
'''simple docstring'''
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


class     _lowerCAmelCase					(					__snake_case  ,							__snake_case  ,							unittest.TestCase   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		UNetaDModel
							lowerCAmelCase_      		=		"sample"

							@property
							def  lowercase							(self						)  ->     Tuple:
														_snake_case     =						4
														_snake_case     =						14
														_snake_case     =						16

														_snake_case     =						floats_tensor((batch_size, num_features, seq_len)						).to(UpperCAmelCase						)
														_snake_case     =						torch.tensor([10] * batch_size						).to(UpperCAmelCase						)

														return {"sample": noise, "timestep": time_step}

							@property
							def  lowercase							(self						)  ->     List[str]:
														return (4, 14, 16)

							@property
							def  lowercase							(self						)  ->     str:
														return (4, 14, 16)

							def  lowercase							(self						)  ->     str:
														pass

							def  lowercase							(self						)  ->     Union[str, Any]:
														pass

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     Dict:
														super().test_determinism()

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     Optional[Any]:
														super().test_outputs_equivalence()

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     Optional[int]:
														super().test_from_save_pretrained()

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     Dict:
														super().test_from_save_pretrained_variant()

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     Optional[int]:
														super().test_model_from_pretrained()

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     Optional[Any]:
														super().test_output()

							def  lowercase							(self						)  ->     Tuple:
														_snake_case     =						{
														    """block_out_channels""": (32, 64, 128, 256),
														    """in_channels""": 14,
														    """out_channels""": 14,
														    """time_embedding_type""": """positional""",
														    """use_timestep_embedding""": True,
														    """flip_sin_to_cos""": False,
														    """freq_shift""": 1.0,
														    """out_block_type""": """OutConv1DBlock""",
														    """mid_block_type""": """MidResTemporalBlock1D""",
														    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
														    """up_block_types""": ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D"""),
														    """act_fn""": """mish""",
														}
														_snake_case     =						self.dummy_input
														return init_dict, inputs_dict

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     int:
														_snake_case,							_snake_case     =						UNetaDModel.from_pretrained(
														    """bglick13/hopper-medium-v2-value-function-hor32"""						,						output_loading_info=UpperCAmelCase						,						subfolder="""unet"""						)
														self.assertIsNotNone(UpperCAmelCase						)
														self.assertEqual(len(loading_info["""missing_keys"""]						)						,						0						)

														model.to(UpperCAmelCase						)
														_snake_case     =						model(**self.dummy_input						)

														assert image is not None, "Make sure output is not None"

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     str:
														_snake_case     =						UNetaDModel.from_pretrained("""bglick13/hopper-medium-v2-value-function-hor32"""						,						subfolder="""unet"""						)
														torch.manual_seed(0						)
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0						)

														_snake_case     =						model.config.in_channels
														_snake_case     =						16
														_snake_case     =						torch.randn((1, seq_len, num_features)						).permute(
														    0						,						2						,						1						)  # match original, we can update values and remove
														_snake_case     =						torch.full((num_features,)						,						0						)

														with torch.no_grad():
																					_snake_case     =						model(UpperCAmelCase						,						UpperCAmelCase						).sample.permute(0						,						2						,						1						)

														_snake_case     =						output[0, -3:, -3:].flatten()
														# fmt: off
														_snake_case     =						torch.tensor([-2.13_7172, 1.142_6016, 0.368_8687, -0.76_6922, 0.730_3146, 0.1103_8864, -0.476_0633, 0.1327_0172, 0.0259_1348]						)
														# fmt: on
														self.assertTrue(torch.allclose(UpperCAmelCase						,						UpperCAmelCase						,						rtol=1e-3						)						)

							def  lowercase							(self						)  ->     int:
														# Not implemented yet for this UNet
														pass





							@slow
							def  lowercase							(self						)  ->     Tuple:
														_snake_case     =						"""harmonai/maestro-150k"""
														_snake_case     =						UNetaDModel.from_pretrained(UpperCAmelCase						,						subfolder="""unet"""						)
														model.to(UpperCAmelCase						)

														_snake_case     =						65536
														_snake_case     =						torch.sin(torch.arange(UpperCAmelCase						)[None, None, :].repeat(1						,						2						,						1						)						).to(UpperCAmelCase						)
														_snake_case     =						torch.tensor([1]						).to(UpperCAmelCase						)

														with torch.no_grad():
																					_snake_case     =						model(UpperCAmelCase						,						UpperCAmelCase						).sample

