




'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_pytesseract_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor







class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   def __init__(   self :     Union[str, Any]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Optional[Any]=7  ,				__lowerCamelCase :     str=3  ,				__lowerCamelCase :     Union[str, Any]=1_8  ,				__lowerCamelCase :     Union[str, Any]=3_0  ,				__lowerCamelCase :     str=4_0_0  ,				__lowerCamelCase :     Optional[int]=True  ,				__lowerCamelCase :     Optional[int]=None  ,				__lowerCamelCase :     List[Any]=True  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				size if size is not None else {"height": 1_8, "width": 1_8}
      _SCREAMING_SNAKE_CASE	=				parent
      _SCREAMING_SNAKE_CASE	=				batch_size
      _SCREAMING_SNAKE_CASE	=				num_channels
      _SCREAMING_SNAKE_CASE	=				image_size
      _SCREAMING_SNAKE_CASE	=				min_resolution
      _SCREAMING_SNAKE_CASE	=				max_resolution
      _SCREAMING_SNAKE_CASE	=				do_resize
      _SCREAMING_SNAKE_CASE	=				size
      _SCREAMING_SNAKE_CASE	=				apply_ocr



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class 		lowercase_   (			A					,  unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						LayoutLMvaImageProcessor if is_pytesseract_available() else None



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				LayoutLMvaImageProcessingTester(self   )



   @property
   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(__lowerCamelCase  ,				"do_resize"   )   )
      self.assertTrue(hasattr(__lowerCamelCase  ,				"size"   )   )
      self.assertTrue(hasattr(__lowerCamelCase  ,				"apply_ocr"   )   )



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.image_processing_class.from_dict(self.image_processor_dict   )
      self.assertEqual(image_processor.size  ,				{"height": 1_8, "width": 1_8}   )

      _SCREAMING_SNAKE_CASE	=				self.image_processing_class.from_dict(self.image_processor_dict  ,				size=4_2   )
      self.assertEqual(image_processor.size  ,				{"height": 4_2, "width": 4_2}   )



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      pass



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      # Initialize image_processing
      _SCREAMING_SNAKE_CASE	=				self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      _SCREAMING_SNAKE_CASE	=				prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=__lowerCamelCase   )
      for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase  ,				Image.Image   )

      # Test not batched input
      _SCREAMING_SNAKE_CASE	=				image_processing(image_inputs[0]  ,				return_tensors="pt"   )
      self.assertEqual(
          encoding.pixel_values.shape  ,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )  ,				)

      self.assertIsInstance(encoding.words  ,				__lowerCamelCase   )
      self.assertIsInstance(encoding.boxes  ,				__lowerCamelCase   )

      # Test batched
      _SCREAMING_SNAKE_CASE	=				image_processing(__lowerCamelCase  ,				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 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      # Initialize image_processing
      _SCREAMING_SNAKE_CASE	=				self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      _SCREAMING_SNAKE_CASE	=				prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=__lowerCamelCase  ,				numpify=__lowerCamelCase   )
      for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase  ,				np.ndarray   )

      # Test not batched input
      _SCREAMING_SNAKE_CASE	=				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
      _SCREAMING_SNAKE_CASE	=				image_processing(__lowerCamelCase  ,				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 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      # Initialize image_processing
      _SCREAMING_SNAKE_CASE	=				self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      _SCREAMING_SNAKE_CASE	=				prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=__lowerCamelCase  ,				torchify=__lowerCamelCase   )
      for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase  ,				torch.Tensor   )

      # Test not batched input
      _SCREAMING_SNAKE_CASE	=				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
      _SCREAMING_SNAKE_CASE	=				image_processing(__lowerCamelCase  ,				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 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      # with apply_OCR = True
      _SCREAMING_SNAKE_CASE	=				LayoutLMvaImageProcessor()

      from datasets import load_dataset

      _SCREAMING_SNAKE_CASE	=				load_dataset("hf-internal-testing/fixtures_docvqa"  ,				split="test"   )

      _SCREAMING_SNAKE_CASE	=				Image.open(ds[0]["file"]   ).convert("RGB"   )

