

"""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      (						UpperCAmelCase     :							Optional[Any]				):


					'''simple docstring'''






					UpperCamelCase__		:    Any         =TimesformerConfig()

					if "large" in model_name:
										UpperCamelCase__		:    Union[str, Any]         =96

					if "hr" in model_name:
										UpperCamelCase__		:    Union[str, Any]         =16
										UpperCamelCase__		:    Union[str, Any]         =448

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

					return config



def        _lowerCAmelCase      (						UpperCAmelCase     :							Tuple				):


					'''simple docstring'''






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

					return name



def        _lowerCAmelCase      (						UpperCAmelCase     :							Optional[int]      ,							UpperCAmelCase     :							Optional[int]				):


					'''simple docstring'''






					for key in orig_state_dict.copy().keys():
										UpperCamelCase__		:    Any         =orig_state_dict.pop(UpperCAmelCase				)

										if key.startswith('''model.'''				):
															UpperCamelCase__		:    Tuple         =key.replace('''model.'''      ,							''''''				)

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

					return orig_state_dict



def        _lowerCAmelCase      (						):


					'''simple docstring'''






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



def        _lowerCAmelCase      (						UpperCAmelCase     :							str      ,							UpperCAmelCase     :							Union[str, Any]      ,							UpperCAmelCase     :							Union[str, Any]      ,							UpperCAmelCase     :							Any				):


					'''simple docstring'''






					UpperCamelCase__		:    List[str]         =get_timesformer_config(UpperCAmelCase				)

					UpperCamelCase__		:    Dict         =TimesformerForVideoClassification(UpperCAmelCase				)

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

					model.load_state_dict(UpperCAmelCase				)
					model.eval()

					# verify model on basic input
					UpperCamelCase__		:    str         =VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]      ,							image_std=[0.5, 0.5, 0.5]				)
					UpperCamelCase__		:    Union[str, Any]         =prepare_video()
					UpperCamelCase__		:    Optional[Any]         =image_processor(video[:8]      ,							return_tensors='''pt'''				)

					UpperCamelCase__		:    List[Any]         =model(**UpperCAmelCase				)
					UpperCamelCase__		:    Optional[Any]         =outputs.logits

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

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





"""simple docstring"""





import io
import json
import subprocess


_SCREAMING_SNAKE_CASE       :  List[Any]        = [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

_SCREAMING_SNAKE_CASE       :  str        = 8



def        _lowerCAmelCase      (						UpperCAmelCase     :							Tuple      ,							UpperCAmelCase     :							List[Any]				):


					'''simple docstring'''






					UpperCamelCase__		:    Any         ={}
					for src, tgt in pairs:
										UpperCamelCase__		:    Optional[int]         =F'''{src}-{tgt}'''
										UpperCamelCase__		:    int         =F'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
										UpperCamelCase__		:    str         =subprocess.run(UpperCAmelCase      ,							stdout=subprocess.PIPE				).stdout.decode('''utf-8'''				).splitlines()
										UpperCamelCase__		:    List[Any]         =F'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
										UpperCamelCase__		:    str         =subprocess.run(UpperCAmelCase      ,							stdout=subprocess.PIPE				).stdout.decode('''utf-8'''				).splitlines()
										UpperCamelCase__		:    Any         ={'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
					return text


_SCREAMING_SNAKE_CASE       :  List[str]        = get_all_data(pairs, n_objs)
_SCREAMING_SNAKE_CASE       :  Optional[Any]        = """./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
				_SCREAMING_SNAKE_CASE       :  Any        = json.dump(text, f, indent=2, ensure_ascii=False)


