
import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase=False				):
    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"""deit.encoder.layer.{i}.layernorm_before.weight""")				)
        rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")				)
        rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")				)
        rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")				)
        rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")				)
        rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")				)
        rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")				)
        rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")				)
        rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")				)
        rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")				)

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

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

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

    return rename_keys





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase=False				):
    for i in range(config.num_hidden_layers				):
        if base_model:
            snake_case__    =					""
        else:
            snake_case__    =					"deit."
        # 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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					dct.pop(__lowerCAmelCase				)
    snake_case__    =					val





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					"http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case__    =					Image.open(requests.get(__lowerCAmelCase     ,		stream=__lowerCAmelCase				).raw				)
    return im





@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					DeiTConfig()
    # all deit models have fine-tuned heads
    snake_case__    =					False
    # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
    snake_case__    =					1_000
    snake_case__    =					"huggingface/label-files"
    snake_case__    =					"imagenet-1k-id2label.json"
    snake_case__    =					json.load(open(hf_hub_download(__lowerCAmelCase     ,		__lowerCAmelCase     ,		repo_type="dataset"				)     ,		"r"				)				)
    snake_case__    =					{int(__lowerCAmelCase				): v for k, v in idalabel.items()}
    snake_case__    =					idalabel
    snake_case__    =					{v: k for k, v in idalabel.items()}
    snake_case__    =					int(deit_name[-6:-4]				)
    snake_case__    =					int(deit_name[-3:]				)
    # size of the architecture
    if deit_name[9:].startswith("tiny"				):
        snake_case__    =					192
        snake_case__    =					768
        snake_case__    =					12
        snake_case__    =					3
    elif deit_name[9:].startswith("small"				):
        snake_case__    =					384
        snake_case__    =					1_536
        snake_case__    =					12
        snake_case__    =					6
    if deit_name[9:].startswith("base"				):
        pass
    elif deit_name[4:].startswith("large"				):
        snake_case__    =					1_024
        snake_case__    =					4_096
        snake_case__    =					24
        snake_case__    =					16

    # load original model from timm
    snake_case__    =					timm.create_model(__lowerCAmelCase     ,		pretrained=__lowerCAmelCase				)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    snake_case__    =					timm_model.state_dict()
    snake_case__    =					create_rename_keys(__lowerCAmelCase     ,		__lowerCAmelCase				)
    for src, dest in rename_keys:
        rename_key(__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)
    read_in_q_k_v(__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)

    # load HuggingFace model
    snake_case__    =					DeiTForImageClassificationWithTeacher(__lowerCAmelCase				).eval()
    model.load_state_dict(__lowerCAmelCase				)

    # Check outputs on an image, prepared by DeiTImageProcessor
    snake_case__    =					int(
        (256 / 224) * config.image_size				)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
    snake_case__    =					DeiTImageProcessor(size=__lowerCAmelCase     ,		crop_size=config.image_size				)
    snake_case__    =					image_processor(images=prepare_img()     ,		return_tensors="pt"				)
    snake_case__    =					encoding["pixel_values"]
    snake_case__    =					model(__lowerCAmelCase				)

    snake_case__    =					timm_model(__lowerCAmelCase				)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(__lowerCAmelCase     ,		outputs.logits     ,		atol=1E-3				)

    Path(__lowerCAmelCase				).mkdir(exist_ok=__lowerCAmelCase				)
    print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""				)
    model.save_pretrained(__lowerCAmelCase				)
    print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
    image_processor.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
     __magic_name__  							=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--deit_name''',
         default='''vit_deit_base_distilled_patch16_224''',
         type=str,
         help='''Name of the DeiT timm 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.'''
     )

     __magic_name__  							=	parser.parse_args()
     convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





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,
)

__magic_name__  							=	logging.getLogger(__name__)

__magic_name__  							=	{'''facebook/bart-base''': BartForConditionalGeneration}
__magic_name__  							=	{'''facebook/bart-base''': BartTokenizer}





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

    snake_case__    =					parser.parse_args()

    return args





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase="cpu"				):
    snake_case__    =					model_dict[model_name].from_pretrained(__lowerCAmelCase				).to(__lowerCAmelCase				)
    snake_case__    =					tokenizer_dict[model_name].from_pretrained(__lowerCAmelCase				)

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

    return huggingface_model, tokenizer





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    model.eval()

    snake_case__    =					None
    snake_case__    =					torch.jit.script(BARTBeamSearchGenerator(__lowerCAmelCase				)				)

    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=__lowerCAmelCase     ,		max_length=__lowerCAmelCase     ,		early_stopping=__lowerCAmelCase     ,		decoder_start_token_id=model.config.decoder_start_token_id     ,		)

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

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

        snake_case__    =					remove_dup_initializers(os.path.abspath(__lowerCAmelCase				)				)

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

        snake_case__    =					onnxruntime.InferenceSession(__lowerCAmelCase				)
        snake_case__    =					ort_sess.run(
            __lowerCAmelCase     ,		{
                "input_ids": inputs["input_ids"].cpu().numpy(),
                "attention_mask": inputs["attention_mask"].cpu().numpy(),
                "num_beams": np.array(__lowerCAmelCase				),
                "max_length": np.array(__lowerCAmelCase				),
                "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 						SCREAMING_SNAKE_CASE__     (   ):
    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     ,		__lowerCAmelCase				)

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

    model.to(__lowerCAmelCase				)

    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(__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)


if __name__ == "__main__":
     main()



