
"""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 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		lowerCamelCase     :			List[str]		=    TimesformerConfig()

		if "large" in model_name:
				lowerCamelCase     :			Optional[Any]		=    96

		if "hr" in model_name:
				lowerCamelCase     :			str		=    16
				lowerCamelCase     :			List[Any]		=    448

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

		return config
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


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

		return name
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				lowerCamelCase     :			Optional[Any]		=    orig_state_dict.pop(a_			)

				if key.startswith('model.'			):
						lowerCamelCase     :			List[str]		=    key.replace('model.',						''			)

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

		return orig_state_dict
def 		UpperCAmelCase					(		):





		'''simple docstring'''


		lowerCamelCase     :			Union[str, Any]		=    hf_hub_download(
		    repo_id='hf-internal-testing/spaghetti-video',						filename='eating_spaghetti.npy',						repo_type='dataset'			)
		lowerCamelCase     :			Optional[int]		=    np.load(a_			)
		return list(a_			)
def 		UpperCAmelCase					(		a_,						a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			Dict		=    get_timesformer_config(a_			)

		lowerCamelCase     :			Dict		=    TimesformerForVideoClassification(a_			)

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

		model.load_state_dict(a_			)
		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     :			Union[str, Any]		=    prepare_video()
		lowerCamelCase     :			Optional[Any]		=    image_processor(video[:8],						return_tensors='pt'			)

		lowerCamelCase     :			Any		=    model(**a_			)
		lowerCamelCase     :			Tuple		=    outputs.logits

		lowerCamelCase     :			Tuple		=    [
		    # 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     :			Optional[Any]		=    torch.Size([1, 400]			)
				lowerCamelCase     :			int		=    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     :			Optional[Any]		=    torch.Size([1, 600]			)
				lowerCamelCase     :			Tuple		=    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     :			Tuple		=    torch.Size([1, 174]			)
				lowerCamelCase     :			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":
				lowerCamelCase     :			Any		=    torch.Size([1, 400]			)
				lowerCamelCase     :			Any		=    torch.tensor([0, 0, 0]			)
		elif model_name == "timesformer-large-finetuned-k600":
				lowerCamelCase     :			Dict		=    torch.Size([1, 600]			)
				lowerCamelCase     :			List[Any]		=    torch.tensor([0, 0, 0]			)
		elif model_name == "timesformer-large-finetuned-ssv2":
				lowerCamelCase     :			Dict		=    torch.Size([1, 174]			)
				lowerCamelCase     :			Dict		=    torch.tensor([0, 0, 0]			)
		elif model_name == "timesformer-hr-finetuned-k400":
				lowerCamelCase     :			Optional[Any]		=    torch.Size([1, 400]			)
				lowerCamelCase     :			Dict		=    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     :			Optional[int]		=    torch.Size([1, 600]			)
				lowerCamelCase     :			Optional[Any]		=    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     :			Tuple		=    torch.Size([1, 174]			)
				lowerCamelCase     :			Tuple		=    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],						a_,						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(a_			)
				model.save_pretrained(a_			)

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


if __name__ == "__main__":
					_A            = 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            = parser.parse_args()
					convert_timesformer_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
					)














"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						str:






     '''simple docstring'''



     UpperCAmelCase__		:  Tuple				=				word.upper()
     UpperCAmelCase__		:  Union[str, Any]				=				0
     UpperCAmelCase__		:  Optional[Any]				=				{}
     UpperCAmelCase__		:  List[Any]				=				[]

     for letter in word:
          if letter not in letter_nums:
               UpperCAmelCase__		:  Tuple				=				str(snake_case							)
               next_num += 1
          word_pattern.append(letter_nums[letter]							)
     return ".".join(snake_case							)


if __name__ == "__main__":
 import pprint
 import time

 _lowerCAmelCase     :     Optional[Any]            =	time.time()
 with open("""dictionary.txt""") as in_file:
  _lowerCAmelCase     :     Optional[int]            =	in_file.read().splitlines()

 _lowerCAmelCase     :     dict            =	{}
 for word in word_list:
  _lowerCAmelCase     :     int            =	get_word_pattern(word)
  if pattern in all_patterns:
   all_patterns[pattern].append(word)
  else:
   _lowerCAmelCase     :     Any            =	[word]

 with open("""word_patterns.txt""", """w""") as out_file:
  out_file.write(pprint.pformat(all_patterns))

 _lowerCAmelCase     :     Union[str, Any]            =	round(time.time() - start_time, 2)
 print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
 # Done!  9,581 word patterns found in 0.58 seconds.


