
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__     (    SCREAMING_SNAKE_CASE_	)							->						Optional[Any]:
			lowerCAmelCase__ : List[str]   =						TimesformerConfig()

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

			if "hr" in model_name:
						lowerCAmelCase__ : Any   =						16
						lowerCAmelCase__ : Optional[int]   =						448

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

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

			return name
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Optional[Any]:
			for key in orig_state_dict.copy().keys():
						lowerCAmelCase__ : str   =						orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

						if key.startswith('model.'	):
									lowerCAmelCase__ : Any   =						key.replace('model.'			,				''	)

						if "qkv" in key:
									lowerCAmelCase__ : Optional[Any]   =						key.split('.'	)
									lowerCAmelCase__ : str   =						int(key_split[1]	)
									lowerCAmelCase__ : int   =						'timesformer.encoder.layer.'
									if "temporal" in key:
												lowerCAmelCase__ : Optional[Any]   =						'.temporal_attention.attention.qkv.'
									else:
												lowerCAmelCase__ : int   =						'.attention.attention.qkv.'
									if "weight" in key:
												lowerCAmelCase__ : List[Any]   =						val
									else:
												lowerCAmelCase__ : List[str]   =						val
						else:
									lowerCAmelCase__ : Any   =						val

			return orig_state_dict
def 					lowerCAmelCase__     (    )							->						str:
			lowerCAmelCase__ : Tuple   =						hf_hub_download(
			    repo_id='hf-internal-testing/spaghetti-video'			,				filename='eating_spaghetti.npy'			,				repo_type='dataset'	)
			lowerCAmelCase__ : List[Any]   =						np.load(SCREAMING_SNAKE_CASE_	)
			return list(SCREAMING_SNAKE_CASE_	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						List[Any]:
			lowerCAmelCase__ : Union[str, Any]   =						get_timesformer_config(SCREAMING_SNAKE_CASE_	)

			lowerCAmelCase__ : Optional[Any]   =						TimesformerForVideoClassification(SCREAMING_SNAKE_CASE_	)

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

			model.load_state_dict(SCREAMING_SNAKE_CASE_	)
			model.eval()

			# verify model on basic input
			lowerCAmelCase__ : Any   =						VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,				image_std=[0.5, 0.5, 0.5]	)
			lowerCAmelCase__ : Dict   =						prepare_video()
			lowerCAmelCase__ : List[Any]   =						image_processor(video[:8]			,				return_tensors='pt'	)

			lowerCAmelCase__ : int   =						model(**SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Dict   =						outputs.logits

			lowerCAmelCase__ : int   =						[
			    # 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__ : Any   =						torch.Size([1, 400]	)
						lowerCAmelCase__ : List[Any]   =						torch.tensor([-0.3016, -0.7713, -0.4205]	)
			elif model_name == "timesformer-base-finetuned-k600":
						lowerCAmelCase__ : List[Any]   =						torch.Size([1, 600]	)
						lowerCAmelCase__ : List[str]   =						torch.tensor([-0.7267, -0.7466, 3.2404]	)
			elif model_name == "timesformer-base-finetuned-ssv2":
						lowerCAmelCase__ : Dict   =						torch.Size([1, 174]	)
						lowerCAmelCase__ : int   =						torch.tensor([-0.9059, 0.6433, -3.1457]	)
			elif model_name == "timesformer-large-finetuned-k400":
						lowerCAmelCase__ : Union[str, Any]   =						torch.Size([1, 400]	)
						lowerCAmelCase__ : int   =						torch.tensor([0, 0, 0]	)
			elif model_name == "timesformer-large-finetuned-k600":
						lowerCAmelCase__ : List[Any]   =						torch.Size([1, 600]	)
						lowerCAmelCase__ : Any   =						torch.tensor([0, 0, 0]	)
			elif model_name == "timesformer-large-finetuned-ssv2":
						lowerCAmelCase__ : List[str]   =						torch.Size([1, 174]	)
						lowerCAmelCase__ : str   =						torch.tensor([0, 0, 0]	)
			elif model_name == "timesformer-hr-finetuned-k400":
						lowerCAmelCase__ : str   =						torch.Size([1, 400]	)
						lowerCAmelCase__ : List[Any]   =						torch.tensor([-0.9617, -3.7311, -3.7708]	)
			elif model_name == "timesformer-hr-finetuned-k600":
						lowerCAmelCase__ : Tuple   =						torch.Size([1, 600]	)
						lowerCAmelCase__ : Optional[int]   =						torch.tensor([2.5273, 0.7127, 1.8848]	)
			elif model_name == "timesformer-hr-finetuned-ssv2":
						lowerCAmelCase__ : List[Any]   =						torch.Size([1, 174]	)
						lowerCAmelCase__ : Dict   =						torch.tensor([-3.6756, -0.7513, 0.7180]	)
			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]			,				SCREAMING_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(SCREAMING_SNAKE_CASE_	)
						model.save_pretrained(SCREAMING_SNAKE_CASE_	)

			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
					)

import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						List[str]:
			# Construct configuration depending on size
			lowerCAmelCase__ : Tuple   =						{'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ : Optional[int]   =						MODELS[model_size]  # set model hyperparameters
			lowerCAmelCase__ : Optional[int]   =						ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE_			,				n_layer=SCREAMING_SNAKE_CASE_			,				n_head=SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : List[Any]   =						ImageGPTForCausalLM(SCREAMING_SNAKE_CASE_	)

			# Load weights from numpy
			load_tf_weights_in_imagegpt(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

			# Save pytorch-model
			lowerCAmelCase__ : Optional[int]   =						pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			lowerCAmelCase__ : Optional[int]   =						pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
			torch.save(model.state_dict()			,				SCREAMING_SNAKE_CASE_	)
			print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
			with open(SCREAMING_SNAKE_CASE_			,				'w'			,				encoding='utf-8'	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
					lowerCamelCase__        =						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--imagegpt_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the TensorFlow checkpoint path.""",
					)
					parser.add_argument(
					    """--model_size""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Size of the model (can be either 'small', 'medium' or 'large').""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					lowerCamelCase__        =						parser.parse_args()
					convert_imagegpt_checkpoint_to_pytorch(
					    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
					)