
"""simple docstring"""


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_lowercase											=			16
_lowercase											=			32






def  _snake_case			(						snake_case__				:       Accelerator		, snake_case__				:       int = 16       ):
	A  				=				AutoTokenizer.from_pretrained('bert-base-cased'       )
	A  				=				load_dataset('glue'		, 'mrpc'       )

	def tokenize_function(snake_case__				:       str       ):
		# max_length=None => use the model max length (it's actually the default)
		A  				=				tokenizer(examples['sentence1']		, examples['sentence2']		, truncation=snake_case__		, max_length=snake_case__       )
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		A  				=				datasets.map(
		    snake_case__		, batched=snake_case__		, remove_columns=['idx', 'sentence1', 'sentence2']		, )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	A  				=				tokenized_datasets.rename_column('label'		, 'labels'       )

	def collate_fn(snake_case__				:       Optional[Any]       ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A  				=				128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			A  				=				16
		elif accelerator.mixed_precision != "no":
			A  				=				8
		else:
			A  				=				None

		return tokenizer.pad(
		    snake_case__		, padding='longest'		, max_length=snake_case__		, pad_to_multiple_of=snake_case__		, return_tensors='pt'		, )

	# Instantiate dataloaders.
	A  				=				DataLoader(
	    tokenized_datasets['train']		, shuffle=snake_case__		, collate_fn=snake_case__		, batch_size=snake_case__       )
	A  				=				DataLoader(
	    tokenized_datasets['validation']		, shuffle=snake_case__		, collate_fn=snake_case__		, batch_size=snake_case__       )

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		_lowercase											=			mocked_dataloaders  # noqa: F811






def  _snake_case			(						snake_case__				:       Dict		, snake_case__				:       Optional[Any]       ):
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'		, snake_case__       ) == "1":
		A  				=				2
	# Initialize accelerator
	A  				=				Accelerator(cpu=args.cpu		, mixed_precision=args.mixed_precision       )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A  				=				config['lr']
	A  				=				int(config['num_epochs']       )
	A  				=				int(config['seed']       )
	A  				=				int(config['batch_size']       )

	# New Code #
	# Parse out whether we are saving every epoch or after a certain number of batches
	if hasattr(args.checkpointing_steps		, 'isdigit'       ):
		if args.checkpointing_steps == "epoch":
			A  				=				args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			A  				=				int(args.checkpointing_steps       )
		else:
			raise ValueError(
			    F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'       )
	else:
		A  				=				None

	set_seed(snake_case__       )

	A				, A  				=				get_dataloaders(snake_case__		, snake_case__       )
	A  				=				evaluate.load('glue'		, 'mrpc'       )

	# If the batch size is too big we use gradient accumulation
	A  				=				1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A  				=				batch_size // MAX_GPU_BATCH_SIZE
		A  				=				MAX_GPU_BATCH_SIZE

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A  				=				AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		, return_dict=snake_case__       )

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	A  				=				model.to(accelerator.device       )

	# Instantiate optimizer
	A  				=				AdamW(params=model.parameters()		, lr=snake_case__       )

	# Instantiate scheduler
	A  				=				get_linear_schedule_with_warmup(
	    optimizer=snake_case__		, num_warmup_steps=100		, num_training_steps=(len(snake_case__       ) * num_epochs) // gradient_accumulation_steps		, )

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	A				, A				, A				, A				, A  				=				accelerator.prepare(
	    snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__       )

	# New Code #
	# We need to keep track of how many total steps we have iterated over
	A  				=				0
	# We also need to keep track of the stating epoch so files are named properly
	A  				=				0

	# We need to load the checkpoint back in before training here with `load_state`
	# The total number of epochs is adjusted based on where the state is being loaded from,
	# as we assume continuation of the same training script
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(F'Resumed from checkpoint: {args.resume_from_checkpoint}'       )
			accelerator.load_state(args.resume_from_checkpoint       )
			A  				=				os.path.basename(args.resume_from_checkpoint       )
		else:
			# Get the most recent checkpoint
			A  				=				[f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
			dirs.sort(key=os.path.getctime       )
			A  				=				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		A  				=				os.path.splitext(snake_case__       )[0]

		if "epoch" in training_difference:
			A  				=				int(training_difference.replace('epoch_'		, ''       )       ) + 1
			A  				=				None
		else:
			A  				=				int(training_difference.replace('step_'		, ''       )       )
			A  				=				resume_step // len(snake_case__       )
			resume_step -= starting_epoch * len(snake_case__       )

    # Now we train the model
	for epoch in range(snake_case__		, snake_case__       ):
		model.train()
		# New Code #
		if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
			# We need to skip steps until we reach the resumed step
			A  				=				accelerator.skip_first_batches(snake_case__		, snake_case__       )
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			A  				=				train_dataloader
		for step, batch in enumerate(snake_case__       ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device       )
			A  				=				model(**snake_case__       )
			A  				=				outputs.loss
			A  				=				loss / gradient_accumulation_steps
			accelerator.backward(snake_case__       )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
			# New Code #
			overall_step += 1

