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 MobileNetVaImageProcessor




class      lowercase_						(						unittest.TestCase				):



	"""simple docstring"""





	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=7	,    __SCREAMING_SNAKE_CASE=3	,    __SCREAMING_SNAKE_CASE=18	,    __SCREAMING_SNAKE_CASE=30	,    __SCREAMING_SNAKE_CASE=400	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=None	,    )		->Tuple:
								lowerCAmelCase				=	size if size is not None else {"shortest_edge": 20}
								lowerCAmelCase				=	crop_size if crop_size is not None else {"height": 18, "width": 18}
								lowerCAmelCase				=	parent
								lowerCAmelCase				=	batch_size
								lowerCAmelCase				=	num_channels
								lowerCAmelCase				=	image_size
								lowerCAmelCase				=	min_resolution
								lowerCAmelCase				=	max_resolution
								lowerCAmelCase				=	do_resize
								lowerCAmelCase				=	size
								lowerCAmelCase				=	do_center_crop
								lowerCAmelCase				=	crop_size




	def     SCREAMING_SNAKE_CASE_       (							self       )		->str:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}




@require_torch
@require_vision
class      lowercase_						(						UpperCamelCase_   ,  unittest.TestCase				):



	"""simple docstring"""





	UpperCAmelCase_						:      List[Any]     =       MobileNetVaImageProcessor if is_vision_available() else None


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
								lowerCAmelCase				=	MobileNetVaImageProcessingTester(self       )


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


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Tuple:
								lowerCAmelCase				=	self.image_processing_class(**self.image_processor_dict       )
								self.assertTrue(hasattr(__lowerCamelCase	,    '''do_resize'''       )       )
								self.assertTrue(hasattr(__lowerCamelCase	,    '''size'''       )       )
								self.assertTrue(hasattr(__lowerCamelCase	,    '''do_center_crop'''       )       )
								self.assertTrue(hasattr(__lowerCamelCase	,    '''center_crop'''       )       )


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Union[str, Any]:
								lowerCAmelCase				=	self.image_processing_class.from_dict(self.image_processor_dict       )
								self.assertEqual(image_processor.size	,    {'''shortest_edge''': 20}       )
								self.assertEqual(image_processor.crop_size	,    {'''height''': 18, '''width''': 18}       )

								lowerCAmelCase				=	self.image_processing_class.from_dict(self.image_processor_dict	,    size=42	,    crop_size=84       )
								self.assertEqual(image_processor.size	,    {'''shortest_edge''': 42}       )
								self.assertEqual(image_processor.crop_size	,    {'''height''': 84, '''width''': 84}       )


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Union[str, Any]:
								pass


	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[Any]:
								# Initialize image_processing
								lowerCAmelCase				=	self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								lowerCAmelCase				=	prepare_image_inputs(self.image_processor_tester	,    equal_resolution=__lowerCamelCase       )
								for image in image_inputs:
															self.assertIsInstance(__lowerCamelCase	,    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )

								# Test batched
								lowerCAmelCase				=	image_processing(__lowerCamelCase	,    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )


	def     SCREAMING_SNAKE_CASE_       (							self       )		->str:
								# Initialize image_processing
								lowerCAmelCase				=	self.image_processing_class(**self.image_processor_dict       )
								# create random numpy tensors
								lowerCAmelCase				=	prepare_image_inputs(self.image_processor_tester	,    equal_resolution=__lowerCamelCase	,    numpify=__lowerCamelCase       )
								for image in image_inputs:
															self.assertIsInstance(__lowerCamelCase	,    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )

								# Test batched
								lowerCAmelCase				=	image_processing(__lowerCamelCase	,    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )




	def     SCREAMING_SNAKE_CASE_       (							self       )		->Tuple:
								# Initialize image_processing
								lowerCAmelCase				=	self.image_processing_class(**self.image_processor_dict       )
								# create random PyTorch tensors
								lowerCAmelCase				=	prepare_image_inputs(self.image_processor_tester	,    equal_resolution=__lowerCamelCase	,    torchify=__lowerCamelCase       )
								for image in image_inputs:
															self.assertIsInstance(__lowerCamelCase	,    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )

								# Test batched
								lowerCAmelCase				=	image_processing(__lowerCamelCase	,    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )




from typing import Callable, Optional, Union

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


lowercase__    :       Optional[Any]						=			logging.get_logger(__name__)

lowercase__    :       Union[str, Any]						=			{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      Optional[int]     =       """prophetnet"""
	UpperCAmelCase_						:      Optional[int]     =       ["""past_key_values"""]
	UpperCAmelCase_						:      Any     =       {
	    """num_attention_heads""": """num_encoder_attention_heads""",
	}


	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = "gelu"	,    __SCREAMING_SNAKE_CASE = 30522	,    __SCREAMING_SNAKE_CASE = 1024	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 512	,    __SCREAMING_SNAKE_CASE = 0.0_2	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 2	,    __SCREAMING_SNAKE_CASE = 32	,    __SCREAMING_SNAKE_CASE = 128	,    __SCREAMING_SNAKE_CASE = False	,    __SCREAMING_SNAKE_CASE = 0.0	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 1	,    __SCREAMING_SNAKE_CASE = 2	,    **__SCREAMING_SNAKE_CASE	,    )		->int:
								lowerCAmelCase				=	vocab_size
								lowerCAmelCase				=	hidden_size
								lowerCAmelCase				=	encoder_ffn_dim
								lowerCAmelCase				=	num_encoder_layers
								lowerCAmelCase				=	num_encoder_attention_heads
								lowerCAmelCase				=	decoder_ffn_dim
								lowerCAmelCase				=	num_decoder_layers
								lowerCAmelCase				=	num_decoder_attention_heads
								lowerCAmelCase				=	max_position_embeddings
								lowerCAmelCase				=	init_std  # Normal(0, this parameter)
								lowerCAmelCase				=	activation_function

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

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

								lowerCAmelCase				=	use_cache

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


	@property
	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
								return self.num_encoder_layers + self.num_decoder_layers




	@num_hidden_layers.setter
	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
								raise NotImplementedError(
								    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
								    ''' `num_decoder_layers`.'''       )



