


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        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		XCLIPTextConfig()

						# derive patch size from model name
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		model_name.find('''patch'''      )
						__SCREAMING_SNAKE_CASE						:     Dict					=		int(model_name[start_idx + len('''patch'''      ) : start_idx + len('''patch'''      ) + 2]      )
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		XCLIPVisionConfig(patch_size=lowercase__    ,       num_frames=lowercase__      )

						if "large" in model_name:
												__SCREAMING_SNAKE_CASE						:     int					=		768
												__SCREAMING_SNAKE_CASE						:     Dict					=		3072
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		12

												__SCREAMING_SNAKE_CASE						:     Any					=		1024
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		4096
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		16
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		24
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		768
												__SCREAMING_SNAKE_CASE						:     str					=		3072

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

						__SCREAMING_SNAKE_CASE						:     int					=		XCLIPConfig.from_text_vision_configs(lowercase__    ,       lowercase__      )

						if "large" in model_name:
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		768

						return config


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

						return name


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						for key in orig_state_dict.copy().keys():
												__SCREAMING_SNAKE_CASE						:     List[str]					=		orig_state_dict.pop(lowercase__      )

												if "attn.in_proj" in key:
																		__SCREAMING_SNAKE_CASE						:     str					=		key.split('''.'''      )
																		if key.startswith('''visual'''      ):
																								__SCREAMING_SNAKE_CASE						:     str					=		key_split[3]
																								__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		config.vision_config.hidden_size
																								if "message_attn" in key:
																														if "weight" in key:
																																				__SCREAMING_SNAKE_CASE						:     int					=		val[
																																				    :dim, :
																																				]
																																				__SCREAMING_SNAKE_CASE						:     str					=		val[
																																				    dim : dim * 2, :
																																				]
																																				__SCREAMING_SNAKE_CASE						:     str					=		val[
																																				    -dim:, :
																																				]
																														else:
																																				__SCREAMING_SNAKE_CASE						:     str					=		val[
																																				    :dim
																																				]
																																				__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		val[
																																				    dim : dim * 2
																																				]
																																				__SCREAMING_SNAKE_CASE						:     Optional[int]					=		val[
																																				    -dim:
																																				]
																								else:
																														if "weight" in key:
																																				__SCREAMING_SNAKE_CASE						:     Any					=		val[
																																				    :dim, :
																																				]
																																				__SCREAMING_SNAKE_CASE						:     Optional[int]					=		val[
																																				    dim : dim * 2, :
																																				]
																																				__SCREAMING_SNAKE_CASE						:     Optional[int]					=		val[
																																				    -dim:, :
																																				]
																														else:
																																				__SCREAMING_SNAKE_CASE						:     Optional[int]					=		val[:dim]
																																				__SCREAMING_SNAKE_CASE						:     Optional[int]					=		val[
																																				    dim : dim * 2
																																				]
																																				__SCREAMING_SNAKE_CASE						:     Tuple					=		val[-dim:]
																		elif key.startswith('''mit'''      ):
																								__SCREAMING_SNAKE_CASE						:     Dict					=		key_split[2]
																								__SCREAMING_SNAKE_CASE						:     Any					=		config.vision_config.mit_hidden_size
																								if "weight" in key:
																														__SCREAMING_SNAKE_CASE						:     Dict					=		val[:dim, :]
																														__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		val[dim : dim * 2, :]
																														__SCREAMING_SNAKE_CASE						:     List[Any]					=		val[-dim:, :]
																								else:
																														__SCREAMING_SNAKE_CASE						:     Dict					=		val[:dim]
																														__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		val[dim : dim * 2]
																														__SCREAMING_SNAKE_CASE						:     List[str]					=		val[-dim:]
																		else:
																								__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		key_split[2]
																								__SCREAMING_SNAKE_CASE						:     Optional[int]					=		config.text_config.hidden_size
																								if "weight" in key:
																														__SCREAMING_SNAKE_CASE						:     Dict					=		val[:dim, :]
																														__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		val[
																														    dim : dim * 2, :
																														]
																														__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		val[-dim:, :]
																								else:
																														__SCREAMING_SNAKE_CASE						:     Tuple					=		val[:dim]
																														__SCREAMING_SNAKE_CASE						:     Any					=		val[
																														    dim : dim * 2
																														]
																														__SCREAMING_SNAKE_CASE						:     Any					=		val[-dim:]
												else:
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		rename_key(lowercase__      )
																		if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																								__SCREAMING_SNAKE_CASE						:     Any					=		val.T
																		__SCREAMING_SNAKE_CASE						:     Dict					=		val

						return orig_state_dict


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


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=None    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, 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''',
						}

