





import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

lowerCAmelCase_   :   List[Any]       =					collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCAmelCase_   :   Dict       =					"https://storage.googleapis.com/cvdf-datasets/mnist/"







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    List[str]:
			a__     = numpy.dtype(numpy.uintaa     ).newbyteorder(">"     )
			return numpy.frombuffer(bytestream.read(4     )					,  dtype=__lowerCamelCase     )[0]







@deprecated(__lowerCamelCase					,  "Please use tf.data to implement this functionality."     )
def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    List[str]:
			print("Extracting"					,  f.name     )
			with gzip.GzipFile(fileobj=__lowerCamelCase     ) as bytestream:
						a__     = _readaa(__lowerCamelCase     )
						if magic != 2051:
									raise ValueError(
									    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)     )
						a__     = _readaa(__lowerCamelCase     )
						a__     = _readaa(__lowerCamelCase     )
						a__     = _readaa(__lowerCamelCase     )
						a__     = bytestream.read(rows * cols * num_images     )
						a__     = numpy.frombuffer(__lowerCamelCase					,  dtype=numpy.uinta     )
						a__     = data.reshape(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  1     )
						return data







@deprecated(__lowerCamelCase					,  "Please use tf.one_hot on tensors."     )
def    _lowerCamelCase  (__lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     Optional[int]     )   ->    Dict:
			a__     = labels_dense.shape[0]
			a__     = numpy.arange(__lowerCamelCase     ) * num_classes
			a__     = numpy.zeros((num_labels, num_classes)     )
			a__     = 1
			return labels_one_hot







@deprecated(__lowerCamelCase					,  "Please use tf.data to implement this functionality."     )
def    _lowerCamelCase  (__lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Tuple=False					,  __lowerCamelCase  :     Dict=10     )   ->    Any:
			print("Extracting"					,  f.name     )
			with gzip.GzipFile(fileobj=__lowerCamelCase     ) as bytestream:
						a__     = _readaa(__lowerCamelCase     )
						if magic != 2049:
									raise ValueError(
									    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)     )
						a__     = _readaa(__lowerCamelCase     )
						a__     = bytestream.read(__lowerCamelCase     )
						a__     = numpy.frombuffer(__lowerCamelCase					,  dtype=numpy.uinta     )
						if one_hot:
									return _dense_to_one_hot(__lowerCamelCase					,  __lowerCamelCase     )
						return labels






class       UpperCamelCase__  :







	@deprecated(
	    lowerCamelCase , "Please use alternatives such as official/mnist/_DataSet.py"
	    " from tensorflow/models." , )
	def __init__(				self			:   List[str] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Any , lowerCamelCase			:   str=False , lowerCamelCase			:   Tuple=False , lowerCamelCase			:   Optional[int]=dtypes.floataa , lowerCamelCase			:   List[str]=True , lowerCamelCase			:   Dict=None , ):




				'''simple docstring'''
				a__   ,				a__     = random_seed.get_seed(lowerCamelCase		)
				# If op level seed is not set, use whatever graph level seed is returned
				numpy.random.seed(seeda if seed is None else seeda		)
				a__     = dtypes.as_dtype(lowerCamelCase		).base_dtype
				if dtype not in (dtypes.uinta, dtypes.floataa):
							raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype		)
				if fake_data:
							a__     = 1_0_0_0_0
							a__     = one_hot
				else:
							assert (
							    images.shape[0] == labels.shape[0]
							), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
							a__     = images.shape[0]

							# Convert shape from [num examples, rows, columns, depth]
							# to [num examples, rows*columns] (assuming depth == 1)
							if reshape:
										assert images.shape[3] == 1
										a__     = images.reshape(
										    images.shape[0] , images.shape[1] * images.shape[2]		)
							if dtype == dtypes.floataa:
										# Convert from [0, 255] -> [0.0, 1.0].
										a__     = images.astype(numpy.floataa		)
										a__     = numpy.multiply(lowerCamelCase , 1.0 / 255.0		)
				a__     = images
				a__     = labels
				a__     = 0
				a__     = 0







	@property
	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				return self._images







	@property
	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				return self._labels







	@property
	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				return self._num_examples







	@property
	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				return self._epochs_completed







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   str , lowerCamelCase			:   Any=False , lowerCamelCase			:   List[Any]=True		):




