





"""simple docstring"""





from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE__					=   "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

SCREAMING_SNAKE_CASE__					=   "\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

SCREAMING_SNAKE_CASE__					=   "\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Union[str, Any]							,						SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						bool							,						SCREAMING_SNAKE_CASE     :						Optional[Dict[int, int]] = None							,						SCREAMING_SNAKE_CASE     :						bool = False							,						):







      '''simple docstring'''




      if label_map is not None:
            for old_id, new_id in label_map.items():
                  lowerCAmelCase  						=       new_id

    # turn into Numpy arrays
      lowerCAmelCase  						=       np.array(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       np.array(SCREAMING_SNAKE_CASE			)

      if reduce_labels:
            lowerCAmelCase  						=       2_55
            lowerCAmelCase  						=       label - 1
            lowerCAmelCase  						=       2_55

      lowerCAmelCase  						=       label != ignore_index
      lowerCAmelCase  						=       np.not_equal(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       pred_label[mask]
      lowerCAmelCase  						=       np.array(SCREAMING_SNAKE_CASE			)[mask]

      lowerCAmelCase  						=       pred_label[pred_label == label]

      lowerCAmelCase  						=       np.histogram(SCREAMING_SNAKE_CASE							,						bins=SCREAMING_SNAKE_CASE							,						range=(0, num_labels - 1)			)[0]
      lowerCAmelCase  						=       np.histogram(SCREAMING_SNAKE_CASE							,						bins=SCREAMING_SNAKE_CASE							,						range=(0, num_labels - 1)			)[0]
      lowerCAmelCase  						=       np.histogram(SCREAMING_SNAKE_CASE							,						bins=SCREAMING_SNAKE_CASE							,						range=(0, num_labels - 1)			)[0]

      lowerCAmelCase  						=       area_pred_label + area_label - area_intersect

      return area_intersect, area_union, area_pred_label, area_label


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						List[str]							,						SCREAMING_SNAKE_CASE     :						bool							,						SCREAMING_SNAKE_CASE     :						Optional[Dict[int, int]] = None							,						SCREAMING_SNAKE_CASE     :						bool = False							,						):







      '''simple docstring'''




      lowerCAmelCase  						=       np.zeros((num_labels,)							,						dtype=np.floataa			)
      lowerCAmelCase  						=       np.zeros((num_labels,)							,						dtype=np.floataa			)
      lowerCAmelCase  						=       np.zeros((num_labels,)							,						dtype=np.floataa			)
      lowerCAmelCase  						=       np.zeros((num_labels,)							,						dtype=np.floataa			)
      for result, gt_seg_map in zip(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			):
            lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       intersect_and_union(
                SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
            total_area_intersect += area_intersect
            total_area_union += area_union
            total_area_pred_label += area_pred_label
            total_area_label += area_label
      return total_area_intersect, total_area_union, total_area_pred_label, total_area_label


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						bool							,						SCREAMING_SNAKE_CASE     :						Optional[int] = None							,						SCREAMING_SNAKE_CASE     :						Optional[Dict[int, int]] = None							,						SCREAMING_SNAKE_CASE     :						bool = False							,						):







      '''simple docstring'''




      lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       total_intersect_and_union(
          SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      # compute metrics
      lowerCAmelCase  						=       {}

      lowerCAmelCase  						=       total_area_intersect.sum() / total_area_label.sum()
      lowerCAmelCase  						=       total_area_intersect / total_area_union
      lowerCAmelCase  						=       total_area_intersect / total_area_label

      lowerCAmelCase  						=       np.nanmean(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       np.nanmean(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       all_acc
      lowerCAmelCase  						=       iou
      lowerCAmelCase  						=       acc

      if nan_to_num is not None:
            lowerCAmelCase  						=       {metric: np.nan_to_num(SCREAMING_SNAKE_CASE							,						nan=SCREAMING_SNAKE_CASE			) for metric, metric_value in metrics.items()}

      return metrics






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,  _KWARGS_DESCRIPTION					)
class 		lowercase					(					datasets.Metric					):





