

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 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= TimesformerConfig()

			if "large" in model_name:
						SCREAMING_SNAKE_CASE__									= 96

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

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

			return config

def 		A    (       snake_case__      ):




			'''simple docstring'''


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

			return name

def 		A    (       snake_case__						,			snake_case__      ):




			'''simple docstring'''


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

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

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

			return orig_state_dict

def 		A    (       ):




			'''simple docstring'''


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

def 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= get_timesformer_config(snake_case__      )

			SCREAMING_SNAKE_CASE__									= TimesformerForVideoClassification(snake_case__      )

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

			model.load_state_dict(snake_case__      )
			model.eval()

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

			SCREAMING_SNAKE_CASE__									= model(**snake_case__      )
			SCREAMING_SNAKE_CASE__									= outputs.logits

			SCREAMING_SNAKE_CASE__									= [
			    # 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":
						SCREAMING_SNAKE_CASE__									= torch.Size([1, 4_00]      )
						SCREAMING_SNAKE_CASE__									= torch.tensor([-0.30_16, -0.77_13, -0.42_05]      )
			elif model_name == "timesformer-base-finetuned-k600":
						SCREAMING_SNAKE_CASE__									= torch.Size([1, 6_00]      )
						SCREAMING_SNAKE_CASE__									= torch.tensor([-0.72_67, -0.74_66, 3.24_04]      )
			elif model_name == "timesformer-base-finetuned-ssv2":
						SCREAMING_SNAKE_CASE__									= torch.Size([1, 1_74]      )
						SCREAMING_SNAKE_CASE__									= torch.tensor([-0.90_59, 0.64_33, -3.14_57]      )
			elif model_name == "timesformer-large-finetuned-k400":
						SCREAMING_SNAKE_CASE__									= torch.Size([1, 4_00]      )
						SCREAMING_SNAKE_CASE__									= torch.tensor([0, 0, 0]      )
			elif model_name == "timesformer-large-finetuned-k600":
						SCREAMING_SNAKE_CASE__									= torch.Size([1, 6_00]      )
						SCREAMING_SNAKE_CASE__									= torch.tensor([0, 0, 0]      )
			elif model_name == "timesformer-large-finetuned-ssv2":
						SCREAMING_SNAKE_CASE__									= torch.Size([1, 1_74]      )
						SCREAMING_SNAKE_CASE__									= torch.tensor([0, 0, 0]      )
			elif model_name == "timesformer-hr-finetuned-k400":
						SCREAMING_SNAKE_CASE__									= torch.Size([1, 4_00]      )
						SCREAMING_SNAKE_CASE__									= torch.tensor([-0.96_17, -3.73_11, -3.77_08]      )
			elif model_name == "timesformer-hr-finetuned-k600":
						SCREAMING_SNAKE_CASE__									= torch.Size([1, 6_00]      )
						SCREAMING_SNAKE_CASE__									= torch.tensor([2.52_73, 0.71_27, 1.88_48]      )
			elif model_name == "timesformer-hr-finetuned-ssv2":
						SCREAMING_SNAKE_CASE__									= torch.Size([1, 1_74]      )
						SCREAMING_SNAKE_CASE__									= torch.tensor([-3.67_56, -0.75_13, 0.71_80]      )
			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]						,			snake_case__						,			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(snake_case__      )
						model.save_pretrained(snake_case__      )

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


if __name__ == "__main__":
						A_							:     List[Any]     =	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."
						)

						A_							:     Optional[int]     =	parser.parse_args()
						convert_timesformer_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
						)





"""simple docstring"""




from .hash_table import HashTable





class 				lowerCamelCase  (A__     ):

			def __init__(   self			:  List[str]      ,			*__UpperCAmelCase			:  Union[str, Any]      ,			**__UpperCAmelCase			:  Any    )				->      Optional[int]:
						super().__init__(*__UpperCAmelCase      ,			**__UpperCAmelCase    )





			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=None    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= 1
						SCREAMING_SNAKE_CASE__									= self.hash_function(key + i * i    )

						while self.values[new_key] is not None and self.values[new_key] != key:
									i += 1
									SCREAMING_SNAKE_CASE__									= (
									    self.hash_function(key + i * i    )
									    if not self.balanced_factor() >= self.lim_charge
									    else None
									)

									if new_key is None:
												break

						return new_key


