



import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard



@slow
@require_flax
class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()




     def 		snake_case_		(					self):
         A__			,						A__					=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-canny'''			,    from_pt=a__			,    dtype=jnp.bfloataa)
         A__			,						A__					=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''			,    controlnet=a__			,    from_pt=a__			,    dtype=jnp.bfloataa)
         A__					=       controlnet_params

         A__					=       '''bird'''
         A__					=       jax.device_count()
         A__					=       pipe.prepare_text_inputs([prompts] * num_samples)

         A__					=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png''')
         A__					=       pipe.prepare_image_inputs([canny_image] * num_samples)

         A__					=       jax.random.PRNGKey(0)
         A__					=       jax.random.split(a__			,    jax.device_count())

         A__					=       replicate(a__)
         A__					=       shard(a__)
         A__					=       shard(a__)

         A__					=       pipe(
             prompt_ids=a__			,    image=a__			,    params=a__			,    prng_seed=a__			,    num_inference_steps=5_0			,    jit=a__			,    ).images
         assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

         A__					=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
         A__					=       images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         A__					=       jnp.asarray(jax.device_get(image_slice.flatten()))
         A__					=       jnp.array(
             [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8])
         print(F"output_slice: {output_slice}")
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2




     def 		snake_case_		(					self):
         A__			,						A__					=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-openpose'''			,    from_pt=a__			,    dtype=jnp.bfloataa)
         A__			,						A__					=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''			,    controlnet=a__			,    from_pt=a__			,    dtype=jnp.bfloataa)
         A__					=       controlnet_params

         A__					=       '''Chef in the kitchen'''
         A__					=       jax.device_count()
         A__					=       pipe.prepare_text_inputs([prompts] * num_samples)

         A__					=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png''')
         A__					=       pipe.prepare_image_inputs([pose_image] * num_samples)

         A__					=       jax.random.PRNGKey(0)
         A__					=       jax.random.split(a__			,    jax.device_count())

         A__					=       replicate(a__)
         A__					=       shard(a__)
         A__					=       shard(a__)

         A__					=       pipe(
             prompt_ids=a__			,    image=a__			,    params=a__			,    prng_seed=a__			,    num_inference_steps=5_0			,    jit=a__			,    ).images
         assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

         A__					=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
         A__					=       images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         A__					=       jnp.asarray(jax.device_get(image_slice.flatten()))
         A__					=       jnp.array(
             [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]])
         print(F"output_slice: {output_slice}")
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2





import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[Any]						)->	Union[str, Any]:
    A__					=       MobileNetVaConfig(layer_norm_eps=0.001						)

    if "_quant" in model_name:
        raise ValueError('''Quantized models are not supported.'''						)

    A__					=       re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''			,							UpperCamelCase_						)
    if matches:
        A__					=       float(matches[1]						)
        A__					=       int(matches[2]						)

    # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
    # the usual 1000. The first class (index 0) is "background".
    A__					=       1_0_0_1
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       '''huggingface/label-files'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						) + 1: v for k, v in idalabel.items()}
    A__					=       '''background'''
    A__					=       idalabel
    A__					=       {v: k for k, v in idalabel.items()}

    return config



def 		lowerCAmelCase__     (       )->	Tuple:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							int			,							UpperCamelCase_					:							List[str]=False						)->	Tuple:
    A__					=       get_mobilenet_va_config(UpperCamelCase_						)

    # Load 🤗 model
    A__					=       MobileNetVaForImageClassification(UpperCamelCase_						).eval()

    # Load weights from TensorFlow checkpoint
    load_tf_weights_in_mobilenet_va(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_						)

    # Check outputs on an image, prepared by MobileNetV1ImageProcessor
    A__					=       MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size}			,							size={'''shortest_edge''': config.image_size + 3_2}			,							)
    A__					=       image_processor(images=prepare_img()			,							return_tensors='''pt'''						)
    A__					=       model(**UpperCamelCase_						)
    A__					=       outputs.logits

    assert logits.shape == (1, 1_0_0_1)

    if model_name == "mobilenet_v1_1.0_224":
        A__					=       torch.tensor([-4.1739, -1.1233, 3.1205]						)
    elif model_name == "mobilenet_v1_0.75_192":
        A__					=       torch.tensor([-3.9440, -2.3141, -0.3333]						)
    else:
        A__					=       None

    if expected_logits is not None:
        assert torch.allclose(logits[0, :3]			,							UpperCamelCase_			,							atol=1E-4						)

    Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
    print(f"Saving model {model_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 push_to_hub:
        print('''Pushing to the hub...'''						)
        A__					=       '''google/''' + model_name
        image_processor.push_to_hub(UpperCamelCase_						)
        model.push_to_hub(UpperCamelCase_						)


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="mobilenet_v1_1.0_224",
       type=str,
       help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.",
   )
   parser.add_argument(
       "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
   )
   parser.add_argument(
       "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
   )

   _lowercase    	=		parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )
