

'''simple docstring'''

import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





class 							_snake_case	(    _a  ):
   def       __UpperCamelCase  (	self     : Tuple       ,SCREAMING_SNAKE_CASE__     : List[Any]       ,SCREAMING_SNAKE_CASE__     : List[Any]=False							):
     SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:List[Any]   =   (
         filename_skip_take["filename"],
         filename_skip_take["skip"] if "skip" in filename_skip_take else None,
         filename_skip_take["take"] if "take" in filename_skip_take else None,
     )
     open(os.path.join(SCREAMING_SNAKE_CASE__							)       ,"wb"							).close()
     SCREAMING_SNAKE_CASE:int   =   pa.Table.from_pydict({"filename": [Path(SCREAMING_SNAKE_CASE__							).name] * 100}							)
     if take == -1:
       SCREAMING_SNAKE_CASE:Optional[Any]   =   len(SCREAMING_SNAKE_CASE__							) - skip
     if skip is not None and take is not None:
       SCREAMING_SNAKE_CASE:Optional[Any]   =   pa_table.slice(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)
     return pa_table





class 							_snake_case	(    _a  ):
   def       __UpperCamelCase  (	self     : Tuple							):
     SCREAMING_SNAKE_CASE:Dict   =   "my_name"
     SCREAMING_SNAKE_CASE:Tuple   =   SplitInfo(name="train"       ,num_examples=100							)
     SCREAMING_SNAKE_CASE:Dict   =   SplitInfo(name="test"       ,num_examples=100							)
     SCREAMING_SNAKE_CASE:Any   =   [train_info, test_info]
     SCREAMING_SNAKE_CASE:Any   =   SplitDict()
     split_dict.add(SCREAMING_SNAKE_CASE__							)
     split_dict.add(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any   =   DatasetInfo(splits=SCREAMING_SNAKE_CASE__							)

     with tempfile.TemporaryDirectory() as tmp_dir:
       SCREAMING_SNAKE_CASE:str   =   ReaderTest(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)

       SCREAMING_SNAKE_CASE:Optional[Any]   =   "test[:33%]"
       SCREAMING_SNAKE_CASE:Optional[Any]   =   Dataset(**reader.read(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)							)
       self.assertEqual(dset["filename"][0]       ,F'''{name}-test'''							)
       self.assertEqual(dset.num_rows       ,33							)
       self.assertEqual(dset.num_columns       ,1							)

       SCREAMING_SNAKE_CASE:List[Any]   =   ["train", "test[:33%]"]
       SCREAMING_SNAKE_CASE:List[Any]   =   [Split.TRAIN, ReadInstruction.from_spec("test[:33%]"							)]
       for instructions in [instructionsa, instructionsa]:
         SCREAMING_SNAKE_CASE:int   =   [reader.read(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							) for instr in instructions]
         SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:str   =   (Dataset(**SCREAMING_SNAKE_CASE__							) for dataset_kwargs in datasets_kwargs)
         self.assertEqual(train_dset["filename"][0]       ,F'''{name}-train'''							)
         self.assertEqual(train_dset.num_rows       ,100							)
         self.assertEqual(train_dset.num_columns       ,1							)
         self.assertIsInstance(train_dset.split       ,SCREAMING_SNAKE_CASE__							)
         self.assertEqual(str(train_dset.split							)       ,"train"							)
         self.assertEqual(test_dset["filename"][0]       ,F'''{name}-test'''							)
         self.assertEqual(test_dset.num_rows       ,33							)
         self.assertEqual(test_dset.num_columns       ,1							)
         self.assertIsInstance(test_dset.split       ,SCREAMING_SNAKE_CASE__							)
         self.assertEqual(str(test_dset.split							)       ,"test[:33%]"							)
         del train_dset, test_dset
   def       __UpperCamelCase  (	self     : List[Any]							):
     SCREAMING_SNAKE_CASE:Dict   =   "my_name"
     SCREAMING_SNAKE_CASE:List[str]   =   SplitInfo(name="train"       ,num_examples=1_000       ,shard_lengths=[100] * 10							)
     SCREAMING_SNAKE_CASE:List[str]   =   [train_info]
     SCREAMING_SNAKE_CASE:Optional[int]   =   SplitDict()
     split_dict.add(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any   =   DatasetInfo(splits=SCREAMING_SNAKE_CASE__							)

     with tempfile.TemporaryDirectory() as tmp_dir:
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   ReaderTest(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)

       SCREAMING_SNAKE_CASE:Dict   =   "train[:33%]"
       SCREAMING_SNAKE_CASE:Optional[int]   =   Dataset(**reader.read(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)							)
       self.assertEqual(dset["filename"][0]       ,F'''{name}-train-00000-of-00010'''							)
       self.assertEqual(dset["filename"][-1]       ,F'''{name}-train-00003-of-00010'''							)
       self.assertEqual(dset.num_rows       ,330							)
       self.assertEqual(dset.num_columns       ,1							)







   def       __UpperCamelCase  (	self     : int							):
     SCREAMING_SNAKE_CASE:Tuple   =   SplitInfo(name="train"       ,num_examples=100							)
     SCREAMING_SNAKE_CASE:Optional[Any]   =   SplitInfo(name="test"       ,num_examples=100							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   SplitDict()
     split_dict.add(SCREAMING_SNAKE_CASE__							)
     split_dict.add(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[Any]   =   DatasetInfo(splits=SCREAMING_SNAKE_CASE__							)

     with tempfile.TemporaryDirectory() as tmp_dir:
       SCREAMING_SNAKE_CASE:Tuple   =   ReaderTest(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)

