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


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import TvltImageProcessor






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Tuple ,	lowerCAmelCase_: Optional[Any]=1_0 ,	lowerCAmelCase_: List[str]=1_0 ,	lowerCAmelCase_: Any=False ,	lowerCAmelCase_: Tuple=False     ):
      snake_case_     :			List[Any]       =       []
      for i in range(image_processor_tester.num_frames     ):
            video.append(np.random.randint(2_5_5 ,	size=(image_processor_tester.num_channels, width, height) ,	dtype=np.uinta     )     )

      if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            snake_case_     :			List[Any]       =       [Image.fromarray(np.moveaxis(lowerCAmelCase_ ,	0 ,	-1     )     ) for frame in video]

      if torchify:
            snake_case_     :			Tuple       =       [torch.from_numpy(lowerCAmelCase_     ) for frame in video]

      return video






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: Optional[Any]=False ,	lowerCAmelCase_: Dict=False ,	lowerCAmelCase_: Dict=False     ):
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      snake_case_     :			Union[str, Any]       =       []
      for i in range(image_processor_tester.batch_size     ):
            if equal_resolution:
                  snake_case_     :			Optional[Any]       =       image_processor_tester.max_resolution
            else:
                  snake_case_     :			Dict       =       np.random.choice(
                      np.arange(image_processor_tester.min_resolution ,	image_processor_tester.max_resolution     ) ,	2     )
                  snake_case_     :			Any       =       prepare_video(
                      image_processor_tester=lowerCAmelCase_ ,	width=lowerCAmelCase_ ,	height=lowerCAmelCase_ ,	numpify=lowerCAmelCase_ ,	torchify=lowerCAmelCase_ ,	)
            video_inputs.append(lowerCAmelCase_     )

      return video_inputs



class 					snake_case__       (				unittest.TestCase							):



   def __init__(				self   :   List[Any]			,	A__   :   int			,	A__   :   List[str]=7			,	A__   :   str=3			,	A__   :   Dict=4			,	A__   :   str=18			,	A__   :   Any=30			,	A__   :   Union[str, Any]=4_00			,	A__   :   Optional[Any]=True			,	A__   :   Any=None			,	A__   :   int=True			,	A__   :   int=[0.5, 0.5, 0.5]			,	A__   :   List[str]=[0.5, 0.5, 0.5]			,	A__   :   Any=True			,	A__   :   Optional[int]=None			,	)							->     Tuple:




         '''simple docstring'''




         snake_case_     :			List[Any]       =       size if size is not None else {"shortest_edge": 18}
         snake_case_     :			Optional[Any]       =       crop_size if crop_size is not None else {"height": 18, "width": 18}

         snake_case_     :			Tuple       =       parent
         snake_case_     :			Optional[Any]       =       batch_size
         snake_case_     :			Union[str, Any]       =       num_channels
         snake_case_     :			List[str]       =       num_frames
         snake_case_     :			Optional[Any]       =       image_size
         snake_case_     :			Optional[Any]       =       min_resolution
         snake_case_     :			Tuple       =       max_resolution
         snake_case_     :			List[Any]       =       do_resize
         snake_case_     :			Tuple       =       size
         snake_case_     :			Any       =       do_normalize
         snake_case_     :			Optional[int]       =       image_mean
         snake_case_     :			List[Any]       =       image_std
         snake_case_     :			str       =       do_center_crop
         snake_case_     :			Any       =       crop_size



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




         '''simple docstring'''




         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "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 					snake_case__       (				_UpperCamelCase					,						unittest.TestCase							):
   _SCREAMING_SNAKE_CASE	: Optional[Any]						    =      TvltImageProcessor if is_vision_available() else None



   def 					UpperCAmelCase__       (				self   :   Any   )							->     Dict:




         '''simple docstring'''




         snake_case_     :			int       =       TvltImageProcessorTester(self   )



   @property
   def 					UpperCAmelCase__       (				self   :   str   )							->     str:




         '''simple docstring'''




         return self.image_processor_tester.prepare_image_processor_dict()



   def 					UpperCAmelCase__       (				self   :   str   )							->     List[str]:




         '''simple docstring'''




         snake_case_     :			Any       =       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__			,	"do_center_crop"   )   )
         self.assertTrue(hasattr(A__			,	"size"   )   )



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




         '''simple docstring'''




         snake_case_     :			str       =       self.image_processing_class(**self.image_processor_dict   )
         # create random PIL videos
         snake_case_     :			Optional[int]       =       prepare_video_inputs(self.image_processor_tester			,	equal_resolution=A__   )
         for video in video_inputs:
               self.assertIsInstance(A__			,	A__   )
               self.assertIsInstance(video[0]			,	Image.Image   )