			# New Code #
			# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
			# These are saved to folders named `step_{overall_step}`
			# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
			# If mixed precision was used, will also save a "scalar.bin" file
			if isinstance(snake_case__		, snake_case__       ):
				A  				=				F'step_{overall_step}'
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						A  				=				os.path.join(args.output_dir		, snake_case__       )
					accelerator.save_state(snake_case__       )

		model.eval()
		for step, batch in enumerate(snake_case__       ):
			# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
			batch.to(accelerator.device       )
			with torch.no_grad():
				A  				=				model(**snake_case__       )
			A  				=				outputs.logits.argmax(dim=-1       )
			A				, A  				=				accelerator.gather_for_metrics((predictions, batch['labels'])       )
			metric.add_batch(
			    predictions=snake_case__		, references=snake_case__		, )

		A  				=				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'epoch {epoch}:'		, snake_case__       )

		# New Code #
		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
		# These are saved to folders named `epoch_{epoch}`
		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
		# If mixed precision was used, will also save a "scalar.bin" file
		if checkpointing_steps == "epoch":
			A  				=				F'epoch_{epoch}'
			if args.output_dir is not None:
				A  				=				os.path.join(args.output_dir		, snake_case__       )
			accelerator.save_state(snake_case__       )






def  _snake_case			(						):
	A  				=				argparse.ArgumentParser(description='Simple example of training script.'       )
	parser.add_argument(
	    '--mixed_precision'		, type=snake_case__		, default=snake_case__		, choices=['no', 'fp16', 'bf16', 'fp8']		, help='Whether to use mixed precision. Choose'
	    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
	    'and an Nvidia Ampere GPU.'		, )
	parser.add_argument('--cpu'		, action='store_true'		, help='If passed, will train on the CPU.'       )
	parser.add_argument(
	    '--checkpointing_steps'		, type=snake_case__		, default=snake_case__		, help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'		, )
	parser.add_argument(
	    '--output_dir'		, type=snake_case__		, default='.'		, help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'		, )
	parser.add_argument(
	    '--resume_from_checkpoint'		, type=snake_case__		, default=snake_case__		, help='If the training should continue from a checkpoint folder.'		, )
	A  				=				parser.parse_args()
	A  				=				{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(snake_case__		, snake_case__       )


if __name__ == "__main__":
		main()

"""simple docstring"""


import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling






def  _snake_case			(						snake_case__				:       Tuple       ):
	A  				=				'https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'

	with open(snake_case__		, 'wb'       ) as f:
		with requests.get(snake_case__		, stream=snake_case__       ) as req:
			for chunk in req.iter_content(chunk_size=2048       ):
				f.write(snake_case__       )






def  _snake_case			(						):
	A  				=				VivitConfig()

	A  				=				400
	A  				=				'huggingface/label-files'
	A  				=				'kinetics400-id2label.json'

	A  				=				json.load(open(hf_hub_download(snake_case__		, snake_case__		, repo_type='dataset'       )		, 'r'       )       )
	A  				=				{int(snake_case__       ): v for k, v in idalabel.items()}
	A  				=				idalabel
	A  				=				{v: k for k, v in idalabel.items()}
	return config






def  _snake_case			(						):
	A  				=				hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video'		, filename='eating_spaghetti_32_frames.npy'		, repo_type='dataset'       )
	A  				=				np.load(snake_case__       )
	return list(snake_case__       )






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	if np.ndim(snake_case__       ) == 2:
		return transform_attention_bias(snake_case__       )

	elif np.ndim(snake_case__       ) == 3:
		return transform_attention_kernel(snake_case__       )

	else:
		raise Exception(F'Invalid number of dimesions: {np.ndim(snake_case__       )}'       )






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	return current.flatten()






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	return np.reshape(snake_case__		, (current.shape[0], current.shape[1] * current.shape[2])       ).T






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	return np.reshape(snake_case__		, (current.shape[0] * current.shape[1], current.shape[2])       ).T






def  _snake_case			(						snake_case__				:       str		, snake_case__				:       List[str]       ):
	A  				=				state_dict['optimizer']['target']['Transformer'][F'encoderblock_{i}']

