
from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
 import pyximport

 pyximport.install(setup_args={"include_dirs": np.get_include()})
 from . import algos_graphormer  # noqa E402
def        __lowercase(							__snake_case				:		Tuple    ,__snake_case				:		int = 5_12					)       ->   Tuple:
       __snake_case						       =   x.shape[1] if len(x.shape					) > 1 else 1
       __snake_case						       =   1 + np.arange(0    ,feature_num * offset    ,__snake_case    ,dtype=np.intaa					)
       __snake_case						       =   x + feature_offset
       return x
def        __lowercase(							__snake_case				:		Optional[Any]    ,__snake_case				:		Tuple=True					)       ->   Optional[Any]:
       requires_backends(__snake_case    ,['cython']					)

       if keep_features and "edge_attr" in item.keys():  # edge_attr
              __snake_case						       =   np.asarray(item['edge_attr']    ,dtype=np.intaa					)
       else:
              __snake_case						       =   np.ones((len(item['edge_index'][0]					), 1)    ,dtype=np.intaa					)  # same embedding for all

       if keep_features and "node_feat" in item.keys():  # input_nodes
              __snake_case						       =   np.asarray(item['node_feat']    ,dtype=np.intaa					)
       else:
              __snake_case						       =   np.ones((item['num_nodes'], 1)    ,dtype=np.intaa					)  # same embedding for all

       __snake_case						       =   np.asarray(item['edge_index']    ,dtype=np.intaa					)

       __snake_case						       =   convert_to_single_emb(__snake_case					) + 1
       __snake_case						       =   item['num_nodes']

       if len(edge_attr.shape					) == 1:
              __snake_case						       =   edge_attr[:, None]
       __snake_case						       =   np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]    ,dtype=np.intaa					)
       __snake_case						       =   convert_to_single_emb(__snake_case					) + 1

       # node adj matrix [num_nodes, num_nodes] bool
       __snake_case						       =   np.zeros([num_nodes, num_nodes]    ,dtype=__snake_case					)
       __snake_case						       =   True

       __snake_case       ,       __snake_case						       =   algos_graphormer.floyd_warshall(__snake_case					)
       __snake_case						       =   np.amax(__snake_case					)

       __snake_case						       =   algos_graphormer.gen_edge_input(__snake_case    ,__snake_case    ,__snake_case					)
       __snake_case						       =   np.zeros([num_nodes + 1, num_nodes + 1]    ,dtype=np.single					)  # with graph token

       # combine
       __snake_case						       =   input_nodes + 1  # we shift all indices by one for padding
       __snake_case						       =   attn_bias
       __snake_case						       =   attn_edge_type
       __snake_case						       =   shortest_path_result.astype(np.intaa					) + 1  # we shift all indices by one for padding
       __snake_case						       =   np.sum(__snake_case    ,axis=1					).reshape(-1					) + 1  # we shift all indices by one for padding
       __snake_case						       =   item['in_degree']  # for undirected graph
       __snake_case						       =   input_edges + 1  # we shift all indices by one for padding
       if "labels" not in item:
              __snake_case						       =   item['y']

       return item







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=20							,       SCREAMING_SNAKE_CASE_=False    ):
            if not is_cython_available():
                   raise ImportError('Graphormer preprocessing needs Cython (pyximport)'    )

            __snake_case						       =   spatial_pos_max
            __snake_case						       =   on_the_fly_processing

     def __call__(  self							,       SCREAMING_SNAKE_CASE_    ):
            if self.on_the_fly_processing:
                   __snake_case						       =   [preprocess_item(SCREAMING_SNAKE_CASE_    ) for i in features]

            if not isinstance(features[0]							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   [vars(SCREAMING_SNAKE_CASE_    ) for f in features]
            __snake_case						       =   {}

