


'''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 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase=False    ,       UpperCAmelCase=False    ,       UpperCAmelCase="data2vec."     ):






       """simple docstring"""





       snake_case__					:	Optional[Any]          =					"""backbone.""" if is_semantic else """"""

       snake_case__					:	Tuple          =					[]
       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 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase=False    ,       UpperCAmelCase=False    ,       UpperCAmelCase="data2vec_vision."     ):






       """simple docstring"""





       for i in range(config.num_hidden_layers     ):
              snake_case__					:	Optional[int]          =					"""backbone.""" if is_semantic else """"""
              # queries, keys and values
              snake_case__					:	Any          =					state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""     )
              snake_case__					:	Any          =					state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""     )
              snake_case__					:	Any          =					state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""     )

              snake_case__					:	int          =					in_proj_weight[
                  : config.hidden_size, :
              ]
              snake_case__					:	List[str]          =					q_bias
              snake_case__					:	Optional[Any]          =					in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              snake_case__					:	Dict          =					in_proj_weight[
                  -config.hidden_size :, :
              ]
              snake_case__					:	int          =					v_bias

              # gamma_1 and gamma_2
              # we call them lambda because otherwise they are renamed when using .from_pretrained
              snake_case__					:	List[str]          =					state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""     )
              snake_case__					:	List[Any]          =					state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""     )

              snake_case__					:	Dict          =					gamma_a
              snake_case__					:	Optional[Any]          =					gamma_a

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

                     snake_case__					:	Any          =					table
                     snake_case__					:	Optional[Any]          =					index



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





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

       return parser.parse_args()



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       def load_state_dict(UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase=""    ,       UpperCAmelCase="relative_position_index"     ):
              snake_case__					:	int          =					[]
              snake_case__					:	str          =					[]
              snake_case__					:	Tuple          =					[]
              # copy state_dict so _load_from_state_dict can modify it
              snake_case__					:	Tuple          =					getattr(UpperCAmelCase    ,       """_metadata"""    ,       UpperCAmelCase     )
              snake_case__					:	Any          =					state_dict.copy()
              if metadata is not None:
                     snake_case__					:	Dict          =					metadata

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

              load(UpperCAmelCase    ,       prefix=UpperCAmelCase     )

              snake_case__					:	Optional[int]          =					[]
              snake_case__					:	Any          =					[]
              for key in missing_keys:
                     snake_case__					:	List[Any]          =					True
                     for ignore_key in ignore_missing.split("""|"""     ):
                            if ignore_key in key:
                                   snake_case__					:	List[Any]          =					False
                                   break
                     if keep_flag:
                            warn_missing_keys.append(UpperCAmelCase     )
                     else:
                            ignore_missing_keys.append(UpperCAmelCase     )

              snake_case__					:	Dict          =					warn_missing_keys

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

       snake_case__					:	Dict          =					{
           """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""": 0.0_0_1,
                      """use_rel_pos_bias""": True,
                  }     )

       snake_case__					:	int          =					create_model(
           """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""    ,       **UpperCAmelCase    ,       )
       snake_case__					:	List[Any]          =					model.patch_embed.patch_size
       snake_case__					:	List[Any]          =					(args.input_size // patch_size[0], args.input_size // patch_size[1])
       snake_case__					:	str          =					torch.load(args.beit_checkpoint    ,       map_location="""cpu"""     )

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

       snake_case__					:	Union[str, Any]          =					list(checkpoint_model.keys()     )
       for key in all_keys:
              if "relative_position_index" in key:
                     checkpoint_model.pop(UpperCAmelCase     )

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

       load_state_dict(UpperCAmelCase    ,       UpperCAmelCase    ,       prefix=""""""     )

       return model



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	Dict          =					get_args()

       snake_case__					:	Optional[int]          =					"""ft1k""" in args.hf_checkpoint_name
       snake_case__					:	int          =					"""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__					:	List[Any]          =					DataaVecVisionConfig()
       if is_finetuned:
              snake_case__					:	Union[str, Any]          =					True
              snake_case__					:	Tuple          =					False
              snake_case__					:	List[str]          =					True
              snake_case__					:	int          =					1000

              snake_case__					:	List[Any]          =					"""huggingface/label-files"""
              snake_case__					:	Tuple          =					"""imagenet-1k-id2label.json"""
              snake_case__					:	int          =					json.load(open(hf_hub_download(UpperCAmelCase    ,       UpperCAmelCase    ,       repo_type="""dataset"""     )    ,       """r"""     )     )
              snake_case__					:	Tuple          =					{int(UpperCAmelCase     ): v for k, v in idalabel.items()}
              snake_case__					:	List[Any]          =					idalabel
              snake_case__					:	Union[str, Any]          =					{v: k for k, v in idalabel.items()}
       else:
              snake_case__					:	Tuple          =					False
              snake_case__					:	str          =					True
              snake_case__					:	Optional[Any]          =					False

       if is_large:
              snake_case__					:	Tuple          =					1024
              snake_case__					:	Any          =					4096
              snake_case__					:	int          =					24
              snake_case__					:	Optional[Any]          =					16

