





'''simple docstring'''


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCAmelCase_   :   Optional[Any]       =					logging.getLogger(__name__)






@dataclass
class       UpperCamelCase__  :
	lowerCAmelCase__					:			str								=						field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
	lowerCAmelCase__					:			Optional[str]								=						field(
	    default=__lowerCAmelCase	,metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
	lowerCAmelCase__					:			Optional[str]								=						field(
	    default=__lowerCAmelCase	,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
	lowerCAmelCase__					:			Optional[str]								=						field(
	    default=__lowerCAmelCase	,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,)
	lowerCAmelCase__					:			bool								=						field(default=__lowerCAmelCase	,metadata={"help": "Whether tp freeze the encoder."}     )
	lowerCAmelCase__					:			bool								=						field(default=__lowerCAmelCase	,metadata={"help": "Whether  to freeze the embeddings."}     )






@dataclass
class       UpperCamelCase__  :
	lowerCAmelCase__					:			str								=						field(
	    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}     )
	lowerCAmelCase__					:			Optional[str]								=						field(
	    default="summarization"	,metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}	,)
	lowerCAmelCase__					:			Optional[int]								=						field(
	    default=1_024	,metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }	,)
	lowerCAmelCase__					:			Optional[int]								=						field(
	    default=128	,metadata={
	        "help": (
	            "The maximum total sequence length for target text after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }	,)
	lowerCAmelCase__					:			Optional[int]								=						field(
	    default=142	,metadata={
	        "help": (
	            "The maximum total sequence length for validation target text after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded. "
	            "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
	            "during ``evaluate`` and ``predict``."
	        )
	    }	,)
	lowerCAmelCase__					:			Optional[int]								=						field(
	    default=142	,metadata={
	        "help": (
	            "The maximum total sequence length for test target text after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }	,)
	lowerCAmelCase__					:			Optional[int]								=						field(default=-1	,metadata={"help": "# training examples. -1 means use all."}     )
	lowerCAmelCase__					:			Optional[int]								=						field(default=-1	,metadata={"help": "# validation examples. -1 means use all."}     )
	lowerCAmelCase__					:			Optional[int]								=						field(default=-1	,metadata={"help": "# test examples. -1 means use all."}     )
	lowerCAmelCase__					:			Optional[str]								=						field(default=__lowerCAmelCase	,metadata={"help": "Source language id for translation."}     )
	lowerCAmelCase__					:			Optional[str]								=						field(default=__lowerCAmelCase	,metadata={"help": "Target language id for translation."}     )
	lowerCAmelCase__					:			Optional[int]								=						field(default=__lowerCAmelCase	,metadata={"help": "# num_beams to use for evaluation."}     )
	lowerCAmelCase__					:			bool								=						field(
	    default=__lowerCAmelCase	,metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}	,)







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     Dict					,  __lowerCamelCase  :     Tuple     )   ->    List[str]:
			logger.info(f'''***** {split} metrics *****'''     )
			for key in sorted(metrics.keys()     ):
						logger.info(f'''  {key} = {metrics[key]}'''     )
			save_json(__lowerCamelCase					,  os.path.join(__lowerCamelCase					,  f'''{split}_results.json'''     )     )







def    _lowerCamelCase  ()   ->    str:
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			a__     = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)     )

			if len(sys.argv     ) == 2 and sys.argv[1].endswith(".json"     ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						a__   ,				a__   ,				a__     = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
			else:
						a__   ,				a__   ,				a__     = parser.parse_args_into_dataclasses()

			check_output_dir(__lowerCamelCase     )

			# Setup logging
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,  datefmt="%m/%d/%Y %H:%M:%S"					,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,  )
			logger.warning(
			    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"					,  training_args.local_rank					,  training_args.device					,  training_args.n_gpu					,  bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED     )					,  training_args.fpaa					,  )
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank     ):
						transformers.utils.logging.set_verbosity_info()
			logger.info("Training/evaluation parameters %s"					,  __lowerCamelCase     )

			# Set seed
			set_seed(training_args.seed     )

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			a__     = AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,  cache_dir=model_args.cache_dir					,  )

			a__     = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
			for p in extra_model_params:
						if getattr(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     ):
									assert hasattr(__lowerCamelCase					,  __lowerCamelCase     ), f'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
									setattr(__lowerCamelCase					,  __lowerCamelCase					,  getattr(__lowerCamelCase					,  __lowerCamelCase     )     )

			a__     = AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,  cache_dir=model_args.cache_dir					,  )
			a__     = AutoModelForSeqaSeqLM.from_pretrained(
			    model_args.model_name_or_path					,  from_tf=".ckpt" in model_args.model_name_or_path					,  config=__lowerCamelCase					,  cache_dir=model_args.cache_dir					,  )