      _SCREAMING_SNAKE_CASE	=				image_processing(__lowerCamelCase  ,				return_tensors="pt"   )

      self.assertEqual(encoding.pixel_values.shape  ,				(1, 3, 2_2_4, 2_2_4)   )
      self.assertEqual(len(encoding.words   )  ,				len(encoding.boxes   )   )

      # fmt: off
      # the words and boxes were obtained with Tesseract 4.1.1
      _SCREAMING_SNAKE_CASE	=				[["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]]  # noqa: E231
      _SCREAMING_SNAKE_CASE	=				[[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
      # fmt: on

      self.assertListEqual(encoding.words  ,				__lowerCamelCase   )
      self.assertListEqual(encoding.boxes  ,				__lowerCamelCase   )

      # with apply_OCR = False
      _SCREAMING_SNAKE_CASE	=				LayoutLMvaImageProcessor(apply_ocr=__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				image_processing(__lowerCamelCase  ,				return_tensors="pt"   )

      self.assertEqual(encoding.pixel_values.shape  ,				(1, 3, 2_2_4, 2_2_4)   )







'''simple docstring'''



import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowerCamelCase_			=						'<<<<<<< This should probably be modified because it mentions: '

lowerCamelCase_			=						'=======\n>>>>>>>\n'

lowerCamelCase_			=						[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

lowerCamelCase_			=						[
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'tfds\.core', r'datasets'),
    (r'tf\.io\.gfile\.GFile', r'open'),
    (r'tf\.([\w\d]+)', r'datasets.Value(\'\1\')'),
    (r'tfds\.features\.Text\(\)', r'datasets.Value(\'string\')'),
    (r'tfds\.features\.Text\(', r'datasets.Value(\'string\'),'),
    (r'features\s*=\s*tfds.features.FeaturesDict\(', r'features=datasets.Features('),
    (r'tfds\.features\.FeaturesDict\(', r'dict('),
    (r'The TensorFlow Datasets Authors', r'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (r'tfds\.', r'datasets.'),
    (r'dl_manager\.manual_dir', r'self.config.data_dir'),
    (r'self\.builder_config', r'self.config'),
]
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Namespace  )			->			List[Any]:
   return ConvertCommand(args.tfds_path	,					args.datasets_directory  )







class 		lowercase_   (			A       ):


   """simple docstring"""

   @staticmethod
   def 							lowerCAmelCase_      (   __lowerCamelCase :     ArgumentParser   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				parser.add_parser(
          "convert"  ,				help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."  ,				)
      train_parser.add_argument(
          "--tfds_path"  ,				type=__lowerCamelCase  ,				required=__lowerCamelCase  ,				help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."  ,				)
      train_parser.add_argument(
          "--datasets_directory"  ,				type=__lowerCamelCase  ,				required=__lowerCamelCase  ,				help="Path to the HuggingFace Datasets folder."   )
      train_parser.set_defaults(func=__lowerCamelCase   )



   def __init__(   self :     Dict  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str  ,				*__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				get_logger("datasets-cli/converting"   )

      _SCREAMING_SNAKE_CASE	=				tfds_path
      _SCREAMING_SNAKE_CASE	=				datasets_directory



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      if os.path.isdir(self._tfds_path   ):
         _SCREAMING_SNAKE_CASE	=				os.path.abspath(self._tfds_path   )
      elif os.path.isfile(self._tfds_path   ):
         _SCREAMING_SNAKE_CASE	=				os.path.dirname(self._tfds_path   )
      else:
         raise ValueError("--tfds_path is neither a directory nor a file. Please check path."   )

      _SCREAMING_SNAKE_CASE	=				os.path.abspath(self._datasets_directory   )

      self._logger.info(F"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}"""   )

      _SCREAMING_SNAKE_CASE	=				[]
      _SCREAMING_SNAKE_CASE	=				[]
      _SCREAMING_SNAKE_CASE	=				{}

      if os.path.isdir(self._tfds_path   ):
         _SCREAMING_SNAKE_CASE	=				os.listdir(__lowerCamelCase   )
      else:
         _SCREAMING_SNAKE_CASE	=				[os.path.basename(self._tfds_path   )]

      for f_name in file_names:
         self._logger.info(F"""Looking at file {f_name}"""   )
         _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )

         if not os.path.isfile(__lowerCamelCase   ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
            self._logger.info("Skipping file"   )
            continue

         with open(__lowerCamelCase  ,				encoding="utf-8"   ) as f:
            _SCREAMING_SNAKE_CASE	=				f.readlines()

