
import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


UpperCamelCase__      :		Any       							=  {
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 131_072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
}
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




   """simple docstring"""
   return torch.atana(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						) / math.pi * 2
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				torch.sin(t * math.pi / 2						) ** 2
   _SCREAMING_SNAKE_CASE      =				(1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

class    _a				(_lowerCamelCase):





       """simple docstring"""




       pass






class    _a				(nn.Module):





       """simple docstring"""




       def __init__(							self       ,							A__      )    ->			Dict:
          super().__init__()

          _SCREAMING_SNAKE_CASE      =				DiffusionAttnUnetaD(A__       ,							n_attn_layers=4      )
          _SCREAMING_SNAKE_CASE      =				deepcopy(self.diffusion      )
          _SCREAMING_SNAKE_CASE      =				torch.quasirandom.SobolEngine(1       ,							scramble=A__      )






def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				MODELS_MAP[model_name]["""url"""]
   os.system(F"wget {url} ./"						)

   return F"./{model_name}.ckpt"


UpperCamelCase__      :		Optional[int]       							=  {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
UpperCamelCase__      :		List[str]       							=  {
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
UpperCamelCase__      :		str       							=  {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
    "8": "resnets.3",
    "9": "attentions.3",
    "10": "resnets.4",
    "11": "attentions.4",
    "12": "resnets.5",
    "13": "attentions.5",
}
UpperCamelCase__      :		Union[str, Any]       							=  {
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

UpperCamelCase__      :		Optional[int]       							=  {
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

UpperCamelCase__      :		Any       							=  {
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   if name.startswith("""skip"""						):
      return name.replace("""skip"""    ,      RES_CONV_MAP["""skip"""]						)

   # name has to be of format main.{digit}
   if not name.startswith("""main."""						):
      raise ValueError(F"ResConvBlock error with {name}"						)

   return name.replace(name[:6]    ,      RES_CONV_MAP[name[:6]]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   for key, value in ATTN_MAP.items():
      if name.startswith(SCREAMING_SNAKE_CASE_						) and not isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
         return name.replace(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
      elif name.startswith(SCREAMING_SNAKE_CASE_						):
         return [name.replace(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						) for v in value]
   raise ValueError(F"Attn error with {name}"						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=13						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				input_string

   if string.split("""."""						)[0] == "timestep_embed":
      return string.replace("""timestep_embed"""    ,      """time_proj"""						)

   _SCREAMING_SNAKE_CASE      =				0
   if string.startswith("""net.3."""						):
      depth += 1
      _SCREAMING_SNAKE_CASE      =				string[6:]
   elif string.startswith("""net."""						):
      _SCREAMING_SNAKE_CASE      =				string[4:]

   while string.startswith("""main.7."""						):
      depth += 1
      _SCREAMING_SNAKE_CASE      =				string[7:]

   if string.startswith("""main."""						):
      _SCREAMING_SNAKE_CASE      =				string[5:]

   # mid block
   if string[:2].isdigit():
      _SCREAMING_SNAKE_CASE      =				string[:2]
      _SCREAMING_SNAKE_CASE      =				string[2:]
   else:
      _SCREAMING_SNAKE_CASE      =				string[0]
      _SCREAMING_SNAKE_CASE      =				string[1:]

   if depth == max_depth:
      _SCREAMING_SNAKE_CASE      =				MID_NUM_TO_LAYER[layer_num]
      _SCREAMING_SNAKE_CASE      =				"""mid_block"""
   elif depth > 0 and int(SCREAMING_SNAKE_CASE_						) < 7:
      _SCREAMING_SNAKE_CASE      =				DOWN_NUM_TO_LAYER[layer_num]
      _SCREAMING_SNAKE_CASE      =				F"down_blocks.{depth}"
   elif depth > 0 and int(SCREAMING_SNAKE_CASE_						) > 7:
      _SCREAMING_SNAKE_CASE      =				UP_NUM_TO_LAYER[layer_num]
      _SCREAMING_SNAKE_CASE      =				F"up_blocks.{max_depth - depth - 1}"
   elif depth == 0:
      _SCREAMING_SNAKE_CASE      =				DEPTH_0_TO_LAYER[layer_num]
      _SCREAMING_SNAKE_CASE      =				F"up_blocks.{max_depth - 1}" if int(SCREAMING_SNAKE_CASE_						) > 3 else """down_blocks.0"""

   if not string_left.startswith("""."""						):
      raise ValueError(F"Naming error with {input_string} and string_left: {string_left}."						)

   _SCREAMING_SNAKE_CASE      =				string_left[1:]

   if "resnets" in new_layer:
      _SCREAMING_SNAKE_CASE      =				convert_resconv_naming(SCREAMING_SNAKE_CASE_						)
   elif "attentions" in new_layer:
      _SCREAMING_SNAKE_CASE      =				convert_attn_naming(SCREAMING_SNAKE_CASE_						)
      _SCREAMING_SNAKE_CASE      =				new_string_left

   if not isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				prefix + """.""" + new_layer + """.""" + string_left
   else:
      _SCREAMING_SNAKE_CASE      =				[prefix + """.""" + new_layer + """.""" + s for s in string_left]
   return new_string
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				{}
   for k, v in state_dict.items():
      if k.endswith("""kernel"""						):
         # up- and downsample layers, don't have trainable weights
         continue

