import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Optional[int]:
     a  :    List[Any]				=						'''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

     with open(lowerCAmelCase_						,      '''wb'''       ) as f:
          with requests.get(lowerCAmelCase_						,      stream=lowerCAmelCase_       ) as req:
               for chunk in req.iter_content(chunk_size=2_0_4_8       ):
                    f.write(lowerCAmelCase_       )







def 	UpperCAmelCase       (		)	->	VivitConfig:
     a  :    Dict				=						VivitConfig()

     a  :    Union[str, Any]				=						4_0_0
     a  :    List[Any]				=						'''huggingface/label-files'''
     a  :    List[Any]				=						'''kinetics400-id2label.json'''

     a  :    Optional[Any]				=						json.load(open(hf_hub_download(lowerCAmelCase_						,      lowerCAmelCase_						,      repo_type='''dataset'''       )						,      '''r'''       )       )
     a  :    Optional[int]				=						{int(lowerCAmelCase_       ): v for k, v in idalabel.items()}
     a  :    Dict				=						idalabel
     a  :    Dict				=						{v: k for k, v in idalabel.items()}
     return config







def 	UpperCAmelCase       (		)	->	str:
     a  :    Any				=						hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video'''						,      filename='''eating_spaghetti_32_frames.npy'''						,      repo_type='''dataset'''       )
     a  :    Optional[Any]				=						np.load(lowerCAmelCase_       )
     return list(lowerCAmelCase_       )







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Any:
     if np.ndim(lowerCAmelCase_       ) == 2:
          return transform_attention_bias(lowerCAmelCase_       )

     elif np.ndim(lowerCAmelCase_       ) == 3:
          return transform_attention_kernel(lowerCAmelCase_       )

     else:
          raise Exception(f"""Invalid number of dimesions: {np.ndim(lowerCAmelCase_       )}"""       )







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Optional[Any]:
     return current.flatten()







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Optional[int]:
     return np.reshape(lowerCAmelCase_						,      (current.shape[0], current.shape[1] * current.shape[2])       ).T







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Tuple:
     return np.reshape(lowerCAmelCase_						,      (current.shape[0] * current.shape[1], current.shape[2])       ).T







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_       )	->	Any:
     a  :    str				=						state_dict['''optimizer''']['''target''']['''Transformer'''][f"""encoderblock_{i}"""]

     a  :    Tuple				=						f"""encoder.layer.{i}."""
     a  :    List[Any]				=						{
         prefix + '''intermediate.dense.bias''': state['''MlpBlock_0''']['''Dense_0''']['''bias'''],
         prefix + '''intermediate.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_0''']['''kernel''']       ),
         prefix + '''output.dense.bias''': state['''MlpBlock_0''']['''Dense_1''']['''bias'''],
         prefix + '''output.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_1''']['''kernel''']       ),
         prefix + '''layernorm_before.bias''': state['''LayerNorm_0''']['''bias'''],
         prefix + '''layernorm_before.weight''': state['''LayerNorm_0''']['''scale'''],
         prefix + '''layernorm_after.bias''': state['''LayerNorm_1''']['''bias'''],
         prefix + '''layernorm_after.weight''': state['''LayerNorm_1''']['''scale'''],
         prefix
         + '''attention.attention.query.bias''': transform_attention(
             state['''MultiHeadDotProductAttention_0''']['''query''']['''bias''']       ),
         prefix
         + '''attention.attention.query.weight''': transform_attention(
             state['''MultiHeadDotProductAttention_0''']['''query''']['''kernel''']       ),
         prefix
         + '''attention.attention.key.bias''': transform_attention(state['''MultiHeadDotProductAttention_0''']['''key''']['''bias''']       ),
         prefix
         + '''attention.attention.key.weight''': transform_attention(
             state['''MultiHeadDotProductAttention_0''']['''key''']['''kernel''']       ),
         prefix
         + '''attention.attention.value.bias''': transform_attention(
             state['''MultiHeadDotProductAttention_0''']['''value''']['''bias''']       ),
         prefix
         + '''attention.attention.value.weight''': transform_attention(
             state['''MultiHeadDotProductAttention_0''']['''value''']['''kernel''']       ),
         prefix + '''attention.output.dense.bias''': state['''MultiHeadDotProductAttention_0''']['''out''']['''bias'''],
         prefix
         + '''attention.output.dense.weight''': transform_attention_output_weight(
             state['''MultiHeadDotProductAttention_0''']['''out''']['''kernel''']       ),
     }

     return new_state







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	int:
     return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]       )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_=False       )	->	Optional[Any]:
     a  :    List[str]				=						get_n_layers(lowerCAmelCase_       )

     a  :    Optional[Any]				=						OrderedDict()

     a  :    List[Any]				=						state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
     a  :    Any				=						state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

     a  :    str				=						np.transpose(
         state_dict['''optimizer''']['''target''']['''embedding''']['''kernel''']						,      (4, 3, 0, 1, 2)       )
     a  :    List[Any]				=						state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

     a  :    int				=						state_dict['''optimizer''']['''target''']['''cls''']
     a  :    Optional[int]				=						state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
         '''pos_embedding'''
     ]

     for i in range(lowerCAmelCase_       ):
          new_state.update(transform_state_encoder_block(lowerCAmelCase_						,      lowerCAmelCase_       )       )

     if classification_head:
          a  :    List[Any]				=						{'''vivit.''' + k: v for k, v in new_state.items()}
          a  :    Any				=						np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel''']       )
          a  :    Optional[Any]				=						np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias''']       )

     return {k: torch.tensor(lowerCAmelCase_       ) for k, v in new_state.items()}







def 	UpperCAmelCase       (		)	->	VivitImageProcessor:
     a  :    Optional[int]				=						VivitImageProcessor()

     assert extractor.do_resize is True
     assert extractor.size == {"shortest_edge": 2_5_6}
     assert extractor.do_center_crop is True
     assert extractor.crop_size == {"width": 2_2_4, "height": 2_2_4}
     assert extractor.resample == PILImageResampling.BILINEAR