	A  				=				F'encoder.layer.{i}.'
	A  				=				{
	    prefix + 'intermediate.dense.bias': state['MlpBlock_0']['Dense_0']['bias'],
	    prefix + 'intermediate.dense.weight': np.transpose(state['MlpBlock_0']['Dense_0']['kernel']       ),
	    prefix + 'output.dense.bias': state['MlpBlock_0']['Dense_1']['bias'],
	    prefix + 'output.dense.weight': np.transpose(state['MlpBlock_0']['Dense_1']['kernel']       ),
	    prefix + 'layernorm_before.bias': state['LayerNorm_0']['bias'],
	    prefix + 'layernorm_before.weight': state['LayerNorm_0']['scale'],
	    prefix + 'layernorm_after.bias': state['LayerNorm_1']['bias'],
	    prefix + 'layernorm_after.weight': state['LayerNorm_1']['scale'],
	    prefix
	    + 'attention.attention.query.bias': transform_attention(
	        state['MultiHeadDotProductAttention_0']['query']['bias']       ),
	    prefix
	    + 'attention.attention.query.weight': transform_attention(
	        state['MultiHeadDotProductAttention_0']['query']['kernel']       ),
	    prefix
	    + 'attention.attention.key.bias': transform_attention(state['MultiHeadDotProductAttention_0']['key']['bias']       ),
	    prefix
	    + 'attention.attention.key.weight': transform_attention(
	        state['MultiHeadDotProductAttention_0']['key']['kernel']       ),
	    prefix
	    + 'attention.attention.value.bias': transform_attention(
	        state['MultiHeadDotProductAttention_0']['value']['bias']       ),
	    prefix
	    + 'attention.attention.value.weight': transform_attention(
	        state['MultiHeadDotProductAttention_0']['value']['kernel']       ),
	    prefix + 'attention.output.dense.bias': state['MultiHeadDotProductAttention_0']['out']['bias'],
	    prefix
	    + 'attention.output.dense.weight': transform_attention_output_weight(
	        state['MultiHeadDotProductAttention_0']['out']['kernel']       ),
	}

	return new_state






def  _snake_case			(						snake_case__				:       Dict       ):
	return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]       )






def  _snake_case			(						snake_case__				:       Union[str, Any]		, snake_case__				:       Optional[int]=False       ):
	A  				=				get_n_layers(snake_case__       )

	A  				=				OrderedDict()

	A  				=				state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
	A  				=				state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

	A  				=				np.transpose(
	    state_dict['optimizer']['target']['embedding']['kernel']		, (4, 3, 0, 1, 2)       )
	A  				=				state_dict['optimizer']['target']['embedding']['bias']

	A  				=				state_dict['optimizer']['target']['cls']
	A  				=				state_dict['optimizer']['target']['Transformer']['posembed_input'][
	    'pos_embedding'
	]

	for i in range(snake_case__       ):
		new_state.update(transform_state_encoder_block(snake_case__		, snake_case__       )       )

	if classification_head:
		A  				=				{'vivit.' + k: v for k, v in new_state.items()}
		A  				=				np.transpose(state_dict['optimizer']['target']['output_projection']['kernel']       )
		A  				=				np.transpose(state_dict['optimizer']['target']['output_projection']['bias']       )

	return {k: torch.tensor(snake_case__       ) for k, v in new_state.items()}






def  _snake_case			(						):
	A  				=				VivitImageProcessor()

	assert extractor.do_resize is True
	assert extractor.size == {"shortest_edge": 256}
	assert extractor.do_center_crop is True
	assert extractor.crop_size == {"width": 224, "height": 224}
	assert extractor.resample == PILImageResampling.BILINEAR

	# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
	# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
	# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
	assert extractor.do_normalize is False
	assert extractor.do_rescale is True
	assert extractor.rescale_factor == 1 / 255

	# zero-centering = True in original implementation
	assert extractor.do_zero_centering is True

	return extractor






def  _snake_case			(						snake_case__				:       str       ):
	A  				=				'checkpoint'

	if not os.path.exists(snake_case__       ):
		download_checkpoint(snake_case__       )

	A  				=				restore_checkpoint(snake_case__		, snake_case__       )
	A  				=				transform_state(snake_case__		, classification_head=snake_case__       )

	A  				=				get_vivit_config()

	assert config.image_size == 224
	assert config.num_frames == 32

	A  				=				VivitForVideoClassification(snake_case__       )
	model.load_state_dict(snake_case__       )
	model.eval()

	A  				=				get_processor()

	A  				=				prepare_video()
	A  				=				extractor(snake_case__		, return_tensors='pt'       )

	A  				=				model(**snake_case__       )

	A  				=				torch.Size([1, 400]       )
	A  				=				torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]       )

	assert outputs.logits.shape == expected_shape
	assert torch.allclose(outputs.logits[0, :5]		, snake_case__		, atol=1e-4       ), outputs.logits[0, :5]

	model.save_pretrained(snake_case__       )
	extractor.save_pretrained(snake_case__       )


if __name__ == "__main__":
		_lowercase											=			argparse.ArgumentParser()

		parser.add_argument('''--output_model_name''', '''-o''', type=str, help='''Output path for the converted HuggingFace model''')

		_lowercase											=			parser.parse_args()
		convert(args.output_model_name)