


"""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 ViTImageProcessor


class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     snake_case__=13		,     snake_case__=3		,     snake_case__=224		,     snake_case__=30		,     snake_case__=400		,     snake_case__=True		,     snake_case__=None		,     snake_case__=True		,     snake_case__=[0.5, 0.5, 0.5]		,     snake_case__=[0.5, 0.5, 0.5]		,     ):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							size if size is not None else {"height": 18, "width": 18}
         lowerCAmelCase     :     Any								=							parent
         lowerCAmelCase     :     List[Any]								=							batch_size
         lowerCAmelCase     :     int								=							num_channels
         lowerCAmelCase     :     List[Any]								=							image_size
         lowerCAmelCase     :     Union[str, Any]								=							min_resolution
         lowerCAmelCase     :     Dict								=							max_resolution
         lowerCAmelCase     :     List[str]								=							do_resize
         lowerCAmelCase     :     Optional[Any]								=							size
         lowerCAmelCase     :     Optional[int]								=							do_normalize
         lowerCAmelCase     :     Optional[Any]								=							image_mean
         lowerCAmelCase     :     Any								=							image_std







       def        lowercase__							(						self						):






         """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,
         }


@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__				(		lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				str   =ViTImageProcessor if is_vision_available() else None

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							EfficientFormerImageProcessorTester(self						)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self.image_proc_tester.prepare_image_processor_dict()

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(snake_case__		,     "image_mean"						)						)
         self.assertTrue(hasattr(snake_case__		,     "image_std"						)						)
         self.assertTrue(hasattr(snake_case__		,     "do_normalize"						)						)
         self.assertTrue(hasattr(snake_case__		,     "do_resize"						)						)
         self.assertTrue(hasattr(snake_case__		,     "size"						)						)

       def        lowercase__							(						self						):






         """simple docstring"""





         pass

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         lowerCAmelCase     :     Optional[Any]								=							prepare_image_inputs(self.image_proc_tester		,     equal_resolution=snake_case__						)
         for image in image_inputs:
           self.assertIsInstance(snake_case__		,     Image.Image						)

         # Test not batched input
         lowerCAmelCase     :     Optional[Any]								=							image_processor(image_inputs[0]		,     return_tensors="pt"						).pixel_values
         self.assertEqual(
             encoded_images.shape		,     (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )		,     )

         # Test batched
         lowerCAmelCase     :     Any								=							image_processor(snake_case__		,     return_tensors="pt"						).pixel_values
         self.assertEqual(
             encoded_images.shape		,     (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )		,     )

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         lowerCAmelCase     :     List[Any]								=							prepare_image_inputs(self.image_proc_tester		,     equal_resolution=snake_case__		,     numpify=snake_case__						)
         for image in image_inputs:
           self.assertIsInstance(snake_case__		,     np.ndarray						)

         # Test not batched input
         lowerCAmelCase     :     int								=							image_processor(image_inputs[0]		,     return_tensors="pt"						).pixel_values
         self.assertEqual(
             encoded_images.shape		,     (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )		,     )

         # Test batched
         lowerCAmelCase     :     Optional[int]								=							image_processor(snake_case__		,     return_tensors="pt"						).pixel_values
         self.assertEqual(
             encoded_images.shape		,     (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )		,     )







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         lowerCAmelCase     :     List[Any]								=							prepare_image_inputs(self.image_proc_tester		,     equal_resolution=snake_case__		,     torchify=snake_case__						)
         for image in image_inputs:
           self.assertIsInstance(snake_case__		,     torch.Tensor						)

         # Test not batched input
         lowerCAmelCase     :     Any								=							image_processor(image_inputs[0]		,     return_tensors="pt"						).pixel_values
         self.assertEqual(
             encoded_images.shape		,     (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )		,     )

         # Test batched
         lowerCAmelCase     :     str								=							image_processor(snake_case__		,     return_tensors="pt"						).pixel_values
         self.assertEqual(
             encoded_images.shape		,     (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )		,     )












import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Tuple=1024,	lowerCAmelCase_      :			Optional[Any]=1024,	lowerCAmelCase_      :			Tuple=False,	**lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='train',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	tok.pad_token_id

   def get_lens(lowerCAmelCase_      :			Optional[Any]	):
      __lowerCAmelCase								=	tqdm(
          DataLoader(lowerCAmelCase_,	batch_size=512,	num_workers=8,	shuffle=lowerCAmelCase_,	collate_fn=ds.collate_fn	),	desc=str(ds.len_file	),	)
      __lowerCAmelCase								=	[]
      for batch in dl:
         __lowerCAmelCase								=	batch['input_ids'].ne(lowerCAmelCase_	).sum(1	).tolist()
         __lowerCAmelCase								=	batch['labels'].ne(lowerCAmelCase_	).sum(1	).tolist()
         if consider_target:
            for src, tgt in zip(lowerCAmelCase_,	lowerCAmelCase_	):
               max_lens.append(max(lowerCAmelCase_,	lowerCAmelCase_	)	)
         else:
            max_lens.extend(lowerCAmelCase_	)
      return max_lens

   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='val',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   pickle_save(lowerCAmelCase_,	train_ds.len_file	)
   pickle_save(lowerCAmelCase_,	val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)