       # 2. Load Beit model
       snake_case__					:	int          =					load_beit_model(UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     )
       orig_model.eval()

       # 3. Forward Beit model
       snake_case__					:	str          =					BeitImageProcessor(size=config.image_size    ,       do_center_crop=UpperCAmelCase     )
       snake_case__					:	Tuple          =					Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""     )
       snake_case__					:	Union[str, Any]          =					image_processor(images=UpperCAmelCase    ,       return_tensors="""pt"""     )
       snake_case__					:	Any          =					encoding["""pixel_values"""]

       snake_case__					:	str          =					(pixel_values,) if is_finetuned else (pixel_values, None)
       with torch.no_grad():
              snake_case__					:	str          =					orig_model(*UpperCAmelCase     )

       # 4. Load HF Data2VecVision model
       if is_finetuned:
              snake_case__					:	Dict          =					DataaVecVisionForImageClassification(UpperCAmelCase     )
              hf_model.eval()
              snake_case__					:	List[str]          =					False
              snake_case__					:	List[str]          =					"""data2vec_vision."""
       else:
              snake_case__					:	List[str]          =					DataaVecVisionModel(UpperCAmelCase     )
              hf_model.eval()
              snake_case__					:	int          =					True
              snake_case__					:	List[Any]          =					""""""

       snake_case__					:	List[str]          =					create_rename_keys(UpperCAmelCase    ,       hf_prefix=UpperCAmelCase    ,       has_lm_head=UpperCAmelCase     )
       snake_case__					:	List[Any]          =					orig_model.state_dict()
       for src, dest in rename_keys:
              snake_case__					:	Dict          =					state_dict.pop(UpperCAmelCase     )
              snake_case__					:	Any          =					val

       read_in_q_k_v(UpperCAmelCase    ,       UpperCAmelCase    ,       hf_prefix=UpperCAmelCase    ,       has_lm_head=UpperCAmelCase     )
       snake_case__       ,   snake_case__					:	Union[str, Any]          =					hf_model.load_state_dict(UpperCAmelCase    ,       strict=UpperCAmelCase     )
       print("""HF missing"""    ,       UpperCAmelCase     )
       print("""HF unexpected_keys"""    ,       UpperCAmelCase     )

       # 5. Forward HF Data2VecVision model
       with torch.no_grad():
              snake_case__					:	List[Any]          =					hf_model(UpperCAmelCase     )

       snake_case__					:	Dict          =					hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

       # 6. Compare
       snake_case__					:	Optional[Any]          =					torch.max(torch.abs(hf_output - orig_model_output     )     ).item()

       print(f"""max_absolute_diff = {max_absolute_diff}"""     )
       snake_case__					:	Any          =					torch.allclose(UpperCAmelCase    ,       UpperCAmelCase    ,       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"








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

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Dict          =					TimesformerConfig()

       if "large" in model_name:
              snake_case__					:	int          =					96

       if "hr" in model_name:
              snake_case__					:	int          =					16
              snake_case__					:	int          =					448

       snake_case__					:	int          =					"""huggingface/label-files"""
       if "k400" in model_name:
              snake_case__					:	List[Any]          =					400
              snake_case__					:	List[str]          =					"""kinetics400-id2label.json"""
       elif "k600" in model_name:
              snake_case__					:	Optional[int]          =					600
              snake_case__					:	int          =					"""kinetics600-id2label.json"""
       elif "ssv2" in model_name:
              snake_case__					:	Any          =					174
              snake_case__					:	Union[str, Any]          =					"""something-something-v2-id2label.json"""
       else:
              raise ValueError("""Model name should either contain 'k400', 'k600' or 'ssv2'."""     )
       snake_case__					:	List[str]          =					json.load(open(hf_hub_download(UpperCAmelCase    ,       UpperCAmelCase    ,       repo_type="""dataset"""     )    ,       """r"""     )     )
       snake_case__					:	Tuple          =					{int(UpperCAmelCase     ): v for k, v in idalabel.items()}
       snake_case__					:	int          =					idalabel
       snake_case__					:	Optional[int]          =					{v: k for k, v in idalabel.items()}

