





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


logging.set_verbosity_info()
_snake_case     :		Dict       						= logging.get_logger(__name__)






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






def 					A__				(			UpperCamelCase       ):
							A		=  ["head.weight", "head.bias"]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase					,  UpperCamelCase       )






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							A		=  dct.pop(UpperCamelCase       )
							A		=  val






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






@torch.no_grad()
def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  ViTConfig()
							A		=  False
							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							if vit_name[-5:] == "in21k":
														A		=  True
														A		=  int(vit_name[-12:-10]       )
														A		=  int(vit_name[-9:-6]       )
							else:
														A		=  1_000
														A		=  "huggingface/label-files"
														A		=  "imagenet-1k-id2label.json"
														A		=  json.load(open(hf_hub_download(UpperCamelCase					,  UpperCamelCase					,  repo_type="dataset"       )					,  "r"       )       )
														A		=  {int(UpperCamelCase       ): v for k, v in idalabel.items()}
														A		=  idalabel
														A		=  {v: k for k, v in idalabel.items()}
														A		=  int(vit_name[-6:-4]       )
														A		=  int(vit_name[-3:]       )
							# size of the architecture
							if "deit" in vit_name:
														if vit_name[9:].startswith("tiny"       ):
																					A		=  192
																					A		=  768
																					A		=  12
																					A		=  3
														elif vit_name[9:].startswith("small"       ):
																					A		=  384
																					A		=  1_536
																					A		=  12
																					A		=  6
														else:
																					pass
							else:
														if vit_name[4:].startswith("small"       ):
																					A		=  768
																					A		=  2_304
																					A		=  8
																					A		=  8
														elif vit_name[4:].startswith("base"       ):
																					pass
														elif vit_name[4:].startswith("large"       ):
																					A		=  1_024
																					A		=  4_096
																					A		=  24
																					A		=  16
														elif vit_name[4:].startswith("huge"       ):
																					A		=  1_280
																					A		=  5_120
																					A		=  32
																					A		=  16

    # load original model from timm
							A		=  timm.create_model(UpperCamelCase					,  pretrained=UpperCamelCase       )
							timm_model.eval()

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

							# load HuggingFace model
							if vit_name[-5:] == "in21k":
														A		=  ViTModel(UpperCamelCase       ).eval()
							else:
														A		=  ViTForImageClassification(UpperCamelCase       ).eval()
							model.load_state_dict(UpperCamelCase       )

							# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
							if "deit" in vit_name:
														A		=  DeiTImageProcessor(size=config.image_size       )
							else:
														A		=  ViTImageProcessor(size=config.image_size       )
							A		=  image_processor(images=prepare_img()					,  return_tensors="pt"       )
							A		=  encoding["pixel_values"]
							A		=  model(UpperCamelCase       )

							if base_model:
														A		=  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:
														A		=  timm_model(UpperCamelCase       )
														assert timm_logits.shape == outputs.logits.shape
														assert torch.allclose(UpperCamelCase					,  outputs.logits					,  atol=1E-3       )

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


if __name__ == "__main__":
		_snake_case     :		Any       						= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--vit_name',
		    default='vit_base_patch16_224',
		    type=str,
		    help='Name of the 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.'
		)

		_snake_case     :		Tuple       						= parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)









"""simple docstring"""
import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort






class 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:Union[str, Any]=0		):
												A		=  floats_tensor((1, 3, 1_28, 1_28)     ,       rng=random.Random(__UpperCamelCase		)		)
												A		=  np.random.RandomState(__UpperCamelCase		)
												A		=  {
												    "prompt": "A painting of a squirrel eating a burger",
												    "image": image,
												    "generator": generator,
												    "num_inference_steps": 3,
												    "strength": 0.75,
												    "guidance_scale": 7.5,
												    "output_type": "numpy",
												}
												return inputs


					def 	lowerCamelCase       (					self			:Any		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]		)
												assert np.abs(image_slice - expected_slice		).max() < 1e-1


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  PNDMScheduler.from_config(pipe.scheduler.config     ,       skip_prk_steps=__UpperCamelCase		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1]

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-1


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												# warmup pass to apply optimizations
												A		=  pipe(**self.get_dummy_inputs()		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1]

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-1


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  EulerDiscreteScheduler.from_config(pipe.scheduler.config		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1]

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-1


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1]

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-1


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1]

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-1







@nightly
@require_onnxruntime
@require_torch_gpu
class 					_UpperCAmelCase   (	unittest.TestCase ):


					@property
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)


					@property
					def 	lowerCamelCase       (					self			:Optional[int]		):
												A		=  ort.SessionOptions()
												A		=  False
												return options


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/img2img/sketch-mountains-input.jpg"		)
												A		=  init_image.resize((7_68, 5_12)		)
												# using the PNDM scheduler by default
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    "CompVis/stable-diffusion-v1-4"     ,       revision="onnx"     ,       safety_checker=__UpperCamelCase     ,       feature_extractor=__UpperCamelCase     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  "A fantasy landscape, trending on artstation"

												A		=  np.random.RandomState(0		)
												A		=  pipe(
												    prompt=__UpperCamelCase     ,       image=__UpperCamelCase     ,       strength=0.75     ,       guidance_scale=7.5     ,       num_inference_steps=10     ,       generator=__UpperCamelCase     ,       output_type="np"     ,       )
												A		=  output.images
												A		=  images[0, 2_55:2_58, 3_83:3_86, -1]

												assert images.shape == (1, 5_12, 7_68, 3)
												A		=  np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]		)
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 2e-2


					def 	lowerCamelCase       (					self			:Any		):
												A		=  load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/img2img/sketch-mountains-input.jpg"		)
												A		=  init_image.resize((7_68, 5_12)		)
												A		=  LMSDiscreteScheduler.from_pretrained(
												    "runwayml/stable-diffusion-v1-5"     ,       subfolder="scheduler"     ,       revision="onnx"		)
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    "runwayml/stable-diffusion-v1-5"     ,       revision="onnx"     ,       scheduler=__UpperCamelCase     ,       safety_checker=__UpperCamelCase     ,       feature_extractor=__UpperCamelCase     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  "A fantasy landscape, trending on artstation"

												A		=  np.random.RandomState(0		)
												A		=  pipe(
												    prompt=__UpperCamelCase     ,       image=__UpperCamelCase     ,       strength=0.75     ,       guidance_scale=7.5     ,       num_inference_steps=20     ,       generator=__UpperCamelCase     ,       output_type="np"     ,       )
												A		=  output.images
												A		=  images[0, 2_55:2_58, 3_83:3_86, -1]

												assert images.shape == (1, 5_12, 7_68, 3)
												A		=  np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]		)
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 2e-2