      def     _snake_case			(       self )		->     Tuple:
            return datasets.MetricInfo(
                description=_DESCRIPTION      ,						citation=_CITATION      ,						inputs_description=_KWARGS_DESCRIPTION      ,						features=datasets.Features(
                    # 1st Seq - height dim, 2nd - width dim
                    {
                        """predictions""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16""" ) ) ),
                        """references""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16""" ) ) ),
                    } )      ,						reference_urls=[
                    """https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"""
                ]      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						lowercase = None      ,						lowercase = False      ,						)		->     Tuple:
            lowerCAmelCase  						=       mean_iou(
                results=lowercase      ,						gt_seg_maps=lowercase      ,						num_labels=lowercase      ,						ignore_index=lowercase      ,						nan_to_num=lowercase      ,						label_map=lowercase      ,						reduce_labels=lowercase      ,						)
            return iou_result











"""simple docstring"""
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    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :int						=			XCLIPTextConfig()

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

						if "large" in model_name:
												snake_case_ :Optional[Any]						=			768
												snake_case_ :Union[str, Any]						=			3072
												snake_case_ :Any						=			12

												snake_case_ :Any						=			1024
												snake_case_ :str						=			4096
												snake_case_ :Union[str, Any]						=			16
												snake_case_ :Union[str, Any]						=			24
												snake_case_ :Tuple						=			768
												snake_case_ :Any						=			3072

						if model_name == "xclip-large-patch14-16-frames":
												snake_case_ :Any						=			336

						snake_case_ :Any						=			XCLIPConfig.from_text_vision_configs(_lowercase,					_lowercase	)

						if "large" in model_name:
												snake_case_ :List[Any]						=			768

						return config
def    A_  (			_lowercase	):







						'''simple docstring'''


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

						return name
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												snake_case_ :Dict						=			orig_state_dict.pop(_lowercase	)

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

						return orig_state_dict
def    A_  (			_lowercase	):







						'''simple docstring'''


						if num_frames == 8:
												snake_case_ :str						=			"""eating_spaghetti_8_frames.npy"""
						elif num_frames == 16:
												snake_case_ :int						=			"""eating_spaghetti.npy"""
						elif num_frames == 32:
												snake_case_ :List[str]						=			"""eating_spaghetti_32_frames.npy"""
						snake_case_ :int						=			hf_hub_download(
						    repo_id="""hf-internal-testing/spaghetti-video""",					filename=_lowercase,					repo_type="""dataset""",					)
						snake_case_ :Union[str, Any]						=			np.load(_lowercase	)
						return list(_lowercase	)
def    A_  (			_lowercase,					_lowercase=None,					_lowercase=False	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			{
						    # 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""",
						}

						snake_case_ :Optional[int]						=			model_to_url[model_name]
						snake_case_ :int						=			8
						if "16-frames" in model_name:
												snake_case_ :List[Any]						=			16
						elif "shot" in model_name:
												snake_case_ :Dict						=			32

						snake_case_ :Optional[int]						=			get_xclip_config(_lowercase,					_lowercase	)
						snake_case_ :Optional[Any]						=			XCLIPModel(_lowercase	)
						model.eval()

						if "drive" in checkpoint_url:
												snake_case_ :List[str]						=			"""pytorch_model.bin"""
												gdown.cached_download(_lowercase,					_lowercase,					quiet=_lowercase	)
												snake_case_ :List[Any]						=			torch.load(_lowercase,					map_location="""cpu"""	)["""model"""]
						else:
												snake_case_ :Tuple						=			torch.hub.load_state_dict_from_url(_lowercase	)["""model"""]

						snake_case_ :Union[str, Any]						=			convert_state_dict(_lowercase,					_lowercase	)

						snake_case_ :str						=			XCLIPModel(_lowercase	)
						snake_case_,       snake_case_ :Optional[int]						=			model.load_state_dict(_lowercase,					strict=_lowercase	)
						assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
						model.eval()

