



import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor
class     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					@property
					def        lowerCAmelCase_    (	self:							int						)       ->     int:
											snake_case_			:Optional[Any]						=      1
											snake_case_			:Any						=      3
											snake_case_			:str						=      8
											snake_case_			:Union[str, Any]						=      8

											snake_case_			:List[str]						=      torch.rand((batch_size, num_channels, height, width)						)

											return sample






					def        lowerCAmelCase_    (	self:							Optional[int]						,					snake_case:							List[str]						)       ->     Optional[int]:
											if isinstance(image[0]						,					PIL.Image.Image						):
																	return np.stack([np.array(__UpperCamelCase						) for i in image]						,					axis=0						)
											elif isinstance(__UpperCamelCase						,					torch.Tensor						):
																	return image.cpu().numpy().transpose(0						,					2						,					3						,					1						)
											return image






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Optional[int]:
											snake_case_			:Optional[Any]						=      VaeImageProcessor(do_resize=__UpperCamelCase						,					do_normalize=__UpperCamelCase						)

											snake_case_			:Dict						=      self.dummy_sample
											snake_case_			:List[Any]						=      self.to_np(__UpperCamelCase						)

											for output_type in ["pt", "np", "pil"]:
																	snake_case_			:str						=      image_processor.postprocess(
																	    image_processor.preprocess(__UpperCamelCase						)						,					output_type=__UpperCamelCase						,					)
																	snake_case_			:Optional[int]						=      self.to_np(__UpperCamelCase						)
																	snake_case_			:Optional[Any]						=      (input_np * 255).round() if output_type == """pil""" else input_np
																	assert (
																	    np.abs(in_np - out_np						).max() < 1E-6
																	), f"""decoded output does not match input for output_type {output_type}"""






					def        lowerCAmelCase_    (	self:							Dict						)       ->     List[Any]:
											snake_case_			:Optional[Any]						=      VaeImageProcessor(do_resize=__UpperCamelCase						,					do_normalize=__UpperCamelCase						)
											snake_case_			:Dict						=      self.dummy_sample.cpu().numpy().transpose(0						,					2						,					3						,					1						)

											for output_type in ["pt", "np", "pil"]:
																	snake_case_			:int						=      image_processor.postprocess(image_processor.preprocess(__UpperCamelCase						)						,					output_type=__UpperCamelCase						)

																	snake_case_			:List[Any]						=      self.to_np(__UpperCamelCase						)
																	snake_case_			:Tuple						=      (input_np * 255).round() if output_type == """pil""" else input_np
																	assert (
																	    np.abs(in_np - out_np						).max() < 1E-6
																	), f"""decoded output does not match input for output_type {output_type}"""






					def        lowerCAmelCase_    (	self:							str						)       ->     Optional[int]:
											snake_case_			:List[str]						=      VaeImageProcessor(do_resize=__UpperCamelCase						,					do_normalize=__UpperCamelCase						)

											snake_case_			:Union[str, Any]						=      self.dummy_sample.cpu().numpy().transpose(0						,					2						,					3						,					1						)
											snake_case_			:str						=      image_processor.numpy_to_pil(__UpperCamelCase						)

											for output_type in ["pt", "np", "pil"]:
																	snake_case_			:Dict						=      image_processor.postprocess(image_processor.preprocess(__UpperCamelCase						)						,					output_type=__UpperCamelCase						)
																	for i, o in zip(__UpperCamelCase						,					__UpperCamelCase						):
																							snake_case_			:Union[str, Any]						=      np.array(__UpperCamelCase						)
																							snake_case_			:Dict						=      self.to_np(__UpperCamelCase						) if output_type == """pil""" else (self.to_np(__UpperCamelCase						) * 255).round()
																							assert (
																							    np.abs(in_np - out_np						).max() < 1E-6
																							), f"""decoded output does not match input for output_type {output_type}"""






					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Optional[int]:
											snake_case_			:Optional[Any]						=      VaeImageProcessor(do_resize=__UpperCamelCase						,					do_normalize=__UpperCamelCase						)

											snake_case_			:List[Any]						=      self.dummy_sample
											snake_case_			:List[str]						=      input_pt_ad.squeeze(0						)

											snake_case_			:Optional[int]						=      image_processor.postprocess(
											    image_processor.preprocess(__UpperCamelCase						)						,					output_type="""np"""						,					)
											snake_case_			:int						=      image_processor.postprocess(
											    image_processor.preprocess(__UpperCamelCase						)						,					output_type="""np"""						,					)

											snake_case_			:List[Any]						=      self.to_np(self.dummy_sample						)
											snake_case_			:Optional[Any]						=      input_np_ad.squeeze(0						)

											snake_case_			:Tuple						=      image_processor.postprocess(
											    image_processor.preprocess(__UpperCamelCase						)						,					output_type="""np"""						,					)
											snake_case_			:Any						=      image_processor.postprocess(
											    image_processor.preprocess(__UpperCamelCase						)						,					output_type="""np"""						,					)

											assert np.abs(out_pt_ad - out_pt_ad						).max() < 1E-6
											assert np.abs(out_np_ad - out_np_ad						).max() < 1E-6






					def        lowerCAmelCase_    (	self:							int						)       ->     Any:
											snake_case_			:str						=      VaeImageProcessor(do_resize=__UpperCamelCase						,					do_normalize=__UpperCamelCase						)

											snake_case_			:Tuple						=      self.dummy_sample
											snake_case_			:Dict						=      list(__UpperCamelCase						)

											snake_case_			:List[str]						=      image_processor.postprocess(
											    image_processor.preprocess(__UpperCamelCase						)						,					output_type="""np"""						,					)

											snake_case_			:Tuple						=      image_processor.postprocess(
											    image_processor.preprocess(__UpperCamelCase						)						,					output_type="""np"""						,					)

											snake_case_			:Optional[Any]						=      self.to_np(self.dummy_sample						)
											list(__UpperCamelCase						)

											snake_case_			:Tuple						=      image_processor.postprocess(
											    image_processor.preprocess(__UpperCamelCase						)						,					output_type="""np"""						,					)

											snake_case_			:Optional[int]						=      image_processor.postprocess(
											    image_processor.preprocess(__UpperCamelCase						)						,					output_type="""np"""						,					)

											assert np.abs(out_pt_ad - out_pt_list						).max() < 1E-6
											assert np.abs(out_np_ad - out_np_list						).max() < 1E-6





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__a     =    {
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a     =    ["XLMProphetNetTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a     =    [
						    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "XLMProphetNetDecoder",
						    "XLMProphetNetEncoder",
						    "XLMProphetNetForCausalLM",
						    "XLMProphetNetForConditionalGeneration",
						    "XLMProphetNetModel",
						    "XLMProphetNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_prophetnet import (
												    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMProphetNetDecoder,
												    XLMProphetNetEncoder,
												    XLMProphetNetForCausalLM,
												    XLMProphetNetForConditionalGeneration,
												    XLMProphetNetModel,
												    XLMProphetNetPreTrainedModel,
												)

else:
						import sys

						__a     =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
