





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


SCREAMING_SNAKE_CASE_      :			Dict		  =      (30, 487)
SCREAMING_SNAKE_CASE_      :			str		  =      (36, 1024)
SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      (100, 100)
SCREAMING_SNAKE_CASE_      :			List[str]		  =      100

SCREAMING_SNAKE_CASE_      :			Optional[Any]		  =      datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

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



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case       )		->						Dict:
		with ArrowWriter(features=snake_case	,      path=os.path.join(snake_case	,      'beta.arrow'       )       ) as writer:
				for key, record in dummy_data:
						__lowercase					=    my_features.encode_example(snake_case       )
						writer.write(snake_case       )
				__lowercase	,			__lowercase					=    writer.finalize()



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Dict:
		__lowercase					=    datasets.Dataset.from_file(
		    filename=os.path.join(snake_case	,      'beta.arrow'       )	,      info=datasets.DatasetInfo(features=snake_case       )       )
		for _ in dataset:
				pass



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Tuple:
		__lowercase					=    datasets.Dataset.from_file(
		    filename=os.path.join(snake_case	,      'beta.arrow'       )	,      info=datasets.DatasetInfo(features=snake_case       )       )
		dataset.set_format('numpy'       )
		for _ in dataset:
				pass



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Optional[int]:
		__lowercase					=    10
		__lowercase					=    datasets.Dataset.from_file(
		    filename=os.path.join(snake_case	,      'beta.arrow'       )	,      info=datasets.DatasetInfo(features=snake_case       )       )
		for i in range(0	,      len(snake_case       )	,      snake_case       ):
				__lowercase					=    dataset[i : i + batch_size]



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Any:
		__lowercase					=    10
		__lowercase					=    datasets.Dataset.from_file(
		    filename=os.path.join(snake_case	,      'beta.arrow'       )	,      info=datasets.DatasetInfo(features=snake_case       )       )
		dataset.set_format('numpy'       )
		for i in range(0	,      len(snake_case       )	,      snake_case       ):
				__lowercase					=    dataset[i : i + batch_size]



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Optional[int]:
		__lowercase					=    datasets.Dataset.from_file(
		    filename=os.path.join(snake_case	,      'beta.arrow'       )	,      info=datasets.DatasetInfo(features=snake_case       )       )
		for col in feats:
				__lowercase					=    dataset[col]



@get_duration
def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Tuple:
		__lowercase					=    datasets.Dataset.from_file(
		    filename=os.path.join(snake_case	,      'beta.arrow'       )	,      info=datasets.DatasetInfo(features=snake_case       )       )
		dataset.set_format('numpy'       )
		for col in feats:
				__lowercase					=    dataset[col]



def    SCREAMING_SNAKE_CASE							(			)		->						List[Any]:
		__lowercase					=    {}
		__lowercase					=    (
		    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:
				__lowercase					=    datasets.Features({'image': ArrayaD(snake_case	,      dtype='float32'       )}       )
				__lowercase					=    generate_examples(features=snake_case	,      num_examples=snake_case       )
				__lowercase					=    write(snake_case	,      snake_case	,      snake_case       )
				for read_func in read_functions:
						__lowercase					=    read_func(snake_case	,      snake_case       )

		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])}
				# )
				__lowercase					=    datasets.Features({'image': datasets.Sequence(datasets.Sequence(datasets.Value('float32'       )       )       )}       )
				__lowercase					=    generate_examples(
				    features=snake_case	,      num_examples=snake_case	,      seq_shapes={'image': SPEED_TEST_SHAPE}       )
				__lowercase					=    write(snake_case	,      snake_case	,      snake_case       )
				for read_func in read_functions:
						__lowercase					=    read_func(snake_case	,      snake_case       )

		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])}
				# )
				__lowercase					=    datasets.Features({'image': datasets.Sequence(datasets.Value('float32'       )       )}       )
				__lowercase					=    generate_examples(
				    features=snake_case	,      num_examples=snake_case	,      seq_shapes={'image': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}	,      )
				__lowercase					=    write(snake_case	,      snake_case	,      snake_case       )
				for read_func in read_functions:
						__lowercase					=    read_func(snake_case	,      snake_case       )

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


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












import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		(VQDiffusionScheduler,)




	def       UpperCAmelCase (	self       :    int				,			**__lowerCamelCase       :    List[Any]      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    {
			    'num_vec_classes': 4_097,
			    'num_train_timesteps': 100,
			}

			config.update(**__lowerCamelCase      )
			return config




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    8
			__lowercase					=    8

			__lowercase					=    torch.randint(0				,			__lowerCamelCase				,			(batch_size, height * width)      )

			return sample




	@property
	def       UpperCAmelCase (	self       :    Any      )					->   List[Any]:

			'''simple docstring'''




			assert False




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			__lowerCamelCase       :    str      )					->   Dict:

			'''simple docstring'''




			def model(__lowerCamelCase       :    Dict				,			__lowerCamelCase       :    Optional[Any]				,			*__lowerCamelCase       :    List[Any]      ):
					__lowercase	,			__lowercase					=    sample.shape
					__lowercase					=    torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)      )
					__lowercase					=    F.log_softmax(logits.double()				,			dim=1      ).float()
					return return_value

			return model




	def       UpperCAmelCase (	self       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			for timesteps in [2, 5, 100, 1_000]:
					self.check_over_configs(num_train_timesteps=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			for num_vec_classes in [5, 100, 1_000, 4_000]:
					self.check_over_configs(num_vec_classes=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Any      )					->   List[Any]:

			'''simple docstring'''




			for t in [0, 50, 99]:
					self.check_over_forward(time_step=__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    str      )					->   Optional[int]:

			'''simple docstring'''




			pass




