


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
A_								=   logging.get_logger()

@dataclass
class     snake_case      :
			'''simple docstring'''

			UpperCAmelCase							:							int							=			42
			UpperCAmelCase							:							str							=			field(default_factory=_UpperCamelCase						)
			UpperCAmelCase							:							Tuple							=			field(default_factory=_UpperCamelCase						)

			def _lowercase			(						self  :  Any				,				lowerCAmelCase_  :  Union[str, Any]				,				lowerCAmelCase_  :  Tensor				,				lowerCAmelCase_  :  Tensor       )       ->       Any:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							len(list(m.modules()       )       ) == 1 or isinstance(UpperCamelCase__				,				nn.Convad       ) or isinstance(UpperCamelCase__				,				nn.BatchNormad       )
								if has_not_submodules:
													self.traced.append(UpperCamelCase__       )

			def __call__(						self  :  List[str]				,				lowerCAmelCase_  :  Tensor       )       ->       Union[str, Any]:




								"""simple docstring"""






								for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook       )       )
								self.module(UpperCamelCase__       )
								[x.remove() for x in self.handles]
								return self




			@property
			def _lowercase			(						self  :  List[str]       )       ->       List[Any]:




								"""simple docstring"""






								return list(filter(lambda lowerCAmelCase_       : len(list(x.state_dict().keys()       )       ) > 0				,				self.traced       )       )

@dataclass
class     snake_case      :
			'''simple docstring'''

			UpperCAmelCase							:							Dict							=			42
			UpperCAmelCase							:							List[str]							=			42
			UpperCAmelCase							:							List[Any]							=			1
			UpperCAmelCase							:							List[str]							=			field(default_factory=_UpperCamelCase						)
			UpperCAmelCase							:							Tuple							=			field(default_factory=_UpperCamelCase						)
			UpperCAmelCase							:							str							=			True

			def __call__(						self  :  List[Any]				,				lowerCAmelCase_  :  Tensor       )       ->       int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							Tracker(self.dest       )(UpperCamelCase__       ).parametrized
								SCREAMING_SNAKE_CASE_						   =							Tracker(self.src       )(UpperCamelCase__       ).parametrized

								SCREAMING_SNAKE_CASE_						   =							list(filter(lambda lowerCAmelCase_       : type(UpperCamelCase__       ) not in self.src_skip				,				UpperCamelCase__       )       )
								SCREAMING_SNAKE_CASE_						   =							list(filter(lambda lowerCAmelCase_       : type(UpperCamelCase__       ) not in self.dest_skip				,				UpperCamelCase__       )       )

								if len(UpperCamelCase__       ) != len(UpperCamelCase__       ) and self.raise_if_mismatch:
													raise Exception(
													    F'''Numbers of operations are different. Source module has {len(UpperCamelCase__       )} operations while'''
													    F''' destination module has {len(UpperCamelCase__       )}.'''       )

								for dest_m, src_m in zip(UpperCamelCase__				,				UpperCamelCase__       ):
													dest_m.load_state_dict(src_m.state_dict()       )
													if self.verbose == 1:
																		print(F'''Transfered from={src_m} to={dest_m}'''       )

class     snake_case      (							nn.Module						):
			'''simple docstring'''

			def __init__(						self  :  Any				,				lowerCAmelCase_  :  nn.Module       )       ->       Dict:




								"""simple docstring"""






								super().__init__()

								SCREAMING_SNAKE_CASE_						   =							[]
								# - 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}'''
													SCREAMING_SNAKE_CASE_						   =							len(UpperCamelCase__       ) + 1
													feature_blocks.append((F'''res{block_index}''', v)       )

								SCREAMING_SNAKE_CASE_						   =							nn.ModuleDict(UpperCamelCase__       )




			def _lowercase			(						self  :  Tuple				,				lowerCAmelCase_  :  Tensor       )       ->       Tuple:




								"""simple docstring"""






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

class     snake_case      (							_UpperCamelCase						):
			'''simple docstring'''

			def _lowercase			(						self  :  Tuple				,				lowerCAmelCase_  :  str       )       ->       str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							x.split('''-'''       )
								return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]       )




			def __getitem__(						self  :  List[str]				,				lowerCAmelCase_  :  str       )       ->       Callable[[], Tuple[nn.Module, Dict]]:




								"""simple docstring"""






								if x not in self:
													SCREAMING_SNAKE_CASE_						   =							self.convert_name_to_timm(UpperCamelCase__       )
													SCREAMING_SNAKE_CASE_						   =							partial(lambda: (timm.create_model(UpperCamelCase__				,				pretrained=UpperCamelCase__       ).eval(), None)       )

								else:
													SCREAMING_SNAKE_CASE_						   =							super().__getitem__(UpperCamelCase__       )

								return val

class     snake_case      (							_UpperCamelCase						):
			'''simple docstring'''

			def __getitem__(						self  :  Optional[int]				,				lowerCAmelCase_  :  str       )       ->       Callable[[], nn.Module]:




