
import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__      :		Tuple       							=  logging.get_logger(__name__)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   print("""Loading config file..."""						)

   def flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=""    ,      SCREAMING_SNAKE_CASE_="."						):
      _SCREAMING_SNAKE_CASE      =				[]
      for k, v in d.items():
         _SCREAMING_SNAKE_CASE      =				parent_key + sep + k if parent_key else k
         if isinstance(UpperCamelCase__    ,      collections.abc.MutableMapping						):
            items.extend(flatten_yaml_as_dict(UpperCamelCase__    ,      UpperCamelCase__    ,      sep=UpperCamelCase__						).items()						)
         else:
            items.append((new_key, v)						)
      return dict(UpperCamelCase__						)

   _SCREAMING_SNAKE_CASE      =				argparse.Namespace()
   with open(UpperCamelCase__    ,      """r"""						) as yaml_file:
      try:
         _SCREAMING_SNAKE_CASE      =				yaml.load(UpperCamelCase__    ,      Loader=yaml.FullLoader						)

         _SCREAMING_SNAKE_CASE      =				flatten_yaml_as_dict(UpperCamelCase__						)
         for k, v in flat_cfg.items():
            setattr(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__						)
      except yaml.YAMLError as exc:
         logger.error("""Error while loading config file: {}. Error message: {}""".format(UpperCamelCase__    ,      str(UpperCamelCase__						)						)						)
   return config
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				MobileViTVaConfig()

   _SCREAMING_SNAKE_CASE      =				False

   # dataset
   if task_name.startswith("""imagenet1k_"""						):
      _SCREAMING_SNAKE_CASE      =				10_00
      if int(task_name.strip().split("""_"""						)[-1]						) == 3_84:
         _SCREAMING_SNAKE_CASE      =				3_84
      else:
         _SCREAMING_SNAKE_CASE      =				2_56
      _SCREAMING_SNAKE_CASE      =				"""imagenet-1k-id2label.json"""
   elif task_name.startswith("""imagenet21k_to_1k_"""						):
      _SCREAMING_SNAKE_CASE      =				2_10_00
      if int(task_name.strip().split("""_"""						)[-1]						) == 3_84:
         _SCREAMING_SNAKE_CASE      =				3_84
      else:
         _SCREAMING_SNAKE_CASE      =				2_56
      _SCREAMING_SNAKE_CASE      =				"""imagenet-22k-id2label.json"""
   elif task_name.startswith("""ade20k_"""						):
      _SCREAMING_SNAKE_CASE      =				1_51
      _SCREAMING_SNAKE_CASE      =				5_12
      _SCREAMING_SNAKE_CASE      =				"""ade20k-id2label.json"""
      _SCREAMING_SNAKE_CASE      =				True
   elif task_name.startswith("""voc_"""						):
      _SCREAMING_SNAKE_CASE      =				21
      _SCREAMING_SNAKE_CASE      =				5_12
      _SCREAMING_SNAKE_CASE      =				"""pascal-voc-id2label.json"""
      _SCREAMING_SNAKE_CASE      =				True

   # orig_config
   _SCREAMING_SNAKE_CASE      =				load_orig_config_file(UpperCamelCase__						)
   assert getattr(UpperCamelCase__    ,      """model.classification.name"""    ,      -1						) == "mobilevit_v2", "Invalid model"
   _SCREAMING_SNAKE_CASE      =				getattr(UpperCamelCase__    ,      """model.classification.mitv2.width_multiplier"""    ,      1.0						)
   assert (
       getattr(UpperCamelCase__    ,      """model.classification.mitv2.attn_norm_layer"""    ,      -1						) == "layer_norm_2d"
   ), "Norm layers other than layer_norm_2d is not supported"
   _SCREAMING_SNAKE_CASE      =				getattr(UpperCamelCase__    ,      """model.classification.activation.name"""    ,      """swish"""						)
   # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

   if is_segmentation_model:
      _SCREAMING_SNAKE_CASE      =				getattr(UpperCamelCase__    ,      """model.segmentation.output_stride"""    ,      16						)
      if "_deeplabv3" in task_name:
         _SCREAMING_SNAKE_CASE      =				getattr(UpperCamelCase__    ,      """model.segmentation.deeplabv3.aspp_rates"""    ,      [12, 24, 36]						)
         _SCREAMING_SNAKE_CASE      =				getattr(UpperCamelCase__    ,      """model.segmentation.deeplabv3.aspp_out_channels"""    ,      5_12						)
         _SCREAMING_SNAKE_CASE      =				getattr(UpperCamelCase__    ,      """model.segmentation.deeplabv3.aspp_dropout"""    ,      0.1						)