														_snake_case     =						output.abs().sum()
														_snake_case     =						output.abs().max()

														assert (output_sum - 224.0896).abs() < 0.5
														assert (output_max - 0.0607).abs() < 4e-4


class     _lowerCAmelCase					(					__snake_case  ,							__snake_case  ,							unittest.TestCase   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		UNetaDModel
							lowerCAmelCase_      		=		"sample"

							@property
							def  lowercase							(self						)  ->     List[str]:
														_snake_case     =						4
														_snake_case     =						14
														_snake_case     =						16

														_snake_case     =						floats_tensor((batch_size, num_features, seq_len)						).to(UpperCAmelCase						)
														_snake_case     =						torch.tensor([10] * batch_size						).to(UpperCAmelCase						)

														return {"sample": noise, "timestep": time_step}

							@property
							def  lowercase							(self						)  ->     str:
														return (4, 14, 16)

							@property
							def  lowercase							(self						)  ->     Any:
														return (4, 14, 1)

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     str:
														super().test_determinism()

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     List[str]:
														super().test_outputs_equivalence()

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     str:
														super().test_from_save_pretrained()

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     str:
														super().test_from_save_pretrained_variant()

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     Optional[int]:
														super().test_model_from_pretrained()

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     str:
														# UNetRL is a value-function is different output shape
														_snake_case,							_snake_case     =						self.prepare_init_args_and_inputs_for_common()
														_snake_case     =						self.model_class(**UpperCAmelCase						)
														model.to(UpperCAmelCase						)
														model.eval()

														with torch.no_grad():
																					_snake_case     =						model(**UpperCAmelCase						)

																					if isinstance(UpperCAmelCase						,						UpperCAmelCase						):
																												_snake_case     =						output.sample

														self.assertIsNotNone(UpperCAmelCase						)
														_snake_case     =						torch.Size((inputs_dict["""sample"""].shape[0], 1)						)
														self.assertEqual(output.shape						,						UpperCAmelCase						,						"""Input and output shapes do not match"""						)

							def  lowercase							(self						)  ->     List[str]:
														pass

							def  lowercase							(self						)  ->     Any:
														pass

							def  lowercase							(self						)  ->     Optional[Any]:
														_snake_case     =						{
														    """in_channels""": 14,
														    """out_channels""": 14,
														    """down_block_types""": ["""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""],
														    """up_block_types""": [],
														    """out_block_type""": """ValueFunction""",
														    """mid_block_type""": """ValueFunctionMidBlock1D""",
														    """block_out_channels""": [32, 64, 128, 256],
														    """layers_per_block""": 1,
														    """downsample_each_block""": True,
														    """use_timestep_embedding""": True,
														    """freq_shift""": 1.0,
														    """flip_sin_to_cos""": False,
														    """time_embedding_type""": """positional""",
														    """act_fn""": """mish""",
														}
														_snake_case     =						self.dummy_input
														return init_dict, inputs_dict

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     int:
														_snake_case,							_snake_case     =						UNetaDModel.from_pretrained(
														    """bglick13/hopper-medium-v2-value-function-hor32"""						,						output_loading_info=UpperCAmelCase						,						subfolder="""value_function"""						)
														self.assertIsNotNone(UpperCAmelCase						)
														self.assertEqual(len(vf_loading_info["""missing_keys"""]						)						,						0						)

														value_function.to(UpperCAmelCase						)
														_snake_case     =						value_function(**self.dummy_input						)

														assert image is not None, "Make sure output is not None"

							@unittest.skipIf(torch_device == """mps"""						,						"""mish op not supported in MPS"""						)
							def  lowercase							(self						)  ->     Tuple:
														_snake_case,							_snake_case     =						UNetaDModel.from_pretrained(
														    """bglick13/hopper-medium-v2-value-function-hor32"""						,						output_loading_info=UpperCAmelCase						,						subfolder="""value_function"""						)
														torch.manual_seed(0						)
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0						)

														_snake_case     =						value_function.config.in_channels
														_snake_case     =						14
														_snake_case     =						torch.randn((1, seq_len, num_features)						).permute(
														    0						,						2						,						1						)  # match original, we can update values and remove
														_snake_case     =						torch.full((num_features,)						,						0						)

														with torch.no_grad():
																					_snake_case     =						value_function(UpperCAmelCase						,						UpperCAmelCase						).sample

