import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__lowerCamelCase     :					Optional[Any]				  =						(30, 487)
__lowerCamelCase     :					Tuple				  =						(36, 1024)
__lowerCamelCase     :					List[str]				  =						(100, 100)
__lowerCamelCase     :					Optional[Any]				  =						100

__lowerCamelCase     :					Dict				  =						datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

__lowerCamelCase  ,		__lowerCamelCase     :					Any				  =						os.path.split(__file__)
__lowerCamelCase     :					Optional[Any]				  =						os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))



@get_duration
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			int					,	__UpperCamelCase       :			Dict					,	__UpperCamelCase       :			List[Any]      )				->   Tuple:







  """simple docstring"""





  with ArrowWriter(features=__UpperCamelCase					,	path=os.path.join(__UpperCamelCase					,	"""beta.arrow"""      )      ) as writer:
    for key, record in dummy_data:
      SCREAMING_SNAKE_CASE__     				= my_features.encode_example(__UpperCamelCase      )
      writer.write(__UpperCamelCase      )
    SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= writer.finalize()



@get_duration
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			int					,	__UpperCamelCase       :			Union[str, Any]      )				->   Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= datasets.Dataset.from_file(
      filename=os.path.join(__UpperCamelCase					,	"""beta.arrow"""      )					,	info=datasets.DatasetInfo(features=__UpperCamelCase      )      )
  for _ in dataset:
    pass



@get_duration
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]					,	__UpperCamelCase       :			str      )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= datasets.Dataset.from_file(
      filename=os.path.join(__UpperCamelCase					,	"""beta.arrow"""      )					,	info=datasets.DatasetInfo(features=__UpperCamelCase      )      )
  dataset.set_format("""numpy"""      )
  for _ in dataset:
    pass



@get_duration
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			int					,	__UpperCamelCase       :			str      )				->   Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= 10
  SCREAMING_SNAKE_CASE__     				= datasets.Dataset.from_file(
      filename=os.path.join(__UpperCamelCase					,	"""beta.arrow"""      )					,	info=datasets.DatasetInfo(features=__UpperCamelCase      )      )
  for i in range(0					,	len(__UpperCamelCase      )					,	__UpperCamelCase      ):
    SCREAMING_SNAKE_CASE__     				= dataset[i : i + batch_size]



@get_duration
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Optional[Any]					,	__UpperCamelCase       :			List[str]      )				->   Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= 10
  SCREAMING_SNAKE_CASE__     				= datasets.Dataset.from_file(
      filename=os.path.join(__UpperCamelCase					,	"""beta.arrow"""      )					,	info=datasets.DatasetInfo(features=__UpperCamelCase      )      )
  dataset.set_format("""numpy"""      )
  for i in range(0					,	len(__UpperCamelCase      )					,	__UpperCamelCase      ):
    SCREAMING_SNAKE_CASE__     				= dataset[i : i + batch_size]



@get_duration
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Optional[int]					,	__UpperCamelCase       :			List[str]      )				->   List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= datasets.Dataset.from_file(
      filename=os.path.join(__UpperCamelCase					,	"""beta.arrow"""      )					,	info=datasets.DatasetInfo(features=__UpperCamelCase      )      )
  for col in feats:
    SCREAMING_SNAKE_CASE__     				= dataset[col]



@get_duration
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			List[Any]					,	__UpperCamelCase       :			Tuple      )				->   Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= datasets.Dataset.from_file(
      filename=os.path.join(__UpperCamelCase					,	"""beta.arrow"""      )					,	info=datasets.DatasetInfo(features=__UpperCamelCase      )      )
  dataset.set_format("""numpy"""      )
  for col in feats:
    SCREAMING_SNAKE_CASE__     				= dataset[col]



def       __SCREAMING_SNAKE_CASE    (   )				->   int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= {}
  SCREAMING_SNAKE_CASE__     				= (
      read_unformated,
      read_formatted_as_numpy,
      read_batch_unformated,
      read_batch_formatted_as_numpy,
      read_col_unformated,
      read_col_formatted_as_numpy,
  )
  with tempfile.TemporaryDirectory() as tmp_dir:
    SCREAMING_SNAKE_CASE__     				= datasets.Features({"""image""": ArrayaD(__UpperCamelCase					,	dtype="""float32"""      )}      )
    SCREAMING_SNAKE_CASE__     				= generate_examples(features=__UpperCamelCase					,	num_examples=__UpperCamelCase      )
    SCREAMING_SNAKE_CASE__     				= write(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase      )
    for read_func in read_functions:
      SCREAMING_SNAKE_CASE__     				= read_func(__UpperCamelCase					,	__UpperCamelCase      )

  with tempfile.TemporaryDirectory() as tmp_dir:
    # don't use fixed length for fair comparison
    # feats = datasets.Features(
    #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
    # )
    SCREAMING_SNAKE_CASE__     				= datasets.Features({"""image""": datasets.Sequence(datasets.Sequence(datasets.Value("""float32"""      )      )      )}      )
    SCREAMING_SNAKE_CASE__     				= generate_examples(
        features=__UpperCamelCase					,	num_examples=__UpperCamelCase					,	seq_shapes={"""image""": SPEED_TEST_SHAPE}      )
    SCREAMING_SNAKE_CASE__     				= write(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase      )
    for read_func in read_functions:
      SCREAMING_SNAKE_CASE__     				= read_func(__UpperCamelCase					,	__UpperCamelCase      )

  with tempfile.TemporaryDirectory() as tmp_dir:
    # don't use fixed length for fair comparison
    # feats = datasets.Features(
    #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
    # )
    SCREAMING_SNAKE_CASE__     				= datasets.Features({"""image""": datasets.Sequence(datasets.Value("""float32"""      )      )}      )
    SCREAMING_SNAKE_CASE__     				= generate_examples(
        features=__UpperCamelCase					,	num_examples=__UpperCamelCase					,	seq_shapes={"""image""": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}					,	)
    SCREAMING_SNAKE_CASE__     				= write(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase      )
    for read_func in read_functions:
      SCREAMING_SNAKE_CASE__     				= read_func(__UpperCamelCase					,	__UpperCamelCase      )

  with open(__UpperCamelCase					,	"""wb"""      ) as f:
    f.write(json.dumps(__UpperCamelCase      ).encode("""utf-8"""      )      )


if __name__ == "__main__":  # useful to run the profiler
   benchmark_array_xd()

import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference







@slow
@require_torch_gpu
class __snake_case							(		unittest.TestCase						):




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """runwayml/stable-diffusion-v1-5"""
     SCREAMING_SNAKE_CASE__     				= TextToVideoZeroPipeline.from_pretrained(_lowercase			,      torch_dtype=torch.floataa ).to("""cuda""" )
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler.from_config(pipe.scheduler.config )
     SCREAMING_SNAKE_CASE__     				= torch.Generator(device="""cuda""" ).manual_seed(0 )

     SCREAMING_SNAKE_CASE__     				= """A bear is playing a guitar on Times Square"""
     SCREAMING_SNAKE_CASE__     				= pipe(prompt=_lowercase			,      generator=_lowercase ).images

     SCREAMING_SNAKE_CASE__     				= load_pt(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt""" )

     assert_mean_pixel_difference(_lowercase			,      _lowercase )
