






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												=      logging.get_logger()



@dataclass
class  __UpperCAmelCase						:
			__snake_case							: nn.Module
			__snake_case							: List[nn.Module]							=				field(default_factory=_UpperCAmelCase       )
			__snake_case							: list							=				field(default_factory=_UpperCAmelCase       )
			__snake_case							: Dict[str, nn.Module]							=				field(default_factory=_UpperCAmelCase       )







			def 				UpperCamelCase (      self:    int ,      UpperCAmelCase_:    Any ,      UpperCAmelCase_:    Tensor ,      UpperCAmelCase_:    Tensor ,      UpperCAmelCase_:    str	):







									'''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_	)
															_SCREAMING_SNAKE_CASE       =    m







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







									'''simple docstring'''




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







			@property
			def 				UpperCamelCase (      self:    Optional[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       ):







			def __init__(      self:    Optional[Any] ,      UpperCAmelCase_:    nn.Module	):







									'''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 				UpperCamelCase (      self:    Any ,      UpperCAmelCase_:    Tensor	):







									'''simple docstring'''




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



class  __UpperCAmelCase						(_UpperCAmelCase       ):







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]

def 			__lowerCamelCase     (						snake_case__						)				->    Dict[str, str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    RegNetConfig(depths=[2, 7, 17, 1]			,hidden_sizes=[8, 8, 8, 8]			,groups_width=8						)
						if "in1k" in model_name:
												_SCREAMING_SNAKE_CASE       =    RegNetForImageClassification(snake_case__						)
						else:
												_SCREAMING_SNAKE_CASE       =    RegNetModel(snake_case__						)
						# create from model (with small weights)
						_SCREAMING_SNAKE_CASE       =    FakeRegNetVisslWrapper(
						    RegNet(FakeRegNetParams(depth=27			,group_width=10_10			,w_a=17_44			,w_a=620.83			,w_m=2.52						)						)						)

						with torch.no_grad():
												_SCREAMING_SNAKE_CASE       =    from_model.eval()
												_SCREAMING_SNAKE_CASE       =    our_model.eval()

												_SCREAMING_SNAKE_CASE       =    torch.randn((1, 3, 32, 32)						)
												# trace both
												_SCREAMING_SNAKE_CASE       =    Tracker(snake_case__						)
												_SCREAMING_SNAKE_CASE       =    dest_tracker(snake_case__						).parametrized

												pprint(dest_tracker.nameamodule						)
												_SCREAMING_SNAKE_CASE       =    Tracker(snake_case__						)
												_SCREAMING_SNAKE_CASE       =    src_tracker(snake_case__						).parametrized

						# convert the keys -> module dict to keys -> params
						def to_params_dict(snake_case__						):
												_SCREAMING_SNAKE_CASE       =    OrderedDict()
												for name, module in dict_with_modules.items():
																		for param_name, param in module.state_dict().items():
																								_SCREAMING_SNAKE_CASE       =    param
												return params_dict

						_SCREAMING_SNAKE_CASE       =    {}

						_SCREAMING_SNAKE_CASE       =    to_params_dict(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    to_params_dict(snake_case__						)

						for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()			,dst_state_dict.items()						):
												_SCREAMING_SNAKE_CASE       =    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:
												_SCREAMING_SNAKE_CASE       =    """classifier.1.weight"""
												_SCREAMING_SNAKE_CASE       =    """classifier.1.bias"""

						return from_to_ours_keys

def 			__lowerCamelCase     (						snake_case__			,snake_case__ = None			,snake_case__ = True						)				->    List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    """imagenet-1k-id2label.json"""
						_SCREAMING_SNAKE_CASE       =    10_00

						_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-y-10b-seer""": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]			,hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]			,groups_width=10_10						),
						    # finetuned on imagenet
						    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]			,hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]			,groups_width=10_10						),
						}

						# add seer weights logic
						def load_using_classy_vision(snake_case__						) -> Tuple[Dict, Dict]:
												_SCREAMING_SNAKE_CASE       =    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
												_SCREAMING_SNAKE_CASE       =    files["""classy_state_dict"""]["""base_model"""]["""model"""]
												return model_state_dict["trunk"], model_state_dict["heads"]