								"""simple docstring"""






								if "seer" in x and "in1k" not in x:
													SCREAMING_SNAKE_CASE_						   =							RegNetModel
								else:
													SCREAMING_SNAKE_CASE_						   =							RegNetForImageClassification
								return val







def 				UpperCAmelCase					(  UpperCAmelCase					,UpperCAmelCase					,UpperCAmelCase   )->		int:







					'''simple docstring'''



					for from_key, to_key in keys:
										SCREAMING_SNAKE_CASE_						   =							from_state_dict[from_key].clone()
										print(f'''Copied key={from_key} to={to_key}'''   )
					return to_state_dict







def 				UpperCAmelCase					(  UpperCAmelCase					,UpperCAmelCase					,UpperCAmelCase					,UpperCAmelCase					,UpperCAmelCase					,UpperCAmelCase = True					,)->		Union[str, Any]:







					'''simple docstring'''



					print(f'''Converting {name}...'''   )
					with torch.no_grad():
										SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_						   =							from_model_func()
										SCREAMING_SNAKE_CASE_						   =							our_model_func(__snake_case   ).eval()
										SCREAMING_SNAKE_CASE_						   =							ModuleTransfer(src=__snake_case					,dest=__snake_case					,raise_if_mismatch=__snake_case   )
										SCREAMING_SNAKE_CASE_						   =							torch.randn((1, 3, 224, 224)   )
										module_transfer(__snake_case   )

					if from_state_dict is not None:
										SCREAMING_SNAKE_CASE_						   =							[]
										# for seer - in1k finetuned we have to manually copy the head
										if "seer" in name and "in1k" in name:
															SCREAMING_SNAKE_CASE_						   =							[('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
										SCREAMING_SNAKE_CASE_						   =							manually_copy_vissl_head(__snake_case					,our_model.state_dict()					,__snake_case   )
										our_model.load_state_dict(__snake_case   )

					SCREAMING_SNAKE_CASE_						   =							our_model(__snake_case					,output_hidden_states=__snake_case   )
					SCREAMING_SNAKE_CASE_						   =							(
					    our_outputs.logits if isinstance(__snake_case					,__snake_case   ) else our_outputs.last_hidden_state
					)

					SCREAMING_SNAKE_CASE_						   =							from_model(__snake_case   )
					SCREAMING_SNAKE_CASE_						   =							from_output[-1] if type(__snake_case   ) is list else from_output

					# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
					if "seer" in name and "in1k" in name:
										SCREAMING_SNAKE_CASE_						   =							our_outputs.hidden_states[-1]

					assert torch.allclose(__snake_case					,__snake_case   ), "The model logits don't match the original one."

					if push_to_hub:
										our_model.push_to_hub(
										    repo_path_or_name=save_directory / name					,commit_message='''Add model'''					,use_temp_dir=__snake_case					,)

										SCREAMING_SNAKE_CASE_						   =							224 if '''seer''' not in name else 384
										# we can use the convnext one
										SCREAMING_SNAKE_CASE_						   =							AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''					,size=__snake_case   )
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / name					,commit_message='''Add image processor'''					,use_temp_dir=__snake_case					,)

										print(f'''Pushed {name}'''   )







def 				UpperCAmelCase					(  UpperCAmelCase					,UpperCAmelCase = None					,UpperCAmelCase = True   )->		Dict:







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_						   =							'''imagenet-1k-id2label.json'''
					SCREAMING_SNAKE_CASE_						   =							1000
					SCREAMING_SNAKE_CASE_						   =							(1, num_labels)

					SCREAMING_SNAKE_CASE_						   =							'''huggingface/label-files'''
					SCREAMING_SNAKE_CASE_						   =							num_labels
					SCREAMING_SNAKE_CASE_						   =							json.load(open(cached_download(hf_hub_url(__snake_case					,__snake_case					,repo_type='''dataset'''   )   )					,'''r'''   )   )
					SCREAMING_SNAKE_CASE_						   =							{int(__snake_case   ): v for k, v in idalabel.items()}

					SCREAMING_SNAKE_CASE_						   =							idalabel
					SCREAMING_SNAKE_CASE_						   =							{v: k for k, v in idalabel.items()}

					SCREAMING_SNAKE_CASE_						   =							partial(__snake_case					,num_labels=__snake_case					,idalabel=__snake_case					,labelaid=__snake_case   )

