





'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import 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 EfficientNetImageProcessor



class  lowerCamelCase__				(						unittest.TestCase						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=1_3   ,     UpperCAmelCase__=3   ,     UpperCAmelCase__=1_8   ,     UpperCAmelCase__=3_0   ,     UpperCAmelCase__=4_0_0   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=[0.5, 0.5, 0.5]   ,     UpperCAmelCase__=[0.5, 0.5, 0.5]   ,     )       ->				Union[str, Any]:
					_A :				List[str]				=  size if size is not None else {'''height''': 1_8, '''width''': 1_8}
					_A :				Optional[Any]				=  parent
					_A :				Dict				=  batch_size
					_A :				List[Any]				=  num_channels
					_A :				int				=  image_size
					_A :				Dict				=  min_resolution
					_A :				List[str]				=  max_resolution
					_A :				List[str]				=  do_resize
					_A :				int				=  size
					_A :				Any				=  do_normalize
					_A :				List[str]				=  image_mean
					_A :				Tuple				=  image_std




	def 				_lowerCamelCase				(					self			)       ->				Dict:
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					}



@require_torch
@require_vision
class  lowerCamelCase__				(						snake_case_  ,							unittest.TestCase						):


	"""simple docstring"""





	__magic_name__							   =		EfficientNetImageProcessor if is_vision_available() else None




	def 				_lowerCamelCase				(					self			)       ->				str:
					_A :				str				=  EfficientNetImageProcessorTester(self			)




	@property
	def 				_lowerCamelCase				(					self			)       ->				Dict:
					return self.image_processor_tester.prepare_image_processor_dict()




	def 				_lowerCamelCase				(					self			)       ->				List[str]:
					_A :				Optional[Any]				=  self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(__UpperCamelCase   ,     '''image_mean'''			)			)
					self.assertTrue(hasattr(__UpperCamelCase   ,     '''image_std'''			)			)
					self.assertTrue(hasattr(__UpperCamelCase   ,     '''do_normalize'''			)			)
					self.assertTrue(hasattr(__UpperCamelCase   ,     '''do_resize'''			)			)
					self.assertTrue(hasattr(__UpperCamelCase   ,     '''size'''			)			)




	def 				_lowerCamelCase				(					self			)       ->				int:
					_A :				List[Any]				=  self.image_processing_class.from_dict(self.image_processor_dict			)
					self.assertEqual(image_processor.size   ,     {'''height''': 1_8, '''width''': 1_8}			)

					_A :				Optional[Any]				=  self.image_processing_class.from_dict(self.image_processor_dict   ,     size=4_2			)
					self.assertEqual(image_processor.size   ,     {'''height''': 4_2, '''width''': 4_2}			)




	def 				_lowerCamelCase				(					self			)       ->				int:
					_A :				List[Any]				=  self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					_A :				Any				=  prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=__UpperCamelCase			)
					for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase   ,     Image.Image			)

					# Test not batched input
					_A :				Any				=  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
					_A :				List[str]				=  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'''],
					    )   ,     )




	def 				_lowerCamelCase				(					self			)       ->				Dict:
					_A :				str				=  self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					_A :				Dict				=  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
					_A :				Optional[Any]				=  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
					_A :				int				=  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'''],
					    )   ,     )




	def 				_lowerCamelCase				(					self			)       ->				List[str]:
					_A :				List[str]				=  self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					_A :				str				=  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
					_A :				List[str]				=  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
					_A :				List[Any]				=  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'''],
					    )   ,     )



from typing import Callable, Optional, Union

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


A__							=				logging.get_logger(__name__)

A__							=				{
    "microsoft/prophetnet-large-uncased": (
        "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"
    ),
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: Union[str, Any]												=	"prophetnet"
	_lowercase	: Any												=	["past_key_values"]
	_lowercase	: int												=	{
	    "num_attention_heads": "num_encoder_attention_heads",
	}







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			Optional[float] = 0.1	,			__UpperCamelCase:			Optional[Union[str, Callable]] = "gelu"	,			__UpperCamelCase:			Optional[int] = 3_05_22	,			__UpperCamelCase:			Optional[int] = 10_24	,			__UpperCamelCase:			Optional[int] = 40_96	,			__UpperCamelCase:			Optional[int] = 12	,			__UpperCamelCase:			Optional[int] = 16	,			__UpperCamelCase:			Optional[int] = 40_96	,			__UpperCamelCase:			Optional[int] = 12	,			__UpperCamelCase:			Optional[int] = 16	,			__UpperCamelCase:			Optional[float] = 0.1	,			__UpperCamelCase:			Optional[float] = 0.1	,			__UpperCamelCase:			Optional[int] = 5_12	,			__UpperCamelCase:			Optional[float] = 0.02	,			__UpperCamelCase:			Optional[bool] = True	,			__UpperCamelCase:			Optional[bool] = True	,			__UpperCamelCase:			Optional[int] = 0	,			__UpperCamelCase:			Optional[int] = 2	,			__UpperCamelCase:			Optional[int] = 32	,			__UpperCamelCase:			Optional[int] = 1_28	,			__UpperCamelCase:			Optional[bool] = False	,			__UpperCamelCase:			Optional[float] = 0.0	,			__UpperCamelCase:			Optional[bool] = True	,			__UpperCamelCase:			Optional[int] = 0	,			__UpperCamelCase:			Optional[int] = 1	,			__UpperCamelCase:			Optional[int] = 2	,			**__UpperCamelCase:			Optional[int]	,			):

						'''simple docstring'''





						__magic_name__				=     vocab_size
						__magic_name__				=     hidden_size
						__magic_name__				=     encoder_ffn_dim
						__magic_name__				=     num_encoder_layers
						__magic_name__				=     num_encoder_attention_heads
						__magic_name__				=     decoder_ffn_dim
						__magic_name__				=     num_decoder_layers
						__magic_name__				=     num_decoder_attention_heads
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     init_std  # Normal(0, this parameter)
						__magic_name__				=     activation_function

						# parameters for prophetnet
						__magic_name__				=     ngram
						__magic_name__				=     num_buckets
						__magic_name__				=     relative_max_distance
						__magic_name__				=     disable_ngram_loss
						__magic_name__				=     eps

						# 3 Types of Dropout
						__magic_name__				=     attention_dropout
						__magic_name__				=     activation_dropout
						__magic_name__				=     dropout

						__magic_name__				=     use_cache

						super().__init__(
						    pad_token_id=__UpperCamelCase	,			bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			is_encoder_decoder=__UpperCamelCase	,			add_cross_attention=__UpperCamelCase	,			decoder_start_token_id=__UpperCamelCase	,			**__UpperCamelCase	,			)







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						return self.num_encoder_layers + self.num_decoder_layers







	@num_hidden_layers.setter
	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			List[Any]   ):

						'''simple docstring'''





						raise NotImplementedError(
						    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
						    ' `num_decoder_layers`.'   )




