'''simple docstring'''




import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

def _lowercase	(      lowerCamelCase__						)					->       Optional[int]:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		np.inf

       def set_batch_size(lowerCamelCase__						) -> None:
              nonlocal batch_size
              if isinstance(lowerCamelCase__							,    lowerCamelCase__						):
                     __UpperCAmelCase  :				Optional[int]       				=		min(lowerCamelCase__							,    config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS						)
              elif isinstance(lowerCamelCase__							,    lowerCamelCase__						):
                     __UpperCAmelCase  :				str       				=		min(lowerCamelCase__							,    config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS						)
              elif isinstance(lowerCamelCase__							,    lowerCamelCase__						) and feature.dtype == "binary":
                     __UpperCAmelCase  :				Tuple       				=		min(lowerCamelCase__							,    config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS						)

       _visit(lowerCamelCase__							,    lowerCamelCase__						)

       return None if batch_size is np.inf else batch_size






class 		__A       (__magic_name__   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = False				,     UpperCamelCase_ = False				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              super().__init__(
                  UpperCamelCase_				,     split=UpperCamelCase_				,     features=UpperCamelCase_				,     cache_dir=UpperCamelCase_				,     keep_in_memory=UpperCamelCase_				,     streaming=UpperCamelCase_				,     num_proc=UpperCamelCase_				,     **UpperCamelCase_				,     )
              __UpperCAmelCase  :				Tuple       				=		path_or_paths if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else {self.split: path_or_paths}
              __UpperCAmelCase  :				Optional[int]       				=		_PACKAGED_DATASETS_MODULES["parquet"][1]
              __UpperCAmelCase  :				str       				=		Parquet(
                  cache_dir=UpperCamelCase_				,     data_files=UpperCamelCase_				,     features=UpperCamelCase_				,     hash=UpperCamelCase_				,     **UpperCamelCase_				,     )







       def 			_snake_case						(      self      ):
              # Build iterable dataset
              if self.streaming:
                     __UpperCAmelCase  :				List[str]       				=		self.builder.as_streaming_dataset(split=self.split      )
              # Build regular (map-style) dataset
              else:
                     __UpperCAmelCase  :				Any       				=		None
                     __UpperCAmelCase  :				List[Any]       				=		None
                     __UpperCAmelCase  :				Union[str, Any]       				=		None
                     __UpperCAmelCase  :				Dict       				=		None

                     self.builder.download_and_prepare(
                         download_config=UpperCamelCase_				,     download_mode=UpperCamelCase_				,     verification_mode=UpperCamelCase_				,     base_path=UpperCamelCase_				,     num_proc=self.num_proc				,     )
                     __UpperCAmelCase  :				int       				=		self.builder.as_dataset(
                         split=self.split				,     verification_mode=UpperCamelCase_				,     in_memory=self.keep_in_memory      )
              return dataset






class 		__A       :






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				List[Any]       				=		dataset
              __UpperCAmelCase  :				Dict       				=		path_or_buf
              __UpperCAmelCase  :				List[str]       				=		batch_size or get_writer_batch_size(dataset.features      )
              __UpperCAmelCase  :				Optional[int]       				=		parquet_writer_kwargs






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

              if isinstance(self.path_or_buf				,     (str, bytes, os.PathLike)      ):
                     with open(self.path_or_buf				,     "wb+"      ) as buffer:
                            __UpperCAmelCase  :				Union[str, Any]       				=		self._write(file_obj=UpperCamelCase_				,     batch_size=UpperCamelCase_				,     **self.parquet_writer_kwargs      )
              else:
                     __UpperCAmelCase  :				int       				=		self._write(file_obj=self.path_or_buf				,     batch_size=UpperCamelCase_				,     **self.parquet_writer_kwargs      )
              return written







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[int]       				=		0
              __UpperCAmelCase  :				List[str]       				=		parquet_writer_kwargs.pop("path_or_buf"				,     UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		self.dataset.features.arrow_schema

              __UpperCAmelCase  :				Dict       				=		pq.ParquetWriter(UpperCamelCase_				,     schema=UpperCamelCase_				,     **UpperCamelCase_      )

              for offset in logging.tqdm(
                  range(0				,     len(self.dataset      )				,     UpperCamelCase_      )				,     unit="ba"				,     disable=not logging.is_progress_bar_enabled()				,     desc="Creating parquet from Arrow format"				,     ):
                     __UpperCAmelCase  :				Dict       				=		query_table(
                         table=self.dataset._data				,     key=slice(UpperCamelCase_				,     offset + batch_size      )				,     indices=self.dataset._indices if self.dataset._indices is not None else None				,     )
                     writer.write_table(UpperCamelCase_      )
                     written += batch.nbytes
              writer.close()
              return written







'''simple docstring'''




import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_a   :	Union[str, Any] 							=   HfApi()

_a   :	int 							=   {}
# fmt: off
_a   :	Optional[int] 							=   torch.tensor([
    -0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
    1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189,
    -1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839,
    0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557
])
_a   :	Optional[Any] 							=   torch.tensor([
    -2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436,
    1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208,
    -2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948,
    2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365
])
_a   :	int 							=   torch.tensor([
    -0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869,
    -0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304,
    -0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925,
    0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943
])
_a   :	str 							=   torch.tensor([
    0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172,
    -0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309,
    0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805,
    -0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505
])
_a   :	Union[str, Any] 							=   torch.tensor([
    0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133,
    -0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395,
    0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559,
    -0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386
])
_a   :	Any 							=   torch.tensor([
    0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078,
    -0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330,
    0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683,
    -0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431
])
_a   :	List[Any] 							=   torch.tensor([
    0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042,
    -0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398,
    0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574,
    -0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390
])
_a   :	Optional[int] 							=   torch.tensor([
    0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042,
    -0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290,
    0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746,
    -0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473
])
_a   :	Tuple 							=   torch.tensor([
    -1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330,
    1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243,
    -2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810,
    1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251])
_a   :	List[Any] 							=   torch.tensor([
    -1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324,
    0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181,
    -2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259,
    1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266
])
_a   :	Optional[Any] 							=   torch.tensor([
    -1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212,
    0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027,
    -2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131,
    1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355
])
_a   :	Union[str, Any] 							=   torch.tensor([
    -2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959,
    1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351,
    -3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341,
    3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066
])
_a   :	Optional[int] 							=   torch.tensor([
    -2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740,
    1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398,
    -2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395,
    2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243
])
_a   :	Union[str, Any] 							=   torch.tensor([
    -2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336,
    1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908,
    -3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560,
    3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343
])
_a   :	str 							=   torch.tensor([
    -1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344,
    1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391,
    -2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439,
    1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219
])
# fmt: on

_a   :	Optional[Any] 							=   api.list_models(filter="diffusers")
for mod in models:
   if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
      _a   :	List[str] 							=   "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]

      print(f"""Started running {mod.modelId}!!!""")

      if mod.modelId.startswith("CompVis"):
         _a   :	int 							=   UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
      else:
         _a   :	Optional[int] 							=   UNetaDModel.from_pretrained(local_checkpoint)

      torch.manual_seed(0)
      random.seed(0)

      _a   :	str 							=   torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
      _a   :	str 							=   torch.tensor([10] * noise.shape[0])
      with torch.no_grad():
         _a   :	str 							=   model(noise, time_step).sample

      assert torch.allclose(
          logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1e-3
      )
      print(f"""{mod.modelId} has passed successfully!!!""")