				'''simple docstring'''
				if fake_data:
							a__     = [1] * 7_8_4
							a__     = [1] + [0] * 9 if self.one_hot else 0
							return (
							    [fake_image for _ in range(lowerCamelCase		)],
							    [fake_label for _ in range(lowerCamelCase		)],
							)
				a__     = self._index_in_epoch
				# Shuffle for the first epoch
				if self._epochs_completed == 0 and start == 0 and shuffle:
							a__     = numpy.arange(self._num_examples		)
							numpy.random.shuffle(lowerCamelCase		)
							a__     = self.images[perma]
							a__     = self.labels[perma]
				# Go to the next epoch
				if start + batch_size > self._num_examples:
							# Finished epoch
							self._epochs_completed += 1
							# Get the rest examples in this epoch
							a__     = self._num_examples - start
							a__     = self._images[start : self._num_examples]
							a__     = self._labels[start : self._num_examples]
							# Shuffle the data
							if shuffle:
										a__     = numpy.arange(self._num_examples		)
										numpy.random.shuffle(lowerCamelCase		)
										a__     = self.images[perm]
										a__     = self.labels[perm]
							# Start next epoch
							a__     = 0
							a__     = batch_size - rest_num_examples
							a__     = self._index_in_epoch
							a__     = self._images[start:end]
							a__     = self._labels[start:end]
							return (
							    numpy.concatenate((images_rest_part, images_new_part) , axis=0		),
							    numpy.concatenate((labels_rest_part, labels_new_part) , axis=0		),
							)
				else:
							self._index_in_epoch += batch_size
							a__     = self._index_in_epoch
							return self._images[start:end], self._labels[start:end]







@deprecated(__lowerCamelCase					,  "Please write your own downloading logic."     )
def    _lowerCamelCase  (__lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     List[Any]     )   ->    Tuple:
			if not gfile.Exists(__lowerCamelCase     ):
						gfile.MakeDirs(__lowerCamelCase     )
			a__     = os.path.join(__lowerCamelCase					,  __lowerCamelCase     )
			if not gfile.Exists(__lowerCamelCase     ):
						urllib.request.urlretrieve(__lowerCamelCase					,  __lowerCamelCase     )  # noqa: S310
						with gfile.GFile(__lowerCamelCase     ) as f:
									a__     = f.size()
						print("Successfully downloaded"					,  __lowerCamelCase					,  __lowerCamelCase					,  "bytes."     )
			return filepath







@deprecated(
    __lowerCamelCase					,  "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"     )
def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[Any]=False					,  __lowerCamelCase  :     Dict=False					,  __lowerCamelCase  :     Union[str, Any]=dtypes.floataa					,  __lowerCamelCase  :     Union[str, Any]=True					,  __lowerCamelCase  :     int=5000					,  __lowerCamelCase  :     List[str]=None					,  __lowerCamelCase  :     Any=DEFAULT_SOURCE_URL					,  )   ->    int:
			if fake_data:

						def fake():
									return _DataSet(
									    []					,  []					,  fake_data=__lowerCamelCase					,  one_hot=__lowerCamelCase					,  dtype=__lowerCamelCase					,  seed=__lowerCamelCase     )

						a__     = fake()
						a__     = fake()
						a__     = fake()
						return _Datasets(train=__lowerCamelCase					,  validation=__lowerCamelCase					,  test=__lowerCamelCase     )

			if not source_url:  # empty string check
						a__     = DEFAULT_SOURCE_URL

			a__     = "train-images-idx3-ubyte.gz"
			a__     = "train-labels-idx1-ubyte.gz"
			a__     = "t10k-images-idx3-ubyte.gz"
			a__     = "t10k-labels-idx1-ubyte.gz"

			a__     = _maybe_download(
			    __lowerCamelCase					,  __lowerCamelCase					,  source_url + train_images_file     )
			with gfile.Open(__lowerCamelCase					,  "rb"     ) as f:
						a__     = _extract_images(__lowerCamelCase     )

			a__     = _maybe_download(
			    __lowerCamelCase					,  __lowerCamelCase					,  source_url + train_labels_file     )
			with gfile.Open(__lowerCamelCase					,  "rb"     ) as f:
						a__     = _extract_labels(__lowerCamelCase					,  one_hot=__lowerCamelCase     )

			a__     = _maybe_download(
			    __lowerCamelCase					,  __lowerCamelCase					,  source_url + test_images_file     )
			with gfile.Open(__lowerCamelCase					,  "rb"     ) as f:
						a__     = _extract_images(__lowerCamelCase     )

			a__     = _maybe_download(
			    __lowerCamelCase					,  __lowerCamelCase					,  source_url + test_labels_file     )
			with gfile.Open(__lowerCamelCase					,  "rb"     ) as f:
						a__     = _extract_labels(__lowerCamelCase					,  one_hot=__lowerCamelCase     )

			if not 0 <= validation_size <= len(__lowerCamelCase     ):
						a__     = (
						    "Validation size should be between 0 and "
						    f'''{len(__lowerCamelCase     )}. Received: {validation_size}.'''
						)
						raise ValueError(__lowerCamelCase     )

			a__     = train_images[:validation_size]
			a__     = train_labels[:validation_size]
			a__     = train_images[validation_size:]
			a__     = train_labels[validation_size:]

			a__     = {"dtype": dtype, "reshape": reshape, "seed": seed}

			a__     = _DataSet(__lowerCamelCase					,  __lowerCamelCase					,  **__lowerCamelCase     )
			a__     = _DataSet(__lowerCamelCase					,  __lowerCamelCase					,  **__lowerCamelCase     )
			a__     = _DataSet(__lowerCamelCase					,  __lowerCamelCase					,  **__lowerCamelCase     )

			return _Datasets(train=__lowerCamelCase					,  validation=__lowerCamelCase					,  test=__lowerCamelCase     )










'''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    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Dict     )   ->    Tuple:
			a__     = XCLIPTextConfig()