					SCREAMING_SNAKE_CASE_						   =							{
					    '''regnet-x-002''': ImageNetPreTrainedConfig(
					        depths=[1, 1, 4, 7]					,hidden_sizes=[24, 56, 152, 368]					,groups_width=8					,layer_type='''x'''   ),
					    '''regnet-x-004''': ImageNetPreTrainedConfig(
					        depths=[1, 2, 7, 12]					,hidden_sizes=[32, 64, 160, 384]					,groups_width=16					,layer_type='''x'''   ),
					    '''regnet-x-006''': ImageNetPreTrainedConfig(
					        depths=[1, 3, 5, 7]					,hidden_sizes=[48, 96, 240, 528]					,groups_width=24					,layer_type='''x'''   ),
					    '''regnet-x-008''': ImageNetPreTrainedConfig(
					        depths=[1, 3, 7, 5]					,hidden_sizes=[64, 128, 288, 672]					,groups_width=16					,layer_type='''x'''   ),
					    '''regnet-x-016''': ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 2]					,hidden_sizes=[72, 168, 408, 912]					,groups_width=24					,layer_type='''x'''   ),
					    '''regnet-x-032''': ImageNetPreTrainedConfig(
					        depths=[2, 6, 15, 2]					,hidden_sizes=[96, 192, 432, 1008]					,groups_width=48					,layer_type='''x'''   ),
					    '''regnet-x-040''': ImageNetPreTrainedConfig(
					        depths=[2, 5, 14, 2]					,hidden_sizes=[80, 240, 560, 1360]					,groups_width=40					,layer_type='''x'''   ),
					    '''regnet-x-064''': ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 1]					,hidden_sizes=[168, 392, 784, 1624]					,groups_width=56					,layer_type='''x'''   ),
					    '''regnet-x-080''': ImageNetPreTrainedConfig(
					        depths=[2, 5, 15, 1]					,hidden_sizes=[80, 240, 720, 1920]					,groups_width=120					,layer_type='''x'''   ),
					    '''regnet-x-120''': ImageNetPreTrainedConfig(
					        depths=[2, 5, 11, 1]					,hidden_sizes=[224, 448, 896, 2240]					,groups_width=112					,layer_type='''x'''   ),
					    '''regnet-x-160''': ImageNetPreTrainedConfig(
					        depths=[2, 6, 13, 1]					,hidden_sizes=[256, 512, 896, 2048]					,groups_width=128					,layer_type='''x'''   ),
					    '''regnet-x-320''': ImageNetPreTrainedConfig(
					        depths=[2, 7, 13, 1]					,hidden_sizes=[336, 672, 1344, 2520]					,groups_width=168					,layer_type='''x'''   ),
					    # y variant
					    '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]					,hidden_sizes=[24, 56, 152, 368]					,groups_width=8   ),
					    '''regnet-y-004''': ImageNetPreTrainedConfig(
					        depths=[1, 3, 6, 6]					,hidden_sizes=[48, 104, 208, 440]					,groups_width=8   ),
					    '''regnet-y-006''': ImageNetPreTrainedConfig(
					        depths=[1, 3, 7, 4]					,hidden_sizes=[48, 112, 256, 608]					,groups_width=16   ),
					    '''regnet-y-008''': ImageNetPreTrainedConfig(
					        depths=[1, 3, 8, 2]					,hidden_sizes=[64, 128, 320, 768]					,groups_width=16   ),
					    '''regnet-y-016''': ImageNetPreTrainedConfig(
					        depths=[2, 6, 17, 2]					,hidden_sizes=[48, 120, 336, 888]					,groups_width=24   ),
					    '''regnet-y-032''': ImageNetPreTrainedConfig(
					        depths=[2, 5, 13, 1]					,hidden_sizes=[72, 216, 576, 1512]					,groups_width=24   ),
					    '''regnet-y-040''': ImageNetPreTrainedConfig(
					        depths=[2, 6, 12, 2]					,hidden_sizes=[128, 192, 512, 1088]					,groups_width=64   ),
					    '''regnet-y-064''': ImageNetPreTrainedConfig(
					        depths=[2, 7, 14, 2]					,hidden_sizes=[144, 288, 576, 1296]					,groups_width=72   ),
					    '''regnet-y-080''': ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 1]					,hidden_sizes=[168, 448, 896, 2016]					,groups_width=56   ),
					    '''regnet-y-120''': ImageNetPreTrainedConfig(
					        depths=[2, 5, 11, 1]					,hidden_sizes=[224, 448, 896, 2240]					,groups_width=112   ),
					    '''regnet-y-160''': ImageNetPreTrainedConfig(
					        depths=[2, 4, 11, 1]					,hidden_sizes=[224, 448, 1232, 3024]					,groups_width=112   ),
					    '''regnet-y-320''': ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1]					,hidden_sizes=[232, 696, 1392, 3712]					,groups_width=232   ),
					    # models created by SEER -> https://arxiv.org/abs/2202.08360
					    '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]					,hidden_sizes=[232, 696, 1392, 3712]					,groups_width=232   ),
					    '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]					,hidden_sizes=[328, 984, 1968, 4920]					,groups_width=328   ),
					    '''regnet-y-1280-seer''': RegNetConfig(
					        depths=[2, 7, 17, 1]					,hidden_sizes=[528, 1056, 2904, 7392]					,groups_width=264   ),
					    '''regnet-y-2560-seer''': RegNetConfig(
					        depths=[3, 7, 16, 1]					,hidden_sizes=[640, 1696, 2544, 5088]					,groups_width=640   ),
					    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]					,hidden_sizes=[2020, 4040, 11110, 28280]					,groups_width=1010   ),
					    # finetuned on imagenet
					    '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1]					,hidden_sizes=[232, 696, 1392, 3712]					,groups_width=232   ),
					    '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1]					,hidden_sizes=[328, 984, 1968, 4920]					,groups_width=328   ),
					    '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]					,hidden_sizes=[528, 1056, 2904, 7392]					,groups_width=264   ),
					    '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
					        depths=[3, 7, 16, 1]					,hidden_sizes=[640, 1696, 2544, 5088]					,groups_width=640   ),
					    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]					,hidden_sizes=[2020, 4040, 11110, 28280]					,groups_width=1010   ),
					}

