



'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__lowerCAmelCase							=logging.get_logger(__name__)

__lowerCAmelCase							={
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      "plbart"
					_UpperCamelCase		 =      ["past_key_values"]
					_UpperCamelCase		 =      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(     self   , UpperCAmelCase__=5_0005   , UpperCAmelCase__=1024   , UpperCAmelCase__=6   , UpperCAmelCase__=3072   , UpperCAmelCase__=12   , UpperCAmelCase__=6   , UpperCAmelCase__=3072   , UpperCAmelCase__=12   , UpperCAmelCase__=0.0   , UpperCAmelCase__=0.0   , UpperCAmelCase__=True   , UpperCAmelCase__=True   , UpperCAmelCase__="gelu"   , UpperCAmelCase__=768   , UpperCAmelCase__=0.1   , UpperCAmelCase__=0.1   , UpperCAmelCase__=0.0   , UpperCAmelCase__=0.0_2   , UpperCAmelCase__=0.0   , UpperCAmelCase__=True   , UpperCAmelCase__=1   , UpperCAmelCase__=0   , UpperCAmelCase__=2   , UpperCAmelCase__=2   , **UpperCAmelCase__   , )      ->	Dict:
										a_									=   vocab_size
										a_									=   max_position_embeddings
										a_									=   d_model
										a_									=   encoder_ffn_dim
										a_									=   encoder_layers
										a_									=   encoder_attention_heads
										a_									=   decoder_ffn_dim
										a_									=   decoder_layers
										a_									=   decoder_attention_heads
										a_									=   dropout
										a_									=   attention_dropout
										a_									=   activation_dropout
										a_									=   activation_function
										a_									=   init_std
										a_									=   encoder_layerdrop
										a_									=   decoder_layerdrop
										a_									=   classifier_dropout
										a_									=   use_cache
										a_									=   encoder_layers
										a_									=   scale_embedding  # scale factor will be sqrt(d_model) if True
										super().__init__(
										    pad_token_id=UpperCAmelCase__   , bos_token_id=UpperCAmelCase__   , eos_token_id=UpperCAmelCase__   , is_encoder_decoder=UpperCAmelCase__   , forced_eos_token_id=UpperCAmelCase__   , **UpperCAmelCase__   , )



class    _snake_case   (				snake_case					):

					"""simple docstring"""





					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Mapping[str, Mapping[int, str]]:
										return OrderedDict(
										    [
										        ('input_ids', {0: 'batch', 1: 'sequence'}),
										        ('attention_mask', {0: 'batch', 1: 'sequence'}),
										    ]     )




					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Mapping[str, Mapping[int, str]]:
										if self.use_past:
															return OrderedDict(
															    [
															        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
															        ('past_keys', {0: 'batch', 2: 'sequence'}),
															        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
															    ]     )
										else:
															return OrderedDict(
															    [
															        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
															        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
															    ]     )









'''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 ConvNextImageProcessor


class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__=7   , UpperCAmelCase__=3   , UpperCAmelCase__=18   , UpperCAmelCase__=30   , UpperCAmelCase__=400   , UpperCAmelCase__=True   , UpperCAmelCase__=None   , UpperCAmelCase__=0.8_7_5   , UpperCAmelCase__=True   , UpperCAmelCase__=[0.5, 0.5, 0.5]   , UpperCAmelCase__=[0.5, 0.5, 0.5]   , )      ->	Optional[int]:
										a_									=   size if size is not None else {'shortest_edge': 20}
										a_									=   parent
										a_									=   batch_size
										a_									=   num_channels
										a_									=   image_size
										a_									=   min_resolution
										a_									=   max_resolution
										a_									=   do_resize
										a_									=   size
										a_									=   crop_pct
										a_									=   do_normalize
										a_									=   image_mean
										a_									=   image_std




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_pct": self.crop_pct,
										}



@require_torch
@require_vision
class    _snake_case   (				snake_case     ,					unittest.TestCase					):

					"""simple docstring"""





					_UpperCamelCase		 =      ConvNextImageProcessor if is_vision_available() else None




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   ConvNextImageProcessingTester(self     )




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




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Any:
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'do_resize'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'size'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'crop_pct'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'do_normalize'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'image_mean'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'image_std'     )     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   self.image_processing_class.from_dict(self.image_processor_dict     )
										self.assertEqual(image_processor.size   , {'shortest_edge': 20}     )

										a_									=   self.image_processing_class.from_dict(self.image_processor_dict   , size=42     )
										self.assertEqual(image_processor.size   , {'shortest_edge': 42}     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										pass




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										a_									=   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_									=   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['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   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['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										a_									=   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_									=   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['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   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['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										a_									=   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_									=   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['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   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['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

