



import argparse
import json

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

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling





def 							A			(  _UpperCAmelCase       :     Optional[int]					)       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
			_UpperCAmelCase	 =     True if 'large' in model_name or 'huge' in model_name else False
			_UpperCAmelCase	 =     True if 'large' in model_name or 'huge' in model_name else False
			_UpperCAmelCase	 =     True if 'large' in model_name or 'huge' in model_name else False

			if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
						if "fl3" in model_name:
									_UpperCAmelCase	 =     [3, 3, 3, 3]
									_UpperCAmelCase	 =     [5, 5, 5, 5]
						elif "fl4" in model_name:
									_UpperCAmelCase	 =     [4, 4, 4, 4]
									_UpperCAmelCase	 =     [3, 3, 3, 3]

			if "tiny" in model_name or "small" in model_name or "base" in model_name:
						_UpperCAmelCase	 =     [3, 3, 3, 3]
						if "lrf" in model_name:
									_UpperCAmelCase	 =     [3, 3, 3, 3]
						else:
									_UpperCAmelCase	 =     [2, 2, 2, 2]

			if "tiny" in model_name:
						_UpperCAmelCase	 =     96
			elif "small" in model_name:
						_UpperCAmelCase	 =     96
			elif "base" in model_name:
						_UpperCAmelCase	 =     128
			elif "large" in model_name:
						_UpperCAmelCase	 =     192
			elif "xlarge" in model_name:
						_UpperCAmelCase	 =     256
			elif "huge" in model_name:
						_UpperCAmelCase	 =     352

			# set label information
			_UpperCAmelCase	 =     'huggingface/label-files'
			if "large" in model_name or "huge" in model_name:
						_UpperCAmelCase	 =     'imagenet-22k-id2label.json'
			else:
						_UpperCAmelCase	 =     'imagenet-1k-id2label.json'

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

			_UpperCAmelCase	 =     FocalNetConfig(
			    embed_dim=_UpperCAmelCase							,     depths=_UpperCAmelCase							,     focal_levels=_UpperCAmelCase							,     focal_windows=_UpperCAmelCase							,     use_conv_embed=_UpperCAmelCase							,     idalabel=_UpperCAmelCase							,     labelaid=_UpperCAmelCase							,     use_post_layernorm=_UpperCAmelCase							,     use_layerscale=_UpperCAmelCase							,     )

			return config





def 							A			(  _UpperCAmelCase       :     List[Any]					)       ->				int:







			'''simple docstring'''
			if "patch_embed.proj" in name:
						_UpperCAmelCase	 =     name.replace('patch_embed.proj'							,     'embeddings.patch_embeddings.projection'					)
			if "patch_embed.norm" in name:
						_UpperCAmelCase	 =     name.replace('patch_embed.norm'							,     'embeddings.norm'					)
			if "layers" in name:
						_UpperCAmelCase	 =     'encoder.' + name
			if "encoder.layers" in name:
						_UpperCAmelCase	 =     name.replace('encoder.layers'							,     'encoder.stages'					)
			if "downsample.proj" in name:
						_UpperCAmelCase	 =     name.replace('downsample.proj'							,     'downsample.projection'					)
			if "blocks" in name:
						_UpperCAmelCase	 =     name.replace('blocks'							,     'layers'					)
			if "modulation.f.weight" in name or "modulation.f.bias" in name:
						_UpperCAmelCase	 =     name.replace('modulation.f'							,     'modulation.projection_in'					)
			if "modulation.h.weight" in name or "modulation.h.bias" in name:
						_UpperCAmelCase	 =     name.replace('modulation.h'							,     'modulation.projection_context'					)
			if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
						_UpperCAmelCase	 =     name.replace('modulation.proj'							,     'modulation.projection_out'					)

			if name == "norm.weight":
						_UpperCAmelCase	 =     'layernorm.weight'
			if name == "norm.bias":
						_UpperCAmelCase	 =     'layernorm.bias'

			if "head" in name:
						_UpperCAmelCase	 =     name.replace('head'							,     'classifier'					)
			else:
						_UpperCAmelCase	 =     'focalnet.' + name

			return name





def 							A			(  _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Union[str, Any]=False					)       ->				str:







			'''simple docstring'''
			# fmt: off
			_UpperCAmelCase	 =     {
			    'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
			    'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
			    'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
			    'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
			    'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
			    'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
			    'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
			    'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
			    'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
			    'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
			}
			# fmt: on

			_UpperCAmelCase	 =     model_name_to_url[model_name]
			print('Checkpoint URL: '							,     _UpperCAmelCase					)
			_UpperCAmelCase	 =     torch.hub.load_state_dict_from_url(_UpperCAmelCase							,     map_location='cpu'					)['model']

			# rename keys
			for key in state_dict.copy().keys():
						_UpperCAmelCase	 =     state_dict.pop(_UpperCAmelCase					)
						_UpperCAmelCase	 =     val

