


"""simple docstring"""


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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase=False   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    []

			# fmt: off
			# stem:
			rename_keys.append(("cls_token", "vit.embeddings.cls_token")   )
			rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")   )

			rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")   )
			rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")   )

			# backbone
			rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")   )
			rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")   )
			rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")   )

			for stage_idx in range(len(config.backbone_config.depths   )   ):
						for layer_idx in range(config.backbone_config.depths[stage_idx]   ):
									rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")   )
									rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")   )
									rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")   )
									rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")   )
									rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")   )
									rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")   )
									rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")   )
									rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")   )
									rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")   )

						rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")   )
						rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")   )
						rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")   )

			# transformer encoder
			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")   )

			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 "vit" from all keys that start with "vit"
						__lowerCAmelCase       					=    [(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"),
						    ]   )
			# fmt: on

			return rename_keys


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase=False   ):

			'''simple docstring'''




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


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    ["head.weight", "head.bias"]
			for k in ignore_keys:
						state_dict.pop(_UpperCamelCase					,				_UpperCamelCase   )


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    dct.pop(_UpperCamelCase   )
			__lowerCAmelCase       					=    val


def _lowerCamelCase			(	):

			'''simple docstring'''




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


@torch.no_grad()
def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase=False   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    BitConfig(
			    global_padding="same"					,				layer_type="bottleneck"					,				depths=(3, 4, 9)					,				out_features=["stage3"]					,				embedding_dynamic_padding=_UpperCamelCase					,				)
			__lowerCAmelCase       					=    ViTHybridConfig(backbone_config=_UpperCamelCase					,				image_size=384					,				num_labels=1000   )
			__lowerCAmelCase       					=    False

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

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

			__lowerCAmelCase       					=    "huggingface/label-files"
			__lowerCAmelCase       					=    "imagenet-1k-id2label.json"
			__lowerCAmelCase       					=    json.load(open(hf_hub_download(_UpperCamelCase					,				_UpperCamelCase					,				repo_type="dataset"   )					,				"r"   )   )
			__lowerCAmelCase       					=    {int(_UpperCamelCase   ): v for k, v in idalabel.items()}
			__lowerCAmelCase       					=    idalabel
			__lowerCAmelCase       					=    {v: k for k, v in idalabel.items()}

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						__lowerCAmelCase       					=    ViTHybridModel(_UpperCamelCase   ).eval()
			else:
						__lowerCAmelCase       					=    ViTHybridForImageClassification(_UpperCamelCase   ).eval()
			model.load_state_dict(_UpperCamelCase   )

			# create image processor
			__lowerCAmelCase       					=    create_transform(**resolve_data_config({}					,				model=_UpperCamelCase   )   )
			__lowerCAmelCase       					=    transform.transforms

			__lowerCAmelCase       					=    {
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

			__lowerCAmelCase       					=    ViTHybridImageProcessor(
			    do_resize=_UpperCamelCase					,				size={"shortest_edge": timm_transforms[0].size}					,				resample=pillow_resamplings[timm_transforms[0].interpolation.value]					,				do_center_crop=_UpperCamelCase					,				crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}					,				do_normalize=_UpperCamelCase					,				image_mean=timm_transforms[-1].mean.tolist()					,				image_std=timm_transforms[-1].std.tolist()					,				)

			__lowerCAmelCase       					=    prepare_img()
			__lowerCAmelCase       					=    transform(_UpperCamelCase   ).unsqueeze(0   )
			__lowerCAmelCase       					=    processor(_UpperCamelCase					,				return_tensors="pt"   ).pixel_values

			# verify pixel values
			assert torch.allclose(_UpperCamelCase					,				_UpperCamelCase   )

			# verify logits
			with torch.no_grad():
						__lowerCAmelCase       					=    model(_UpperCamelCase   )
						__lowerCAmelCase       					=    outputs.logits

			print("Predicted class:"					,				logits.argmax(-1   ).item()   )
			if base_model:
						__lowerCAmelCase       					=    timm_model.forward_features(_UpperCamelCase   )
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(_UpperCamelCase					,				outputs.pooler_output					,				atol=1e-3   )
			else:
						__lowerCAmelCase       					=    timm_model(_UpperCamelCase   )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(_UpperCamelCase					,				outputs.logits					,				atol=1e-3   )
			print("Looks ok!"   )

			if pytorch_dump_folder_path is not None:
						Path(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
						print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"   )
						model.save_pretrained(_UpperCamelCase   )
						print(f"Saving processor to {pytorch_dump_folder_path}"   )
						processor.save_pretrained(_UpperCamelCase   )

			if push_to_hub:
						print(f"Pushing model and processor to the hub {vit_name}"   )
						model.push_to_hub(f"ybelkada/{vit_name}"   )
						processor.push_to_hub(f"ybelkada/{vit_name}"   )


if __name__ == "__main__":
	A  :     Dict    					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--vit_name",
	    default="vit_base_r50_s16_384",
	    type=str,
	    help="Name of the hybrid ViT 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."
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
	)

