
"""simple docstring"""



import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DonutImageProcessor
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def __init__(					self	:			Optional[int]				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			Dict=7				,     UpperCAmelCase_	:			Any=3				,     UpperCAmelCase_	:			Any=18				,     UpperCAmelCase_	:			Dict=30				,     UpperCAmelCase_	:			Dict=400				,     UpperCAmelCase_	:			List[str]=True				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			List[str]=True				,     UpperCAmelCase_	:			Optional[Any]=False				,     UpperCAmelCase_	:			Union[str, Any]=True				,     UpperCAmelCase_	:			Tuple=True				,     UpperCAmelCase_	:			str=[0.5, 0.5, 0.5]				,     UpperCAmelCase_	:			Optional[int]=[0.5, 0.5, 0.5]				,     )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			parent
											_lowerCAmelCase  	=			batch_size
											_lowerCAmelCase  	=			num_channels
											_lowerCAmelCase  	=			image_size
											_lowerCAmelCase  	=			min_resolution
											_lowerCAmelCase  	=			max_resolution
											_lowerCAmelCase  	=			do_resize
											_lowerCAmelCase  	=			size if size is not None else {'height': 18, 'width': 20}
											_lowerCAmelCase  	=			do_thumbnail
											_lowerCAmelCase  	=			do_align_axis
											_lowerCAmelCase  	=			do_pad
											_lowerCAmelCase  	=			do_normalize
											_lowerCAmelCase  	=			image_mean
											_lowerCAmelCase  	=			image_std







				def 				__lowerCamelCase	(					self	:			str  )		->  Optional[Any]:







											"""simple docstring"""




											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_thumbnail": self.do_thumbnail,
											    "do_align_long_axis": self.do_align_axis,
											    "do_pad": self.do_pad,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}






@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					DonutImageProcessor if is_vision_available() else None


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			DonutImageProcessingTester(self  )


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







											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_resize'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'size'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_thumbnail'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_align_long_axis'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_pad'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_normalize'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'image_mean'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'image_std'  )  )


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







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class.from_dict(self.image_processor_dict  )
											self.assertEqual(image_processor.size				,     {'height': 18, 'width': 20}  )

											_lowerCAmelCase  	=			self.image_processing_class.from_dict(self.image_processor_dict				,     size=42  )
											self.assertEqual(image_processor.size				,     {'height': 42, 'width': 42}  )

											# Previous config had dimensions in (width, height) order
											_lowerCAmelCase  	=			self.image_processing_class.from_dict(self.image_processor_dict				,     size=(42, 84)  )
											self.assertEqual(image_processor.size				,     {'height': 84, 'width': 42}  )


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







											"""simple docstring"""




											pass


				@is_flaky()
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											_lowerCAmelCase  	=			prepare_image_inputs(self.image_processor_tester				,     equal_resolution=UpperCAmelCase_  )
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_				,     Image.Image  )

											# Test not batched input
											_lowerCAmelCase  	=			image_processing(image_inputs[0]				,     return_tensors='pt'  ).pixel_values
											self.assertEqual(
											    encoded_images.shape				,     (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )				,     )

											# Test batched
											_lowerCAmelCase  	=			image_processing(UpperCAmelCase_				,     return_tensors='pt'  ).pixel_values
											self.assertEqual(
											    encoded_images.shape				,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )				,     )


				@is_flaky()
				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											# create random numpy tensors
											_lowerCAmelCase  	=			prepare_image_inputs(self.image_processor_tester				,     equal_resolution=UpperCAmelCase_				,     numpify=UpperCAmelCase_  )
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_				,     np.ndarray  )

											# Test not batched input
											_lowerCAmelCase  	=			image_processing(image_inputs[0]				,     return_tensors='pt'  ).pixel_values
											self.assertEqual(
											    encoded_images.shape				,     (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )				,     )

											# Test batched
											_lowerCAmelCase  	=			image_processing(UpperCAmelCase_				,     return_tensors='pt'  ).pixel_values
											self.assertEqual(
											    encoded_images.shape				,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )				,     )







				@is_flaky()
				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											# create random PyTorch tensors
											_lowerCAmelCase  	=			prepare_image_inputs(self.image_processor_tester				,     equal_resolution=UpperCAmelCase_				,     torchify=UpperCAmelCase_  )
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_				,     torch.Tensor  )

											# Test not batched input
											_lowerCAmelCase  	=			image_processing(image_inputs[0]				,     return_tensors='pt'  ).pixel_values
											self.assertEqual(
											    encoded_images.shape				,     (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )				,     )

											# Test batched
											_lowerCAmelCase  	=			image_processing(UpperCAmelCase_				,     return_tensors='pt'  ).pixel_values
											self.assertEqual(
											    encoded_images.shape				,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )				,     )








"""simple docstring"""



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


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					"gpt_neox_japanese"


				def __init__(					self	:			Optional[Any]				,     UpperCAmelCase_	:			Any=32_000				,     UpperCAmelCase_	:			Dict=2_560				,     UpperCAmelCase_	:			Union[str, Any]=32				,     UpperCAmelCase_	:			Any=32				,     UpperCAmelCase_	:			Optional[Any]=4				,     UpperCAmelCase_	:			int="gelu"				,     UpperCAmelCase_	:			Union[str, Any]=1.00				,     UpperCAmelCase_	:			Any=10_000				,     UpperCAmelCase_	:			Optional[int]=2_048				,     UpperCAmelCase_	:			Optional[Any]=0.02				,     UpperCAmelCase_	:			Tuple=1E-5				,     UpperCAmelCase_	:			int=True				,     UpperCAmelCase_	:			Tuple=31_996				,     UpperCAmelCase_	:			List[Any]=31_999				,     UpperCAmelCase_	:			str=0.1				,     UpperCAmelCase_	:			List[Any]=0.0				,     **UpperCAmelCase_	:			str				,     )		->  int:







											"""simple docstring"""




											super().__init__(bos_token_id=UpperCAmelCase_				,     eos_token_id=UpperCAmelCase_				,     **UpperCAmelCase_  )
											_lowerCAmelCase  	=			vocab_size
											_lowerCAmelCase  	=			max_position_embeddings
											_lowerCAmelCase  	=			hidden_size
											_lowerCAmelCase  	=			num_hidden_layers
											_lowerCAmelCase  	=			num_attention_heads
											_lowerCAmelCase  	=			intermediate_multiple_size
											_lowerCAmelCase  	=			hidden_act
											_lowerCAmelCase  	=			rotary_pct
											_lowerCAmelCase  	=			rotary_emb_base
											_lowerCAmelCase  	=			initializer_range
											_lowerCAmelCase  	=			layer_norm_eps
											_lowerCAmelCase  	=			use_cache
											_lowerCAmelCase  	=			attention_dropout
											_lowerCAmelCase  	=			hidden_dropout