			_UpperCAmelCase	 =     get_focalnet_config(_UpperCAmelCase					)
			_UpperCAmelCase	 =     FocalNetForImageClassification(_UpperCAmelCase					)
			model.eval()

			# load state dict
			model.load_state_dict(_UpperCAmelCase					)

			# verify conversion
			_UpperCAmelCase	 =     'http://images.cocodataset.org/val2017/000000039769.jpg'

			_UpperCAmelCase	 =     BitImageProcessor(
			    do_resize=_UpperCAmelCase							,     size={'shortest_edge': 256}							,     resample=PILImageResampling.BILINEAR							,     do_center_crop=_UpperCAmelCase							,     crop_size=224							,     do_normalize=_UpperCAmelCase							,     image_mean=_UpperCAmelCase							,     image_std=_UpperCAmelCase							,     )
			_UpperCAmelCase	 =     Image.open(requests.get(_UpperCAmelCase							,     stream=_UpperCAmelCase					).raw					)
			_UpperCAmelCase	 =     processor(images=_UpperCAmelCase							,     return_tensors='pt'					)

			_UpperCAmelCase	 =     transforms.Compose(
			    [
			        transforms.Resize(256					),
			        transforms.CenterCrop(224					),
			        transforms.ToTensor(),
			        transforms.Normalize(mean=[0.485, 0.456, 0.406]							,     std=[0.229, 0.224, 0.225]					),
			    ]					)

			_UpperCAmelCase	 =     image_transforms(_UpperCAmelCase					).unsqueeze(0					)

			# verify pixel_values
			assert torch.allclose(inputs.pixel_values							,     _UpperCAmelCase							,     atol=1E-4					)

			_UpperCAmelCase	 =     model(**_UpperCAmelCase					)

			_UpperCAmelCase	 =     outputs.logits.argmax(-1					).item()
			print('Predicted class:'							,     model.config.idalabel[predicted_class_idx]					)

			print('First values of logits:'							,     outputs.logits[0, :3]					)

			if model_name == "focalnet-tiny":
						_UpperCAmelCase	 =     torch.tensor([0.2166, -0.4368, 0.2191]					)
			elif model_name == "focalnet-tiny-lrf":
						_UpperCAmelCase	 =     torch.tensor([1.1669, 0.0125, -0.1695]					)
			elif model_name == "focalnet-small":
						_UpperCAmelCase	 =     torch.tensor([0.4917, -0.0430, 0.1341]					)
			elif model_name == "focalnet-small-lrf":
						_UpperCAmelCase	 =     torch.tensor([-0.2588, -0.5342, -0.2331]					)
			elif model_name == "focalnet-base":
						_UpperCAmelCase	 =     torch.tensor([-0.1655, -0.4090, -0.1730]					)
			elif model_name == "focalnet-base-lrf":
						_UpperCAmelCase	 =     torch.tensor([0.5306, -0.0483, -0.3928]					)
			assert torch.allclose(outputs.logits[0, :3]							,     _UpperCAmelCase							,     atol=1E-4					)
			print('Looks ok!'					)

			if pytorch_dump_folder_path is not None:
						print(F"Saving model and processor of {model_name} to {pytorch_dump_folder_path}"					)
						model.save_pretrained(_UpperCAmelCase					)
						processor.save_pretrained(_UpperCAmelCase					)

			if push_to_hub:
						print(F"Pushing model and processor of {model_name} to the hub..."					)
						model.push_to_hub(F"{model_name}"					)
						processor.push_to_hub(F"{model_name}"					)


if __name__ == "__main__":
							UpperCAmelCase__     = argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default="focalnet-tiny",
							    type=str,
							    help="Name of the FocalNet model you'd like to convert.",
							)
							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 to push the model and processor to the hub.",
							)

							UpperCAmelCase__     = parser.parse_args()
							convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format="%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=os.environ.get("LOGLEVEL", "INFO").upper(),
    stream=sys.stdout,
)

UpperCAmelCase__     = logging.getLogger(__name__)

UpperCAmelCase__     = {"facebook/bart-base": BartForConditionalGeneration}
UpperCAmelCase__     = {"facebook/bart-base": BartTokenizer}





def 							A			(  )       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.'					)
			parser.add_argument(
			    '--validation_file'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='A csv or a json file containing the validation data.'					)
			parser.add_argument(
			    '--max_length'							,     type=_UpperCAmelCase							,     default=5							,     help='The maximum total input sequence length after tokenization.'							,     )
			parser.add_argument(
			    '--num_beams'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help=(
			        'Number of beams to use for evaluation. This argument will be '
			        'passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'
			    )							,     )
			parser.add_argument(
			    '--model_name_or_path'							,     type=_UpperCAmelCase							,     help='Path to pretrained model or model identifier from huggingface.co/models.'							,     required=_UpperCAmelCase							,     )
			parser.add_argument(
			    '--config_name'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='Pretrained config name or path if not the same as model_name'							,     )
			parser.add_argument(
			    '--device'							,     type=_UpperCAmelCase							,     default='cpu'							,     help='Device where the model will be run'							,     )
			parser.add_argument('--output_file_path'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='Where to store the final ONNX file.'					)

