



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor
class 				_snake_case			(					unittest.TestCase):




    def __init__(						self					:						Optional[int],					__lowercase					:						int,					__lowercase					:						List[str]=7,					__lowercase					:						List[Any]=3,					__lowercase					:						int=30,					__lowercase					:						str=400,					__lowercase					:						Any=True,					__lowercase					:						int=None,					__lowercase					:						Dict=True,					__lowercase					:						List[Any]=[0.5, 0.5, 0.5],					__lowercase					:						List[Any]=[0.5, 0.5, 0.5],					__lowercase					:						int=True,					__lowercase					:						Optional[Any]=1 / 255,					__lowercase					:						Optional[Any]=True,					):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       lowercase__					  =				size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
       lowercase__					  =				parent
       lowercase__					  =				batch_size
       lowercase__					  =				num_channels
       lowercase__					  =				min_resolution
       lowercase__					  =				max_resolution
       lowercase__					  =				do_resize
       lowercase__					  =				size
       lowercase__					  =				do_normalize
       lowercase__					  =				image_mean
       lowercase__					  =				image_std
       lowercase__					  =				do_rescale
       lowercase__					  =				rescale_factor
       lowercase__					  =				do_pad




    def 							A__      (						self					:						List[Any]						):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }




    def 							A__      (						self					:						List[str],					__lowercase					:						int,					__lowercase					:						List[Any]=False						):
       if not batched:
          lowercase__					  =				image_inputs[0]
          if isinstance(__lowercase,					Image.Image						):
             lowercase__  ,       lowercase__					  =				image.size
          else:
             lowercase__  ,       lowercase__					  =				image.shape[1], image.shape[2]
          if w < h:
             lowercase__					  =				int(self.size["shortest_edge"] * h / w						)
             lowercase__					  =				self.size["shortest_edge"]
          elif w > h:
             lowercase__					  =				self.size["shortest_edge"]
             lowercase__					  =				int(self.size["shortest_edge"] * w / h						)
          else:
             lowercase__					  =				self.size["shortest_edge"]
             lowercase__					  =				self.size["shortest_edge"]

       else:
          lowercase__					  =				[]
          for image in image_inputs:
             lowercase__  ,       lowercase__					  =				self.get_expected_values([image]						)
             expected_values.append((expected_height, expected_width)						)
          lowercase__					  =				max(__lowercase,					key=lambda __lowercase						: item[0]						)[0]
          lowercase__					  =				max(__lowercase,					key=lambda __lowercase						: item[1]						)[1]

       return expected_height, expected_width
@require_torch
@require_vision
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
    UpperCamelCase__				:					Any     		=ConditionalDetrImageProcessor if is_vision_available() else None




    def 							A__      (						self					:						List[str]						):
       lowercase__					  =				ConditionalDetrImageProcessingTester(self						)




    @property
    def 							A__      (						self					:						List[Any]						):
       return self.image_processor_tester.prepare_image_processor_dict()




    def 							A__      (						self					:						Optional[Any]						):
       lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(__lowercase,					"image_mean"						)						)
       self.assertTrue(hasattr(__lowercase,					"image_std"						)						)
       self.assertTrue(hasattr(__lowercase,					"do_normalize"						)						)
       self.assertTrue(hasattr(__lowercase,					"do_resize"						)						)
       self.assertTrue(hasattr(__lowercase,					"size"						)						)




    def 							A__      (						self					:						Any						):
       lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size,					{"shortest_edge": 18, "longest_edge": 1333}						)
       self.assertEqual(image_processor.do_pad,					__lowercase						)

       lowercase__					  =				self.image_processing_class.from_dict(
           self.image_processor_dict,					size=42,					max_size=84,					pad_and_return_pixel_mask=__lowercase						)
       self.assertEqual(image_processor.size,					{"shortest_edge": 42, "longest_edge": 84}						)
       self.assertEqual(image_processor.do_pad,					__lowercase						)




    def 							A__      (						self					:						List[str]						):
       pass




    def 							A__      (						self					:						List[Any]						):
       # Initialize image_processing
       lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase						)
       for image in image_inputs:
          self.assertIsInstance(__lowercase,					Image.Image						)