       return config



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       if "encoder." in name:
              snake_case__					:	Union[str, Any]          =					name.replace("""encoder."""    ,       """"""     )
       if "cls_token" in name:
              snake_case__					:	Any          =					name.replace("""cls_token"""    ,       """timesformer.embeddings.cls_token"""     )
       if "pos_embed" in name:
              snake_case__					:	Tuple          =					name.replace("""pos_embed"""    ,       """timesformer.embeddings.position_embeddings"""     )
       if "time_embed" in name:
              snake_case__					:	Optional[int]          =					name.replace("""time_embed"""    ,       """timesformer.embeddings.time_embeddings"""     )
       if "patch_embed.proj" in name:
              snake_case__					:	Optional[int]          =					name.replace("""patch_embed.proj"""    ,       """timesformer.embeddings.patch_embeddings.projection"""     )
       if "patch_embed.norm" in name:
              snake_case__					:	Tuple          =					name.replace("""patch_embed.norm"""    ,       """timesformer.embeddings.norm"""     )
       if "blocks" in name:
              snake_case__					:	Optional[int]          =					name.replace("""blocks"""    ,       """timesformer.encoder.layer"""     )
       if "attn.proj" in name:
              snake_case__					:	Optional[int]          =					name.replace("""attn.proj"""    ,       """attention.output.dense"""     )
       if "attn" in name and "bias" not in name and "temporal" not in name:
              snake_case__					:	Any          =					name.replace("""attn"""    ,       """attention.self"""     )
       if "attn" in name and "temporal" not in name:
              snake_case__					:	Optional[Any]          =					name.replace("""attn"""    ,       """attention.attention"""     )
       if "temporal_norm1" in name:
              snake_case__					:	Optional[int]          =					name.replace("""temporal_norm1"""    ,       """temporal_layernorm"""     )
       if "temporal_attn.proj" in name:
              snake_case__					:	Any          =					name.replace("""temporal_attn"""    ,       """temporal_attention.output.dense"""     )
       if "temporal_fc" in name:
              snake_case__					:	Union[str, Any]          =					name.replace("""temporal_fc"""    ,       """temporal_dense"""     )
       if "norm1" in name and "temporal" not in name:
              snake_case__					:	Any          =					name.replace("""norm1"""    ,       """layernorm_before"""     )
       if "norm2" in name:
              snake_case__					:	Union[str, Any]          =					name.replace("""norm2"""    ,       """layernorm_after"""     )
       if "mlp.fc1" in name:
              snake_case__					:	List[str]          =					name.replace("""mlp.fc1"""    ,       """intermediate.dense"""     )
       if "mlp.fc2" in name:
              snake_case__					:	Tuple          =					name.replace("""mlp.fc2"""    ,       """output.dense"""     )
       if "norm.weight" in name and "fc" not in name and "temporal" not in name:
              snake_case__					:	Optional[int]          =					name.replace("""norm.weight"""    ,       """timesformer.layernorm.weight"""     )
       if "norm.bias" in name and "fc" not in name and "temporal" not in name:
              snake_case__					:	Tuple          =					name.replace("""norm.bias"""    ,       """timesformer.layernorm.bias"""     )
       if "head" in name:
              snake_case__					:	str          =					name.replace("""head"""    ,       """classifier"""     )

       return name



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       for key in orig_state_dict.copy().keys():
              snake_case__					:	Any          =					orig_state_dict.pop(UpperCAmelCase     )

              if key.startswith("""model."""     ):
                     snake_case__					:	Tuple          =					key.replace("""model."""    ,       """"""     )

              if "qkv" in key:
                     snake_case__					:	int          =					key.split("""."""     )
                     snake_case__					:	Union[str, Any]          =					int(key_split[1]     )
                     snake_case__					:	int          =					"""timesformer.encoder.layer."""
                     if "temporal" in key:
                            snake_case__					:	Any          =					""".temporal_attention.attention.qkv."""
                     else:
                            snake_case__					:	Any          =					""".attention.attention.qkv."""
                     if "weight" in key:
                            snake_case__					:	Dict          =					val
                     else:
                            snake_case__					:	str          =					val
              else:
                     snake_case__					:	Any          =					val

       return orig_state_dict



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	Dict          =					hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video"""    ,       filename="""eating_spaghetti.npy"""    ,       repo_type="""dataset"""     )
       snake_case__					:	Any          =					np.load(UpperCAmelCase     )
       return list(UpperCAmelCase     )



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Any          =					get_timesformer_config(UpperCAmelCase     )

       snake_case__					:	Optional[int]          =					TimesformerForVideoClassification(UpperCAmelCase     )

