

import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[Any]  ,      lowerCAmelCase:  int )   ->       List[Any]:
				_UpperCAmelCase       :		Dict 		=    XCLIPTextConfig()

				# derive patch size from model name
				_UpperCAmelCase       :		str 		=    model_name.find("patch" )
				_UpperCAmelCase       :		Any 		=    int(model_name[start_idx + len("patch" ) : start_idx + len("patch" ) + 2] )
				_UpperCAmelCase       :		Union[str, Any] 		=    XCLIPVisionConfig(patch_size=_SCREAMING_SNAKE_CASE  ,      num_frames=_SCREAMING_SNAKE_CASE )

				if "large" in model_name:
								_UpperCAmelCase       :		Dict 		=    768
								_UpperCAmelCase       :		Optional[int] 		=    3072
								_UpperCAmelCase       :		str 		=    12

								_UpperCAmelCase       :		str 		=    1024
								_UpperCAmelCase       :		List[str] 		=    4096
								_UpperCAmelCase       :		Optional[Any] 		=    16
								_UpperCAmelCase       :		Optional[Any] 		=    24
								_UpperCAmelCase       :		int 		=    768
								_UpperCAmelCase       :		Union[str, Any] 		=    3072

				if model_name == "xclip-large-patch14-16-frames":
								_UpperCAmelCase       :		List[Any] 		=    336

				_UpperCAmelCase       :		Any 		=    XCLIPConfig.from_text_vision_configs(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE )

				if "large" in model_name:
								_UpperCAmelCase       :		Dict 		=    768

				return config

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any] )   ->       Union[str, Any]:
				# text encoder
				if name == "token_embedding.weight":
								_UpperCAmelCase       :		int 		=    name.replace("token_embedding.weight"  ,      "text_model.embeddings.token_embedding.weight" )
				if name == "positional_embedding":
								_UpperCAmelCase       :		Union[str, Any] 		=    name.replace("positional_embedding"  ,      "text_model.embeddings.position_embedding.weight" )
				if "ln_1" in name:
								_UpperCAmelCase       :		Any 		=    name.replace("ln_1"  ,      "layer_norm1" )
				if "ln_2" in name:
								_UpperCAmelCase       :		int 		=    name.replace("ln_2"  ,      "layer_norm2" )
				if "c_fc" in name:
								_UpperCAmelCase       :		Any 		=    name.replace("c_fc"  ,      "fc1" )
				if "c_proj" in name:
								_UpperCAmelCase       :		List[Any] 		=    name.replace("c_proj"  ,      "fc2" )
				if name.startswith("transformer.resblocks" ):
								_UpperCAmelCase       :		Any 		=    name.replace("transformer.resblocks"  ,      "text_model.encoder.layers" )
				if "attn.out_proj" in name and "message" not in name:
								_UpperCAmelCase       :		int 		=    name.replace("attn.out_proj"  ,      "self_attn.out_proj" )
				if "ln_final" in name:
								_UpperCAmelCase       :		int 		=    name.replace("ln_final"  ,      "text_model.final_layer_norm" )
				# visual encoder
				if name == "visual.class_embedding":
								_UpperCAmelCase       :		int 		=    name.replace("visual.class_embedding"  ,      "vision_model.embeddings.class_embedding" )
				if name == "visual.positional_embedding":
								_UpperCAmelCase       :		List[str] 		=    name.replace("visual.positional_embedding"  ,      "vision_model.embeddings.position_embedding.weight" )
				if name.startswith("visual.transformer.resblocks" ):
								_UpperCAmelCase       :		List[Any] 		=    name.replace("visual.transformer.resblocks"  ,      "vision_model.encoder.layers" )
				if "visual.conv1" in name:
								_UpperCAmelCase       :		Optional[Any] 		=    name.replace("visual.conv1"  ,      "vision_model.embeddings.patch_embedding" )
				if "visual.ln_pre" in name:
								_UpperCAmelCase       :		List[str] 		=    name.replace("visual.ln_pre"  ,      "vision_model.pre_layernorm" )
				if "visual.ln_post" in name:
								_UpperCAmelCase       :		str 		=    name.replace("visual.ln_post"  ,      "vision_model.post_layernorm" )
				if "visual.proj" in name:
								_UpperCAmelCase       :		Tuple 		=    name.replace("visual.proj"  ,      "visual_projection.weight" )
				if "text_projection" in name:
								_UpperCAmelCase       :		Dict 		=    name.replace("text_projection"  ,      "text_projection.weight" )
				# things on top
				if "prompts_visual_proj" in name:
								_UpperCAmelCase       :		Optional[Any] 		=    name.replace("prompts_visual_proj"  ,      "prompts_visual_projection" )
				if "prompts_visual_ln" in name:
								_UpperCAmelCase       :		Any 		=    name.replace("prompts_visual_ln"  ,      "prompts_visual_layernorm" )
				# mit
				if name == "mit.positional_embedding":
								_UpperCAmelCase       :		int 		=    name.replace("positional"  ,      "position" )
				if name.startswith("mit.resblocks" ):
								_UpperCAmelCase       :		str 		=    name.replace("mit.resblocks"  ,      "mit.encoder.layers" )
				# prompts generator
				if name.startswith("prompts_generator.norm" ):
								_UpperCAmelCase       :		Dict 		=    name.replace("prompts_generator.norm"  ,      "prompts_generator.layernorm" )