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

						__SCREAMING_SNAKE_CASE						:     Any					=		get_xclip_config(lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		XCLIPModel(lowercase__      )
						model.eval()

						if "drive" in checkpoint_url:
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		'''pytorch_model.bin'''
												gdown.cached_download(lowercase__    ,       lowercase__    ,       quiet=lowercase__      )
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		torch.load(lowercase__    ,       map_location='''cpu'''      )['''model''']
						else:
												__SCREAMING_SNAKE_CASE						:     List[str]					=		torch.hub.load_state_dict_from_url(lowercase__      )['''model''']

						__SCREAMING_SNAKE_CASE						:     str					=		convert_state_dict(lowercase__    ,       lowercase__      )

						__SCREAMING_SNAKE_CASE						:     Dict					=		XCLIPModel(lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		model.load_state_dict(lowercase__    ,       strict=lowercase__      )
						assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
						model.eval()

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

						__SCREAMING_SNAKE_CASE						:     Dict					=		prepare_video(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     int					=		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():
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		model(**lowercase__      )

						# Verify outputs
						__SCREAMING_SNAKE_CASE						:     Tuple					=		outputs.logits_per_video
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		logits_per_video.softmax(dim=1      )
						print('''Probs:'''    ,       lowercase__      )
						# kinetics-400
						if model_name == "xclip-base-patch32":
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		torch.tensor([[0.0019, 0.9951, 0.0030]]      )
						elif model_name == "xclip-base-patch32-16-frames":
												__SCREAMING_SNAKE_CASE						:     Dict					=		torch.tensor([[7.09_99e-04, 9.98_83e-01, 4.55_80e-04]]      )
						elif model_name == "xclip-base-patch16":
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		torch.tensor([[0.0083, 0.9681, 0.0236]]      )
						elif model_name == "xclip-base-patch16-16-frames":
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		torch.tensor([[7.69_37e-04, 9.97_28e-01, 1.94_73e-03]]      )
						elif model_name == "xclip-large-patch14":
												__SCREAMING_SNAKE_CASE						:     Any					=		torch.tensor([[0.0062, 0.9864, 0.0075]]      )
						elif model_name == "xclip-large-patch14-16-frames":
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		torch.tensor([[3.38_77e-04, 9.99_37e-01, 2.88_88e-04]]      )
						# kinetics-600
						elif model_name == "xclip-base-patch16-kinetics-600":
												__SCREAMING_SNAKE_CASE						:     Any					=		torch.tensor([[0.0555, 0.8914, 0.0531]]      )
						elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
												__SCREAMING_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":
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		torch.tensor([[0.0036, 0.9920, 0.0045]]      )
						# few shot
						elif model_name == "xclip-base-patch16-hmdb-2-shot":
												__SCREAMING_SNAKE_CASE						:     str					=		torch.tensor([[7.18_90e-06, 9.99_94e-01, 5.65_59e-05]]      )
						elif model_name == "xclip-base-patch16-hmdb-4-shot":
												__SCREAMING_SNAKE_CASE						:     Dict					=		torch.tensor([[1.03_20e-05, 9.99_93e-01, 6.24_35e-05]]      )
						elif model_name == "xclip-base-patch16-hmdb-8-shot":
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		torch.tensor([[4.13_77e-06, 9.99_90e-01, 9.83_86e-05]]      )
						elif model_name == "xclip-base-patch16-hmdb-16-shot":
												__SCREAMING_SNAKE_CASE						:     Any					=		torch.tensor([[4.13_47e-05, 9.99_62e-01, 3.34_11e-04]]      )
						elif model_name == "xclip-base-patch16-ucf-2-shot":
												__SCREAMING_SNAKE_CASE						:     Dict					=		torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]      )
						elif model_name == "xclip-base-patch16-ucf-4-shot":
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]      )
						elif model_name == "xclip-base-patch16-ucf-8-shot":
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		torch.tensor([[0.0027, 0.9904, 0.0070]]      )
						elif model_name == "xclip-base-patch16-ucf-16-shot":
												__SCREAMING_SNAKE_CASE						:     str					=		torch.tensor([[9.82_19e-04, 9.95_93e-01, 3.08_63e-03]]      )
						# zero shot
						elif model_name == "xclip-base-patch16-zero-shot":
												__SCREAMING_SNAKE_CASE						:     List[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__":
						__lowerCAmelCase	: Union[str, 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	: Optional[int]  				=parser.parse_args()
						convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__lowerCAmelCase	: List[str]  				='true'


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=82    ,       lowercase__=16      ):
						set_seed(42      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		RegressionModel()
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		deepcopy(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Any					=		RegressionDataset(length=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		DataLoader(lowercase__    ,       batch_size=lowercase__      )
						model.to(accelerator.device      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		accelerator.prepare(lowercase__    ,       lowercase__      )
						return model, ddp_model, dataloader


