

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()
_UpperCAmelCase		:       List[str]						    =	logging.get_logger()







@dataclass
class 			lowercase    :
				__SCREAMING_SNAKE_CASE :			nn.Module
				__SCREAMING_SNAKE_CASE :			List[nn.Module]	      =      field(default_factory=lowercase_      )
				__SCREAMING_SNAKE_CASE :			list	      =      field(default_factory=lowercase_      )




				def    a							( self				,       snake_case				,       snake_case				,       snake_case	):
											snake_case_          =   len(list(m.modules()	)	) == 1 or isinstance(snake_case				,       nn.Convad	) or isinstance(snake_case				,       nn.BatchNormad	)
											if has_not_submodules:
																		self.traced.append(snake_case	)




				def __call__( self				,       snake_case	):
											for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook	)	)
											self.module(snake_case	)
											[x.remove() for x in self.handles]
											return self




				@property
				def    a							( self	):
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case	: len(list(x.state_dict().keys()	)	) > 0				,       self.traced	)	)







@dataclass
class 			lowercase    :
				__SCREAMING_SNAKE_CASE :			nn.Module
				__SCREAMING_SNAKE_CASE :			nn.Module
				__SCREAMING_SNAKE_CASE :			int	      =      1
				__SCREAMING_SNAKE_CASE :			List	      =      field(default_factory=lowercase_      )
				__SCREAMING_SNAKE_CASE :			List	      =      field(default_factory=lowercase_      )
				__SCREAMING_SNAKE_CASE :			bool	      =      True




				def __call__( self				,       snake_case	):
											snake_case_          =   Tracker(self.dest	)(snake_case	).parametrized
											snake_case_          =   Tracker(self.src	)(snake_case	).parametrized

											snake_case_          =   list(filter(lambda snake_case	: type(snake_case	) not in self.src_skip				,       snake_case	)	)
											snake_case_          =   list(filter(lambda snake_case	: type(snake_case	) not in self.dest_skip				,       snake_case	)	)

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

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







class 			lowercase    (	nn.Module      ):




				def __init__( self				,       snake_case	):
											super().__init__()

											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}'''
																		snake_case_          =   len(snake_case	) + 1
																		feature_blocks.append((F'''res{block_index}''', v)	)

											snake_case_          =   nn.ModuleDict(snake_case	)




				def    a							( self				,       snake_case	):
											return get_trunk_forward_outputs(
											    snake_case				,       out_feat_keys=snake_case				,       feature_blocks=self._feature_blocks				,       )







class 			lowercase    (	lowercase_      ):




				def    a							( self				,       snake_case	):
											snake_case_          =   x.split('-'	)
											return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]	)




				def __getitem__( self				,       snake_case	):
											# default to timm!
											if x not in self:
																		snake_case_          =   self.convert_name_to_timm(snake_case	)
																		snake_case_          =   partial(lambda: (timm.create_model(snake_case				,       pretrained=snake_case	).eval(), None)	)

											else:
																		snake_case_          =   super().__getitem__(snake_case	)

											return val







class 			lowercase    (	lowercase_      ):




				def __getitem__( self				,       snake_case	):
											if "seer" in x and "in1k" not in x:
																		snake_case_          =   RegNetModel
											else:
																		snake_case_          =   RegNetForImageClassification
											return val





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__ = True       ,							):







							'''simple docstring'''






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

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

							snake_case_          =   our_model(UpperCamelCase__       ,							output_hidden_states=UpperCamelCase__				)
							snake_case_          =   (
							    our_outputs.logits if isinstance(UpperCamelCase__       ,							UpperCamelCase__				) else our_outputs.last_hidden_state
							)

							snake_case_          =   from_model(UpperCamelCase__				)
							snake_case_          =   from_output[-1] if type(UpperCamelCase__				) 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:
														snake_case_          =   our_outputs.hidden_states[-1]

							assert torch.allclose(UpperCamelCase__       ,							UpperCamelCase__				), "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=UpperCamelCase__       ,							)

