
'''simple docstring'''
import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
       _snake_case       =  MobileViTConfig()

       # size of the architecture
       if "mobilevit_s" in mobilevit_name:
              _snake_case       =  [144, 192, 240]
              _snake_case       =  [16, 32, 64, 96, 128, 160, 640]
       elif "mobilevit_xs" in mobilevit_name:
              _snake_case       =  [96, 120, 144]
              _snake_case       =  [16, 32, 48, 64, 80, 96, 384]
       elif "mobilevit_xxs" in mobilevit_name:
              _snake_case       =  [64, 80, 96]
              _snake_case       =  [16, 16, 24, 48, 64, 80, 320]
              _snake_case       =  0.05
              _snake_case       =  2.0

       if mobilevit_name.startswith("""deeplabv3_"""				):
              _snake_case       =  512
              _snake_case       =  16
              _snake_case       =  21
              _snake_case       =  """pascal-voc-id2label.json"""
       else:
              _snake_case       =  1000
              _snake_case       =  """imagenet-1k-id2label.json"""

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

       return config






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=False				):
       for i in range(1							, 6				):
              if f"""layer_{i}.""" in name:
                     _snake_case       =  name.replace(f"""layer_{i}."""							, f"""encoder.layer.{i - 1}."""				)

       if "conv_1." in name:
              _snake_case       =  name.replace("""conv_1."""							, """conv_stem."""				)
       if ".block." in name:
              _snake_case       =  name.replace(""".block."""							, """."""				)

       if "exp_1x1" in name:
              _snake_case       =  name.replace("""exp_1x1"""							, """expand_1x1"""				)
       if "red_1x1" in name:
              _snake_case       =  name.replace("""red_1x1"""							, """reduce_1x1"""				)
       if ".local_rep.conv_3x3." in name:
              _snake_case       =  name.replace(""".local_rep.conv_3x3."""							, """.conv_kxk."""				)
       if ".local_rep.conv_1x1." in name:
              _snake_case       =  name.replace(""".local_rep.conv_1x1."""							, """.conv_1x1."""				)
       if ".norm." in name:
              _snake_case       =  name.replace(""".norm."""							, """.normalization."""				)
       if ".conv." in name:
              _snake_case       =  name.replace(""".conv."""							, """.convolution."""				)
       if ".conv_proj." in name:
              _snake_case       =  name.replace(""".conv_proj."""							, """.conv_projection."""				)

       for i in range(0							, 2				):
              for j in range(0							, 4				):
                     if f""".{i}.{j}.""" in name:
                            _snake_case       =  name.replace(f""".{i}.{j}."""							, f""".{i}.layer.{j}."""				)

       for i in range(2							, 6				):
              for j in range(0							, 4				):
                     if f""".{i}.{j}.""" in name:
                            _snake_case       =  name.replace(f""".{i}.{j}."""							, f""".{i}."""				)
                            if "expand_1x1" in name:
                                   _snake_case       =  name.replace("""expand_1x1"""							, """downsampling_layer.expand_1x1"""				)
                            if "conv_3x3" in name:
                                   _snake_case       =  name.replace("""conv_3x3"""							, """downsampling_layer.conv_3x3"""				)
                            if "reduce_1x1" in name:
                                   _snake_case       =  name.replace("""reduce_1x1"""							, """downsampling_layer.reduce_1x1"""				)

       for i in range(2							, 5				):
              if f""".global_rep.{i}.weight""" in name:
                     _snake_case       =  name.replace(f""".global_rep.{i}.weight"""							, """.layernorm.weight"""				)
              if f""".global_rep.{i}.bias""" in name:
                     _snake_case       =  name.replace(f""".global_rep.{i}.bias"""							, """.layernorm.bias"""				)

       if ".global_rep." in name:
              _snake_case       =  name.replace(""".global_rep."""							, """.transformer."""				)
       if ".pre_norm_mha.0." in name:
              _snake_case       =  name.replace(""".pre_norm_mha.0."""							, """.layernorm_before."""				)
       if ".pre_norm_mha.1.out_proj." in name:
              _snake_case       =  name.replace(""".pre_norm_mha.1.out_proj."""							, """.attention.output.dense."""				)
       if ".pre_norm_ffn.0." in name:
              _snake_case       =  name.replace(""".pre_norm_ffn.0."""							, """.layernorm_after."""				)
       if ".pre_norm_ffn.1." in name:
              _snake_case       =  name.replace(""".pre_norm_ffn.1."""							, """.intermediate.dense."""				)
       if ".pre_norm_ffn.4." in name:
              _snake_case       =  name.replace(""".pre_norm_ffn.4."""							, """.output.dense."""				)
       if ".transformer." in name:
              _snake_case       =  name.replace(""".transformer."""							, """.transformer.layer."""				)

       if ".aspp_layer." in name:
              _snake_case       =  name.replace(""".aspp_layer."""							, """."""				)
       if ".aspp_pool." in name:
              _snake_case       =  name.replace(""".aspp_pool."""							, """."""				)
       if "seg_head." in name:
              _snake_case       =  name.replace("""seg_head."""							, """segmentation_head."""				)
       if "segmentation_head.classifier.classifier." in name:
              _snake_case       =  name.replace("""segmentation_head.classifier.classifier."""							, """segmentation_head.classifier."""				)

       if "classifier.fc." in name:
              _snake_case       =  name.replace("""classifier.fc."""							, """classifier."""				)
       elif (not base_model) and ("segmentation_head." not in name):
              _snake_case       =  """mobilevit.""" + name

       return name






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=False				):
       if base_model:
              _snake_case       =  """"""
       else:
              _snake_case       =  """mobilevit."""