				return name

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str] )   ->       List[str]:
				for key in orig_state_dict.copy().keys():
								_UpperCAmelCase       :		Union[str, Any] 		=    orig_state_dict.pop(_SCREAMING_SNAKE_CASE )

								if "attn.in_proj" in key:
												_UpperCAmelCase       :		int 		=    key.split("." )
												if key.startswith("visual" ):
																_UpperCAmelCase       :		Optional[Any] 		=    key_split[3]
																_UpperCAmelCase       :		Any 		=    config.vision_config.hidden_size
																if "message_attn" in key:
																				if "weight" in key:
																								_UpperCAmelCase       :		List[str] 		=    val[
																								    :dim, :
																								]
																								_UpperCAmelCase       :		Tuple 		=    val[
																								    dim : dim * 2, :
																								]
																								_UpperCAmelCase       :		Optional[Any] 		=    val[
																								    -dim:, :
																								]
																				else:
																								_UpperCAmelCase       :		List[str] 		=    val[
																								    :dim
																								]
																								_UpperCAmelCase       :		str 		=    val[
																								    dim : dim * 2
																								]
																								_UpperCAmelCase       :		int 		=    val[
																								    -dim:
																								]
																else:
																				if "weight" in key:
																								_UpperCAmelCase       :		int 		=    val[
																								    :dim, :
																								]
																								_UpperCAmelCase       :		Any 		=    val[
																								    dim : dim * 2, :
																								]
																								_UpperCAmelCase       :		Union[str, Any] 		=    val[
																								    -dim:, :
																								]
																				else:
																								_UpperCAmelCase       :		Optional[int] 		=    val[:dim]
																								_UpperCAmelCase       :		str 		=    val[
																								    dim : dim * 2
																								]
																								_UpperCAmelCase       :		int 		=    val[-dim:]
												elif key.startswith("mit" ):
																_UpperCAmelCase       :		int 		=    key_split[2]
																_UpperCAmelCase       :		str 		=    config.vision_config.mit_hidden_size
																if "weight" in key:
																				_UpperCAmelCase       :		str 		=    val[:dim, :]
																				_UpperCAmelCase       :		Any 		=    val[dim : dim * 2, :]
																				_UpperCAmelCase       :		Union[str, Any] 		=    val[-dim:, :]
																else:
																				_UpperCAmelCase       :		Union[str, Any] 		=    val[:dim]
																				_UpperCAmelCase       :		Any 		=    val[dim : dim * 2]
																				_UpperCAmelCase       :		Optional[Any] 		=    val[-dim:]
												else:
																_UpperCAmelCase       :		Tuple 		=    key_split[2]
																_UpperCAmelCase       :		str 		=    config.text_config.hidden_size
																if "weight" in key:
																				_UpperCAmelCase       :		str 		=    val[:dim, :]
																				_UpperCAmelCase       :		Any 		=    val[
																				    dim : dim * 2, :
																				]
																				_UpperCAmelCase       :		List[str] 		=    val[-dim:, :]
																else:
																				_UpperCAmelCase       :		Union[str, Any] 		=    val[:dim]
																				_UpperCAmelCase       :		Optional[Any] 		=    val[
																				    dim : dim * 2
																				]
																				_UpperCAmelCase       :		int 		=    val[-dim:]
								else:
												_UpperCAmelCase       :		Optional[int] 		=    rename_key(_SCREAMING_SNAKE_CASE )
												if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																_UpperCAmelCase       :		List[Any] 		=    val.T
												_UpperCAmelCase       :		Optional[Any] 		=    val

