



'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase     		=   logging.get_logger(__name__)

UpperCAmelCase     		=   torch.device('cpu')






def 		_snake_case		( )      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
	lowerCAmelCase   =     Image.open(requests.get(__SCREAMING_SNAKE_CASE							,		stream=__SCREAMING_SNAKE_CASE							).raw							)
	return im






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							)      ->						Tuple:







	"""simple docstring"""
	if swiftformer_name == "swiftformer_xs":
		return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]							)

	elif swiftformer_name == "swiftformer_s":
		return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]							)

	elif swiftformer_name == "swiftformer_l1":
		return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]							)

	elif swiftformer_name == "swiftformer_l3":
		return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						Optional[int]:







	"""simple docstring"""
	lowerCAmelCase   =     dct.pop(__SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     val






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							)      ->						Optional[int]:







	"""simple docstring"""
	lowerCAmelCase   =     []
	for k in state_dict.keys():
		lowerCAmelCase   =     k
		if ".pwconv" in k:
			lowerCAmelCase   =     k_new.replace(""".pwconv"""							,		""".point_wise_conv"""							)
		if ".dwconv" in k:
			lowerCAmelCase   =     k_new.replace(""".dwconv"""							,		""".depth_wise_conv"""							)
		if ".Proj." in k:
			lowerCAmelCase   =     k_new.replace(""".Proj."""							,		""".proj."""							)
		if "patch_embed" in k_new:
			lowerCAmelCase   =     k_new.replace("""patch_embed"""							,		"""swiftformer.patch_embed.patch_embedding"""							)
		if "network" in k_new:
			lowerCAmelCase   =     k_new.split("""."""							)
			if ls[2].isdigit():
				lowerCAmelCase   =     "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]							)
			else:
				lowerCAmelCase   =     k_new.replace("""network"""							,		"""swiftformer.encoder.network"""							)
		rename_keys.append((k, k_new)							)
	return rename_keys






@torch.no_grad()
def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     SwiftFormerConfig()

	# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
	lowerCAmelCase   =     1_000
	lowerCAmelCase   =     "huggingface/label-files"
	lowerCAmelCase   =     "imagenet-1k-id2label.json"
	lowerCAmelCase   =     json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE							,		__SCREAMING_SNAKE_CASE							,		repo_type="""dataset"""							)							,		"""r"""							)							)
	lowerCAmelCase   =     {int(__SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
	lowerCAmelCase   =     idalabel
	lowerCAmelCase   =     {v: k for k, v in idalabel.items()}

	# size of the architecture
	if swiftformer_name == "swiftformer_xs":
		lowerCAmelCase   =     [3, 3, 6, 4]
		lowerCAmelCase   =     [48, 56, 112, 220]

	elif swiftformer_name == "swiftformer_s":
		lowerCAmelCase   =     [3, 3, 9, 6]
		lowerCAmelCase   =     [48, 64, 168, 224]

	elif swiftformer_name == "swiftformer_l1":
		lowerCAmelCase   =     [4, 3, 10, 5]
		lowerCAmelCase   =     [48, 96, 192, 384]

	elif swiftformer_name == "swiftformer_l3":
		lowerCAmelCase   =     [4, 4, 12, 6]
		lowerCAmelCase   =     [64, 128, 320, 512]

	# load state_dict of original model, remove and rename some keys
	if original_ckpt:
		if original_ckpt.startswith("""https"""							):
			lowerCAmelCase   =     torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE							,		map_location="""cpu"""							,		check_hash=__SCREAMING_SNAKE_CASE							)
		else:
			lowerCAmelCase   =     torch.load(__SCREAMING_SNAKE_CASE							,		map_location="""cpu"""							)
	lowerCAmelCase   =     checkpoint

	lowerCAmelCase   =     create_rename_keys(__SCREAMING_SNAKE_CASE							)
	for rename_key_src, rename_key_dest in rename_keys:
		rename_key(__SCREAMING_SNAKE_CASE							,		__SCREAMING_SNAKE_CASE							,		__SCREAMING_SNAKE_CASE							)

	# load HuggingFace model
	lowerCAmelCase   =     SwiftFormerForImageClassification(__SCREAMING_SNAKE_CASE							).eval()
	hf_model.load_state_dict(__SCREAMING_SNAKE_CASE							)

	# prepare test inputs
	lowerCAmelCase   =     prepare_img()
	lowerCAmelCase   =     ViTImageProcessor.from_pretrained("""preprocessor_config"""							)
	lowerCAmelCase   =     processor(images=__SCREAMING_SNAKE_CASE							,		return_tensors="""pt"""							)

	# compare outputs from both models
	lowerCAmelCase   =     get_expected_output(__SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     hf_model(inputs["""pixel_values"""]							).logits

	assert hf_logits.shape == torch.Size([1, 1_000]							)
	assert torch.allclose(hf_logits[0, 0:5]							,		__SCREAMING_SNAKE_CASE							,		atol=1E-3							)

	Path(__SCREAMING_SNAKE_CASE							).mkdir(exist_ok=__SCREAMING_SNAKE_CASE							)
	print(f'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'							)
	hf_model.save_pretrained(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
				UpperCAmelCase     		=   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--swiftformer_name',
				    default='swiftformer_xs',
				    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
				    type=str,
				    help='Name of the SwiftFormer model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default='./converted_outputs/',
				    type=str,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