       # download original checkpoint, hosted on Google Drive
       snake_case__					:	str          =					"""pytorch_model.bin"""
       gdown.cached_download(UpperCAmelCase    ,       UpperCAmelCase    ,       quiet=UpperCAmelCase     )
       snake_case__					:	Dict          =					torch.load(UpperCAmelCase    ,       map_location="""cpu"""     )
       if "model" in files:
              snake_case__					:	Optional[Any]          =					files["""model"""]
       elif "module" in files:
              snake_case__					:	List[str]          =					files["""module"""]
       else:
              snake_case__					:	str          =					files["""model_state"""]
       snake_case__					:	List[Any]          =					convert_state_dict(UpperCAmelCase    ,       UpperCAmelCase     )

       model.load_state_dict(UpperCAmelCase     )
       model.eval()

       # verify model on basic input
       snake_case__					:	Any          =					VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]    ,       image_std=[0.5, 0.5, 0.5]     )
       snake_case__					:	Union[str, Any]          =					prepare_video()
       snake_case__					:	str          =					image_processor(video[:8]    ,       return_tensors="""pt"""     )

       snake_case__					:	int          =					model(**UpperCAmelCase     )
       snake_case__					:	Tuple          =					outputs.logits

       snake_case__					:	List[str]          =					[
           # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
           """timesformer-base-finetuned-k400""",
           """timesformer-large-finetuned-k400""",
           """timesformer-hr-finetuned-k400""",
           # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
           """timesformer-base-finetuned-k600""",
           """timesformer-large-finetuned-k600""",
           """timesformer-hr-finetuned-k600""",
           # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
           """timesformer-base-finetuned-ssv2""",
           """timesformer-large-finetuned-ssv2""",
           """timesformer-hr-finetuned-ssv2""",
       ]

       # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
       if model_name == "timesformer-base-finetuned-k400":
              snake_case__					:	str          =					torch.Size([1, 400]     )
              snake_case__					:	str          =					torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]     )
       elif model_name == "timesformer-base-finetuned-k600":
              snake_case__					:	Dict          =					torch.Size([1, 600]     )
              snake_case__					:	str          =					torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]     )
       elif model_name == "timesformer-base-finetuned-ssv2":
              snake_case__					:	List[Any]          =					torch.Size([1, 174]     )
              snake_case__					:	Union[str, Any]          =					torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]     )
       elif model_name == "timesformer-large-finetuned-k400":
              snake_case__					:	Optional[Any]          =					torch.Size([1, 400]     )
              snake_case__					:	Tuple          =					torch.tensor([0, 0, 0]     )
       elif model_name == "timesformer-large-finetuned-k600":
              snake_case__					:	Optional[int]          =					torch.Size([1, 600]     )
              snake_case__					:	int          =					torch.tensor([0, 0, 0]     )
       elif model_name == "timesformer-large-finetuned-ssv2":
              snake_case__					:	Union[str, Any]          =					torch.Size([1, 174]     )
              snake_case__					:	List[Any]          =					torch.tensor([0, 0, 0]     )
       elif model_name == "timesformer-hr-finetuned-k400":
              snake_case__					:	str          =					torch.Size([1, 400]     )
              snake_case__					:	List[Any]          =					torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]     )
       elif model_name == "timesformer-hr-finetuned-k600":
              snake_case__					:	Optional[int]          =					torch.Size([1, 600]     )
              snake_case__					:	str          =					torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]     )
       elif model_name == "timesformer-hr-finetuned-ssv2":
              snake_case__					:	int          =					torch.Size([1, 174]     )
              snake_case__					:	Optional[Any]          =					torch.tensor([-3.6_7_5_6, -0.7_5_1_3, 0.7_1_8_0]     )
       else:
              raise ValueError(f"""Model name not supported. Should be one of {model_names}"""     )

       # verify logits
       assert logits.shape == expected_shape
       assert torch.allclose(logits[0, :3]    ,       UpperCAmelCase    ,       atol=1E-4     )
       print("""Logits ok!"""     )

       if pytorch_dump_folder_path is not None:
              print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""     )
              image_processor.save_pretrained(UpperCAmelCase     )
              model.save_pretrained(UpperCAmelCase     )

       if push_to_hub:
              print("""Pushing to the hub..."""     )
              model.push_to_hub(f"""fcakyon/{model_name}"""     )


if __name__ == "__main__":
     lowerCAmelCase__  =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--checkpoint_url',
         default='https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download',
         type=str,
         help=(
             'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
             ' download link.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default='',
         type=str,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument('--model_name', default='timesformer-base-finetuned-k400', type=str, help='Name of the model.')
     parser.add_argument(
         '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
     )

     lowerCAmelCase__  =     parser.parse_args()
     convert_timesformer_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
     )