				return orig_state_dict

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[Any] )   ->       str:
				if num_frames == 8:
								_UpperCAmelCase       :		Dict 		=    "eating_spaghetti_8_frames.npy"
				elif num_frames == 16:
								_UpperCAmelCase       :		List[Any] 		=    "eating_spaghetti.npy"
				elif num_frames == 32:
								_UpperCAmelCase       :		int 		=    "eating_spaghetti_32_frames.npy"
				_UpperCAmelCase       :		Union[str, Any] 		=    hf_hub_download(
				    repo_id="hf-internal-testing/spaghetti-video"  ,      filename=_SCREAMING_SNAKE_CASE  ,      repo_type="dataset"  ,      )
				_UpperCAmelCase       :		Dict 		=    np.load(_SCREAMING_SNAKE_CASE )
				return list(_SCREAMING_SNAKE_CASE )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str]=None  ,      lowerCAmelCase:  Any=False )   ->       List[str]:
				_UpperCAmelCase       :		int 		=    {
				    # fully supervised kinetics-400 checkpoints
				    "xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
				    "xclip-base-patch32-16-frames": (
				        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
				    ),
				    "xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
				    "xclip-base-patch16-16-frames": (
				        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
				    ),
				    "xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
				    "xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f",
				    # fully supervised kinetics-600 checkpoints
				    "xclip-base-patch16-kinetics-600": (
				        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
				    ),
				    "xclip-base-patch16-kinetics-600-16-frames": (
				        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
				    ),
				    "xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
				    # few shot
				    "xclip-base-patch16-hmdb-2-shot": (
				        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
				    ),
				    "xclip-base-patch16-hmdb-4-shot": (
				        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
				    ),
				    "xclip-base-patch16-hmdb-8-shot": (
				        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
				    ),
				    "xclip-base-patch16-hmdb-16-shot": (
				        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
				    ),
				    "xclip-base-patch16-ucf-2-shot": (
				        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
				    ),
				    "xclip-base-patch16-ucf-4-shot": (
				        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
				    ),
				    "xclip-base-patch16-ucf-8-shot": (
				        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
				    ),
				    "xclip-base-patch16-ucf-16-shot": (
				        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
				    ),
				    # zero shot
				    "xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
				}

				_UpperCAmelCase       :		str 		=    model_to_url[model_name]
				_UpperCAmelCase       :		int 		=    8
				if "16-frames" in model_name:
								_UpperCAmelCase       :		Optional[Any] 		=    16
				elif "shot" in model_name:
								_UpperCAmelCase       :		Optional[Any] 		=    32

				_UpperCAmelCase       :		Dict 		=    get_xclip_config(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE )
				_UpperCAmelCase       :		str 		=    XCLIPModel(_SCREAMING_SNAKE_CASE )
				model.eval()

				if "drive" in checkpoint_url:
								_UpperCAmelCase       :		Optional[Any] 		=    "pytorch_model.bin"
								gdown.cached_download(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE  ,      quiet=_SCREAMING_SNAKE_CASE )
								_UpperCAmelCase       :		int 		=    torch.load(_SCREAMING_SNAKE_CASE  ,      map_location="cpu" )["model"]
				else:
								_UpperCAmelCase       :		Dict 		=    torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE )["model"]

				_UpperCAmelCase       :		Any 		=    convert_state_dict(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE )

				_UpperCAmelCase       :		str 		=    XCLIPModel(_SCREAMING_SNAKE_CASE )
				_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    model.load_state_dict(_SCREAMING_SNAKE_CASE  ,      strict=_SCREAMING_SNAKE_CASE )
				assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
				model.eval()

				_UpperCAmelCase       :		int 		=    336 if model_name == "xclip-large-patch14-16-frames" else 224
				_UpperCAmelCase       :		Any 		=    VideoMAEImageProcessor(size=_SCREAMING_SNAKE_CASE )
				_UpperCAmelCase       :		Optional[Any] 		=    CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32" )
				_UpperCAmelCase       :		int 		=    CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32" )
				_UpperCAmelCase       :		int 		=    XCLIPProcessor(image_processor=_SCREAMING_SNAKE_CASE  ,      tokenizer=_SCREAMING_SNAKE_CASE )