														# fmt: off
														_snake_case     =						torch.tensor([165.25] * seq_len						)
														# fmt: on
														self.assertTrue(torch.allclose(UpperCAmelCase						,						UpperCAmelCase						,						rtol=1e-3						)						)





							def  lowercase							(self						)  ->     int:
														# Not implemented yet for this UNet
														pass

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

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=False							,      _SCREAMING_SNAKE_CASE=False							,      _SCREAMING_SNAKE_CASE="data2vec."   ):
							_snake_case     =						"""backbone.""" if is_semantic else """"""

							_snake_case     =						[]
							for i in range(config.num_hidden_layers   ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (f"""{prefix}blocks.{i}.norm1.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")   )
														rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")   )
														rename_keys.append(
														    (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")   )
														rename_keys.append(
														    (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")   )
														rename_keys.append(
														    (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")   )
														rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")   )
														rename_keys.append(
														    (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")   )
														rename_keys.append(
														    (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")   )
														rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")   )
														rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")   )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
							        (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
							        (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
							    ]   )

							if has_lm_head:
														# mask token + shared relative position bias + layernorm
														rename_keys.extend(
														    [
														        ("""mask_token""", f"""{hf_prefix}embeddings.mask_token"""),
														        (
														            """rel_pos_bias.relative_position_bias_table""",
														            f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
														        ),
														        (
														            """rel_pos_bias.relative_position_index""",
														            f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
														        ),
														        ("""norm.weight""", """layernorm.weight"""),
														        ("""norm.bias""", """layernorm.bias"""),
														    ]   )
							elif is_semantic:
														# semantic segmentation classification heads
														rename_keys.extend(
														    [
														        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
														        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
														        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
														        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
														    ]   )
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("""fc_norm.weight""", f"""{hf_prefix}pooler.layernorm.weight"""),
														        ("""fc_norm.bias""", f"""{hf_prefix}pooler.layernorm.bias"""),
														        ("""head.weight""", """classifier.weight"""),
														        ("""head.bias""", """classifier.bias"""),
														    ]   )

							return rename_keys






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=False							,      _SCREAMING_SNAKE_CASE=False							,      _SCREAMING_SNAKE_CASE="data2vec_vision."   ):
							for i in range(config.num_hidden_layers   ):
														_snake_case     =						"""backbone.""" if is_semantic else """"""
														# queries, keys and values
														_snake_case     =						state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""   )
														_snake_case     =						state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""   )
														_snake_case     =						state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""   )

														_snake_case     =						in_proj_weight[
														    : config.hidden_size, :
														]
														_snake_case     =						q_bias
														_snake_case     =						in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														_snake_case     =						in_proj_weight[
														    -config.hidden_size :, :
														]
														_snake_case     =						v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														_snake_case     =						state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""   )
														_snake_case     =						state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""   )

														_snake_case     =						gamma_a
														_snake_case     =						gamma_a

														# relative_position bias table + index
														if not has_lm_head:
																					# each layer has its own relative position bias
																					_snake_case     =						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""   )
																					_snake_case     =						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""   )

																					_snake_case     =						table
																					_snake_case     =						index






def       __SCREAMING_SNAKE_CASE   (						):
							_snake_case     =						argparse.ArgumentParser(
							    """Convert Data2VecVision to HF for image classification and pretraining"""							,      add_help=_SCREAMING_SNAKE_CASE   )
							parser.add_argument("""--hf_checkpoint_name"""							,      type=_SCREAMING_SNAKE_CASE   )
							parser.add_argument("""--input_size"""							,      default=224							,      type=_SCREAMING_SNAKE_CASE							,      help="""images input size"""   )
							parser.add_argument("""--beit_checkpoint"""							,      default=""""""							,      help="""beit checkpoint"""   )

							return parser.parse_args()






