


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE      :			Tuple      				=   logging.get_logger(__name__)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






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

     snake_case					=    '''std_conv''' if '''bit''' in model_name else False

     # note that when using BiT as backbone for ViT-hybrid checkpoints,
     # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
     # config.conv_layer = "std_conv_same"
     snake_case					=    BitConfig(
         conv_layer=UpperCamelCase_							,num_labels=10_00							,idalabel=UpperCamelCase_							,labelaid=UpperCamelCase_							,)

     return config
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     if "stem.conv" in name:
          snake_case					=    name.replace('''stem.conv'''							,'''bit.embedder.convolution'''    )
     if "blocks" in name:
          snake_case					=    name.replace('''blocks'''							,'''layers'''    )
     if "head.fc" in name:
          snake_case					=    name.replace('''head.fc'''							,'''classifier.1'''    )
     if name.startswith('''norm'''    ):
          snake_case					=    '''bit.''' + name
     if "bit" not in name and "classifier" not in name:
          snake_case					=    '''bit.encoder.''' + name

     return name
def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     snake_case					=    Image.open(requests.get(UpperCamelCase_							,stream=UpperCamelCase_    ).raw    )
     return im
@torch.no_grad()
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=False    ):



     """simple docstring"""






     snake_case					=    get_config(UpperCamelCase_    )

     # load original model from timm
     snake_case					=    create_model(UpperCamelCase_							,pretrained=UpperCamelCase_    )
     timm_model.eval()

     # load state_dict of original model
     snake_case					=    timm_model.state_dict()
     for key in state_dict.copy().keys():
          snake_case					=    state_dict.pop(UpperCamelCase_    )
          snake_case					=    val.squeeze() if '''head''' in key else val

     # load HuggingFace model
     snake_case					=    BitForImageClassification(UpperCamelCase_    )
     model.eval()
     model.load_state_dict(UpperCamelCase_    )

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

     snake_case					=    {
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     snake_case					=    BitImageProcessor(
         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()							,)

     snake_case					=    prepare_img()
     snake_case					=    transform(UpperCamelCase_    ).unsqueeze(0    )
     snake_case					=    processor(UpperCamelCase_							,return_tensors='''pt'''    ).pixel_values

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

     # verify logits
     with torch.no_grad():
          snake_case					=    model(UpperCamelCase_    )
          snake_case					=    outputs.logits

     print('''Logits:'''							,logits[0, :3]    )
     print('''Predicted class:'''							,model.config.idalabel[logits.argmax(-1    ).item()]    )
     snake_case					=    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 {model_name} and processor to {pytorch_dump_folder_path}'''    )
          model.save_pretrained(UpperCamelCase_    )
          processor.save_pretrained(UpperCamelCase_    )

     if push_to_hub:
          print(F'''Pushing model {model_name} and processor to the hub'''    )
          model.push_to_hub(F'''ybelkada/{model_name}'''    )
          processor.push_to_hub(F'''ybelkada/{model_name}'''    )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			Tuple      				=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="resnetv2_50x1_bitm",
     type=str,
     help="Name of the BiT 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 push the model to the hub.",
 )

 _SCREAMING_SNAKE_CASE      :			str      				=   parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








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   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
    def 		a_      (	self      , __snake_case=0   ):
         snake_case					=    floats_tensor((1, 3, 1_2_8, 1_2_8)      , rng=random.Random(__snake_case   )   )
         snake_case					=    np.random.RandomState(__snake_case   )
         snake_case					=    {
             '''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 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         pipe.set_progress_bar_config(disable=__snake_case   )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087]   )
         assert np.abs(image_slice - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    PNDMScheduler.from_config(pipe.scheduler.config      , skip_prk_steps=__snake_case   )
         pipe.set_progress_bar_config(disable=__snake_case   )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

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

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]   )

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




    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552]   )

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






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







    """simple docstring"""

    @property
    def 		a_      (	self   ):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )
    @property
    def 		a_      (	self   ):
         snake_case					=    ort.SessionOptions()
         snake_case					=    False
         return options
    def 		a_      (	self   ):
         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''   )
         snake_case					=    init_image.resize((7_6_8, 5_1_2)   )
         # using the PNDM scheduler by default
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''      , revision='''onnx'''      , safety_checker=__snake_case      , feature_extractor=__snake_case      , provider=self.gpu_provider      , sess_options=self.gpu_options      , )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    '''A fantasy landscape, trending on artstation'''

         snake_case					=    np.random.RandomState(0   )
         snake_case					=    pipe(
             prompt=__snake_case      , image=__snake_case      , strength=0.75      , guidance_scale=7.5      , num_inference_steps=1_0      , generator=__snake_case      , output_type='''np'''      , )
         snake_case					=    output.images
         snake_case					=    images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         snake_case					=    np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]   )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




    def 		a_      (	self   ):
         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''   )
         snake_case					=    init_image.resize((7_6_8, 5_1_2)   )
         snake_case					=    LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''      , subfolder='''scheduler'''      , revision='''onnx'''   )
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''      , revision='''onnx'''      , scheduler=__snake_case      , safety_checker=__snake_case      , feature_extractor=__snake_case      , provider=self.gpu_provider      , sess_options=self.gpu_options      , )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    '''A fantasy landscape, trending on artstation'''

         snake_case					=    np.random.RandomState(0   )
         snake_case					=    pipe(
             prompt=__snake_case      , image=__snake_case      , strength=0.75      , guidance_scale=7.5      , num_inference_steps=2_0      , generator=__snake_case      , output_type='''np'''      , )
         snake_case					=    output.images
         snake_case					=    images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         snake_case					=    np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]   )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




