




"""simple docstring"""
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,
)

__SCREAMING_SNAKE_CASE							:      str						=      logging.getLogger(__name__)

__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      {'facebook/bart-base': BartForConditionalGeneration}
__SCREAMING_SNAKE_CASE							:      int						=      {'facebook/bart-base': BartTokenizer}







def   _a			( )      ->			Optional[int]:
							snake_case_          =						argparse.ArgumentParser(description="""Export Bart model + Beam Search to ONNX graph."""			)
							parser.add_argument(
							    """--validation_file"""			,	type=_SCREAMING_SNAKE_CASE			,	default=_SCREAMING_SNAKE_CASE			,	help="""A csv or a json file containing the validation data."""			)
							parser.add_argument(
							    """--max_length"""			,	type=_SCREAMING_SNAKE_CASE			,	default=5			,	help="""The maximum total input sequence length after tokenization."""			,	)
							parser.add_argument(
							    """--num_beams"""			,	type=_SCREAMING_SNAKE_CASE			,	default=_SCREAMING_SNAKE_CASE			,	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=_SCREAMING_SNAKE_CASE			,	help="""Path to pretrained model or model identifier from huggingface.co/models."""			,	required=_SCREAMING_SNAKE_CASE			,	)
							parser.add_argument(
							    """--config_name"""			,	type=_SCREAMING_SNAKE_CASE			,	default=_SCREAMING_SNAKE_CASE			,	help="""Pretrained config name or path if not the same as model_name"""			,	)
							parser.add_argument(
							    """--device"""			,	type=_SCREAMING_SNAKE_CASE			,	default="""cpu"""			,	help="""Device where the model will be run"""			,	)
							parser.add_argument("""--output_file_path"""			,	type=_SCREAMING_SNAKE_CASE			,	default=_SCREAMING_SNAKE_CASE			,	help="""Where to store the final ONNX file."""			)

							snake_case_          =						parser.parse_args()

							return args







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE="cpu"			)      ->			Union[str, Any]:
							snake_case_          =						model_dict[model_name].from_pretrained(_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						tokenizer_dict[model_name].from_pretrained(_SCREAMING_SNAKE_CASE			)

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

							return huggingface_model, tokenizer







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							model.eval()

							snake_case_          =						None
							snake_case_          =						torch.jit.script(BARTBeamSearchGenerator(_SCREAMING_SNAKE_CASE			)			)

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

														snake_case_          =						model.generate(
														    inputs["""input_ids"""]			,	attention_mask=inputs["""attention_mask"""]			,	num_beams=_SCREAMING_SNAKE_CASE			,	max_length=_SCREAMING_SNAKE_CASE			,	early_stopping=_SCREAMING_SNAKE_CASE			,	decoder_start_token_id=model.config.decoder_start_token_id			,	)

														torch.onnx.export(
														    _SCREAMING_SNAKE_CASE			,	(
														        inputs["""input_ids"""],
														        inputs["""attention_mask"""],
														        num_beams,
														        max_length,
														        model.config.decoder_start_token_id,
														    )			,	_SCREAMING_SNAKE_CASE			,	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=_SCREAMING_SNAKE_CASE			,	)

														logger.info("""Model exported to {}""".format(_SCREAMING_SNAKE_CASE			)			)

														snake_case_          =						remove_dup_initializers(os.path.abspath(_SCREAMING_SNAKE_CASE			)			)

														logger.info("""Deduplicated and optimized model written to {}""".format(_SCREAMING_SNAKE_CASE			)			)

														snake_case_          =						onnxruntime.InferenceSession(_SCREAMING_SNAKE_CASE			)
														snake_case_          =						ort_sess.run(
														    _SCREAMING_SNAKE_CASE			,	{
														        """input_ids""": inputs["""input_ids"""].cpu().numpy(),
														        """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
														        """num_beams""": np.array(_SCREAMING_SNAKE_CASE			),
														        """max_length""": np.array(_SCREAMING_SNAKE_CASE			),
														        """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			( )      ->			Optional[int]:
							snake_case_          =						parse_args()
							snake_case_          =						5
							snake_case_          =						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()

							snake_case_          =						torch.device(args.device			)

							snake_case_    ,  snake_case_          =						load_model_tokenizer(args.model_name_or_path			,	_SCREAMING_SNAKE_CASE			)

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

							model.to(_SCREAMING_SNAKE_CASE			)

							if args.max_length:
														snake_case_          =						args.max_length

							if args.num_beams:
														snake_case_          =						args.num_beams

							if args.output_file_path:
														snake_case_          =						args.output_file_path
							else:
														snake_case_          =						"""BART.onnx"""

							logger.info("""Exporting model to ONNX"""			)
							export_and_validate_model(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				main()







"""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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE							:      List[str]						=      logging.get_logger(__name__)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=False			)      ->			int:
							snake_case_          =						[]
							for i in range(config.num_hidden_layers			):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")			)
														rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")			)
														rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")			)
														rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")			)
														rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")			)
														rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")			)
														rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")			)
														rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")			)
														rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")			)
														rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")			)

