





"""simple docstring"""





import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__     			=			logging.getLogger()


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	argparse.ArgumentParser()
		parser.add_argument("-f"    )
		UpperCamelCase   =	parser.parse_args()
		return args.f


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	{}
		UpperCamelCase   =	os.path.join(_SCREAMING_SNAKE_CASE  ,	"all_results.json"    )
		if os.path.exists(_SCREAMING_SNAKE_CASE    ):
				with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
						UpperCamelCase   =	json.load(_SCREAMING_SNAKE_CASE    )
		else:
				raise ValueError(F"can't find {path}"    )
		return results


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	torch.cuda.is_available() and torch_device == "cuda"
		return is_using_cuda and is_apex_available()


lowerCAmelCase__     			=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class        _lowerCamelCase      (       _lowercase ):


						@classmethod
						def     snake_case_   (cls		) ->   Union[str, Any]:
								# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
								UpperCamelCase   =	tempfile.mkdtemp()
								UpperCamelCase   =	os.path.join(cls.tmpdir   ,							"default_config.yml"		)
								write_basic_config(save_location=cls.configPath		)
								UpperCamelCase   =	["accelerate", "launch", "--config_file", cls.configPath]


						@classmethod
						def     snake_case_   (cls		) ->   int:
								shutil.rmtree(cls.tmpdir		)


						@mock.patch.dict(os.environ   ,							{"WANDB_MODE": "offline"}		)
						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	self.get_auto_remove_tmp_dir()
								UpperCamelCase   =	F"\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

								if is_cuda_and_apex_available():
										testargs.append("--fp16"		)

								run_command(self._launch_args + testargs		)
								UpperCamelCase   =	get_results(__a		)
								self.assertGreaterEqual(result["eval_accuracy"]   ,							0.75		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"epoch_0"		)		)		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"glue_no_trainer"		)		)		)


						@mock.patch.dict(os.environ   ,							{"WANDB_MODE": "offline"}		)
						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	self.get_auto_remove_tmp_dir()
								UpperCamelCase   =	F"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

								if torch.cuda.device_count() > 1:
										# Skipping because there are not enough batches to train the model + would need a drop_last to work.
										return

								run_command(self._launch_args + testargs		)
								UpperCamelCase   =	get_results(__a		)
								self.assertLess(result["perplexity"]   ,							1_00		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"epoch_0"		)		)		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"clm_no_trainer"		)		)		)


						@mock.patch.dict(os.environ   ,							{"WANDB_MODE": "offline"}		)
						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	self.get_auto_remove_tmp_dir()
								UpperCamelCase   =	F"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

								run_command(self._launch_args + testargs		)
								UpperCamelCase   =	get_results(__a		)
								self.assertLess(result["perplexity"]   ,							42		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"epoch_0"		)		)		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"mlm_no_trainer"		)		)		)


						@mock.patch.dict(os.environ   ,							{"WANDB_MODE": "offline"}		)
						def     snake_case_   (self		) ->   Tuple:
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								UpperCamelCase   =	7 if get_gpu_count() > 1 else 2

								UpperCamelCase   =	self.get_auto_remove_tmp_dir()
								UpperCamelCase   =	F"\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

								run_command(self._launch_args + testargs		)
								UpperCamelCase   =	get_results(__a		)
								self.assertGreaterEqual(result["eval_accuracy"]   ,							0.75		)
								self.assertLess(result["train_loss"]   ,							0.5		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"epoch_0"		)		)		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"ner_no_trainer"		)		)		)


						@unittest.skip(reason="Fix me @muellerzr"		)
						@mock.patch.dict(os.environ   ,							{"WANDB_MODE": "offline"}		)
						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	self.get_auto_remove_tmp_dir()
								UpperCamelCase   =	F"\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

								run_command(self._launch_args + testargs		)
								UpperCamelCase   =	get_results(__a		)
								# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
								self.assertGreaterEqual(result["eval_f1"]   ,							28		)
								self.assertGreaterEqual(result["eval_exact"]   ,							28		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"epoch_0"		)		)		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"qa_no_trainer"		)		)		)


						@mock.patch.dict(os.environ   ,							{"WANDB_MODE": "offline"}		)
						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	self.get_auto_remove_tmp_dir()
								UpperCamelCase   =	F"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

								run_command(self._launch_args + testargs		)
								UpperCamelCase   =	get_results(__a		)
								self.assertGreaterEqual(result["eval_accuracy"]   ,							0.8		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"swag_no_trainer"		)		)		)


						@slow
						@mock.patch.dict(os.environ   ,							{"WANDB_MODE": "offline"}		)
						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	self.get_auto_remove_tmp_dir()
								UpperCamelCase   =	F"\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

								run_command(self._launch_args + testargs		)
								UpperCamelCase   =	get_results(__a		)
								self.assertGreaterEqual(result["eval_rouge1"]   ,							10		)
								self.assertGreaterEqual(result["eval_rouge2"]   ,							2		)
								self.assertGreaterEqual(result["eval_rougeL"]   ,							7		)
								self.assertGreaterEqual(result["eval_rougeLsum"]   ,							7		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"epoch_0"		)		)		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"summarization_no_trainer"		)		)		)


						@slow
						@mock.patch.dict(os.environ   ,							{"WANDB_MODE": "offline"}		)
						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	self.get_auto_remove_tmp_dir()
								UpperCamelCase   =	F"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

								run_command(self._launch_args + testargs		)
								UpperCamelCase   =	get_results(__a		)
								self.assertGreaterEqual(result["eval_bleu"]   ,							30		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"epoch_0"		)		)		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"translation_no_trainer"		)		)		)