			# use task specific params
			use_task_specific_params(__lowerCamelCase					,  data_args.task     )

			# set num_beams for evaluation
			if data_args.eval_beams is None:
						a__     = model.config.num_beams

			# set decoder_start_token_id for MBart
			if model.config.decoder_start_token_id is None and isinstance(__lowerCamelCase					,  (MBartTokenizer, MBartTokenizerFast)     ):
						assert (
						    data_args.tgt_lang is not None and data_args.src_lang is not None
						), "mBart requires --tgt_lang and --src_lang"
						if isinstance(__lowerCamelCase					,  __lowerCamelCase     ):
									a__     = tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									a__     = tokenizer.convert_tokens_to_ids(data_args.tgt_lang     )

			if model_args.freeze_embeds:
						freeze_embeds(__lowerCamelCase     )
			if model_args.freeze_encoder:
						freeze_params(model.get_encoder()     )
						assert_all_frozen(model.get_encoder()     )

			a__     = SeqaSeqDataset

			# Get datasets
			a__     = (
			    dataset_class(
			        __lowerCamelCase					,  type_path="train"					,  data_dir=data_args.data_dir					,  n_obs=data_args.n_train					,  max_target_length=data_args.max_target_length					,  max_source_length=data_args.max_source_length					,  prefix=model.config.prefix or ""					,  )
			    if training_args.do_train
			    else None
			)
			a__     = (
			    dataset_class(
			        __lowerCamelCase					,  type_path="val"					,  data_dir=data_args.data_dir					,  n_obs=data_args.n_val					,  max_target_length=data_args.val_max_target_length					,  max_source_length=data_args.max_source_length					,  prefix=model.config.prefix or ""					,  )
			    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
			    else None
			)
			a__     = (
			    dataset_class(
			        __lowerCamelCase					,  type_path="test"					,  data_dir=data_args.data_dir					,  n_obs=data_args.n_test					,  max_target_length=data_args.test_max_target_length					,  max_source_length=data_args.max_source_length					,  prefix=model.config.prefix or ""					,  )
			    if training_args.do_predict
			    else None
			)

			# Initialize our Trainer
			a__     = (
			    build_compute_metrics_fn(data_args.task					,  __lowerCamelCase     ) if training_args.predict_with_generate else None
			)
			a__     = SeqaSeqTrainer(
			    model=__lowerCamelCase					,  args=__lowerCamelCase					,  data_args=__lowerCamelCase					,  train_dataset=__lowerCamelCase					,  eval_dataset=__lowerCamelCase					,  data_collator=SeqaSeqDataCollator(
			        __lowerCamelCase					,  __lowerCamelCase					,  model.config.decoder_start_token_id					,  training_args.tpu_num_cores     )					,  compute_metrics=__lowerCamelCase					,  tokenizer=__lowerCamelCase					,  )

			a__     = {}
			# Training
			if training_args.do_train:
						logger.info("*** Train ***"     )

						a__     = trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
						a__     = train_result.metrics
						a__     = data_args.n_train

						trainer.save_model()  # this also saves the tokenizer

						if trainer.is_world_process_zero():
									handle_metrics("train"					,  __lowerCamelCase					,  training_args.output_dir     )
									all_metrics.update(__lowerCamelCase     )

									# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
									trainer.state.save_to_json(os.path.join(training_args.output_dir					,  "trainer_state.json"     )     )

									# For convenience, we also re-save the tokenizer to the same directory,
									# so that you can share your model easily on huggingface.co/models =)
									tokenizer.save_pretrained(training_args.output_dir     )

    # Evaluation
			if training_args.do_eval:
						logger.info("*** Evaluate ***"     )

						a__     = trainer.evaluate(metric_key_prefix="val"     )
						a__     = data_args.n_val
						a__     = round(metrics["val_loss"]					,  4     )

						if trainer.is_world_process_zero():
									handle_metrics("val"					,  __lowerCamelCase					,  training_args.output_dir     )
									all_metrics.update(__lowerCamelCase     )

			if training_args.do_predict:
						logger.info("*** Predict ***"     )

						a__     = trainer.predict(test_dataset=__lowerCamelCase					,  metric_key_prefix="test"     )
						a__     = test_output.metrics
						a__     = data_args.n_test

						if trainer.is_world_process_zero():
									a__     = round(metrics["test_loss"]					,  4     )
									handle_metrics("test"					,  __lowerCamelCase					,  training_args.output_dir     )
									all_metrics.update(__lowerCamelCase     )

									if training_args.predict_with_generate:
												a__     = tokenizer.batch_decode(
												    test_output.predictions					,  skip_special_tokens=__lowerCamelCase					,  clean_up_tokenization_spaces=__lowerCamelCase     )
												a__     = lmap(str.strip					,  __lowerCamelCase     )
												write_txt_file(__lowerCamelCase					,  os.path.join(training_args.output_dir					,  "test_generations.txt"     )     )

			if trainer.is_world_process_zero():
						save_json(__lowerCamelCase					,  os.path.join(training_args.output_dir					,  "all_results.json"     )     )

			return all_metrics







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    List[Any]:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
		main()










'''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)