      _SCREAMING_SNAKE_CASE      =				rename(SCREAMING_SNAKE_CASE_						)

      # check if we need to transform from Conv => Linear for attention
      if isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
         _SCREAMING_SNAKE_CASE      =				transform_conv_attns(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
      else:
         _SCREAMING_SNAKE_CASE      =				v

   return new_state_dict
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Union[str, Any]:




   """simple docstring"""
   if len(SCREAMING_SNAKE_CASE_						) == 1:
      if len(v.shape						) == 3:
         # weight
         _SCREAMING_SNAKE_CASE      =				v[:, :, 0]
      else:
         # bias
         _SCREAMING_SNAKE_CASE      =				v
   else:
      # qkv matrices
      _SCREAMING_SNAKE_CASE      =				v.shape[0]
      _SCREAMING_SNAKE_CASE      =				trippled_shape // 3
      for i in range(3						):
         if len(v.shape						) == 3:
            _SCREAMING_SNAKE_CASE      =				v[i * single_shape : (i + 1) * single_shape, :, 0]
         else:
            _SCREAMING_SNAKE_CASE      =				v[i * single_shape : (i + 1) * single_shape]
   return new_state_dict
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)

   _SCREAMING_SNAKE_CASE      =				args.model_path.split("""/"""						)[-1].split("""."""						)[0]
   if not os.path.isfile(args.model_path						):
      assert (
          model_name == args.model_path
      ), F"Make sure to provide one of the official model names {MODELS_MAP.keys()}"
      _SCREAMING_SNAKE_CASE      =				download(SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				MODELS_MAP[model_name]["""sample_rate"""]
   _SCREAMING_SNAKE_CASE      =				MODELS_MAP[model_name]["""sample_size"""]

   _SCREAMING_SNAKE_CASE      =				Object()
   _SCREAMING_SNAKE_CASE      =				sample_size
   _SCREAMING_SNAKE_CASE      =				sample_rate
   _SCREAMING_SNAKE_CASE      =				0

   _SCREAMING_SNAKE_CASE      =				UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_    ,      sample_rate=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				diffusers_model.state_dict()

   _SCREAMING_SNAKE_CASE      =				DiffusionUncond(SCREAMING_SNAKE_CASE_						)
   orig_model.load_state_dict(torch.load(args.model_path    ,      map_location=SCREAMING_SNAKE_CASE_						)["""state_dict"""]						)
   _SCREAMING_SNAKE_CASE      =				orig_model.diffusion_ema.eval()
   _SCREAMING_SNAKE_CASE      =				orig_model.state_dict()
   _SCREAMING_SNAKE_CASE      =				rename_orig_weights(SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				set(renamed_state_dict.keys()						) - set(diffusers_state_dict.keys()						)
   _SCREAMING_SNAKE_CASE      =				set(diffusers_state_dict.keys()						) - set(renamed_state_dict.keys()						)

   assert len(SCREAMING_SNAKE_CASE_						) == 0, F"Problem with {renamed_minus_diffusers}"
   assert all(k.endswith("""kernel"""						) for k in list(SCREAMING_SNAKE_CASE_						)						), F"Problem with {diffusers_minus_renamed}"

   for key, value in renamed_state_dict.items():
      assert (
          diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
      ), F"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"
      if key == "time_proj.weight":
         _SCREAMING_SNAKE_CASE      =				value.squeeze()

      _SCREAMING_SNAKE_CASE      =				value

   diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				1_00
   _SCREAMING_SNAKE_CASE      =				33

   _SCREAMING_SNAKE_CASE      =				IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				torch.manual_seed(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				torch.randn([1, 2, config.sample_size]    ,      generator=SCREAMING_SNAKE_CASE_						).to(SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				torch.linspace(1    ,      0    ,      steps + 1    ,      device=SCREAMING_SNAKE_CASE_						)[:-1]
   _SCREAMING_SNAKE_CASE      =				get_crash_schedule(SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_    ,      scheduler=SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				torch.manual_seed(33						)
   _SCREAMING_SNAKE_CASE      =				pipe(num_inference_steps=SCREAMING_SNAKE_CASE_    ,      generator=SCREAMING_SNAKE_CASE_						).audios

   _SCREAMING_SNAKE_CASE      =				sampling.iplms_sample(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      {}						)
   _SCREAMING_SNAKE_CASE      =				generated.clamp(-1    ,      1						)

   _SCREAMING_SNAKE_CASE      =				(generated - audio).abs().sum()
   _SCREAMING_SNAKE_CASE      =				(generated - audio).abs().max()

   if args.save:
      pipe.save_pretrained(args.checkpoint_path						)

   print("""Diff sum"""    ,      SCREAMING_SNAKE_CASE_						)
   print("""Diff max"""    ,      SCREAMING_SNAKE_CASE_						)

   assert diff_max < 1e-3, F"Diff max: {diff_max} is too much :-/"

   print(F"Conversion for {model_name} successful!"						)


if __name__ == "__main__":
     UpperCamelCase__      :		Union[str, Any]       							=  argparse.ArgumentParser()

     parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
     parser.add_argument(
         "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
     )
     parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
     UpperCamelCase__      :		int       							=  parser.parse_args()

     main(args)








'''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






