
'''simple docstring'''





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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Tuple      ):
      snake_case_    							=				MobileNetVaConfig(layer_norm_eps=0.0_0_1      )

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

      snake_case_    							=				re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$"			,       _a      )
      if matches:
            snake_case_    							=				float(matches[1]      )
            snake_case_    							=				int(matches[2]      )

      if model_name.startswith("deeplabv3_"      ):
            snake_case_    							=				8
            snake_case_    							=				21
            snake_case_    							=				"pascal-voc-id2label.json"
      else:
            # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
            # of the usual 1000. The first class (index 0) is "background".
            snake_case_    							=				1_001
            snake_case_    							=				"imagenet-1k-id2label.json"

      snake_case_    							=				"huggingface/label-files"
      snake_case_    							=				json.load(open(hf_hub_download(_a			,       _a			,       repo_type="dataset"      )			,       "r"      )      )

      if config.num_labels == 1_001:
            snake_case_    							=				{int(_a      ) + 1: v for k, v in idalabel.items()}
            snake_case_    							=				"background"
      else:
            snake_case_    							=				{int(_a      ): v for k, v in idalabel.items()}

      snake_case_    							=				idalabel
      snake_case_    							=				{v: k for k, v in idalabel.items()}

      return config
def 	snake_case_							(				):
      snake_case_    							=				"http://images.cocodataset.org/val2017/000000039769.jpg"
      snake_case_    							=				Image.open(requests.get(_a			,       stream=_a      ).raw      )
      return im
@torch.no_grad()
def 	snake_case_							(				_a  :				Any			,       _a  :				Dict			,       _a  :				Union[str, Any]			,       _a  :				Any=False      ):
      snake_case_    							=				get_mobilenet_va_config(_a      )

      # Load 🤗 model
      if model_name.startswith("deeplabv3_"      ):
            snake_case_    							=				MobileNetVaForSemanticSegmentation(_a      ).eval()
      else:
            snake_case_    							=				MobileNetVaForImageClassification(_a      ).eval()

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

      # Check outputs on an image, prepared by MobileNetV2ImageProcessor
      snake_case_    							=				MobileNetVaImageProcessor(
          crop_size={"width": config.image_size, "height": config.image_size}			,       size={"shortest_edge": config.image_size + 32}			,       )
      snake_case_    							=				image_processor(images=prepare_img()			,       return_tensors="pt"      )
      snake_case_    							=				model(**_a      )
      snake_case_    							=				outputs.logits

      if model_name.startswith("deeplabv3_"      ):
            assert logits.shape == (1, 21, 65, 65)

            if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                  snake_case_    							=				torch.tensor(
                      [
                          [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
                          [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                          [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
                      ]      )

            else:
                  raise ValueError(F'''Unknown model name: {model_name}'''      )

            assert torch.allclose(logits[0, :3, :3, :3]			,       _a			,       atol=1E-4      )
      else:
            assert logits.shape == (1, 1_001)

            if model_name == "mobilenet_v2_1.4_224":
                  snake_case_    							=				torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]      )
            elif model_name == "mobilenet_v2_1.0_224":
                  snake_case_    							=				torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]      )
            elif model_name == "mobilenet_v2_0.75_160":
                  snake_case_    							=				torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]      )
            elif model_name == "mobilenet_v2_0.35_96":
                  snake_case_    							=				torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]      )
            else:
                  snake_case_    							=				None

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

      Path(_a      ).mkdir(exist_ok=_a      )
      print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''      )
      model.save_pretrained(_a      )
      print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
      image_processor.save_pretrained(_a      )

      if push_to_hub:
            print("Pushing to the hub..."      )
            snake_case_    							=				"google/" + model_name
            image_processor.push_to_hub(_a      )
            model.push_to_hub(_a      )


if __name__ == "__main__":
     A							     = argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--model_name",
         default="mobilenet_v2_1.0_224",
         type=str,
         help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<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."
     )

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



'''simple docstring'''