					SCREAMING_SNAKE_CASE_						   =							NameToOurModelFuncMap()
					SCREAMING_SNAKE_CASE_						   =							NameToFromModelFuncMap()
					# add seer weights logic

					def load_using_classy_vision(UpperCAmelCase					,UpperCAmelCase   ) -> Tuple[nn.Module, Dict]:
										SCREAMING_SNAKE_CASE_						   =							torch.hub.load_state_dict_from_url(__snake_case					,model_dir=str(__snake_case   )					,map_location='''cpu'''   )
										SCREAMING_SNAKE_CASE_						   =							model_func()
										# check if we have a head, if yes add it
										SCREAMING_SNAKE_CASE_						   =							files['''classy_state_dict''']['''base_model''']['''model''']
										SCREAMING_SNAKE_CASE_						   =							model_state_dict['''trunk''']
										model.load_state_dict(__snake_case   )
										return model.eval(), model_state_dict["heads"]

					# pretrained
					SCREAMING_SNAKE_CASE_						   =							partial(
					    __snake_case					,'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''					,lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )					,)

					SCREAMING_SNAKE_CASE_						   =							partial(
					    __snake_case					,'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''					,lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )					,)

					SCREAMING_SNAKE_CASE_						   =							partial(
					    __snake_case					,'''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'''					,lambda: FakeRegNetVisslWrapper(RegNetYaaagf()   )					,)

					SCREAMING_SNAKE_CASE_						   =							partial(
					    __snake_case					,'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''					,lambda: FakeRegNetVisslWrapper(
					        RegNet(RegNetParams(depth=27					,group_width=1010					,w_a=1744					,w_a=620.83					,w_m=2.5_2   )   )   )					,)

					# IN1K finetuned
					SCREAMING_SNAKE_CASE_						   =							partial(
					    __snake_case					,'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''					,lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )					,)

					SCREAMING_SNAKE_CASE_						   =							partial(
					    __snake_case					,'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''					,lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )					,)

					SCREAMING_SNAKE_CASE_						   =							partial(
					    __snake_case					,'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''					,lambda: FakeRegNetVisslWrapper(RegNetYaaagf()   )					,)

					SCREAMING_SNAKE_CASE_						   =							partial(
					    __snake_case					,'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''					,lambda: FakeRegNetVisslWrapper(
					        RegNet(RegNetParams(depth=27					,group_width=1010					,w_a=1744					,w_a=620.83					,w_m=2.5_2   )   )   )					,)

					if model_name:
										convert_weight_and_push(
										    __snake_case					,names_to_from_model_map[model_name]					,names_to_ours_model_map[model_name]					,names_to_config[model_name]					,__snake_case					,__snake_case					,)
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(
															    __snake_case					,names_to_from_model_map[model_name]					,names_to_ours_model_map[model_name]					,__snake_case					,__snake_case					,__snake_case					,)
					return config, expected_shape


if __name__ == "__main__":
		A_								=   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.",
		)

		A_								=   parser.parse_args()

		A_								=   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)



'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @slow
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        _A    		=  TFCamembertModel.from_pretrained('jplu/tf-camembert-base'     )

        _A    		=  tf.convert_to_tensor(
            [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

        _A    		=  model(UpperCamelCase__     )['last_hidden_state']
        _A    		=  tf.TensorShape((1, 10, 7_68)     )
        self.assertEqual(output.shape, UpperCamelCase__     )
        # compare the actual values for a slice.
        _A    		=  tf.convert_to_tensor(
            [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4     )     )