            __snake_case						       =   max(len(i['input_nodes']    ) for i in features    )
            __snake_case						       =   len(features[0]['input_nodes'][0]    )
            __snake_case						       =   len(features[0]['attn_edge_type'][0][0]    )
            __snake_case						       =   max(len(i['input_edges'][0][0]    ) for i in features    )
            __snake_case						       =   len(features[0]['input_edges'][0][0][0]    )
            __snake_case						       =   len(SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   torch.zeros(SCREAMING_SNAKE_CASE_							,       max_node_num + 1							,       max_node_num + 1							,       dtype=torch.float    )
            __snake_case						       =   torch.zeros(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       dtype=torch.long    )
            __snake_case						       =   torch.zeros(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       dtype=torch.long    )
            __snake_case						       =   torch.zeros(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       dtype=torch.long    )
            __snake_case						       =   torch.zeros(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       dtype=torch.long    )
            __snake_case						       =   torch.zeros(
                SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       dtype=torch.long    )

            for ix, f in enumerate(SCREAMING_SNAKE_CASE_    ):
                   for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
                          __snake_case						       =   torch.tensor(f[k]    )

                   if len(f['attn_bias'][1:, 1:][f['spatial_pos'] >= self.spatial_pos_max]    ) > 0:
                          __snake_case						       =   float('-inf'    )

                   __snake_case						       =   f['attn_bias']
                   __snake_case						       =   f[
                       'attn_edge_type'
                   ]
                   __snake_case						       =   f['spatial_pos']
                   __snake_case						       =   f['in_degree']
                   __snake_case						       =   f['input_nodes']
                   __snake_case						       =   f['input_edges']

            __snake_case						       =   batch['in_degree']

            __snake_case						       =   features[0]['labels']
            if len(SCREAMING_SNAKE_CASE_    ) == 1:  # one task
                   if isinstance(sample[0]							,       SCREAMING_SNAKE_CASE_    ):  # regression
                          __snake_case						       =   torch.from_numpy(np.concatenate([i['labels'] for i in features]    )    )
                   else:  # binary classification
                          __snake_case						       =   torch.from_numpy(np.concatenate([i['labels'] for i in features]    )    )
            else:  # multi task classification, left to float to keep the NaNs
                   __snake_case						       =   torch.from_numpy(np.stack([i['labels'] for i in features]							,       axis=0    )    )

            return batch





'''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       (				lowerCamelCase :    str       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	TimesformerConfig()

	if "large" in model_name:
		__lowerCAmelCase       				=	96

	if "hr" in model_name:
		__lowerCAmelCase       				=	16
		__lowerCAmelCase       				=	4_48

	__lowerCAmelCase       				=	"huggingface/label-files"
	if "k400" in model_name:
		__lowerCAmelCase       				=	4_00
		__lowerCAmelCase       				=	"kinetics400-id2label.json"
	elif "k600" in model_name:
		__lowerCAmelCase       				=	6_00
		__lowerCAmelCase       				=	"kinetics600-id2label.json"
	elif "ssv2" in model_name:
		__lowerCAmelCase       				=	1_74
		__lowerCAmelCase       				=	"something-something-v2-id2label.json"
	else:
		raise ValueError("Model name should either contain 'k400', 'k600' or 'ssv2'."       )
	__lowerCAmelCase       				=	json.load(open(hf_hub_download(lowerCamelCase					,		lowerCamelCase					,		repo_type="dataset"       )					,		"r"       )       )
	__lowerCAmelCase       				=	{int(lowerCamelCase       ): v for k, v in idalabel.items()}
	__lowerCAmelCase       				=	idalabel
	__lowerCAmelCase       				=	{v: k for k, v in idalabel.items()}

	return config



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




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

	return name



def 	__lowerCAmelCase       (				lowerCamelCase :    int					,		lowerCamelCase :    List[str]       ):






	'''simple docstring'''




	for key in orig_state_dict.copy().keys():
		__lowerCAmelCase       				=	orig_state_dict.pop(lowerCamelCase       )