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def 	snake_case_							(				):
      snake_case_    							=				Dataset.from_dict({"i": range(17      )}      )
      snake_case_    							=				len(_a      )
      snake_case_    							=				3
      snake_case_    							=				[
          split_dataset_by_node(_a			,       rank=_a			,       world_size=_a      ) for rank in range(_a      )
      ]
      assert sum(len(_a      ) for ds in datasets_per_rank      ) == full_size
      assert len({tuple(x.values()      ) for ds in datasets_per_rank for x in ds}      ) == full_size
def 	snake_case_							(				):
      def gen():
            return ({"i": i} for i in range(17      ))

      snake_case_    							=				3
      snake_case_    							=				IterableDataset.from_generator(_a      )
      snake_case_    							=				len(list(_a      )      )
      snake_case_    							=				[
          split_dataset_by_node(_a			,       rank=_a			,       world_size=_a      ) for rank in range(_a      )
      ]
      assert sum(len(list(_a      )      ) for ds in datasets_per_rank      ) == full_size
      assert len({tuple(x.values()      ) for ds in datasets_per_rank for x in ds}      ) == full_size
@pytest.mark.parametrize("shards_per_node"			,       [1, 2, 3]      )
def 	snake_case_							(				_a  :				List[Any]      ):
      def gen(_a  :				Dict      ):
            for shard in shards:
                  yield from ({"i": i, "shard": shard} for i in range(17      ))

      snake_case_    							=				3
      snake_case_    							=				shards_per_node * world_size
      snake_case_    							=				{"shards": [F'''shard_{shard_idx}.txt''' for shard_idx in range(_a      )]}
      snake_case_    							=				IterableDataset.from_generator(_a			,       gen_kwargs=_a      )
      snake_case_    							=				len(list(_a      )      )
      assert full_ds.n_shards == world_size * shards_per_node
      snake_case_    							=				[
          split_dataset_by_node(_a			,       rank=_a			,       world_size=_a      ) for rank in range(_a      )
      ]
      assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
      assert sum(len(list(_a      )      ) for ds in datasets_per_rank      ) == full_size
      assert len({tuple(x.values()      ) for ds in datasets_per_rank for x in ds}      ) == full_size
def 	snake_case_							(				):
      def gen():
            return ({"i": i} for i in range(17      ))

      snake_case_    							=				2
      snake_case_    							=				IterableDataset.from_generator(_a      )
      snake_case_    							=				len(list(_a      )      )

      snake_case_    							=				split_dataset_by_node(_a			,       rank=0			,       world_size=_a      ).shuffle(seed=42      )
      assert len(list(_a      )      ) == 1 + full_size // world_size
      with pytest.raises(_a      ):
            split_dataset_by_node(_a			,       rank=0			,       world_size=_a      ).shuffle()

      snake_case_    							=				split_dataset_by_node(full_ds.shuffle(seed=42      )			,       rank=0			,       world_size=_a      )
      assert len(list(_a      )      ) == 1 + full_size // world_size
      with pytest.raises(_a      ):
            split_dataset_by_node(full_ds.shuffle()			,       rank=0			,       world_size=_a      )
@pytest.mark.parametrize("streaming"			,       [False, True]      )
@require_torch
@pytest.mark.skipif(os.name == "nt"			,       reason="execute_subprocess_async doesn't support windows"      )
@pytest.mark.integration
def 	snake_case_							(				_a  :				Any      ):
      snake_case_    							=				2
      snake_case_    							=				get_torch_dist_unique_port()
      snake_case_    							=				Path(_a      ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
      snake_case_    							=				F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
      snake_case_    							=				F'''
        --streaming={streaming}
    '''.split()
      snake_case_    							=				[sys.executable] + distributed_args + args
      execute_subprocess_async(_a			,       env=os.environ.copy()      )




@pytest.mark.parametrize(
    "nproc_per_node, num_workers"			,       [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]			,       )
@require_torch
@pytest.mark.skipif(os.name == "nt"			,       reason="execute_subprocess_async doesn't support windows"      )
@pytest.mark.integration
def 	snake_case_							(				_a  :				int			,       _a  :				List[Any]      ):
      snake_case_    							=				True
      snake_case_    							=				get_torch_dist_unique_port()
      snake_case_    							=				Path(_a      ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
      snake_case_    							=				F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
      snake_case_    							=				F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
      snake_case_    							=				[sys.executable] + distributed_args + args
      execute_subprocess_async(_a			,       env=os.environ.copy()      )