	A  :     str    					=    parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Tuple          =[
							    # (model_name, model_kwargs)
							    ("""bert-base-cased""", {}),
							    ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
							]

							@require_tf
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(__a					,     "tf"					,     12					,     **__a			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(__a					,     "pt"					,     12					,     **__a			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
										with NamedTemporaryFile(mode="w+t"			) as vocab_file:
													vocab_file.write("\n".join(__a			)			)
													vocab_file.flush()
													__lowerCAmelCase       					=    BertTokenizerFast(vocab_file.name			)

										with TemporaryDirectory() as bert_save_dir:
													__lowerCAmelCase       					=    BertModel(BertConfig(vocab_size=len(__a			)			)			)
													model.save_pretrained(__a			)
													self._test_export(__a					,     "pt"					,     12					,     __a			)

							@require_tf
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "tf"					,     12					,     **__a			)
													__lowerCAmelCase       					=    quantize(Path(__a			)			)

													# Ensure the actual quantized model is not bigger than the original one
													if quantized_path.stat().st_size >= Path(__a			).stat().st_size:
																self.fail("Quantized model is bigger than initial ONNX model"			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "pt"					,     12					,     **__a			)
													__lowerCAmelCase       					=    quantize(__a			)

													# Ensure the actual quantized model is not bigger than the original one
													if quantized_path.stat().st_size >= Path(__a			).stat().st_size:
																self.fail("Quantized model is bigger than initial ONNX model"			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a=None					,     **__a			):
										try:
													# Compute path
													with TemporaryDirectory() as tempdir:
																__lowerCAmelCase       					=    Path(__a			).joinpath("model.onnx"			)

													# Remove folder if exists
													if path.parent.exists():
																path.parent.rmdir()

													# Export
													convert(__a					,     __a					,     __a					,     __a					,     __a					,     **__a			)

													return path
										except Exception as e:
													self.fail(__a			)

							@require_torch
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										__lowerCAmelCase       					=    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__a					,     __a					,     "pt"			)

							@require_tf
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import TFBertModel

										__lowerCAmelCase       					=    TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										__lowerCAmelCase       					=    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__a					,     __a					,     "tf"			)

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    FeatureExtractionPipeline(__a					,     __a			)

										__lowerCAmelCase       					=    ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    infer_shapes(__a					,     __a			)

										# Assert all variables are present
										self.assertEqual(len(__a			)					,     len(__a			)			)
										self.assertTrue(all(var_name in shapes for var_name in variable_names			)			)
										self.assertSequenceEqual(variable_names[:3]					,     __a			)
										self.assertSequenceEqual(variable_names[3:]					,     __a			)

										# Assert inputs are {0: batch, 1: sequence}
										for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
													self.assertDictEqual(shapes[var_name]					,     {0: "batch", 1: "sequence"}			)

										# Assert outputs are {0: batch, 1: sequence} and {0: batch}
										self.assertDictEqual(shapes["output_0"]					,     {0: "batch", 1: "sequence"}			)
										self.assertDictEqual(shapes["output_1"]					,     {0: "batch"}			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ["input_ids", "attention_mask", "token_type_ids"]
										__lowerCAmelCase       					=    {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
										__lowerCAmelCase					,			__lowerCAmelCase       					=    ensure_valid_input(FuncContiguousArgs()					,     __a					,     __a			)

										# Should have exactly the same number of args (all are valid)
										self.assertEqual(len(__a			)					,     3			)

										# Should have exactly the same input names
										self.assertEqual(set(__a			)					,     set(__a			)			)

										# Parameter should be reordered according to their respective place in the function:
										# (input_ids, token_type_ids, attention_mask)
										self.assertEqual(__a					,     (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])			)

										# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
										__lowerCAmelCase					,			__lowerCAmelCase       					=    ensure_valid_input(FuncNonContiguousArgs()					,     __a					,     __a			)

										# Should have exactly the one arg (all before the one not provided "some_other_args")
										self.assertEqual(len(__a			)					,     1			)
										self.assertEqual(len(__a			)					,     1			)

										# Should have only "input_ids"
										self.assertEqual(inputs_args[0]					,     tokens["input_ids"]			)
										self.assertEqual(ordered_input_names[0]					,     "input_ids"			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    generate_identified_filename(Path("/home/something/my_fake_model.onnx"			)					,     "-test"			)
										self.assertEqual("/home/something/my_fake_model-test.onnx"					,     generated.as_posix()			)