    # id2label
   _SCREAMING_SNAKE_CASE      =				"""huggingface/label-files"""
   _SCREAMING_SNAKE_CASE      =				json.load(open(hf_hub_download(UpperCamelCase__    ,      UpperCamelCase__    ,      repo_type="""dataset"""						)    ,      """r"""						)						)
   _SCREAMING_SNAKE_CASE      =				{int(UpperCamelCase__						): v for k, v in idalabel.items()}
   _SCREAMING_SNAKE_CASE      =				idalabel
   _SCREAMING_SNAKE_CASE      =				{v: k for k, v in idalabel.items()}

   return config
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Tuple:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				dct.pop(UpperCamelCase__						)
   _SCREAMING_SNAKE_CASE      =				val
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False						)     ->     int:




   """simple docstring"""
   if base_model:
      _SCREAMING_SNAKE_CASE      =				""""""
   else:
      _SCREAMING_SNAKE_CASE      =				"""mobilevitv2."""

   _SCREAMING_SNAKE_CASE      =				[]
   for k in state_dict.keys():
      if k[:8] == "encoder.":
         _SCREAMING_SNAKE_CASE      =				k[8:]
      else:
         _SCREAMING_SNAKE_CASE      =				k

      if ".block." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace(""".block."""    ,      """."""						)
      if ".conv." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace(""".conv."""    ,      """.convolution."""						)
      if ".norm." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace(""".norm."""    ,      """.normalization."""						)

      if "conv_1." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace("""conv_1."""    ,      F"{model_prefix}conv_stem."						)
      for i in [1, 2]:
         if F"layer_{i}." in k:
            _SCREAMING_SNAKE_CASE      =				k_new.replace(F"layer_{i}."    ,      F"{model_prefix}encoder.layer.{i-1}.layer."						)
      if ".exp_1x1." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace(""".exp_1x1."""    ,      """.expand_1x1."""						)
      if ".red_1x1." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace(""".red_1x1."""    ,      """.reduce_1x1."""						)

      for i in [3, 4, 5]:
         if F"layer_{i}.0." in k:
            _SCREAMING_SNAKE_CASE      =				k_new.replace(F"layer_{i}.0."    ,      F"{model_prefix}encoder.layer.{i-1}.downsampling_layer."						)
         if F"layer_{i}.1.local_rep.0." in k:
            _SCREAMING_SNAKE_CASE      =				k_new.replace(F"layer_{i}.1.local_rep.0."    ,      F"{model_prefix}encoder.layer.{i-1}.conv_kxk."						)
         if F"layer_{i}.1.local_rep.1." in k:
            _SCREAMING_SNAKE_CASE      =				k_new.replace(F"layer_{i}.1.local_rep.1."    ,      F"{model_prefix}encoder.layer.{i-1}.conv_1x1."						)

      for i in [3, 4, 5]:
         if i == 3:
            _SCREAMING_SNAKE_CASE      =				[0, 1]
         elif i == 4:
            _SCREAMING_SNAKE_CASE      =				[0, 1, 2, 3]
         elif i == 5:
            _SCREAMING_SNAKE_CASE      =				[0, 1, 2]

         for j in j_in:
            if F"layer_{i}.1.global_rep.{j}." in k:
               _SCREAMING_SNAKE_CASE      =				k_new.replace(
                   F"layer_{i}.1.global_rep.{j}."    ,      F"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."						)
         if F"layer_{i}.1.global_rep.{j+1}." in k:
            _SCREAMING_SNAKE_CASE      =				k_new.replace(
                F"layer_{i}.1.global_rep.{j+1}."    ,      F"{model_prefix}encoder.layer.{i-1}.layernorm."						)

         if F"layer_{i}.1.conv_proj." in k:
            _SCREAMING_SNAKE_CASE      =				k_new.replace(F"layer_{i}.1.conv_proj."    ,      F"{model_prefix}encoder.layer.{i-1}.conv_projection."						)

      if "pre_norm_attn.0." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace("""pre_norm_attn.0."""    ,      """layernorm_before."""						)
      if "pre_norm_attn.1." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace("""pre_norm_attn.1."""    ,      """attention."""						)
      if "pre_norm_ffn.0." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace("""pre_norm_ffn.0."""    ,      """layernorm_after."""						)
      if "pre_norm_ffn.1." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace("""pre_norm_ffn.1."""    ,      """ffn.conv1."""						)
      if "pre_norm_ffn.3." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace("""pre_norm_ffn.3."""    ,      """ffn.conv2."""						)

      if "classifier.1." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace("""classifier.1."""    ,      """classifier."""						)

      if "seg_head." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace("""seg_head."""    ,      """segmentation_head."""						)
      if ".aspp_layer." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace(""".aspp_layer."""    ,      """."""						)
      if ".aspp_pool." in k:
         _SCREAMING_SNAKE_CASE      =				k_new.replace(""".aspp_pool."""    ,      """."""						)

      rename_keys.append((k, k_new)						)
   return rename_keys
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[]
   for k in state_dict.keys():
      if k.startswith("""seg_head.aux_head."""						):
         keys_to_ignore.append(UpperCamelCase__						)
   for k in keys_to_ignore:
      state_dict.pop(UpperCamelCase__    ,      UpperCamelCase__						)
def    lowerCAmelCase_	(				)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
   _SCREAMING_SNAKE_CASE      =				Image.open(requests.get(UpperCamelCase__    ,      stream=UpperCamelCase__						).raw						)
   return im
@torch.no_grad()
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				get_mobilevitva_config(UpperCamelCase__    ,      UpperCamelCase__						)