						_SCREAMING_SNAKE_CASE       =    {
						    """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"""			,),
						}

						_SCREAMING_SNAKE_CASE       =    get_from_to_our_keys(snake_case__						)

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

												_SCREAMING_SNAKE_CASE       =    {}

												_SCREAMING_SNAKE_CASE       =    list(from_state_dict.keys()						)
												_SCREAMING_SNAKE_CASE       =    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
																		_SCREAMING_SNAKE_CASE       =    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
																		_SCREAMING_SNAKE_CASE       =    from_to_ours_keys[src_key]
																		# store the parameter with our key
																		_SCREAMING_SNAKE_CASE       =    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
												_SCREAMING_SNAKE_CASE       =    names_to_config[model_name]
												_SCREAMING_SNAKE_CASE       =    RegNetModel
												if "in1k" in model_name:
																		_SCREAMING_SNAKE_CASE       =    RegNetForImageClassification
												_SCREAMING_SNAKE_CASE       =    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
												_SCREAMING_SNAKE_CASE       =    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			,)
												_SCREAMING_SNAKE_CASE       =    3_84
												# 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 / model_name			,commit_message="""Add image processor"""			,output_dir=save_directory / model_name			,)


if __name__ == "__main__":
		UpperCamelCase												=      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												=      parser.parse_args()

		UpperCamelCase												=      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 DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _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 (
		    MODEL_MAPPING,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		)
		from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import BeitImageProcessor



class  __UpperCAmelCase						:







			def __init__(      self:    str ,      UpperCAmelCase_:    List[Any] ,      UpperCAmelCase_:    Tuple=100 ,      UpperCAmelCase_:    str=13 ,      UpperCAmelCase_:    str=30 ,      UpperCAmelCase_:    Tuple=2 ,      UpperCAmelCase_:    Tuple=3 ,      UpperCAmelCase_:    List[str]=True ,      UpperCAmelCase_:    List[Any]=True ,      UpperCAmelCase_:    Optional[int]=32 ,      UpperCAmelCase_:    Optional[int]=4 ,      UpperCAmelCase_:    Tuple=4 ,      UpperCAmelCase_:    List[Any]=37 ,      UpperCAmelCase_:    Optional[Any]="gelu" ,      UpperCAmelCase_:    Optional[int]=0.1 ,      UpperCAmelCase_:    int=0.1 ,      UpperCAmelCase_:    Dict=10 ,      UpperCAmelCase_:    Dict=0.02 ,      UpperCAmelCase_:    Union[str, Any]=3 ,      UpperCAmelCase_:    List[Any]=None ,      UpperCAmelCase_:    Any=[0, 1, 2, 3] ,      ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    parent
									_SCREAMING_SNAKE_CASE       =    100
									_SCREAMING_SNAKE_CASE       =    batch_size
									_SCREAMING_SNAKE_CASE       =    image_size
									_SCREAMING_SNAKE_CASE       =    patch_size
									_SCREAMING_SNAKE_CASE       =    num_channels
									_SCREAMING_SNAKE_CASE       =    is_training
									_SCREAMING_SNAKE_CASE       =    use_labels
									_SCREAMING_SNAKE_CASE       =    hidden_size
									_SCREAMING_SNAKE_CASE       =    num_hidden_layers
									_SCREAMING_SNAKE_CASE       =    num_attention_heads
									_SCREAMING_SNAKE_CASE       =    intermediate_size
									_SCREAMING_SNAKE_CASE       =    hidden_act
									_SCREAMING_SNAKE_CASE       =    hidden_dropout_prob
									_SCREAMING_SNAKE_CASE       =    attention_probs_dropout_prob
									_SCREAMING_SNAKE_CASE       =    type_sequence_label_size
									_SCREAMING_SNAKE_CASE       =    initializer_range
									_SCREAMING_SNAKE_CASE       =    scope
									_SCREAMING_SNAKE_CASE       =    out_indices
									_SCREAMING_SNAKE_CASE       =    num_labels