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							def load_state_dict(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=""							,      _SCREAMING_SNAKE_CASE="relative_position_index"   ):
														_snake_case     =						[]
														_snake_case     =						[]
														_snake_case     =						[]
														# copy state_dict so _load_from_state_dict can modify it
														_snake_case     =						getattr(_SCREAMING_SNAKE_CASE							,      """_metadata"""							,      _SCREAMING_SNAKE_CASE   )
														_snake_case     =						state_dict.copy()
														if metadata is not None:
																					_snake_case     =						metadata

														def load(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=""   ):
																					_snake_case     =						{} if metadata is None else metadata.get(prefix[:-1]							,      {}   )
																					module._load_from_state_dict(
																					    _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   )
																					for name, child in module._modules.items():
																												if child is not None:
																																			load(_SCREAMING_SNAKE_CASE							,      prefix + name + """."""   )

														load(_SCREAMING_SNAKE_CASE							,      prefix=_SCREAMING_SNAKE_CASE   )

														_snake_case     =						[]
														_snake_case     =						[]
														for key in missing_keys:
																					_snake_case     =						True
																					for ignore_key in ignore_missing.split("""|"""   ):
																												if ignore_key in key:
																																			_snake_case     =						False
																																			break
																					if keep_flag:
																												warn_missing_keys.append(_SCREAMING_SNAKE_CASE   )
																					else:
																												ignore_missing_keys.append(_SCREAMING_SNAKE_CASE   )

														_snake_case     =						warn_missing_keys

														if len(_SCREAMING_SNAKE_CASE   ) > 0:
																					print(
																					    """Weights of {} not initialized from pretrained model: {}""".format(
																					        model.__class__.__name__							,      _SCREAMING_SNAKE_CASE   )   )
														if len(_SCREAMING_SNAKE_CASE   ) > 0:
																					print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__							,      _SCREAMING_SNAKE_CASE   )   )
														if len(_SCREAMING_SNAKE_CASE   ) > 0:
																					print(
																					    """Ignored weights of {} not initialized from pretrained model: {}""".format(
																					        model.__class__.__name__							,      _SCREAMING_SNAKE_CASE   )   )
														if len(_SCREAMING_SNAKE_CASE   ) > 0:
																					print("""\n""".join(_SCREAMING_SNAKE_CASE   )   )

							_snake_case     =						{
							    """pretrained""": False,
							    """use_shared_rel_pos_bias""": True,
							    """use_abs_pos_emb""": False,
							    """init_values""": 0.1,
							}

							if is_finetuned:
														model_kwargs.update(
														    {
														        """num_classes""": 1000,
														        """use_mean_pooling""": True,
														        """init_scale""": 0.001,
														        """use_rel_pos_bias""": True,
														    }   )

							_snake_case     =						create_model(
							    """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""							,      **_SCREAMING_SNAKE_CASE							,      )
							_snake_case     =						model.patch_embed.patch_size
							_snake_case     =						(args.input_size // patch_size[0], args.input_size // patch_size[1])
							_snake_case     =						torch.load(args.beit_checkpoint							,      map_location="""cpu"""   )

							print(f"""Load ckpt from {args.beit_checkpoint}"""   )
							_snake_case     =						None
							for model_key in ("model", "module"):
														if model_key in checkpoint:
																					_snake_case     =						checkpoint[model_key]
																					print(f"""Load state_dict by model_key = {model_key}"""   )
																					break

							_snake_case     =						list(checkpoint_model.keys()   )
							for key in all_keys:
														if "relative_position_index" in key:
																					checkpoint_model.pop(_SCREAMING_SNAKE_CASE   )

														if "relative_position_bias_table" in key:
																					_snake_case     =						checkpoint_model[key]
																					_snake_case,							_snake_case     =						rel_pos_bias.size()
																					_snake_case,							_snake_case     =						model.state_dict()[key].size()
																					_snake_case     =						model.patch_embed.patch_shape
																					if dst_patch_shape[0] != dst_patch_shape[1]:
																												raise NotImplementedError()

							load_state_dict(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      prefix=""""""   )

							return model






def       __SCREAMING_SNAKE_CASE   (						):
							_snake_case     =						get_args()

							_snake_case     =						"""ft1k""" in args.hf_checkpoint_name
							_snake_case     =						"""large""" in args.hf_checkpoint_name

							if is_finetuned:
														# To convert Beit's data2vec_vision to HF you need to copy
														# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
														# into this folder.
														import modeling_finetune  # noqa: F401
							else:
														# To convert Beit's data2vec_vision to HF you need to copy
														# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
														# into this folder
														# IMPORTANT: Note that for now we've only converted the down-stream
														# model and not the full pretrained model. This means for the integration
														# test you need to add a `return x` after the following line:
														# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
														# to make the integration test pass.
														import modeling_cyclical  # noqa: F401

