



"""simple docstring"""




import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor

class 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			@property
			def 			snake_case__					(       self							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					Tuple													=     1
					__UpperCAmelCase   :					Tuple													=     3
					__UpperCAmelCase   :					str													=     8
					__UpperCAmelCase   :					Optional[int]													=     8

					__UpperCAmelCase   :					Optional[int]													=     torch.rand((batch_size, num_channels, height, width)			)

					return sample




			def 			snake_case__					(       self							:			Dict	,	a_							:			int			):
					'''simple docstring'''





					if isinstance(image[0]	,	PIL.Image.Image			):
							return np.stack([np.array(a_			) for i in image]	,	axis=0			)
					elif isinstance(a_	,	torch.Tensor			):
							return image.cpu().numpy().transpose(0	,	2	,	3	,	1			)
					return image




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     VaeImageProcessor(do_resize=a_	,	do_normalize=a_			)

					__UpperCAmelCase   :					Optional[int]													=     self.dummy_sample
					__UpperCAmelCase   :					str													=     self.to_np(a_			)

					for output_type in ["pt", "np", "pil"]:
							__UpperCAmelCase   :					str													=     image_processor.postprocess(
							    image_processor.preprocess(a_			)	,	output_type=a_	,	)
							__UpperCAmelCase   :					List[Any]													=     self.to_np(a_			)
							__UpperCAmelCase   :					int													=     (input_np * 2_55).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 			snake_case__					(       self							:			List[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     VaeImageProcessor(do_resize=a_	,	do_normalize=a_			)
					__UpperCAmelCase   :					Tuple													=     self.dummy_sample.cpu().numpy().transpose(0	,	2	,	3	,	1			)

					for output_type in ["pt", "np", "pil"]:
							__UpperCAmelCase   :					Optional[int]													=     image_processor.postprocess(image_processor.preprocess(a_			)	,	output_type=a_			)

							__UpperCAmelCase   :					List[Any]													=     self.to_np(a_			)
							__UpperCAmelCase   :					List[Any]													=     (input_np * 2_55).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 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     VaeImageProcessor(do_resize=a_	,	do_normalize=a_			)

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

					for output_type in ["pt", "np", "pil"]:
							__UpperCAmelCase   :					Optional[Any]													=     image_processor.postprocess(image_processor.preprocess(a_			)	,	output_type=a_			)
							for i, o in zip(a_	,	a_			):
									__UpperCAmelCase   :					List[Any]													=     np.array(a_			)
									__UpperCAmelCase   :					Optional[Any]													=     self.to_np(a_			) if output_type == '''pil''' else (self.to_np(a_			) * 2_55).round()
									assert (
									    np.abs(in_np - out_np			).max() < 1e-6
									), F'decoded output does not match input for output_type {output_type}'




			def 			snake_case__					(       self							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     VaeImageProcessor(do_resize=a_	,	do_normalize=a_			)

					__UpperCAmelCase   :					Optional[Any]													=     self.dummy_sample
					__UpperCAmelCase   :					str													=     input_pt_ad.squeeze(0			)

					__UpperCAmelCase   :					Dict													=     image_processor.postprocess(
					    image_processor.preprocess(a_			)	,	output_type='''np'''	,	)
					__UpperCAmelCase   :					Dict													=     image_processor.postprocess(
					    image_processor.preprocess(a_			)	,	output_type='''np'''	,	)

					__UpperCAmelCase   :					Union[str, Any]													=     self.to_np(self.dummy_sample			)
					__UpperCAmelCase   :					List[str]													=     input_np_ad.squeeze(0			)

					__UpperCAmelCase   :					str													=     image_processor.postprocess(
					    image_processor.preprocess(a_			)	,	output_type='''np'''	,	)
					__UpperCAmelCase   :					List[str]													=     image_processor.postprocess(
					    image_processor.preprocess(a_			)	,	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 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Dict													=     VaeImageProcessor(do_resize=a_	,	do_normalize=a_			)

					__UpperCAmelCase   :					List[str]													=     self.dummy_sample
					__UpperCAmelCase   :					Tuple													=     list(a_			)

					__UpperCAmelCase   :					List[str]													=     image_processor.postprocess(
					    image_processor.preprocess(a_			)	,	output_type='''np'''	,	)

					__UpperCAmelCase   :					List[Any]													=     image_processor.postprocess(
					    image_processor.preprocess(a_			)	,	output_type='''np'''	,	)

					__UpperCAmelCase   :					Optional[int]													=     self.to_np(self.dummy_sample			)
					list(a_			)

					__UpperCAmelCase   :					Optional[Any]													=     image_processor.postprocess(
					    image_processor.preprocess(a_			)	,	output_type='''np'''	,	)

					__UpperCAmelCase   :					str													=     image_processor.postprocess(
					    image_processor.preprocess(a_			)	,	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










from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A             =logging.get_logger(__name__)

__A             ={
    "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/config.json",
    "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json",
    "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/config.json",
    "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	"""splinter"""




			def __init__(       self							:			Any	,	a_							:			Optional[int]=3_05_22	,	a_							:			Union[str, Any]=7_68	,	a_							:			int=12	,	a_							:			int=12	,	a_							:			List[Any]=30_72	,	a_							:			Union[str, Any]="gelu"	,	a_							:			Any=0.1	,	a_							:			Union[str, Any]=0.1	,	a_							:			Any=5_12	,	a_							:			str=2	,	a_							:			Dict=0.0_2	,	a_							:			int=1e-12	,	a_							:			str=True	,	a_							:			Tuple=0	,	a_							:			List[Any]=1_04	,	**a_							:			str	,	):
					'''simple docstring'''





					super().__init__(pad_token_id=a_	,	**a_			)

					__UpperCAmelCase   :					Union[str, Any]													=     vocab_size
					__UpperCAmelCase   :					List[Any]													=     max_position_embeddings
					__UpperCAmelCase   :					str													=     hidden_size
					__UpperCAmelCase   :					Union[str, Any]													=     num_hidden_layers
					__UpperCAmelCase   :					Any													=     num_attention_heads
					__UpperCAmelCase   :					Any													=     intermediate_size
					__UpperCAmelCase   :					Tuple													=     hidden_act
					__UpperCAmelCase   :					str													=     hidden_dropout_prob
					__UpperCAmelCase   :					Any													=     attention_probs_dropout_prob
					__UpperCAmelCase   :					Optional[int]													=     initializer_range
					__UpperCAmelCase   :					Any													=     type_vocab_size
					__UpperCAmelCase   :					int													=     layer_norm_eps
					__UpperCAmelCase   :					List[Any]													=     use_cache
					__UpperCAmelCase   :					Optional[int]													=     question_token_id