						@slow
						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	logging.StreamHandler(sys.stdout		)
								logger.addHandler(__a		)

								UpperCamelCase   =	self.get_auto_remove_tmp_dir()
								UpperCamelCase   =	F"\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        ".split()

								run_command(self._launch_args + testargs		)
								UpperCamelCase   =	get_results(__a		)
								self.assertGreaterEqual(result["eval_overall_accuracy"]   ,							0.10		)




						@mock.patch.dict(os.environ   ,							{"WANDB_MODE": "offline"}		)
						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	self.get_auto_remove_tmp_dir()
								UpperCamelCase   =	F"\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        ".split()

								if is_cuda_and_apex_available():
										testargs.append("--fp16"		)

								run_command(self._launch_args + testargs		)
								UpperCamelCase   =	get_results(__a		)
								# The base model scores a 25%
								self.assertGreaterEqual(result["eval_accuracy"]   ,							0.6		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"step_1"		)		)		)
								self.assertTrue(os.path.exists(os.path.join(__a   ,							"image_classification_no_trainer"		)		)		)













"""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__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	XCLIPTextConfig()

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

		if "large" in model_name:
				UpperCamelCase   =	768
				UpperCamelCase   =	3_072
				UpperCamelCase   =	12

				UpperCamelCase   =	1_024
				UpperCamelCase   =	4_096
				UpperCamelCase   =	16
				UpperCamelCase   =	24
				UpperCamelCase   =	768
				UpperCamelCase   =	3_072

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

		UpperCamelCase   =	XCLIPConfig.from_text_vision_configs(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		if "large" in model_name:
				UpperCamelCase   =	768

		return config


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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

		return name


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		for key in orig_state_dict.copy().keys():
				UpperCamelCase   =	orig_state_dict.pop(_SCREAMING_SNAKE_CASE    )

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

		return orig_state_dict


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if num_frames == 8:
				UpperCamelCase   =	"eating_spaghetti_8_frames.npy"
		elif num_frames == 16:
				UpperCamelCase   =	"eating_spaghetti.npy"
		elif num_frames == 32:
				UpperCamelCase   =	"eating_spaghetti_32_frames.npy"
		UpperCamelCase   =	hf_hub_download(
		    repo_id="hf-internal-testing/spaghetti-video"  ,	filename=_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"  ,	)
		UpperCamelCase   =	np.load(_SCREAMING_SNAKE_CASE    )
		return list(_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=False    ):




		"""simple docstring"""


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

		UpperCamelCase   =	get_xclip_config(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	XCLIPModel(_SCREAMING_SNAKE_CASE    )
		model.eval()

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

		UpperCamelCase   =	convert_state_dict(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

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

		UpperCamelCase   =	prepare_video(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	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   =	model(**_SCREAMING_SNAKE_CASE    )

		# Verify outputs
		UpperCamelCase   =	outputs.logits_per_video
		UpperCamelCase   =	logits_per_video.softmax(dim=1    )
		print("Probs:"  ,	_SCREAMING_SNAKE_CASE    )
		# kinetics-400
		if model_name == "xclip-base-patch32":
				UpperCamelCase   =	torch.tensor([[0.00_19, 0.99_51, 0.00_30]]    )
		elif model_name == "xclip-base-patch32-16-frames":
				UpperCamelCase   =	torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]    )
		elif model_name == "xclip-base-patch16":
				UpperCamelCase   =	torch.tensor([[0.00_83, 0.96_81, 0.02_36]]    )
		elif model_name == "xclip-base-patch16-16-frames":
				UpperCamelCase   =	torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]    )
		elif model_name == "xclip-large-patch14":
				UpperCamelCase   =	torch.tensor([[0.00_62, 0.98_64, 0.00_75]]    )
		elif model_name == "xclip-large-patch14-16-frames":
				UpperCamelCase   =	torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]    )
		# kinetics-600
		elif model_name == "xclip-base-patch16-kinetics-600":
				UpperCamelCase   =	torch.tensor([[0.05_55, 0.89_14, 0.05_31]]    )
		elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
				UpperCamelCase   =	torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]    )
		elif model_name == "xclip-large-patch14-kinetics-600":
				UpperCamelCase   =	torch.tensor([[0.00_36, 0.99_20, 0.00_45]]    )
		# few shot
		elif model_name == "xclip-base-patch16-hmdb-2-shot":
				UpperCamelCase   =	torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]    )
		elif model_name == "xclip-base-patch16-hmdb-4-shot":
				UpperCamelCase   =	torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]    )
		elif model_name == "xclip-base-patch16-hmdb-8-shot":
				UpperCamelCase   =	torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]    )
		elif model_name == "xclip-base-patch16-hmdb-16-shot":
				UpperCamelCase   =	torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]    )
		elif model_name == "xclip-base-patch16-ucf-2-shot":
				UpperCamelCase   =	torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]    )
		elif model_name == "xclip-base-patch16-ucf-4-shot":
				UpperCamelCase   =	torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]    )
		elif model_name == "xclip-base-patch16-ucf-8-shot":
				UpperCamelCase   =	torch.tensor([[0.00_27, 0.99_04, 0.00_70]]    )
		elif model_name == "xclip-base-patch16-ucf-16-shot":
				UpperCamelCase   =	torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]    )
		# zero shot
		elif model_name == "xclip-base-patch16-zero-shot":
				UpperCamelCase   =	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__":
						lowerCAmelCase__     			=			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__     			=			parser.parse_args()
						convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