       for key in orig_state_dict.copy().keys():
              _snake_case       =  orig_state_dict.pop(_SCREAMING_SNAKE_CASE				)

              if key[:8] == "encoder.":
                     _snake_case       =  key[8:]

              if "qkv" in key:
                     _snake_case       =  key.split("""."""				)
                     _snake_case       =  int(key_split[0][6:]				) - 1
                     _snake_case       =  int(key_split[3]				)
                     _snake_case       =  model.get_submodule(f"""{model_prefix}encoder.layer.{layer_num}"""				)
                     _snake_case       =  layer.transformer.layer[transformer_num].attention.attention.all_head_size
                     _snake_case       =  (
                         f"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
                     )
                     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				(  ):
       _snake_case       =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
       _snake_case       =  Image.open(requests.get(_SCREAMING_SNAKE_CASE							, stream=_SCREAMING_SNAKE_CASE				).raw				)
       return im






@torch.no_grad()
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=False				):
       _snake_case       =  get_mobilevit_config(_SCREAMING_SNAKE_CASE				)

       # load original state_dict
       _snake_case       =  torch.load(_SCREAMING_SNAKE_CASE							, map_location="""cpu"""				)

       # load 🤗 model
       if mobilevit_name.startswith("""deeplabv3_"""				):
              _snake_case       =  MobileViTForSemanticSegmentation(_SCREAMING_SNAKE_CASE				).eval()
       else:
              _snake_case       =  MobileViTForImageClassification(_SCREAMING_SNAKE_CASE				).eval()

       _snake_case       =  convert_state_dict(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
       model.load_state_dict(_SCREAMING_SNAKE_CASE				)

       # Check outputs on an image, prepared by MobileViTImageProcessor
       _snake_case       =  MobileViTImageProcessor(crop_size=config.image_size							, size=config.image_size + 32				)
       _snake_case       =  image_processor(images=prepare_img()							, return_tensors="""pt"""				)
       _snake_case       =  model(**_SCREAMING_SNAKE_CASE				)
       _snake_case       =  outputs.logits

       if mobilevit_name.startswith("""deeplabv3_"""				):
              assert logits.shape == (1, 21, 32, 32)

              if mobilevit_name == "deeplabv3_mobilevit_s":
                     _snake_case       =  torch.tensor(
                         [
                             [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
                             [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
                             [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
                         ]				)
              elif mobilevit_name == "deeplabv3_mobilevit_xs":
                     _snake_case       =  torch.tensor(
                         [
                             [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
                             [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
                             [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
                         ]				)
              elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                     _snake_case       =  torch.tensor(
                         [
                             [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                             [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
                             [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
                         ]				)
              else:
                     raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""				)

              assert torch.allclose(logits[0, :3, :3, :3]							, _SCREAMING_SNAKE_CASE							, atol=1E-4				)
       else:
              assert logits.shape == (1, 1000)

              if mobilevit_name == "mobilevit_s":
                     _snake_case       =  torch.tensor([-0.9866, 0.2392, -1.1241]				)
              elif mobilevit_name == "mobilevit_xs":
                     _snake_case       =  torch.tensor([-2.4761, -0.9399, -1.9587]				)
              elif mobilevit_name == "mobilevit_xxs":
                     _snake_case       =  torch.tensor([-1.9364, -1.2327, -0.4653]				)
              else:
                     raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""				)

              assert torch.allclose(logits[0, :3]							, _SCREAMING_SNAKE_CASE							, atol=1E-4				)

       Path(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
       print(f"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""				)
       model.save_pretrained(_SCREAMING_SNAKE_CASE				)
       print(f"""Saving image processor to {pytorch_dump_folder_path}"""				)
       image_processor.save_pretrained(_SCREAMING_SNAKE_CASE				)

       if push_to_hub:
              _snake_case       =  {
                  """mobilevit_s""": """mobilevit-small""",
                  """mobilevit_xs""": """mobilevit-x-small""",
                  """mobilevit_xxs""": """mobilevit-xx-small""",
                  """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
                  """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
                  """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
              }

              print("""Pushing to the hub..."""				)
              _snake_case       =  model_mapping[mobilevit_name]
              image_processor.push_to_hub(_SCREAMING_SNAKE_CASE							, organization="""apple"""				)
              model.push_to_hub(_SCREAMING_SNAKE_CASE							, organization="""apple"""				)


if __name__ == "__main__":
    __lowerCAmelCase												=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--mobilevit_name',
        default='mobilevit_s',
        type=str,
        help=(
            'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
            ' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
        ),
    )
    parser.add_argument(
        '--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt 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.'
    )

    __lowerCAmelCase												=	parser.parse_args()
    convert_movilevit_checkpoint(
        args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )

'''simple docstring'''
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       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[int]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()

							def 	lowercase							(self  )					->       Dict:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-canny"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """bird"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

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

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2





							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-openpose"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

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

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

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2