
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__							(    __magic_name__  :       Tuple					):


					'''simple docstring'''

					lowerCAmelCase						:   Dict							=       TimesformerConfig()

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

					if "hr" in model_name:
										lowerCAmelCase						:   Union[str, Any]							=       16
										lowerCAmelCase						:   Dict							=       4_48

					lowerCAmelCase						:   Optional[int]							=       '''huggingface/label-files'''
					if "k400" in model_name:
										lowerCAmelCase						:   Optional[int]							=       4_00
										lowerCAmelCase						:   List[str]							=       '''kinetics400-id2label.json'''
					elif "k600" in model_name:
										lowerCAmelCase						:   Optional[Any]							=       6_00
										lowerCAmelCase						:   Union[str, Any]							=       '''kinetics600-id2label.json'''
					elif "ssv2" in model_name:
										lowerCAmelCase						:   Optional[int]							=       1_74
										lowerCAmelCase						:   List[Any]							=       '''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(__magic_name__							,					__magic_name__							,					repo_type='''dataset'''					)							,					'''r'''					)					)
					lowerCAmelCase						:   str							=       {int(__magic_name__					): v for k, v in idalabel.items()}
					lowerCAmelCase						:   List[Any]							=       idalabel
					lowerCAmelCase						:   str							=       {v: k for k, v in idalabel.items()}

					return config

def       UpperCAmelCase__							(    __magic_name__  :       Tuple					):


					'''simple docstring'''

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

					return name

def       UpperCAmelCase__							(    __magic_name__  :       Optional[Any]							,					__magic_name__  :       Dict					):


					'''simple docstring'''

					for key in orig_state_dict.copy().keys():
										lowerCAmelCase						:   Union[str, Any]							=       orig_state_dict.pop(__magic_name__					)

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

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

					return orig_state_dict

def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   List[Any]							=       hf_hub_download(
					    repo_id='''hf-internal-testing/spaghetti-video'''							,					filename='''eating_spaghetti.npy'''							,					repo_type='''dataset'''					)
					lowerCAmelCase						:   Dict							=       np.load(__magic_name__					)
					return list(__magic_name__					)

def       UpperCAmelCase__							(    __magic_name__  :       Dict							,					__magic_name__  :       Tuple							,					__magic_name__  :       Union[str, Any]							,					__magic_name__  :       Tuple					):


					'''simple docstring'''

					lowerCAmelCase						:   Tuple							=       get_timesformer_config(__magic_name__					)

					lowerCAmelCase						:   Any							=       TimesformerForVideoClassification(__magic_name__					)

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

					model.load_state_dict(__magic_name__					)
					model.eval()

					# verify model on basic input
					lowerCAmelCase						:   Tuple							=       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]							,					image_std=[0.5, 0.5, 0.5]					)
					lowerCAmelCase						:   List[str]							=       prepare_video()
					lowerCAmelCase						:   int							=       image_processor(video[:8]							,					return_tensors='''pt'''					)

					lowerCAmelCase						:   Optional[int]							=       model(**__magic_name__					)
					lowerCAmelCase						:   Tuple							=       outputs.logits

					lowerCAmelCase						:   Optional[Any]							=       [
					    # 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						:   Dict							=       torch.Size([1, 4_00]					)
										lowerCAmelCase						:   Optional[Any]							=       torch.tensor([-0.3016, -0.7713, -0.4205]					)
					elif model_name == "timesformer-base-finetuned-k600":
										lowerCAmelCase						:   Optional[int]							=       torch.Size([1, 6_00]					)
										lowerCAmelCase						:   Optional[Any]							=       torch.tensor([-0.7267, -0.7466, 3.2404]					)
					elif model_name == "timesformer-base-finetuned-ssv2":
										lowerCAmelCase						:   Optional[Any]							=       torch.Size([1, 1_74]					)
										lowerCAmelCase						:   Tuple							=       torch.tensor([-0.9059, 0.6433, -3.1457]					)
					elif model_name == "timesformer-large-finetuned-k400":
										lowerCAmelCase						:   List[str]							=       torch.Size([1, 4_00]					)
										lowerCAmelCase						:   Union[str, Any]							=       torch.tensor([0, 0, 0]					)
					elif model_name == "timesformer-large-finetuned-k600":
										lowerCAmelCase						:   Optional[Any]							=       torch.Size([1, 6_00]					)
										lowerCAmelCase						:   Dict							=       torch.tensor([0, 0, 0]					)
					elif model_name == "timesformer-large-finetuned-ssv2":
										lowerCAmelCase						:   List[str]							=       torch.Size([1, 1_74]					)
										lowerCAmelCase						:   str							=       torch.tensor([0, 0, 0]					)
					elif model_name == "timesformer-hr-finetuned-k400":
										lowerCAmelCase						:   Dict							=       torch.Size([1, 4_00]					)
										lowerCAmelCase						:   Optional[int]							=       torch.tensor([-0.9617, -3.7311, -3.7708]					)
					elif model_name == "timesformer-hr-finetuned-k600":
										lowerCAmelCase						:   Tuple							=       torch.Size([1, 6_00]					)
										lowerCAmelCase						:   Optional[int]							=       torch.tensor([2.5273, 0.7127, 1.8848]					)
					elif model_name == "timesformer-hr-finetuned-ssv2":
										lowerCAmelCase						:   str							=       torch.Size([1, 1_74]					)
										lowerCAmelCase						:   Any							=       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]							,					__magic_name__							,					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(__magic_name__					)
										model.save_pretrained(__magic_name__					)

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


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				List[str]             =       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.'
		)

		__SCREAMING_SNAKE_CASE     :				int             =       parser.parse_args()
		convert_timesformer_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
		)


from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def       UpperCAmelCase__							(    __magic_name__  :       str=10					):


					'''simple docstring'''

					for _ in tqdm(range(0							,					args.n_examples							,					__magic_name__					)					):
										yield [next(__magic_name__					)[args.text_column] for _ in range(__magic_name__					)]


# Configuration
__SCREAMING_SNAKE_CASE     :				Dict             =       HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE     :				str             =       parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE     :				Tuple             =       AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE     :				List[str]             =       list(bytes_to_unicode().values())

# Load dataset
__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       load_dataset(args.dataset_name, split='train', streaming=True)
__SCREAMING_SNAKE_CASE     :				Any             =       iter(dataset)


# Training and saving
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
