






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_video_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import VivitImageProcessor







class 				UpperCAmelCase_  (		unittest.TestCase		):




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase=7	,						_lowerCAmelCase=3	,						_lowerCAmelCase=10	,						_lowerCAmelCase=18	,						_lowerCAmelCase=30	,						_lowerCAmelCase=400	,						_lowerCAmelCase=True	,						_lowerCAmelCase=None	,						_lowerCAmelCase=True	,						_lowerCAmelCase=[0.5, 0.5, 0.5]	,						_lowerCAmelCase=[0.5, 0.5, 0.5]	,						_lowerCAmelCase=None	,						):
          UpperCAmelCase__      :  Optional[int]				      =			size if size is not None else {"""shortest_edge""": 18}
          UpperCAmelCase__      :  Any				      =			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

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




     def   __UpperCAmelCase  (				self		):
          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_size": self.crop_size,
          }







@require_torch
@require_vision
class 				UpperCAmelCase_  (		__lowerCamelCase							,      unittest.TestCase		):
     __lowerCamelCase      				=  VivitImageProcessor if is_vision_available() else None




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  int				      =			VivitImageProcessingTester(self		)




     @property
     def   __UpperCAmelCase  (				self		):
          return self.image_processor_tester.prepare_image_processor_dict()




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Dict				      =			self.image_processing_class(**self.image_processor_dict		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""image_mean"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""image_std"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""do_normalize"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""do_resize"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""do_center_crop"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""size"""		)		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  int				      =			self.image_processing_class.from_dict(self.image_processor_dict		)
          self.assertEqual(image_processor.size	,						{"""shortest_edge""": 18}		)
          self.assertEqual(image_processor.crop_size	,						{"""height""": 18, """width""": 18}		)

          UpperCAmelCase__      :  str				      =			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   __UpperCAmelCase  (				self		):
          # Initialize image_processing
          UpperCAmelCase__      :  Optional[int]				      =			self.image_processing_class(**self.image_processor_dict		)
          # create random PIL videos
          UpperCAmelCase__      :  List[Any]				      =			prepare_video_inputs(self.image_processor_tester	,						equal_resolution=_lowerCAmelCase		)
          for video in video_inputs:
               self.assertIsInstance(_lowerCAmelCase	,						_lowerCAmelCase		)
               self.assertIsInstance(video[0]	,						Image.Image		)

          # Test not batched input
          UpperCAmelCase__      :  Tuple				      =			image_processing(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
          UpperCAmelCase__      :  str				      =			image_processing(_lowerCAmelCase	,						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		):
          # Initialize image_processing
          UpperCAmelCase__      :  int				      =			self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          UpperCAmelCase__      :  List[Any]				      =			prepare_video_inputs(self.image_processor_tester	,						equal_resolution=_lowerCAmelCase	,						numpify=_lowerCAmelCase		)
          for video in video_inputs:
               self.assertIsInstance(_lowerCAmelCase	,						_lowerCAmelCase		)
               self.assertIsInstance(video[0]	,						np.ndarray		)

          # Test not batched input
          UpperCAmelCase__      :  List[str]				      =			image_processing(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
          UpperCAmelCase__      :  int				      =			image_processing(_lowerCAmelCase	,						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		):
          # Initialize image_processing
          UpperCAmelCase__      :  str				      =			self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          UpperCAmelCase__      :  List[Any]				      =			prepare_video_inputs(self.image_processor_tester	,						equal_resolution=_lowerCAmelCase	,						torchify=_lowerCAmelCase		)
          for video in video_inputs:
               self.assertIsInstance(_lowerCAmelCase	,						_lowerCAmelCase		)
               self.assertIsInstance(video[0]	,						torch.Tensor		)

          # Test not batched input
          UpperCAmelCase__      :  str				      =			image_processing(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
          UpperCAmelCase__      :  str				      =			image_processing(_lowerCAmelCase	,						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 dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							field(
           metadata={"help": "The output directory where the model will be written."}						,       )
       _a   							=							field(
           metadata={
               "help": (
                   "The encoder model checkpoint for weights initialization."
                   "Don't set if you want to train an encoder model from scratch."
               )
           }						,       )
       _a   							=							field(
           metadata={
               "help": (
                   "The decoder model checkpoint for weights initialization."
                   "Don't set if you want to train a decoder model from scratch."
               )
           }						,       )
       _a   							=							field(
           default=a__						,       metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}						)
       _a   							=							field(
           default=a__						,       metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}						)







def    __lowerCamelCase   (      )     ->    Optional[Any]:
   lowerCamelCase_						:   List[Any]		= HfArgumentParser((ModelArguments,)					)
   ((lowerCamelCase_),	)						:   List[Any]		= parser.parse_args_into_dataclasses()

   # Load pretrained model and tokenizer

   # Use explicit specified encoder config
   if model_args.encoder_config_name:
      lowerCamelCase_						:   Any		= AutoConfig.from_pretrained(model_args.encoder_config_name					)
   # Use pretrained encoder model's config
   else:
      lowerCamelCase_						:   Dict		= AutoConfig.from_pretrained(model_args.encoder_model_name_or_path					)

   # Use explicit specified decoder config
   if model_args.decoder_config_name:
      lowerCamelCase_						:   Dict		= AutoConfig.from_pretrained(model_args.decoder_config_name					)
   # Use pretrained decoder model's config
   else:
      lowerCamelCase_						:   Any		= AutoConfig.from_pretrained(model_args.decoder_model_name_or_path					)

   # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
   lowerCamelCase_						:   Optional[Any]		= True
   lowerCamelCase_						:   List[Any]		= True

   lowerCamelCase_						:   List[Any]		= FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
       encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,						decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,						encoder_config=A__						,						decoder_config=A__						,						)

   # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
   lowerCamelCase_						:   Tuple		= decoder_config.decoder_start_token_id
   lowerCamelCase_						:   int		= decoder_config.pad_token_id
   if decoder_start_token_id is None:
      lowerCamelCase_						:   Any		= decoder_config.bos_token_id
   if pad_token_id is None:
      lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id

   # This is necessary to make Flax's generate() work
   lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id
   lowerCamelCase_						:   Tuple		= decoder_start_token_id
   lowerCamelCase_						:   Optional[Any]		= pad_token_id

   lowerCamelCase_						:   str		= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

   lowerCamelCase_						:   Any		= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
   lowerCamelCase_						:   Dict		= tokenizer.convert_ids_to_tokens(model.config.pad_token_id					)

   model.save_pretrained(model_args.output_dir					)
   image_processor.save_pretrained(model_args.output_dir					)
   tokenizer.save_pretrained(model_args.output_dir					)


if __name__ == "__main__":
      main()