         # Test not batched input
         snake_case_     :			Optional[int]       =       image_processor(video_inputs[0]			,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_videos.shape			,	(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )			,	)

         # Test batched
         snake_case_     :			str       =       image_processor(A__			,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_videos.shape			,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )			,	)



   def 					UpperCAmelCase__       (				self   :   str   )							->     Dict:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         snake_case_     :			Optional[int]       =       prepare_video_inputs(self.image_processor_tester			,	equal_resolution=A__			,	numpify=A__   )
         for video in video_inputs:
               self.assertIsInstance(A__			,	A__   )
               self.assertIsInstance(video[0]			,	np.ndarray   )

         # Test not batched input
         snake_case_     :			List[str]       =       image_processor(video_inputs[0]			,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_videos.shape			,	(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )			,	)

         # Test batched
         snake_case_     :			Optional[Any]       =       image_processor(A__			,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_videos.shape			,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )			,	)



   def 					UpperCAmelCase__       (				self   :   Any   )							->     Dict:




         '''simple docstring'''




         snake_case_     :			str       =       self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         snake_case_     :			Any       =       prepare_video_inputs(self.image_processor_tester			,	equal_resolution=A__			,	torchify=A__   )
         for video in video_inputs:
               self.assertIsInstance(A__			,	A__   )
               self.assertIsInstance(video[0]			,	torch.Tensor   )

         # Test not batched input
         snake_case_     :			str       =       image_processor(video_inputs[0]			,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_videos.shape			,	(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )			,	)

         # Test batched
         snake_case_     :			int       =       image_processor(A__			,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_videos.shape			,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )			,	)




from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    "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__       (				_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: List[str]						    =      "plbart"
   _SCREAMING_SNAKE_CASE	: Union[str, Any]						    =      ["past_key_values"]
   _SCREAMING_SNAKE_CASE	: Dict						    =      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



   def __init__(				self   :   Dict			,	A__   :   Any=5_00_05			,	A__   :   Optional[int]=10_24			,	A__   :   Tuple=6			,	A__   :   Optional[Any]=30_72			,	A__   :   Dict=12			,	A__   :   Union[str, Any]=6			,	A__   :   Union[str, Any]=30_72			,	A__   :   Tuple=12			,	A__   :   List[str]=0.0			,	A__   :   Dict=0.0			,	A__   :   int=True			,	A__   :   Tuple=True			,	A__   :   List[str]="gelu"			,	A__   :   List[Any]=7_68			,	A__   :   Optional[Any]=0.1			,	A__   :   List[Any]=0.1			,	A__   :   Any=0.0			,	A__   :   int=0.02			,	A__   :   Optional[Any]=0.0			,	A__   :   List[str]=True			,	A__   :   Any=1			,	A__   :   Tuple=0			,	A__   :   Any=2			,	A__   :   int=2			,	**A__   :   Dict			,	)							->     int:




         '''simple docstring'''




         snake_case_     :			int       =       vocab_size
         snake_case_     :			List[Any]       =       max_position_embeddings
         snake_case_     :			str       =       d_model
         snake_case_     :			Tuple       =       encoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       encoder_layers
         snake_case_     :			Dict       =       encoder_attention_heads
         snake_case_     :			Union[str, Any]       =       decoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       decoder_layers
         snake_case_     :			List[str]       =       decoder_attention_heads
         snake_case_     :			List[Any]       =       dropout
         snake_case_     :			Union[str, Any]       =       attention_dropout
         snake_case_     :			Optional[int]       =       activation_dropout
         snake_case_     :			List[str]       =       activation_function
         snake_case_     :			Tuple       =       init_std
         snake_case_     :			str       =       encoder_layerdrop
         snake_case_     :			int       =       decoder_layerdrop
         snake_case_     :			Optional[int]       =       classifier_dropout
         snake_case_     :			List[str]       =       use_cache
         snake_case_     :			List[Any]       =       encoder_layers
         snake_case_     :			Tuple       =       scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=A__			,	bos_token_id=A__			,	eos_token_id=A__			,	is_encoder_decoder=A__			,	forced_eos_token_id=A__			,	**A__			,	)



class 					snake_case__       (				_UpperCamelCase							):



   @property
   def 					UpperCAmelCase__       (				self   :   str   )							->     Mapping[str, Mapping[int, str]]:




         '''simple docstring'''




         return OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "sequence"}),
                 ("attention_mask", {0: "batch", 1: "sequence"}),
             ]   )



   @property
   def 					UpperCAmelCase__       (				self   :   List[str]   )							->     Mapping[str, Mapping[int, str]]:




         '''simple docstring'''




         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"}),
                   ]   )



