
'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


A							     = logging.get_logger(__name__)

A							     = {
    "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 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






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

  def __init__(self	:			List[str]						,			lowerCAmelCase_	:			str=5_00_05						,			lowerCAmelCase_	:			Any=10_24						,			lowerCAmelCase_	:			Dict=6						,			lowerCAmelCase_	:			int=30_72						,			lowerCAmelCase_	:			Union[str, Any]=12						,			lowerCAmelCase_	:			Union[str, Any]=6						,			lowerCAmelCase_	:			Any=30_72						,			lowerCAmelCase_	:			List[Any]=12						,			lowerCAmelCase_	:			str=0.0						,			lowerCAmelCase_	:			Any=0.0						,			lowerCAmelCase_	:			Optional[int]=True						,			lowerCAmelCase_	:			Optional[Any]=True						,			lowerCAmelCase_	:			Optional[int]="gelu"						,			lowerCAmelCase_	:			Tuple=7_68						,			lowerCAmelCase_	:			str=0.1						,			lowerCAmelCase_	:			Union[str, Any]=0.1						,			lowerCAmelCase_	:			str=0.0						,			lowerCAmelCase_	:			int=0.02						,			lowerCAmelCase_	:			List[Any]=0.0						,			lowerCAmelCase_	:			Optional[Any]=True						,			lowerCAmelCase_	:			List[str]=1						,			lowerCAmelCase_	:			Union[str, Any]=0						,			lowerCAmelCase_	:			List[str]=2						,			lowerCAmelCase_	:			Union[str, Any]=2						,			**lowerCAmelCase_	:			List[str]						,			) ->							Dict:
        snake_case_    							=				vocab_size
        snake_case_    							=				max_position_embeddings
        snake_case_    							=				d_model
        snake_case_    							=				encoder_ffn_dim
        snake_case_    							=				encoder_layers
        snake_case_    							=				encoder_attention_heads
        snake_case_    							=				decoder_ffn_dim
        snake_case_    							=				decoder_layers
        snake_case_    							=				decoder_attention_heads
        snake_case_    							=				dropout
        snake_case_    							=				attention_dropout
        snake_case_    							=				activation_dropout
        snake_case_    							=				activation_function
        snake_case_    							=				init_std
        snake_case_    							=				encoder_layerdrop
        snake_case_    							=				decoder_layerdrop
        snake_case_    							=				classifier_dropout
        snake_case_    							=				use_cache
        snake_case_    							=				encoder_layers
        snake_case_    							=				scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowerCAmelCase_						,			bos_token_id=lowerCAmelCase_						,			eos_token_id=lowerCAmelCase_						,			is_encoder_decoder=lowerCAmelCase_						,			forced_eos_token_id=lowerCAmelCase_						,			**lowerCAmelCase_						,			)



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






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


  @property
  def 		_lowerCamelCase						(self	:			Dict     ) ->							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"}),
                  ]     )





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        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_=13   ,  A_=3   ,  A_=18   ,  A_=30   ,  A_=400   ,  A_=True   ,  A_=None   ,  A_=True   ,  A_=[0.5, 0.5, 0.5]   ,  A_=[0.5, 0.5, 0.5]   ,  )    ->			int:
         __UpperCamelCase       				=size if size is not None else {'height': 18, 'width': 18}
         __UpperCamelCase       				=parent
         __UpperCamelCase       				=batch_size
         __UpperCamelCase       				=num_channels
         __UpperCamelCase       				=image_size
         __UpperCamelCase       				=min_resolution
         __UpperCamelCase       				=max_resolution
         __UpperCamelCase       				=do_resize
         __UpperCamelCase       				=size
         __UpperCamelCase       				=do_normalize
         __UpperCamelCase       				=image_mean
         __UpperCamelCase       				=image_std






    def _a   (     self       )    ->			Optional[Any]:
         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        UpperCAmelCase__			(   A_  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						List[Any]    =    EfficientNetImageProcessor if is_vision_available() else None






    def _a   (     self       )    ->			List[str]:
         __UpperCamelCase       				=EfficientNetImageProcessorTester(self       )






    @property
    def _a   (     self       )    ->			Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(A_   ,  'image_mean'       )       )
         self.assertTrue(hasattr(A_   ,  'image_std'       )       )
         self.assertTrue(hasattr(A_   ,  'do_normalize'       )       )
         self.assertTrue(hasattr(A_   ,  'do_resize'       )       )
         self.assertTrue(hasattr(A_   ,  'size'       )       )






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size   ,  {'height': 18, 'width': 18}       )

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






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  Image.Image       )

         # Test not batched input
         __UpperCamelCase       				=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
         __UpperCamelCase       				=image_processing(A_   ,  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 _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  numpify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  np.ndarray       )

         # Test not batched input
         __UpperCamelCase       				=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
         __UpperCamelCase       				=image_processing(A_   ,  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 _a   (     self       )    ->			Dict:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  torchify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  torch.Tensor       )

         # Test not batched input
         __UpperCamelCase       				=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
         __UpperCamelCase       				=image_processing(A_   ,  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'],
             )   ,  )