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

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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = True				):







							'''simple docstring'''






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

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

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

							snake_case_          =   partial(UpperCamelCase__       ,							num_labels=UpperCamelCase__       ,							idalabel=UpperCamelCase__       ,							labelaid=UpperCamelCase__				)

							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				),
							}

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

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

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

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

							snake_case_          =   partial(
							    UpperCamelCase__       ,							'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()				)       ,							)

							snake_case_          =   partial(
							    UpperCamelCase__       ,							'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=6_20.83       ,							w_m=2.52				)				)				)       ,							)

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

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

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

							snake_case_          =   partial(
							    UpperCamelCase__       ,							'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=6_20.83       ,							w_m=2.52				)				)				)       ,							)

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


if __name__ == "__main__":
					_UpperCAmelCase		:       Optional[int]						    =	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		:       str						    =	parser.parse_args()

					_UpperCAmelCase		:       Path						    =	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 os
import sys
import unittest


_UpperCAmelCase		:       str						    =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


_UpperCAmelCase		:       List[Any]						    =	os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_UpperCAmelCase		:       Dict						    =	os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")







class 			lowercase    (	unittest.TestCase      ):




				def    a							( self	):
											snake_case_          =   get_test_to_tester_mapping(snake_case	)
											snake_case_          =   get_test_to_tester_mapping(snake_case	)

											snake_case_          =   {'BertModelTest': 'BertModelTester'}

											snake_case_          =   {
											    'BlipModelTest': 'BlipModelTester',
											    'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
											    'BlipTextModelTest': 'BlipTextModelTester',
											    'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
											    'BlipVQAModelTest': 'BlipVQAModelTester',
											    'BlipVisionModelTest': 'BlipVisionModelTester',
											}

											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)
											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)




				def    a							( self	):
											snake_case_          =   get_model_to_test_mapping(snake_case	)
											snake_case_          =   get_model_to_test_mapping(snake_case	)

											snake_case_          =   {
											    'BertForMaskedLM': ['BertModelTest'],
											    'BertForMultipleChoice': ['BertModelTest'],
											    'BertForNextSentencePrediction': ['BertModelTest'],
											    'BertForPreTraining': ['BertModelTest'],
											    'BertForQuestionAnswering': ['BertModelTest'],
											    'BertForSequenceClassification': ['BertModelTest'],
											    'BertForTokenClassification': ['BertModelTest'],
											    'BertLMHeadModel': ['BertModelTest'],
											    'BertModel': ['BertModelTest'],
											}

											snake_case_          =   {
											    'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
											    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
											    'BlipForQuestionAnswering': ['BlipVQAModelTest'],
											    'BlipModel': ['BlipModelTest'],
											    'BlipTextModel': ['BlipTextModelTest'],
											    'BlipVisionModel': ['BlipVisionModelTest'],
											}

											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)
											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)




				def    a							( self	):
											snake_case_          =   get_model_to_tester_mapping(snake_case	)
											snake_case_          =   get_model_to_tester_mapping(snake_case	)

											snake_case_          =   {
											    'BertForMaskedLM': ['BertModelTester'],
											    'BertForMultipleChoice': ['BertModelTester'],
											    'BertForNextSentencePrediction': ['BertModelTester'],
											    'BertForPreTraining': ['BertModelTester'],
											    'BertForQuestionAnswering': ['BertModelTester'],
											    'BertForSequenceClassification': ['BertModelTester'],
											    'BertForTokenClassification': ['BertModelTester'],
											    'BertLMHeadModel': ['BertModelTester'],
											    'BertModel': ['BertModelTester'],
											}

											snake_case_          =   {
											    'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
											    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
											    'BlipForQuestionAnswering': ['BlipVQAModelTester'],
											    'BlipModel': ['BlipModelTester'],
											    'BlipTextModel': ['BlipTextModelTester'],
											    'BlipVisionModel': ['BlipVisionModelTester'],
											}

											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)
											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)