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ("""cls_token""", """vit.embeddings.cls_token"""),
							        ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
							        ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
							        ("""pos_embed""", """vit.embeddings.position_embeddings"""),
							    ]			)

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ("""norm.weight""", """layernorm.weight"""),
														        ("""norm.bias""", """layernorm.bias"""),
														    ]			)

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														snake_case_          =						[(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""			) else pair for pair in rename_keys]
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("""norm.weight""", """vit.layernorm.weight"""),
														        ("""norm.bias""", """vit.layernorm.bias"""),
														        ("""head.weight""", """classifier.weight"""),
														        ("""head.bias""", """classifier.bias"""),
														    ]			)

							return rename_keys







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=False			)      ->			List[Any]:
							for i in range(config.num_hidden_layers			):
														if base_model:
																					snake_case_          =						""""""
														else:
																					snake_case_          =						"""vit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														snake_case_          =						state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""			)
														snake_case_          =						state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""			)
														# next, add query, keys and values (in that order) to the state dict
														snake_case_          =						in_proj_weight[
														    : config.hidden_size, :
														]
														snake_case_          =						in_proj_bias[: config.hidden_size]
														snake_case_          =						in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														snake_case_          =						in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														snake_case_          =						in_proj_weight[
														    -config.hidden_size :, :
														]
														snake_case_          =						in_proj_bias[-config.hidden_size :]







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Union[str, Any]:
							snake_case_          =						["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							snake_case_          =						dct.pop(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						val







def   _a			( )      ->			Dict:
							snake_case_          =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							snake_case_          =						Image.open(requests.get(_SCREAMING_SNAKE_CASE			,	stream=_SCREAMING_SNAKE_CASE			).raw			)
							return im







@torch.no_grad()
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=True			)      ->			Any:
							snake_case_          =						ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														snake_case_          =						8
							# set labels if required
							if not base_model:
														snake_case_          =						1_000
														snake_case_          =						"""huggingface/label-files"""
														snake_case_          =						"""imagenet-1k-id2label.json"""
														snake_case_          =						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	repo_type="""dataset"""			)			,	"""r"""			)			)
														snake_case_          =						{int(_SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
														snake_case_          =						idalabel
														snake_case_          =						{v: k for k, v in idalabel.items()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														snake_case_          =						384
														snake_case_          =						1_536
														snake_case_          =						12
														snake_case_          =						6

							# load original model from torch hub
							snake_case_          =						torch.hub.load("""facebookresearch/dino:main"""			,	_SCREAMING_SNAKE_CASE			)
							original_model.eval()

							# load state_dict of original model, remove and rename some keys
							snake_case_          =						original_model.state_dict()
							if base_model:
														remove_classification_head_(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						create_rename_keys(_SCREAMING_SNAKE_CASE			,	base_model=_SCREAMING_SNAKE_CASE			)
							for src, dest in rename_keys:
														rename_key(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							read_in_q_k_v(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							# load HuggingFace model
							if base_model:
														snake_case_          =						ViTModel(_SCREAMING_SNAKE_CASE			,	add_pooling_layer=_SCREAMING_SNAKE_CASE			).eval()
							else:
														snake_case_          =						ViTForImageClassification(_SCREAMING_SNAKE_CASE			).eval()
							model.load_state_dict(_SCREAMING_SNAKE_CASE			)

							# Check outputs on an image, prepared by ViTImageProcessor
							snake_case_          =						ViTImageProcessor()
							snake_case_          =						image_processor(images=prepare_img()			,	return_tensors="""pt"""			)
							snake_case_          =						encoding["""pixel_values"""]
							snake_case_          =						model(_SCREAMING_SNAKE_CASE			)

							if base_model:
														snake_case_          =						original_model(_SCREAMING_SNAKE_CASE			)
														assert torch.allclose(_SCREAMING_SNAKE_CASE			,	outputs.last_hidden_state[:, 0, :]			,	atol=1E-1			)
							else:
														snake_case_          =						original_model(_SCREAMING_SNAKE_CASE			)
														assert logits.shape == outputs.logits.shape
														assert torch.allclose(_SCREAMING_SNAKE_CASE			,	outputs.logits			,	atol=1E-3			)

							Path(_SCREAMING_SNAKE_CASE			).mkdir(exist_ok=_SCREAMING_SNAKE_CASE			)
							print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""			)
							model.save_pretrained(_SCREAMING_SNAKE_CASE			)
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
							image_processor.save_pretrained(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='dino_vitb16',
				    type=str,
				    help='Name of the model trained with DINO 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(
				    '--base_model',
				    action='store_true',
				    help='Whether to only convert the base model (no projection head weights).',
				)

				parser.set_defaults(base_model=True)
				__SCREAMING_SNAKE_CASE							:      int						=      parser.parse_args()
				convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)