       SCREAMING_SNAKE_CASE:Optional[Any]   =   [
           {"filename": os.path.join(SCREAMING_SNAKE_CASE__       ,"train"							)},
           {"filename": os.path.join(SCREAMING_SNAKE_CASE__       ,"test"							), "skip": 10, "take": 10},
       ]
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   Dataset(**reader.read_files(SCREAMING_SNAKE_CASE__       ,original_instructions="train+test[10:20]"							)							)
       self.assertEqual(dset.num_rows       ,110							)
       self.assertEqual(dset.num_columns       ,1							)
       del dset





@pytest.mark.parametrize("in_memory" ,    [False, True]	)
def 				A_					(	snake_case ,    snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:str   =   arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE:Tuple   =   ArrowReader.read_table(snake_case ,    in_memory=snake_case	)
  assert table.shape == dataset.data.shape
  assert set(table.column_names	) == set(dataset.data.column_names	)
  assert dict(table.to_pydict()	) == dict(dataset.data.to_pydict()	)  # to_pydict returns OrderedDict





@pytest.mark.parametrize("in_memory" ,    [False, True]	)
def 				A_					(	snake_case ,    snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:Dict   =   arrow_file
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   ArrowReader("" ,    snake_case	)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE:Optional[Any]   =   reader.read_files([{"filename": filename}] ,    in_memory=snake_case	)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   dataset_kwargs["arrow_table"]
  assert table.shape == dataset.data.shape
  assert set(table.column_names	) == set(dataset.data.column_names	)
  assert dict(table.to_pydict()	) == dict(dataset.data.to_pydict()	)  # to_pydict returns OrderedDict





def 				A_					(	):
  assert ReadInstruction("train" ,    to=10 ,    unit="abs"	).to_spec() == "train[:10]"
  assert ReadInstruction("train" ,    from_=-80 ,    to=10 ,    unit="%"	).to_spec() == "train[-80%:10%]"

  SCREAMING_SNAKE_CASE:Dict   =   "train+test"
  assert ReadInstruction.from_spec(snake_case	).to_spec() == spec_train_test

  SCREAMING_SNAKE_CASE:Union[str, Any]   =   "train[2:10]"
  assert ReadInstruction.from_spec(snake_case	).to_spec() == spec_train_abs

  SCREAMING_SNAKE_CASE:str   =   "train[15%:-20%]"
  assert ReadInstruction.from_spec(snake_case	).to_spec() == spec_train_pct

  SCREAMING_SNAKE_CASE:Union[str, Any]   =   "train[:10%](closest)"
  assert ReadInstruction.from_spec(snake_case	).to_spec() == "train[:10%]"

  SCREAMING_SNAKE_CASE:Any   =   "train[:10%](pct1_dropremainder)"
  assert ReadInstruction.from_spec(snake_case	).to_spec() == spec_train_pct_rounding

  SCREAMING_SNAKE_CASE:List[Any]   =   "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
  assert ReadInstruction.from_spec(snake_case	).to_spec() == spec_train_test_pct_rounding





def 				A_					(	):
  SCREAMING_SNAKE_CASE:str   =   "dummy"
  SCREAMING_SNAKE_CASE:Any   =   [SplitInfo(name="train" ,    num_examples=100	)]
  SCREAMING_SNAKE_CASE:Optional[int]   =   "train[:33%]"
  SCREAMING_SNAKE_CASE:Tuple   =   "arrow"
  SCREAMING_SNAKE_CASE:List[Any]   =   "prefix"

  SCREAMING_SNAKE_CASE:Optional[Any]   =   make_file_instructions(snake_case ,    snake_case ,    snake_case ,    snake_case ,    snake_case	)
  assert isinstance(snake_case ,    snake_case	)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(snake_case ,    F'''{name}-train.arrow'''	), "skip": 0, "take": 33}
  ]

  SCREAMING_SNAKE_CASE:Dict   =   [SplitInfo(name="train" ,    num_examples=100 ,    shard_lengths=[10] * 10	)]
  SCREAMING_SNAKE_CASE:List[Any]   =   make_file_instructions(snake_case ,    snake_case ,    snake_case ,    snake_case ,    snake_case	)
  assert isinstance(snake_case ,    snake_case	)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(snake_case ,    F'''{name}-train-00000-of-00010.arrow'''	), "skip": 0, "take": -1},
      {"filename": os.path.join(snake_case ,    F'''{name}-train-00001-of-00010.arrow'''	), "skip": 0, "take": -1},
      {"filename": os.path.join(snake_case ,    F'''{name}-train-00002-of-00010.arrow'''	), "skip": 0, "take": -1},
      {"filename": os.path.join(snake_case ,    F'''{name}-train-00003-of-00010.arrow'''	), "skip": 0, "take": 3},
  ]