				_UpperCAmelCase       :		List[Any] 		=    prepare_video(_SCREAMING_SNAKE_CASE )
				_UpperCAmelCase       :		str 		=    processor(
				    text=["playing sports", "eating spaghetti", "go shopping"]  ,      videos=_SCREAMING_SNAKE_CASE  ,      return_tensors="pt"  ,      padding=_SCREAMING_SNAKE_CASE )

				print("Shape of pixel values:"  ,      inputs.pixel_values.shape )

				with torch.no_grad():
								_UpperCAmelCase       :		str 		=    model(**_SCREAMING_SNAKE_CASE )

				# Verify outputs
				_UpperCAmelCase       :		List[str] 		=    outputs.logits_per_video
				_UpperCAmelCase       :		str 		=    logits_per_video.softmax(dim=1 )
				print("Probs:"  ,      _SCREAMING_SNAKE_CASE )
				# kinetics-400
				if model_name == "xclip-base-patch32":
								_UpperCAmelCase       :		str 		=    torch.tensor([[0.0_019, 0.9_951, 0.0_030]] )
				elif model_name == "xclip-base-patch32-16-frames":
								_UpperCAmelCase       :		List[Any] 		=    torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]] )
				elif model_name == "xclip-base-patch16":
								_UpperCAmelCase       :		Dict 		=    torch.tensor([[0.0_083, 0.9_681, 0.0_236]] )
				elif model_name == "xclip-base-patch16-16-frames":
								_UpperCAmelCase       :		List[str] 		=    torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]] )
				elif model_name == "xclip-large-patch14":
								_UpperCAmelCase       :		Tuple 		=    torch.tensor([[0.0_062, 0.9_864, 0.0_075]] )
				elif model_name == "xclip-large-patch14-16-frames":
								_UpperCAmelCase       :		int 		=    torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]] )
				# kinetics-600
				elif model_name == "xclip-base-patch16-kinetics-600":
								_UpperCAmelCase       :		Optional[int] 		=    torch.tensor([[0.0_555, 0.8_914, 0.0_531]] )
				elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
								_UpperCAmelCase       :		Dict 		=    torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]] )
				elif model_name == "xclip-large-patch14-kinetics-600":
								_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor([[0.0_036, 0.9_920, 0.0_045]] )
				# few shot
				elif model_name == "xclip-base-patch16-hmdb-2-shot":
								_UpperCAmelCase       :		Optional[Any] 		=    torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]] )
				elif model_name == "xclip-base-patch16-hmdb-4-shot":
								_UpperCAmelCase       :		str 		=    torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]] )
				elif model_name == "xclip-base-patch16-hmdb-8-shot":
								_UpperCAmelCase       :		Any 		=    torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]] )
				elif model_name == "xclip-base-patch16-hmdb-16-shot":
								_UpperCAmelCase       :		int 		=    torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]] )
				elif model_name == "xclip-base-patch16-ucf-2-shot":
								_UpperCAmelCase       :		Dict 		=    torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]] )
				elif model_name == "xclip-base-patch16-ucf-4-shot":
								_UpperCAmelCase       :		Tuple 		=    torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]] )
				elif model_name == "xclip-base-patch16-ucf-8-shot":
								_UpperCAmelCase       :		Dict 		=    torch.tensor([[0.0_027, 0.9_904, 0.0_070]] )
				elif model_name == "xclip-base-patch16-ucf-16-shot":
								_UpperCAmelCase       :		Dict 		=    torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]] )
				# zero shot
				elif model_name == "xclip-base-patch16-zero-shot":
								_UpperCAmelCase       :		Optional[Any] 		=    torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]] )
				else:
								raise ValueError(F'Model name {model_name} not supported' )
				assert torch.allclose(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE  ,      atol=1E-3 )
				print("Looks ok!" )

				if pytorch_dump_folder_path is not None:
								print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
								model.save_pretrained(_SCREAMING_SNAKE_CASE )

				if push_to_hub:
								print("Pushing model, processor and slow tokenizer files to the hub..." )
								model.push_to_hub(_SCREAMING_SNAKE_CASE  ,      organization="nielsr" )
								processor.push_to_hub(_SCREAMING_SNAKE_CASE  ,      organization="nielsr" )
								slow_tokenizer.push_to_hub(_SCREAMING_SNAKE_CASE  ,      organization="nielsr" )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_        =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='xclip-base-patch32',
					    type=str,
					    help='Name of the model.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
					)

					SCREAMING_SNAKE_CASE_        =      parser.parse_args()
					convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										warnings.warn(
										    "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use DeformableDetrImageProcessor instead."						,					A_						,					)
										super().__init__(*A_						,					**A_	)