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







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




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

									_SCREAMING_SNAKE_CASE       =    None
									_SCREAMING_SNAKE_CASE       =    None
									if self.use_labels:
															_SCREAMING_SNAKE_CASE       =    ids_tensor([self.batch_size] ,      self.type_sequence_label_size	)
															_SCREAMING_SNAKE_CASE       =    ids_tensor([self.batch_size, self.image_size, self.image_size] ,      self.num_labels	)

									_SCREAMING_SNAKE_CASE       =    self.get_config()

									return config, pixel_values, labels, pixel_labels







			def 				UpperCamelCase (      self:    Any	):







									'''simple docstring'''




									return DataaVecVisionConfig(
									    vocab_size=self.vocab_size ,      image_size=self.image_size ,      patch_size=self.patch_size ,      num_channels=self.num_channels ,      hidden_size=self.hidden_size ,      num_hidden_layers=self.num_hidden_layers ,      num_attention_heads=self.num_attention_heads ,      intermediate_size=self.intermediate_size ,      hidden_act=self.hidden_act ,      hidden_dropout_prob=self.hidden_dropout_prob ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob ,      is_decoder=UpperCAmelCase_ ,      initializer_range=self.initializer_range ,      out_indices=self.out_indices ,      )







			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    Optional[Any] ,      UpperCAmelCase_:    List[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    DataaVecVisionModel(config=UpperCAmelCase_	)
									model.to(UpperCAmelCase_	)
									model.eval()
									_SCREAMING_SNAKE_CASE       =    model(UpperCAmelCase_	)
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									_SCREAMING_SNAKE_CASE       =    (self.image_size // self.patch_size) ** 2
									self.parent.assertEqual(result.last_hidden_state.shape ,      (self.batch_size, num_patches + 1, self.hidden_size)	)







			def 				UpperCamelCase (      self:    Union[str, Any] ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.type_sequence_label_size
									_SCREAMING_SNAKE_CASE       =    DataaVecVisionForImageClassification(UpperCAmelCase_	)
									model.to(UpperCAmelCase_	)
									model.eval()
									_SCREAMING_SNAKE_CASE       =    model(UpperCAmelCase_ ,      labels=UpperCAmelCase_	)
									self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.type_sequence_label_size)	)







			def 				UpperCamelCase (      self:    List[str] ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    List[Any] ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    List[str]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.num_labels
									_SCREAMING_SNAKE_CASE       =    DataaVecVisionForSemanticSegmentation(UpperCAmelCase_	)
									model.to(UpperCAmelCase_	)
									model.eval()
									_SCREAMING_SNAKE_CASE       =    model(UpperCAmelCase_	)
									self.parent.assertEqual(
									    result.logits.shape ,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
									_SCREAMING_SNAKE_CASE       =    model(UpperCAmelCase_ ,      labels=UpperCAmelCase_	)
									self.parent.assertEqual(
									    result.logits.shape ,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)







			def 				UpperCamelCase (      self:    Optional[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.prepare_config_and_inputs()
									_SCREAMING_SNAKE_CASE ,	_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  __UpperCAmelCase						(_UpperCAmelCase			,_UpperCAmelCase			,unittest.TestCase       ):
			__snake_case							: Tuple							=				(
			    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			__snake_case							: Optional[Any]							=				(
			    {
			        "feature-extraction": DataaVecVisionModel,
			        "image-classification": DataaVecVisionForImageClassification,
			        "image-segmentation": DataaVecVisionForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			__snake_case							: Dict							=				False
			__snake_case							: List[str]							=				False
			__snake_case							: Dict							=				False







			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    DataaVecVisionModelTester(self	)
									_SCREAMING_SNAKE_CASE       =    ConfigTester(
									    self ,      config_class=UpperCAmelCase_ ,      has_text_modality=UpperCAmelCase_ ,      hidden_size=37	)







			def 				UpperCamelCase (      self:    Optional[Any]	):







									'''simple docstring'''




									self.config_tester.run_common_tests()







			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									pass







			@require_torch_multi_gpu
			@unittest.skip(
			    reason="""Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""	)
			def 				UpperCamelCase (      self:    List[str]	):