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''      )
						__SCREAMING_SNAKE_CASE						:     str					=		load_dataset('''glue'''    ,       '''mrpc'''    ,       split='''validation'''      )

						def tokenize_function(lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     Dict					=		tokenizer(examples['''sentence1''']    ,       examples['''sentence2''']    ,       truncation=lowercase__    ,       max_length=lowercase__      )
												return outputs

						with accelerator.main_process_first():
												__SCREAMING_SNAKE_CASE						:     Tuple					=		dataset.map(
												    lowercase__    ,       batched=lowercase__    ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,       )

						__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenized_datasets.rename_column('''label'''    ,       '''labels'''      )

						def collate_fn(lowercase__      ):
												if use_longest:
																		return tokenizer.pad(lowercase__    ,       padding='''longest'''    ,       return_tensors='''pt'''      )
												return tokenizer.pad(lowercase__    ,       padding='''max_length'''    ,       max_length=128    ,       return_tensors='''pt'''      )

						return DataLoader(lowercase__    ,       shuffle=lowercase__    ,       collate_fn=lowercase__    ,       batch_size=16      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     str					=		Accelerator(dispatch_batches=lowercase__    ,       split_batches=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		get_dataloader(lowercase__    ,       not dispatch_batches      )
						__SCREAMING_SNAKE_CASE						:     List[str]					=		AutoModelForSequenceClassification.from_pretrained(
						    '''hf-internal-testing/mrpc-bert-base-cased'''    ,       return_dict=lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		accelerator.prepare(lowercase__    ,       lowercase__      )
						return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[str]					=		[]
						for batch in dataloader:
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		batch.values()
												with torch.no_grad():
																		__SCREAMING_SNAKE_CASE						:     Dict					=		model(lowercase__      )
																		__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		accelerator.gather_for_metrics((logit, target)      )
																		logits_and_targets.append((logit, target)      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		[], []
						for logit, targ in logits_and_targets:
												logits.append(lowercase__      )
												targs.append(lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		torch.cat(lowercase__      ), torch.cat(lowercase__      )
						return logits, targs


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=82    ,       lowercase__=False    ,       lowercase__=False    ,       lowercase__=16      ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		get_basic_setup(lowercase__    ,       lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		generate_predictions(lowercase__    ,       lowercase__    ,       lowercase__      )
						assert (
						    len(lowercase__      ) == num_samples
						), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowercase__      )}'''


def        _UpperCamelCase							(      lowercase__ = False    ,       lowercase__ = False      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		evaluate.load('''glue'''    ,       '''mrpc'''      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[Any]					=		get_mrpc_setup(lowercase__    ,       lowercase__      )
						# First do baseline
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		setup['''no''']
						model.to(lowercase__      )
						model.eval()
						for batch in dataloader:
												batch.to(lowercase__      )
												with torch.inference_mode():
																		__SCREAMING_SNAKE_CASE						:     Dict					=		model(**lowercase__      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		outputs.logits.argmax(dim=-1      )
												metric.add_batch(predictions=lowercase__    ,       references=batch['''labels''']      )
						__SCREAMING_SNAKE_CASE						:     int					=		metric.compute()

						# Then do distributed
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		setup['''ddp''']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		__SCREAMING_SNAKE_CASE						:     int					=		model(**lowercase__      )
												__SCREAMING_SNAKE_CASE						:     str					=		outputs.logits.argmax(dim=-1      )
												__SCREAMING_SNAKE_CASE						:     Any					=		batch['''labels''']
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		accelerator.gather_for_metrics((preds, references)      )
												metric.add_batch(predictions=lowercase__    ,       references=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		metric.compute()

						for key in "accuracy f1".split():
												assert math.isclose(
												    baseline[key]    ,       distributed[key]      ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		Accelerator(split_batches=lowercase__    ,       dispatch_batches=lowercase__      )
						if accelerator.is_local_main_process:
												datasets.utils.logging.set_verbosity_warning()
												transformers.utils.logging.set_verbosity_warning()
						else:
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						# These are a bit slower so they should only be ran on the GPU or TPU
						if torch.cuda.is_available() or is_tpu_available():
												if accelerator.is_local_main_process:
																		print('''**Testing gather_for_metrics**'''      )
												for split_batches in [True, False]:
																		for dispatch_batches in [True, False]:
																								if accelerator.is_local_main_process:
																														print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''      )
																								test_mrpc(lowercase__    ,       lowercase__      )
																								accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test torch metrics**'''      )
						for split_batches in [True, False]:
												for dispatch_batches in [True, False]:
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		Accelerator(split_batches=lowercase__    ,       dispatch_batches=lowercase__      )
																		if accelerator.is_local_main_process:
																								print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''      )
																		test_torch_metrics(lowercase__    ,       99      )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test last batch is not dropped when perfectly divisible**'''      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		Accelerator()
						test_torch_metrics(lowercase__    ,       512      )
						accelerator.state._reset_state()


def        _UpperCamelCase							(      lowercase__      ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()