			# derive patch size from model name
			a__     = model_name.find("patch"     )
			a__     = int(model_name[start_idx + len("patch"     ) : start_idx + len("patch"     ) + 2]     )
			a__     = XCLIPVisionConfig(patch_size=__lowerCamelCase					,  num_frames=__lowerCamelCase     )

			if "large" in model_name:
						a__     = 768
						a__     = 3072
						a__     = 12

						a__     = 1024
						a__     = 4096
						a__     = 16
						a__     = 24
						a__     = 768
						a__     = 3072

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

			a__     = XCLIPConfig.from_text_vision_configs(__lowerCamelCase					,  __lowerCamelCase     )

			if "large" in model_name:
						a__     = 768

			return config







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

			return name







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     str     )   ->    Optional[Any]:
			for key in orig_state_dict.copy().keys():
						a__     = orig_state_dict.pop(__lowerCamelCase     )

						if "attn.in_proj" in key:
									a__     = key.split("."     )
									if key.startswith("visual"     ):
												a__     = key_split[3]
												a__     = config.vision_config.hidden_size
												if "message_attn" in key:
															if "weight" in key:
																		a__     = val[
																		    :dim, :
																		]
																		a__     = val[
																		    dim : dim * 2, :
																		]
																		a__     = val[
																		    -dim:, :
																		]
															else:
																		a__     = val[
																		    :dim
																		]
																		a__     = val[
																		    dim : dim * 2
																		]
																		a__     = val[
																		    -dim:
																		]
												else:
															if "weight" in key:
																		a__     = val[
																		    :dim, :
																		]
																		a__     = val[
																		    dim : dim * 2, :
																		]
																		a__     = val[
																		    -dim:, :
																		]
															else:
																		a__     = val[:dim]
																		a__     = val[
																		    dim : dim * 2
																		]
																		a__     = val[-dim:]
									elif key.startswith("mit"     ):
												a__     = key_split[2]
												a__     = config.vision_config.mit_hidden_size
												if "weight" in key:
															a__     = val[:dim, :]
															a__     = val[dim : dim * 2, :]
															a__     = val[-dim:, :]
												else:
															a__     = val[:dim]
															a__     = val[dim : dim * 2]
															a__     = val[-dim:]
									else:
												a__     = key_split[2]
												a__     = config.text_config.hidden_size
												if "weight" in key:
															a__     = val[:dim, :]
															a__     = val[
															    dim : dim * 2, :
															]
															a__     = val[-dim:, :]
												else:
															a__     = val[:dim]
															a__     = val[
															    dim : dim * 2
															]
															a__     = val[-dim:]
						else:
									a__     = rename_key(__lowerCamelCase     )
									if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
												a__     = val.T
									a__     = val

			return orig_state_dict







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    List[str]:
			if num_frames == 8:
						a__     = "eating_spaghetti_8_frames.npy"
			elif num_frames == 16:
						a__     = "eating_spaghetti.npy"
			elif num_frames == 32:
						a__     = "eating_spaghetti_32_frames.npy"
			a__     = hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video"					,  filename=__lowerCamelCase					,  repo_type="dataset"					,  )
			a__     = np.load(__lowerCamelCase     )
			return list(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Any=None					,  __lowerCamelCase  :     Optional[Any]=False     )   ->    Optional[Any]:
			a__     = {
			    # 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",
			}

			a__     = model_to_url[model_name]
			a__     = 8
			if "16-frames" in model_name:
						a__     = 16
			elif "shot" in model_name:
						a__     = 32

			a__     = get_xclip_config(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = XCLIPModel(__lowerCamelCase     )
			model.eval()

			if "drive" in checkpoint_url:
						a__     = "pytorch_model.bin"
						gdown.cached_download(__lowerCamelCase					,  __lowerCamelCase					,  quiet=__lowerCamelCase     )
						a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )["model"]
			else:
						a__     = torch.hub.load_state_dict_from_url(__lowerCamelCase     )["model"]

			a__     = convert_state_dict(__lowerCamelCase					,  __lowerCamelCase     )

			a__     = XCLIPModel(__lowerCamelCase     )
			a__   ,				a__     = model.load_state_dict(__lowerCamelCase					,  strict=__lowerCamelCase     )
			assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
			model.eval()

			a__     = 336 if model_name == "xclip-large-patch14-16-frames" else 224
			a__     = VideoMAEImageProcessor(size=__lowerCamelCase     )
			a__     = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"     )
			a__     = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"     )
			a__     = XCLIPProcessor(image_processor=__lowerCamelCase					,  tokenizer=__lowerCamelCase     )

			a__     = prepare_video(__lowerCamelCase     )
			a__     = processor(
			    text=["playing sports", "eating spaghetti", "go shopping"]					,  videos=__lowerCamelCase					,  return_tensors="pt"					,  padding=__lowerCamelCase     )

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

			with torch.no_grad():
						a__     = model(**__lowerCamelCase     )

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

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


if __name__ == "__main__":
		lowerCAmelCase_   :   Any       =					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."
		)

		lowerCAmelCase_   :   Dict       =					parser.parse_args()
		convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