							# 1. Create model config
							_snake_case     =						DataaVecVisionConfig()
							if is_finetuned:
														_snake_case     =						True
														_snake_case     =						False
														_snake_case     =						True
														_snake_case     =						1000

														_snake_case     =						"""huggingface/label-files"""
														_snake_case     =						"""imagenet-1k-id2label.json"""
														_snake_case     =						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      repo_type="""dataset"""   )							,      """r"""   )   )
														_snake_case     =						{int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
														_snake_case     =						idalabel
														_snake_case     =						{v: k for k, v in idalabel.items()}
							else:
														_snake_case     =						False
														_snake_case     =						True
														_snake_case     =						False

							if is_large:
														_snake_case     =						1024
														_snake_case     =						4096
														_snake_case     =						24
														_snake_case     =						16

							# 2. Load Beit model
							_snake_case     =						load_beit_model(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   )
							orig_model.eval()

							# 3. Forward Beit model
							_snake_case     =						BeitImageProcessor(size=config.image_size							,      do_center_crop=_SCREAMING_SNAKE_CASE   )
							_snake_case     =						Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							_snake_case     =						image_processor(images=_SCREAMING_SNAKE_CASE							,      return_tensors="""pt"""   )
							_snake_case     =						encoding["""pixel_values"""]

							_snake_case     =						(pixel_values,) if is_finetuned else (pixel_values, None)
							with torch.no_grad():
														_snake_case     =						orig_model(*_SCREAMING_SNAKE_CASE   )

							# 4. Load HF Data2VecVision model
							if is_finetuned:
														_snake_case     =						DataaVecVisionForImageClassification(_SCREAMING_SNAKE_CASE   )
														hf_model.eval()
														_snake_case     =						False
														_snake_case     =						"""data2vec_vision."""
							else:
														_snake_case     =						DataaVecVisionModel(_SCREAMING_SNAKE_CASE   )
														hf_model.eval()
														_snake_case     =						True
														_snake_case     =						""""""

							_snake_case     =						create_rename_keys(_SCREAMING_SNAKE_CASE							,      hf_prefix=_SCREAMING_SNAKE_CASE							,      has_lm_head=_SCREAMING_SNAKE_CASE   )
							_snake_case     =						orig_model.state_dict()
							for src, dest in rename_keys:
														_snake_case     =						state_dict.pop(_SCREAMING_SNAKE_CASE   )
														_snake_case     =						val

							read_in_q_k_v(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      hf_prefix=_SCREAMING_SNAKE_CASE							,      has_lm_head=_SCREAMING_SNAKE_CASE   )
							_snake_case,							_snake_case     =						hf_model.load_state_dict(_SCREAMING_SNAKE_CASE							,      strict=_SCREAMING_SNAKE_CASE   )
							print("""HF missing"""							,      _SCREAMING_SNAKE_CASE   )
							print("""HF unexpected_keys"""							,      _SCREAMING_SNAKE_CASE   )

							# 5. Forward HF Data2VecVision model
							with torch.no_grad():
														_snake_case     =						hf_model(_SCREAMING_SNAKE_CASE   )

							_snake_case     =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

							# 6. Compare
							_snake_case     =						torch.max(torch.abs(hf_output - orig_model_output   )   ).item()

							print(f"""max_absolute_diff = {max_absolute_diff}"""   )
							_snake_case     =						torch.allclose(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      atol=1E-3   )
							print("""Do both models output the same tensors?"""							,      """🔥""" if success else """💩"""   )
							if not success:
														raise Exception("""Something went wRoNg"""   )

							# 7. Save
							print(f"""Saving to {args.hf_checkpoint_name}"""   )
							hf_model.save_pretrained(args.hf_checkpoint_name   )
							image_processor.save_pretrained(args.hf_checkpoint_name   )


if __name__ == "__main__":
				main()
				# Run the following to convert checkpoints
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./pretrained_base.pt \
				#          --hf_checkpoint_name "./data2vec-vision-base"
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./finetuned_base.pt \
				#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./pretrained_large.pt \
				#          --hf_checkpoint_name "./data2vec-vision-large"
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./finetuned_large.pt \
				#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"