         _SCREAMING_SNAKE_CASE	=				[]
         _SCREAMING_SNAKE_CASE	=				False
         _SCREAMING_SNAKE_CASE	=				False
         _SCREAMING_SNAKE_CASE	=				[]
         for line in lines:
            _SCREAMING_SNAKE_CASE	=				line

            # Convert imports
            if "import tensorflow.compat.v2 as tf" in out_line:
               continue
            elif "@tfds.core" in out_line:
               continue
            elif "builder=self" in out_line:
               continue
            elif "import tensorflow_datasets.public_api as tfds" in out_line:
               _SCREAMING_SNAKE_CASE	=				"import datasets\n"
            elif "import tensorflow" in out_line:
               # order is important here
               _SCREAMING_SNAKE_CASE	=				""
               continue
            elif "from absl import logging" in out_line:
               _SCREAMING_SNAKE_CASE	=				"from datasets import logging\n"
            elif "getLogger" in out_line:
               _SCREAMING_SNAKE_CASE	=				out_line.replace("getLogger"  ,				"get_logger"   )
            elif any(expression in out_line for expression in TO_HIGHLIGHT   ):
               _SCREAMING_SNAKE_CASE	=				True
               _SCREAMING_SNAKE_CASE	=				list(filter(lambda __lowerCamelCase   : e in out_line  ,				__lowerCamelCase   )   )
               out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__lowerCamelCase   ) + "\n"   )
               out_lines.append(__lowerCamelCase   )
               out_lines.append(__lowerCamelCase   )
               continue
            else:
               for pattern, replacement in TO_CONVERT:
                  _SCREAMING_SNAKE_CASE	=				re.sub(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase   )

                # Take care of saving utilities (to later move them together with main script)
            if "tensorflow_datasets" in out_line:
               _SCREAMING_SNAKE_CASE	=				re.match(r"from\stensorflow_datasets.*import\s([^\.\r\n]+)"  ,				__lowerCamelCase   )
               tfds_imports.extend(imp.strip() for imp in match.group(1   ).split(","   )   )
               _SCREAMING_SNAKE_CASE	=				"from . import " + match.group(1   )

            # Check we have not forget anything
            if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
               raise ValueError(F"""Error converting {out_line.strip()}"""   )

            if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
               _SCREAMING_SNAKE_CASE	=				True
            out_lines.append(__lowerCamelCase   )

         if is_builder or "wmt" in f_name:
            # We create a new directory for each dataset
            _SCREAMING_SNAKE_CASE	=				f_name.replace(".py"  ,				""   )
            _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )
            _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )
            os.makedirs(__lowerCamelCase  ,				exist_ok=__lowerCamelCase   )
            self._logger.info(F"""Adding directory {output_dir}"""   )
            imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}   )
         else:
            # Utilities will be moved at the end
            utils_files.append(__lowerCamelCase   )

         if needs_manual_update:
            with_manual_update.append(__lowerCamelCase   )

         with open(__lowerCamelCase  ,				"w"  ,				encoding="utf-8"   ) as f:
            f.writelines(__lowerCamelCase   )
         self._logger.info(F"""Converted in {output_file}"""   )

      for utils_file in utils_files:
         try:
            _SCREAMING_SNAKE_CASE	=				os.path.basename(__lowerCamelCase   )
            _SCREAMING_SNAKE_CASE	=				imports_to_builder_map[f_name.replace(".py"  ,				""   )]
            self._logger.info(F"""Moving {dest_folder} to {utils_file}"""   )
            shutil.copy(__lowerCamelCase  ,				__lowerCamelCase   )
         except KeyError:
            self._logger.error(F"""Cannot find destination folder for {utils_file}. Please copy manually."""   )

      if with_manual_update:
         for file_path in with_manual_update:
            self._logger.warning(
                F"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."""   )