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

   # load huggingface model
   if task_name.startswith("""ade20k_"""						) or task_name.startswith("""voc_"""						):
      _SCREAMING_SNAKE_CASE      =				MobileViTVaForSemanticSegmentation(UpperCamelCase__						).eval()
      _SCREAMING_SNAKE_CASE      =				False
   else:
      _SCREAMING_SNAKE_CASE      =				MobileViTVaForImageClassification(UpperCamelCase__						).eval()
      _SCREAMING_SNAKE_CASE      =				False

   # remove and rename some keys of load the original model
   _SCREAMING_SNAKE_CASE      =				checkpoint
   remove_unused_keys(UpperCamelCase__						)
   _SCREAMING_SNAKE_CASE      =				create_rename_keys(UpperCamelCase__    ,      base_model=UpperCamelCase__						)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__						)

   # load modified state_dict
   model.load_state_dict(UpperCamelCase__						)

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

   # verify classification model
   if task_name.startswith("""imagenet"""						):
      _SCREAMING_SNAKE_CASE      =				outputs.logits
      _SCREAMING_SNAKE_CASE      =				logits.argmax(-1						).item()
      print("""Predicted class:"""    ,      model.config.idalabel[predicted_class_idx]						)
      if task_name.startswith("""imagenet1k_256"""						) and config.width_multiplier == 1.0:
         # expected_logits for base variant
         _SCREAMING_SNAKE_CASE      =				torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]						)
         assert torch.allclose(logits[0, :3]    ,      UpperCamelCase__    ,      atol=1e-4						)

   Path(UpperCamelCase__						).mkdir(exist_ok=UpperCamelCase__						)
   print(F"Saving model {task_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 __name__ == "__main__":
     UpperCamelCase__      :		List[Any]       							=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--task",
         default="imagenet1k_256",
         type=str,
         help=(
             "Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . "
             "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
         ),
         choices=[
             "imagenet1k_256",
             "imagenet1k_384",
             "imagenet21k_to_1k_256",
             "imagenet21k_to_1k_384",
             "ade20k_deeplabv3",
             "voc_deeplabv3",
         ],
     )

     parser.add_argument(
         "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
     )
     parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
     parser.add_argument(
         "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
     )

     UpperCamelCase__      :		Any       							=  parser.parse_args()
     convert_mobilevitva_checkpoint(
         args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
     )








'''simple docstring'''



import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""    ,      [False, True]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize(
    """features"""    ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      split=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type"""    ,      [str, list]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   if issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				parquet_path
   elif issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				[parquet_path]
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=("train",)						)     ->     List[str]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   for split in splits:
      _SCREAMING_SNAKE_CASE      =				dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""    ,      [False, True]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(
          {"""train""": parquet_path}    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize(
    """features"""    ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader({"""train""": parquet_path}    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   if split:
      _SCREAMING_SNAKE_CASE      =				{split: parquet_path}
   else:
      _SCREAMING_SNAKE_CASE      =				"""train"""
      _SCREAMING_SNAKE_CASE      =				{"""train""": parquet_path, """test""": parquet_path}
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      splits=list(path.keys()						)						)
   assert all(dataset[split].split == split for split in path.keys()						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0
   _SCREAMING_SNAKE_CASE      =				pq.ParquetFile(tmp_path / """foo.parquet"""						)
   _SCREAMING_SNAKE_CASE      =				pf.read()
   assert dataset.data.table == output_table
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				str(shared_datadir / """test_image_rgb.jpg"""						)
   _SCREAMING_SNAKE_CASE      =				{"""image""": [image_path]}
   _SCREAMING_SNAKE_CASE      =				Features({"""image""": Image()}						)
   _SCREAMING_SNAKE_CASE      =				Dataset.from_dict(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0

   _SCREAMING_SNAKE_CASE      =				Dataset.from_parquet(str(tmp_path / """foo.parquet"""						)						)
   assert dataset.features == reloaded_dataset.features

   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(str(tmp_path / """foo.parquet"""						)    ,      streaming=SCREAMING_SNAKE_CASE_						).read()
   assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    """feature, expected"""    ,      [
        (Features({"""foo""": Value("""int32"""						)}						), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   assert get_writer_batch_size(SCREAMING_SNAKE_CASE_						) == expected