@pytest.mark.parametrize("name, expected_exception" ,    [(None, TypeError), ("", ValueError)]	)
def 				A_					(	snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:Any   =   [SplitInfo(name="train" ,    num_examples=100	)]
  SCREAMING_SNAKE_CASE:int   =   "train"
  SCREAMING_SNAKE_CASE:Optional[Any]   =   "arrow"
  SCREAMING_SNAKE_CASE:Any   =   "prefix_path"
  with pytest.raises(snake_case	):
    SCREAMING_SNAKE_CASE:Dict   =   make_file_instructions(snake_case ,    snake_case ,    snake_case ,    snake_case ,    snake_case	)









'''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 				A_					(	snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   MobileNetVaConfig(layer_norm_eps=0.001	)

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

  SCREAMING_SNAKE_CASE:Any   =   re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$" ,    snake_case	)
  if matches:
    SCREAMING_SNAKE_CASE:str   =   float(matches[1]	)
    SCREAMING_SNAKE_CASE:Optional[int]   =   int(matches[2]	)

  if model_name.startswith("deeplabv3_"	):
    SCREAMING_SNAKE_CASE:str   =   8
    SCREAMING_SNAKE_CASE:Optional[int]   =   21
    SCREAMING_SNAKE_CASE:Dict   =   "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".
    SCREAMING_SNAKE_CASE:Optional[int]   =   1001
    SCREAMING_SNAKE_CASE:Tuple   =   "imagenet-1k-id2label.json"

  SCREAMING_SNAKE_CASE:List[str]   =   "huggingface/label-files"
  SCREAMING_SNAKE_CASE:List[Any]   =   json.load(open(hf_hub_download(snake_case ,    snake_case ,    repo_type="dataset"	) ,    "r"	)	)

  if config.num_labels == 1001:
    SCREAMING_SNAKE_CASE:Optional[Any]   =   {int(snake_case	) + 1: v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE:Dict   =   "background"
  else:
    SCREAMING_SNAKE_CASE:Union[str, Any]   =   {int(snake_case	): v for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE:str   =   idalabel
  SCREAMING_SNAKE_CASE:int   =   {v: k for k, v in idalabel.items()}

  return config





def 				A_					(	):
  SCREAMING_SNAKE_CASE:Optional[Any]   =   "http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE:str   =   Image.open(requests.get(snake_case ,    stream=snake_case	).raw	)
  return im





@torch.no_grad()
def 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case=False	):
  SCREAMING_SNAKE_CASE:Any   =   get_mobilenet_va_config(snake_case	)

  # Load 🤗 model
  if model_name.startswith("deeplabv3_"	):
    SCREAMING_SNAKE_CASE:Tuple   =   MobileNetVaForSemanticSegmentation(snake_case	).eval()
  else:
    SCREAMING_SNAKE_CASE:List[str]   =   MobileNetVaForImageClassification(snake_case	).eval()

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

  # Check outputs on an image, prepared by MobileNetV2ImageProcessor
  SCREAMING_SNAKE_CASE:List[str]   =   MobileNetVaImageProcessor(
      crop_size={"width": config.image_size, "height": config.image_size} ,    size={"shortest_edge": config.image_size + 32} ,    )
  SCREAMING_SNAKE_CASE:List[Any]   =   image_processor(images=prepare_img() ,    return_tensors="pt"	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   model(**snake_case	)
  SCREAMING_SNAKE_CASE:str   =   outputs.logits

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

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      SCREAMING_SNAKE_CASE:int   =   torch.tensor(
          [
              [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
              [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
              [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
          ]	)

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

    assert torch.allclose(logits[0, :3, :3, :3] ,    snake_case ,    atol=1e-4	)
  else:
    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v2_1.4_224":
      SCREAMING_SNAKE_CASE:List[str]   =   torch.tensor([0.0181, -1.0015, 0.4688]	)
    elif model_name == "mobilenet_v2_1.0_224":
      SCREAMING_SNAKE_CASE:Optional[int]   =   torch.tensor([0.2445, -1.1993, 0.1905]	)
    elif model_name == "mobilenet_v2_0.75_160":
      SCREAMING_SNAKE_CASE:Optional[Any]   =   torch.tensor([0.2482, 0.4136, 0.6669]	)
    elif model_name == "mobilenet_v2_0.35_96":
      SCREAMING_SNAKE_CASE:int   =   torch.tensor([0.1451, -0.4624, 0.7192]	)
    else:
      SCREAMING_SNAKE_CASE:Optional[Any]   =   None

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

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

  if push_to_hub:
    print("Pushing to the hub..."	)
    SCREAMING_SNAKE_CASE:Tuple   =   "google/" + model_name
    image_processor.push_to_hub(snake_case	)
    model.push_to_hub(snake_case	)


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
    )






