



'''simple docstring'''




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class 						_UpperCamelCase  (							TensorFormatter[Mapping, np.ndarray, Mapping]   ):


			'''simple docstring'''

			def __init__(							self					:			Union[str, Any]							,					lowerCAmelCase__					:			Any=None							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					super().__init__(features=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		np_array_kwargs

			def UpperCamelCase__ (							self					:			Any							,					lowerCAmelCase__					:			List[Any]						):





					"""simple docstring"""






					if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							if column and all(
							    isinstance(lowerCAmelCase__							,					np.ndarray						) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column						):
									return np.stack(lowerCAmelCase__						)
							else:
									# don't use np.array(column, dtype=object)
									# since it fails in certain cases
									# see https://stackoverflow.com/q/51005699
									__SCREAMING_SNAKE_CASE	:		Any   	=		np.empty(len(lowerCAmelCase__						)							,					dtype=lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE	:		Any   	=		column
									return out
					return column

			def UpperCamelCase__ (							self					:			int							,					lowerCAmelCase__					:			Optional[Any]						):





					"""simple docstring"""






					if isinstance(lowerCAmelCase__							,					(str, bytes, type(lowerCAmelCase__						))						):
							return value
					elif isinstance(lowerCAmelCase__							,					(np.character, np.ndarray)						) and np.issubdtype(value.dtype							,					np.character						):
							return value
					elif isinstance(lowerCAmelCase__							,					np.number						):
							return value

					__SCREAMING_SNAKE_CASE	:		Dict   	=		{}

					if isinstance(lowerCAmelCase__							,					np.ndarray						) and np.issubdtype(value.dtype							,					np.integer						):
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		{"""dtype""": np.intaa}
					elif isinstance(lowerCAmelCase__							,					np.ndarray						) and np.issubdtype(value.dtype							,					np.floating						):
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		{"""dtype""": np.floataa}
					elif config.PIL_AVAILABLE and "PIL" in sys.modules:
							import PIL.Image

							if isinstance(lowerCAmelCase__							,					PIL.Image.Image						):
									return np.asarray(lowerCAmelCase__							,					**self.np_array_kwargs						)

					return np.asarray(lowerCAmelCase__							,					**{**default_dtype, **self.np_array_kwargs}						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			Union[str, Any]						):





					"""simple docstring"""






					if config.TORCH_AVAILABLE and "torch" in sys.modules:
							import torch

							if isinstance(lowerCAmelCase__							,					torch.Tensor						):
									return self._tensorize(data_struct.detach().cpu().numpy()[()]						)
					if hasattr(lowerCAmelCase__							,					"""__array__"""						) and not isinstance(lowerCAmelCase__							,					(np.ndarray, np.character, np.number)						):
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		data_struct.__array__()
					# support for nested types like struct of list of struct
					if isinstance(lowerCAmelCase__							,					np.ndarray						):
							if data_struct.dtype == object:
									return self._consolidate([self.recursive_tensorize(lowerCAmelCase__						) for substruct in data_struct]						)
					if isinstance(lowerCAmelCase__							,					(list, tuple)						):
							return self._consolidate([self.recursive_tensorize(lowerCAmelCase__						) for substruct in data_struct]						)
					return self._tensorize(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			dict						):





					"""simple docstring"""






					return map_nested(self._recursive_tensorize							,					lowerCAmelCase__							,					map_list=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			pa.Table						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.numpy_arrow_extractor().extract_row(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.python_features_decoder.decode_row(lowerCAmelCase__						)
					return self.recursive_tensorize(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			List[Any]							,					lowerCAmelCase__					:			pa.Table						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		self.numpy_arrow_extractor().extract_column(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.python_features_decoder.decode_column(lowerCAmelCase__							,					pa_table.column_names[0]						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		self.recursive_tensorize(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		int   	=		self._consolidate(lowerCAmelCase__						)
					return column



			def UpperCamelCase__ (							self					:			Optional[int]							,					lowerCAmelCase__					:			pa.Table						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		int   	=		self.python_features_decoder.decode_batch(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.recursive_tensorize(lowerCAmelCase__						)
					for column_name in batch:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		self._consolidate(batch[column_name]						)
					return batch




'''simple docstring'''




import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		(VQDiffusionScheduler,)

			def UpperCamelCase__ (							self					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		{
					    """num_vec_classes""": 4_0_9_7,
					    """num_train_timesteps""": 1_0_0,
					}

					config.update(**lowerCAmelCase__						)
					return config

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		4
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		8
					__SCREAMING_SNAKE_CASE	:		int   	=		8

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.randint(0							,					lowerCAmelCase__							,					(batch_size, height * width)						)

					return sample

			@property
			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					assert False

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					def model(lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Any							,					*lowerCAmelCase__					:			Optional[int]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		sample.shape
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
							__SCREAMING_SNAKE_CASE	:		str   	=		F.log_softmax(logits.double()							,					dim=1						).float()
							return return_value

					return model

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
							self.check_over_configs(num_vec_classes=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					for t in [0, 5_0, 9_9]:
							self.check_over_forward(time_step=lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					pass