									'''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(UpperCAmelCase_	)
															self.assertIsInstance(model.get_input_embeddings() ,      (nn.Module)	)
															_SCREAMING_SNAKE_CASE       =    model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(UpperCAmelCase_ ,      nn.Linear	)	)







			def 				UpperCamelCase (      self:    Optional[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(UpperCAmelCase_	)
															_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] ,      UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_segmentation(*UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									if not self.model_tester.is_training:
															return

									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs_for_common()
									_SCREAMING_SNAKE_CASE       =    True

									for model_class in self.all_model_classes:
															if model_class in [*get_values(UpperCAmelCase_	)]:
																					continue

															_SCREAMING_SNAKE_CASE       =    model_class(UpperCAmelCase_	)
															model.to(UpperCAmelCase_	)
															model.train()
															_SCREAMING_SNAKE_CASE       =    self._prepare_for_class(UpperCAmelCase_ ,      UpperCAmelCase_ ,      return_labels=UpperCAmelCase_	)
															_SCREAMING_SNAKE_CASE       =    model(**UpperCAmelCase_	).loss
															loss.backward()







			def 				UpperCamelCase (      self:    Dict	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
															return

									_SCREAMING_SNAKE_CASE       =    False
									_SCREAMING_SNAKE_CASE       =    True

									for model_class in self.all_model_classes:
															if model_class in [*get_values(UpperCAmelCase_	)] or not model_class.supports_gradient_checkpointing:
																					continue
															# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
															# this can then be incorporated into _prepare_for_class in test_modeling_common.py
															elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
																					_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    inputs_dict["""pixel_values"""].shape
																					_SCREAMING_SNAKE_CASE       =    torch.zeros(
																					    [self.model_tester.batch_size, height, width] ,      device=UpperCAmelCase_	).long()
															_SCREAMING_SNAKE_CASE       =    model_class(UpperCAmelCase_	)
															model.gradient_checkpointing_enable()
															model.to(UpperCAmelCase_	)
															model.train()
															_SCREAMING_SNAKE_CASE       =    self._prepare_for_class(UpperCAmelCase_ ,      UpperCAmelCase_ ,      return_labels=UpperCAmelCase_	)
															_SCREAMING_SNAKE_CASE       =    model(**UpperCAmelCase_	).loss
															loss.backward()







			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs_for_common()

									_SCREAMING_SNAKE_CASE       =    _config_zero_init(UpperCAmelCase_	)
									for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE       =    model_class(config=UpperCAmelCase_	)
															for name, param in model.named_parameters():
																					# we skip lambda parameters as these require special initial values
																					# determined by config.layer_scale_init_value
																					if "lambda" 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 				UpperCamelCase (      self:    int ,      UpperCAmelCase_:    Optional[Any] ,      UpperCAmelCase_:    int ,      UpperCAmelCase_:    Any ,      UpperCAmelCase_:    Union[str, Any]=2E-4 ,      UpperCAmelCase_:    Tuple="outputs" ,      UpperCAmelCase_:    Union[str, Any]=None	):







									'''simple docstring'''




									super().check_pt_tf_outputs(UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_	)







			@slow
			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




									for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE       =    DataaVecVisionModel.from_pretrained(UpperCAmelCase_	)
															self.assertIsNotNone(UpperCAmelCase_	)

def 			__lowerCamelCase     (						)				->    int:






						"""simple docstring"""





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




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







			@cached_property
			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									return (
									    BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""	) if is_vision_available() else None
									)







			@slow
			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""	).to(
									    UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    self.default_image_processor
									_SCREAMING_SNAKE_CASE       =    prepare_img()
									_SCREAMING_SNAKE_CASE       =    image_processor(images=UpperCAmelCase_ ,      return_tensors="""pt"""	).to(UpperCAmelCase_	)

									# forward pass
									with torch.no_grad():
															_SCREAMING_SNAKE_CASE       =    model(**UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    outputs.logits

									# verify the logits
									_SCREAMING_SNAKE_CASE       =    torch.Size((1, 1_000)	)
									self.assertEqual(logits.shape ,      UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    torch.tensor([0.32_77, -0.13_95, 0.09_11]	).to(UpperCAmelCase_	)

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

									_SCREAMING_SNAKE_CASE       =    [model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
									self.assertEqual(logits[0].topk(2	).indices.cpu().tolist() ,      UpperCAmelCase_	)