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

		if "qkv" in key:
			__lowerCAmelCase       				=	key.split("."       )
			__lowerCAmelCase       				=	int(key_split[1]       )
			__lowerCAmelCase       				=	"timesformer.encoder.layer."
			if "temporal" in key:
				__lowerCAmelCase       				=	".temporal_attention.attention.qkv."
			else:
				__lowerCAmelCase       				=	".attention.attention.qkv."
			if "weight" in key:
				__lowerCAmelCase       				=	val
			else:
				__lowerCAmelCase       				=	val
		else:
			__lowerCAmelCase       				=	val

	return orig_state_dict



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	__lowerCAmelCase       				=	hf_hub_download(
	    repo_id="hf-internal-testing/spaghetti-video"					,		filename="eating_spaghetti.npy"					,		repo_type="dataset"       )
	__lowerCAmelCase       				=	np.load(lowerCamelCase       )
	return list(lowerCamelCase       )



def 	__lowerCAmelCase       (				lowerCamelCase :    Union[str, Any]					,		lowerCamelCase :    Dict					,		lowerCamelCase :    List[Any]					,		lowerCamelCase :    Optional[Any]       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	get_timesformer_config(lowerCamelCase       )

	__lowerCAmelCase       				=	TimesformerForVideoClassification(lowerCamelCase       )

	# download original checkpoint, hosted on Google Drive
	__lowerCAmelCase       				=	"pytorch_model.bin"
	gdown.cached_download(lowerCamelCase					,		lowerCamelCase					,		quiet=lowerCamelCase       )
	__lowerCAmelCase       				=	torch.load(lowerCamelCase					,		map_location="cpu"       )
	if "model" in files:
		__lowerCAmelCase       				=	files["model"]
	elif "module" in files:
		__lowerCAmelCase       				=	files["module"]
	else:
		__lowerCAmelCase       				=	files["model_state"]
	__lowerCAmelCase       				=	convert_state_dict(lowerCamelCase					,		lowerCamelCase       )

	model.load_state_dict(lowerCamelCase       )
	model.eval()

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

	__lowerCAmelCase       				=	model(**lowerCamelCase       )
	__lowerCAmelCase       				=	outputs.logits

	__lowerCAmelCase       				=	[
	    # 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":
		__lowerCAmelCase       				=	torch.Size([1, 4_00]       )
		__lowerCAmelCase       				=	torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]       )
	elif model_name == "timesformer-base-finetuned-k600":
		__lowerCAmelCase       				=	torch.Size([1, 6_00]       )
		__lowerCAmelCase       				=	torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]       )
	elif model_name == "timesformer-base-finetuned-ssv2":
		__lowerCAmelCase       				=	torch.Size([1, 1_74]       )
		__lowerCAmelCase       				=	torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]       )
	elif model_name == "timesformer-large-finetuned-k400":
		__lowerCAmelCase       				=	torch.Size([1, 4_00]       )
		__lowerCAmelCase       				=	torch.tensor([0, 0, 0]       )
	elif model_name == "timesformer-large-finetuned-k600":
		__lowerCAmelCase       				=	torch.Size([1, 6_00]       )
		__lowerCAmelCase       				=	torch.tensor([0, 0, 0]       )
	elif model_name == "timesformer-large-finetuned-ssv2":
		__lowerCAmelCase       				=	torch.Size([1, 1_74]       )
		__lowerCAmelCase       				=	torch.tensor([0, 0, 0]       )
	elif model_name == "timesformer-hr-finetuned-k400":
		__lowerCAmelCase       				=	torch.Size([1, 4_00]       )
		__lowerCAmelCase       				=	torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]       )
	elif model_name == "timesformer-hr-finetuned-k600":
		__lowerCAmelCase       				=	torch.Size([1, 6_00]       )
		__lowerCAmelCase       				=	torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]       )
	elif model_name == "timesformer-hr-finetuned-ssv2":
		__lowerCAmelCase       				=	torch.Size([1, 1_74]       )
		__lowerCAmelCase       				=	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]					,		lowerCamelCase					,		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(lowerCamelCase       )
		model.save_pretrained(lowerCamelCase       )

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


if __name__ == "__main__":
	lowerCAmelCase						:		Dict						      = 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						:		Optional[int]						      = parser.parse_args()
	convert_timesformer_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
	)