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

						snake_case_ :Optional[int]						=			prepare_video(_lowercase	)
						snake_case_ :Optional[Any]						=			processor(
						    text=["""playing sports""", """eating spaghetti""", """go shopping"""],					videos=_lowercase,					return_tensors="""pt""",					padding=_lowercase	)

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

						with torch.no_grad():
												snake_case_ :List[Any]						=			model(**_lowercase	)

						# Verify outputs
						snake_case_ :List[Any]						=			outputs.logits_per_video
						snake_case_ :Any						=			logits_per_video.softmax(dim=1	)
						print("""Probs:""",					_lowercase	)
						# kinetics-400
						if model_name == "xclip-base-patch32":
												snake_case_ :Union[str, Any]						=			torch.tensor([[0.0019, 0.9951, 0.0030]]	)
						elif model_name == "xclip-base-patch32-16-frames":
												snake_case_ :str						=			torch.tensor([[7.09_99e-04, 9.98_83e-01, 4.55_80e-04]]	)
						elif model_name == "xclip-base-patch16":
												snake_case_ :Tuple						=			torch.tensor([[0.0083, 0.9681, 0.0236]]	)
						elif model_name == "xclip-base-patch16-16-frames":
												snake_case_ :Any						=			torch.tensor([[7.69_37e-04, 9.97_28e-01, 1.94_73e-03]]	)
						elif model_name == "xclip-large-patch14":
												snake_case_ :str						=			torch.tensor([[0.0062, 0.9864, 0.0075]]	)
						elif model_name == "xclip-large-patch14-16-frames":
												snake_case_ :Tuple						=			torch.tensor([[3.38_77e-04, 9.99_37e-01, 2.88_88e-04]]	)
						# kinetics-600
						elif model_name == "xclip-base-patch16-kinetics-600":
												snake_case_ :List[Any]						=			torch.tensor([[0.0555, 0.8914, 0.0531]]	)
						elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
												snake_case_ :Union[str, Any]						=			torch.tensor([[3.85_54e-04, 9.99_29e-01, 3.27_54e-04]]	)
						elif model_name == "xclip-large-patch14-kinetics-600":
												snake_case_ :List[Any]						=			torch.tensor([[0.0036, 0.9920, 0.0045]]	)
						# few shot
						elif model_name == "xclip-base-patch16-hmdb-2-shot":
												snake_case_ :Dict						=			torch.tensor([[7.18_90e-06, 9.99_94e-01, 5.65_59e-05]]	)
						elif model_name == "xclip-base-patch16-hmdb-4-shot":
												snake_case_ :Union[str, Any]						=			torch.tensor([[1.03_20e-05, 9.99_93e-01, 6.24_35e-05]]	)
						elif model_name == "xclip-base-patch16-hmdb-8-shot":
												snake_case_ :str						=			torch.tensor([[4.13_77e-06, 9.99_90e-01, 9.83_86e-05]]	)
						elif model_name == "xclip-base-patch16-hmdb-16-shot":
												snake_case_ :str						=			torch.tensor([[4.13_47e-05, 9.99_62e-01, 3.34_11e-04]]	)
						elif model_name == "xclip-base-patch16-ucf-2-shot":
												snake_case_ :int						=			torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]	)
						elif model_name == "xclip-base-patch16-ucf-4-shot":
												snake_case_ :Optional[int]						=			torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]	)
						elif model_name == "xclip-base-patch16-ucf-8-shot":
												snake_case_ :Any						=			torch.tensor([[0.0027, 0.9904, 0.0070]]	)
						elif model_name == "xclip-base-patch16-ucf-16-shot":
												snake_case_ :Tuple						=			torch.tensor([[9.82_19e-04, 9.95_93e-01, 3.08_63e-03]]	)
						# zero shot
						elif model_name == "xclip-base-patch16-zero-shot":
												snake_case_ :Union[str, Any]						=			torch.tensor([[3.50_82e-04, 9.97_85e-01, 1.79_66e-03]]	)
						else:
												raise ValueError(f"""Model name {model_name} not supported"""	)
						assert torch.allclose(_lowercase,					_lowercase,					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(_lowercase	)

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


if __name__ == "__main__":
						__a      =				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."
						)

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