in zip(outputs.feature_maps		,     backbone.channels      ):
                 self.assertTrue(feature_map.shape[:2]		,     (batch_size, n_channels)      )
             self.assertIsNone(outputs.hidden_states      )
             self.assertIsNone(outputs.attentions      )

             # Test output_hidden_states=True
             A			:  Optional[int]   =		backbone(**SCREAMING_SNAKE_CASE		,     output_hidden_states=SCREAMING_SNAKE_CASE      )
             self.assertIsNotNone(outputs.hidden_states      )
             self.assertTrue(len(outputs.hidden_states      )		,     len(backbone.stage_names      )      )
             # We skip the stem layer
             for hidden_states, n_channels in zip(outputs.hidden_states[1:]		,     backbone.channels      ):
                 for hidden_state in hidden_states:
                     # Hidden states are in the format (batch_size, (height * width), n_channels)
                     A,    A,    A			:  List[Any]   =		hidden_state.shape
                     self.assertTrue((h_batch_size, h_n_channels)		,     (batch_size, n_channels)      )

            # Test output_attentions=True
             if self.has_attentions:
                 A			:  Dict   =		backbone(**SCREAMING_SNAKE_CASE		,     output_attentions=SCREAMING_SNAKE_CASE      )
                 self.assertIsNotNone(outputs.attentions      )