     # here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
     # one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
     # which effectively means no normalization (and ViViT does not overwrite those when calling this func)
     assert extractor.do_normalize is False
     assert extractor.do_rescale is True
     assert extractor.rescale_factor == 1 / 2_5_5

     # zero-centering = True in original implementation
     assert extractor.do_zero_centering is True

     return extractor







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Tuple:
     a  :    Union[str, Any]				=						'''checkpoint'''

     if not os.path.exists(lowerCAmelCase_       ):
          download_checkpoint(lowerCAmelCase_       )

     a  :    Any				=						restore_checkpoint(lowerCAmelCase_						,      lowerCAmelCase_       )
     a  :    str				=						transform_state(lowerCAmelCase_						,      classification_head=lowerCAmelCase_       )

     a  :    Optional[int]				=						get_vivit_config()

     assert config.image_size == 2_2_4
     assert config.num_frames == 3_2

     a  :    str				=						VivitForVideoClassification(lowerCAmelCase_       )
     model.load_state_dict(lowerCAmelCase_       )
     model.eval()

     a  :    int				=						get_processor()

     a  :    Dict				=						prepare_video()
     a  :    Optional[Any]				=						extractor(lowerCAmelCase_						,      return_tensors='''pt'''       )

     a  :    Any				=						model(**lowerCAmelCase_       )

     a  :    Optional[int]				=						torch.Size([1, 4_0_0]       )
     a  :    Dict				=						torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]       )

     assert outputs.logits.shape == expected_shape
     assert torch.allclose(outputs.logits[0, :5]						,      lowerCAmelCase_						,      atol=1e-4       ), outputs.logits[0, :5]

     model.save_pretrained(lowerCAmelCase_       )
     extractor.save_pretrained(lowerCAmelCase_       )


if __name__ == "__main__":
  UpperCAmelCase							   =      argparse.ArgumentParser()

  parser.add_argument("""--output_model_name""", """-o""", type=str, help="""Output path for the converted HuggingFace model""")

  UpperCAmelCase							   =      parser.parse_args()
  convert(args.output_model_name)
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						int 			=   'graphormer'
   UpperCamelCase_				:						Tuple 			=   ['past_key_values']
   def __init__(    self							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 5_12 * 9							,     lowerCAmelCase_ = 5_12 * 3							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 1_28							,     lowerCAmelCase_ = 5							,     lowerCAmelCase_ = 10_24							,     lowerCAmelCase_ = "multi_hop"							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 12							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 32							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = "gelu"							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = 8							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = True							,     lowerCAmelCase_=0							,     lowerCAmelCase_=1							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->Optional[int]:
        a  :    List[Any]				=						num_classes
        a  :    Any				=						num_atoms
        a  :    Dict				=						num_in_degree
        a  :    int				=						num_out_degree
        a  :    Dict				=						num_edges
        a  :    Tuple				=						num_spatial
        a  :    Dict				=						num_edge_dis
        a  :    Optional[int]				=						edge_type
        a  :    List[Any]				=						multi_hop_max_dist
        a  :    Optional[int]				=						spatial_pos_max
        a  :    str				=						max_nodes
        a  :    Optional[Any]				=						num_hidden_layers
        a  :    Any				=						embedding_dim
        a  :    Dict				=						embedding_dim
        a  :    Dict				=						ffn_embedding_dim
        a  :    List[Any]				=						num_attention_heads
        a  :    List[str]				=						dropout
        a  :    int				=						attention_dropout
        a  :    Optional[Any]				=						layerdrop
        a  :    str				=						encoder_normalize_before
        a  :    Optional[int]				=						pre_layernorm
        a  :    Tuple				=						apply_graphormer_init
        a  :    int				=						activation_fn
        a  :    Any				=						embed_scale
        a  :    Optional[int]				=						freeze_embeddings
        a  :    Tuple				=						num_trans_layers_to_freeze
        a  :    List[str]				=						share_input_output_embed
        a  :    List[Any]				=						traceable
        a  :    Optional[Any]				=						q_noise
        a  :    Dict				=						qn_block_size

        # These parameters are here for future extensions
        # atm, the model only supports self attention
        a  :    List[Any]				=						kdim
        a  :    List[str]				=						vdim
        a  :    int				=						self_attention
        a  :    List[Any]				=						bias

        super().__init__(
            pad_token_id=lowerCAmelCase_							,     bos_token_id=lowerCAmelCase_							,     eos_token_id=lowerCAmelCase_							,     **lowerCAmelCase_							,     )