			_UpperCAmelCase	 =     parser.parse_args()

			return args





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     List[Any]="cpu"					)       ->				Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase	 =     model_dict[model_name].from_pretrained(_UpperCAmelCase					).to(_UpperCAmelCase					)
			_UpperCAmelCase	 =     tokenizer_dict[model_name].from_pretrained(_UpperCAmelCase					)

			if model_name in ["facebook/bart-base"]:
						_UpperCAmelCase	 =     0
						_UpperCAmelCase	 =     None
						_UpperCAmelCase	 =     0

			return huggingface_model, tokenizer





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Tuple					)       ->				List[Any]:







			'''simple docstring'''
			model.eval()

			_UpperCAmelCase	 =     None
			_UpperCAmelCase	 =     torch.jit.script(BARTBeamSearchGenerator(_UpperCAmelCase					)					)

			with torch.no_grad():
						_UpperCAmelCase	 =     'My friends are cool but they eat too many carbs.'
						_UpperCAmelCase	 =     tokenizer([ARTICLE_TO_SUMMARIZE]							,     max_length=1_024							,     return_tensors='pt'					).to(model.device					)

						_UpperCAmelCase	 =     model.generate(
						    inputs['input_ids']							,     attention_mask=inputs['attention_mask']							,     num_beams=_UpperCAmelCase							,     max_length=_UpperCAmelCase							,     early_stopping=_UpperCAmelCase							,     decoder_start_token_id=model.config.decoder_start_token_id							,     )

						torch.onnx.export(
						    _UpperCAmelCase							,     (
						        inputs['input_ids'],
						        inputs['attention_mask'],
						        num_beams,
						        max_length,
						        model.config.decoder_start_token_id,
						    )							,     _UpperCAmelCase							,     opset_version=14							,     input_names=['input_ids', 'attention_mask', 'num_beams', 'max_length', 'decoder_start_token_id']							,     output_names=['output_ids']							,     dynamic_axes={
						        'input_ids': {0: 'batch', 1: 'seq'},
						        'output_ids': {0: 'batch', 1: 'seq_out'},
						    }							,     example_outputs=_UpperCAmelCase							,     )

						logger.info('Model exported to {}'.format(_UpperCAmelCase					)					)

						_UpperCAmelCase	 =     remove_dup_initializers(os.path.abspath(_UpperCAmelCase					)					)

						logger.info('Deduplicated and optimized model written to {}'.format(_UpperCAmelCase					)					)

						_UpperCAmelCase	 =     onnxruntime.InferenceSession(_UpperCAmelCase					)
						_UpperCAmelCase	 =     ort_sess.run(
						    _UpperCAmelCase							,     {
						        'input_ids': inputs['input_ids'].cpu().numpy(),
						        'attention_mask': inputs['attention_mask'].cpu().numpy(),
						        'num_beams': np.array(_UpperCAmelCase					),
						        'max_length': np.array(_UpperCAmelCase					),
						        'decoder_start_token_id': np.array(model.config.decoder_start_token_id					),
						    }							,     )

						np.testing.assert_allclose(summary_ids.cpu().numpy()							,     ort_out[0]							,     rtol=1E-3							,     atol=1E-3					)

						logger.info('Model outputs from torch and ONNX Runtime are similar.'					)
						logger.info('Success.'					)





def 							A			(  )       ->				Dict:







			'''simple docstring'''
			_UpperCAmelCase	 =     parse_args()
			_UpperCAmelCase	 =     5
			_UpperCAmelCase	 =     4

			# Make one log on every process with the configuration for debugging.
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,     datefmt='%m/%d/%Y %H:%M:%S'							,     level=logging.INFO							,     )

			logger.setLevel(logging.INFO					)
			transformers.utils.logging.set_verbosity_error()

			_UpperCAmelCase	 =     torch.device(args.device					)

			_UpperCAmelCase ,  _UpperCAmelCase	 =     load_model_tokenizer(args.model_name_or_path							,     _UpperCAmelCase					)

			if model.config.decoder_start_token_id is None:
						raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined'					)

			model.to(_UpperCAmelCase					)

			if args.max_length:
						_UpperCAmelCase	 =     args.max_length

			if args.num_beams:
						_UpperCAmelCase	 =     args.num_beams

			if args.output_file_path:
						_UpperCAmelCase	 =     args.output_file_path
			else:
						_UpperCAmelCase	 =     'BART.onnx'

			logger.info('Exporting model to ONNX'					)
			export_and_validate_model(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)


if __name__ == "__main__":
							main()