				UpperCAmelCase     		=   parser.parse_args()
				convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




'''simple docstring'''

import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
				raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
UpperCAmelCase     		=   False
UpperCAmelCase     		=   False






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Namespace							)      ->						Tuple:







	"""simple docstring"""
	return TrainCommand(_SCREAMING_SNAKE_CASE							)





class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						@staticmethod
						def     __snake_case   ( A_				)     ->     Optional[int]:
							lowerCAmelCase   =     parser.add_parser("""train"""     ,	help="""CLI tool to train a model on a task."""				)

							train_parser.add_argument(
							    """--train_data"""     ,	type=A_     ,	required=A_     ,	help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""     ,	)
							train_parser.add_argument(
							    """--column_label"""     ,	type=A_     ,	default=0     ,	help="""Column of the dataset csv file with example labels."""				)
							train_parser.add_argument(
							    """--column_text"""     ,	type=A_     ,	default=1     ,	help="""Column of the dataset csv file with example texts."""				)
							train_parser.add_argument(
							    """--column_id"""     ,	type=A_     ,	default=2     ,	help="""Column of the dataset csv file with example ids."""				)
							train_parser.add_argument(
							    """--skip_first_row"""     ,	action="""store_true"""     ,	help="""Skip the first row of the csv file (headers)."""				)

							train_parser.add_argument("""--validation_data"""     ,	type=A_     ,	default=""""""     ,	help="""path to validation dataset."""				)
							train_parser.add_argument(
							    """--validation_split"""     ,	type=A_     ,	default=0.1     ,	help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""     ,	)

							train_parser.add_argument("""--output"""     ,	type=A_     ,	default="""./"""     ,	help="""path to saved the trained model."""				)

							train_parser.add_argument(
							    """--task"""     ,	type=A_     ,	default="""text_classification"""     ,	help="""Task to train the model on."""				)
							train_parser.add_argument(
							    """--model"""     ,	type=A_     ,	default="""bert-base-uncased"""     ,	help="""Model's name or path to stored model."""				)
							train_parser.add_argument("""--train_batch_size"""     ,	type=A_     ,	default=32     ,	help="""Batch size for training."""				)
							train_parser.add_argument("""--valid_batch_size"""     ,	type=A_     ,	default=64     ,	help="""Batch size for validation."""				)
							train_parser.add_argument("""--learning_rate"""     ,	type=A_     ,	default=3e-5     ,	help="""Learning rate."""				)
							train_parser.add_argument("""--adam_epsilon"""     ,	type=A_     ,	default=1e-08     ,	help="""Epsilon for Adam optimizer."""				)
							train_parser.set_defaults(func=A_				)


						def __init__( self     ,	A_				)     ->     Tuple:
							lowerCAmelCase   =     logging.get_logger("""transformers-cli/training"""				)

							lowerCAmelCase   =     """tf""" if is_tf_available() else """torch"""

							os.makedirs(args.output     ,	exist_ok=A_				)
							lowerCAmelCase   =     args.output

							lowerCAmelCase   =     args.column_label
							lowerCAmelCase   =     args.column_text
							lowerCAmelCase   =     args.column_id

							self.logger.info(f'Loading {args.task} pipeline for {args.model}'				)
							if args.task == "text_classification":
								lowerCAmelCase   =     TextClassificationPipeline.from_pretrained(args.model				)
							elif args.task == "token_classification":
								raise NotImplementedError
							elif args.task == "question_answering":
								raise NotImplementedError

							self.logger.info(f'Loading dataset from {args.train_data}'				)
							lowerCAmelCase   =     Processor.create_from_csv(
							    args.train_data     ,	column_label=args.column_label     ,	column_text=args.column_text     ,	column_id=args.column_id     ,	skip_first_row=args.skip_first_row     ,	)
							lowerCAmelCase   =     None
							if args.validation_data:
								self.logger.info(f'Loading validation dataset from {args.validation_data}'				)
								lowerCAmelCase   =     Processor.create_from_csv(
								    args.validation_data     ,	column_label=args.column_label     ,	column_text=args.column_text     ,	column_id=args.column_id     ,	skip_first_row=args.skip_first_row     ,	)

							lowerCAmelCase   =     args.validation_split
							lowerCAmelCase   =     args.train_batch_size
							lowerCAmelCase   =     args.valid_batch_size
							lowerCAmelCase   =     args.learning_rate
							lowerCAmelCase   =     args.adam_epsilon


						def     __snake_case   ( self				)     ->     Optional[int]:
							if self.framework == "tf":
								return self.run_tf()
							return self.run_torch()


						def     __snake_case   ( self				)     ->     Tuple:
							raise NotImplementedError




						def     __snake_case   ( self				)     ->     Tuple:
							self.pipeline.fit(
							    self.train_dataset     ,	validation_data=self.valid_dataset     ,	validation_split=self.validation_split     ,	learning_rate=self.learning_rate     ,	adam_epsilon=self.adam_epsilon     ,	train_batch_size=self.train_batch_size     ,	valid_batch_size=self.valid_batch_size     ,	)

							# Save trained pipeline
							self.pipeline.save_pretrained(self.output				)