
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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					SwinvaConfig()
    snake_case__    =					swinva_name.split("_"				)

    snake_case__    =					name_split[1]
    if "to" in name_split[3]:
        snake_case__    =					int(name_split[3][-3:]				)
    else:
        snake_case__    =					int(name_split[3]				)
    if "to" in name_split[2]:
        snake_case__    =					int(name_split[2][-2:]				)
    else:
        snake_case__    =					int(name_split[2][6:]				)

    if model_size == "tiny":
        snake_case__    =					96
        snake_case__    =					(2, 2, 6, 2)
        snake_case__    =					(3, 6, 12, 24)
    elif model_size == "small":
        snake_case__    =					96
        snake_case__    =					(2, 2, 18, 2)
        snake_case__    =					(3, 6, 12, 24)
    elif model_size == "base":
        snake_case__    =					128
        snake_case__    =					(2, 2, 18, 2)
        snake_case__    =					(4, 8, 16, 32)
    else:
        snake_case__    =					192
        snake_case__    =					(2, 2, 18, 2)
        snake_case__    =					(6, 12, 24, 48)

    if "to" in swinva_name:
        snake_case__    =					(12, 12, 12, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        snake_case__    =					21_841
        snake_case__    =					"huggingface/label-files"
        snake_case__    =					"imagenet-22k-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()}

    else:
        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__    =					img_size
    snake_case__    =					num_classes
    snake_case__    =					embed_dim
    snake_case__    =					depths
    snake_case__    =					num_heads
    snake_case__    =					window_size

    return config





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if "patch_embed.proj" in name:
        snake_case__    =					name.replace("patch_embed.proj"     ,		"embeddings.patch_embeddings.projection"				)
    if "patch_embed.norm" in name:
        snake_case__    =					name.replace("patch_embed.norm"     ,		"embeddings.norm"				)
    if "layers" in name:
        snake_case__    =					"encoder." + name
    if "attn.proj" in name:
        snake_case__    =					name.replace("attn.proj"     ,		"attention.output.dense"				)
    if "attn" in name:
        snake_case__    =					name.replace("attn"     ,		"attention.self"				)
    if "norm1" in name:
        snake_case__    =					name.replace("norm1"     ,		"layernorm_before"				)
    if "norm2" in name:
        snake_case__    =					name.replace("norm2"     ,		"layernorm_after"				)
    if "mlp.fc1" in name:
        snake_case__    =					name.replace("mlp.fc1"     ,		"intermediate.dense"				)
    if "mlp.fc2" in name:
        snake_case__    =					name.replace("mlp.fc2"     ,		"output.dense"				)
    if "q_bias" in name:
        snake_case__    =					name.replace("q_bias"     ,		"query.bias"				)
    if "k_bias" in name:
        snake_case__    =					name.replace("k_bias"     ,		"key.bias"				)
    if "v_bias" in name:
        snake_case__    =					name.replace("v_bias"     ,		"value.bias"				)
    if "cpb_mlp" in name:
        snake_case__    =					name.replace("cpb_mlp"     ,		"continuous_position_bias_mlp"				)
    if name == "norm.weight":
        snake_case__    =					"layernorm.weight"
    if name == "norm.bias":
        snake_case__    =					"layernorm.bias"

    if "head" in name:
        snake_case__    =					name.replace("head"     ,		"classifier"				)
    else:
        snake_case__    =					"swinv2." + name

    return name





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    for key in orig_state_dict.copy().keys():
        snake_case__    =					orig_state_dict.pop(__lowerCAmelCase				)

        if "mask" in key:
            continue
        elif "qkv" in key:
            snake_case__    =					key.split("."				)
            snake_case__    =					int(key_split[1]				)
            snake_case__    =					int(key_split[3]				)
            snake_case__    =					model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case__    =					val[:dim, :]
                snake_case__    =					val[dim : dim * 2, :]
                snake_case__    =					val[-dim:, :]
            else:
                snake_case__    =					val[:dim]
                snake_case__    =					val[
                    dim : dim * 2
                ]
                snake_case__    =					val[-dim:]
        else:
            snake_case__    =					val

    return orig_state_dict





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					timm.create_model(__lowerCAmelCase     ,		pretrained=__lowerCAmelCase				)
    timm_model.eval()

    snake_case__    =					get_swinva_config(__lowerCAmelCase				)
    snake_case__    =					SwinvaForImageClassification(__lowerCAmelCase				)
    model.eval()

    snake_case__    =					convert_state_dict(timm_model.state_dict()     ,		__lowerCAmelCase				)
    model.load_state_dict(__lowerCAmelCase				)

    snake_case__    =					"http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case__    =					AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"     ,		"-"				)				)				)
    snake_case__    =					Image.open(requests.get(__lowerCAmelCase     ,		stream=__lowerCAmelCase				).raw				)
    snake_case__    =					image_processor(images=__lowerCAmelCase     ,		return_tensors="pt"				)

    snake_case__    =					timm_model(inputs["pixel_values"]				)
    snake_case__    =					model(**__lowerCAmelCase				).logits

    assert torch.allclose(__lowerCAmelCase     ,		__lowerCAmelCase     ,		atol=1E-3				)

    print(F"""Saving model {swinva_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				)

    model.push_to_hub(
        repo_path_or_name=Path(__lowerCAmelCase     ,		__lowerCAmelCase				)     ,		organization="nandwalritik"     ,		commit_message="Add model"     ,		)


if __name__ == "__main__":
     __magic_name__  							=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--swinv2_name''',
         default='''swinv2_tiny_patch4_window8_256''',
         type=str,
         help='''Name of the Swinv2 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_swinva_checkpoint(args.swinva_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()