       # Test not batched input
       lowercase__					  =				image_processing(image_inputs[0],					return_tensors="pt"						).pixel_values

       lowercase__  ,       lowercase__					  =				self.image_processor_tester.get_expected_values(__lowercase						)

       self.assertEqual(
           encoded_images.shape,					(1, self.image_processor_tester.num_channels, expected_height, expected_width),					)

       # Test batched
       lowercase__  ,       lowercase__					  =				self.image_processor_tester.get_expected_values(__lowercase,					batched=__lowercase						)

       lowercase__					  =				image_processing(__lowercase,					return_tensors="pt"						).pixel_values
       self.assertEqual(
           encoded_images.shape,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           ),					)




    def 							A__      (						self					:						List[str]						):
       # Initialize image_processing
       lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					numpify=__lowercase						)
       for image in image_inputs:
          self.assertIsInstance(__lowercase,					np.ndarray						)

       # Test not batched input
       lowercase__					  =				image_processing(image_inputs[0],					return_tensors="pt"						).pixel_values

       lowercase__  ,       lowercase__					  =				self.image_processor_tester.get_expected_values(__lowercase						)

       self.assertEqual(
           encoded_images.shape,					(1, self.image_processor_tester.num_channels, expected_height, expected_width),					)

       # Test batched
       lowercase__					  =				image_processing(__lowercase,					return_tensors="pt"						).pixel_values

       lowercase__  ,       lowercase__					  =				self.image_processor_tester.get_expected_values(__lowercase,					batched=__lowercase						)

       self.assertEqual(
           encoded_images.shape,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           ),					)




    def 							A__      (						self					:						List[Any]						):
       # Initialize image_processing
       lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					torchify=__lowercase						)
       for image in image_inputs:
          self.assertIsInstance(__lowercase,					torch.Tensor						)

       # Test not batched input
       lowercase__					  =				image_processing(image_inputs[0],					return_tensors="pt"						).pixel_values

       lowercase__  ,       lowercase__					  =				self.image_processor_tester.get_expected_values(__lowercase						)

       self.assertEqual(
           encoded_images.shape,					(1, self.image_processor_tester.num_channels, expected_height, expected_width),					)

       # Test batched
       lowercase__					  =				image_processing(__lowercase,					return_tensors="pt"						).pixel_values

       lowercase__  ,       lowercase__					  =				self.image_processor_tester.get_expected_values(__lowercase,					batched=__lowercase						)

       self.assertEqual(
           encoded_images.shape,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           ),					)




    @slow
    def 							A__      (						self					:						int						):
       # prepare image and target
       lowercase__					  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
       with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt",					"r"						) as f:
          lowercase__					  =				json.loads(f.read()						)

       lowercase__					  =				{"image_id": 3_9769, "annotations": target}

       # encode them
       lowercase__					  =				ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"						)
       lowercase__					  =				image_processing(images=__lowercase,					annotations=__lowercase,					return_tensors="pt"						)

       # verify pixel values
       lowercase__					  =				torch.Size([1, 3, 800, 1066]						)
       self.assertEqual(encoding["pixel_values"].shape,					__lowercase						)

       lowercase__					  =				torch.tensor([0.2796, 0.3138, 0.3481]						)
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3],					__lowercase,					atol=1e-4						)						)

       # verify area
       lowercase__					  =				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"],					__lowercase						)						)
       # verify boxes
       lowercase__					  =				torch.Size([6, 4]						)
       self.assertEqual(encoding["labels"][0]["boxes"].shape,					__lowercase						)
       lowercase__					  =				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0],					__lowercase,					atol=1e-3						)						)
       # verify image_id
       lowercase__					  =				torch.tensor([3_9769]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"],					__lowercase						)						)
       # verify is_crowd
       lowercase__					  =				torch.tensor([0, 0, 0, 0, 0, 0]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"],					__lowercase						)						)
       # verify class_labels
       lowercase__					  =				torch.tensor([75, 75, 63, 65, 17, 17]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"],					__lowercase						)						)
       # verify orig_size
       lowercase__					  =				torch.tensor([480, 640]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"],					__lowercase						)						)
       # verify size
       lowercase__					  =				torch.tensor([800, 1066]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"],					__lowercase						)						)




    @slow
    def 							A__      (						self					:						Optional[int]						):
       # prepare image, target and masks_path
       lowercase__					  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
       with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt",					"r"						) as f:
          lowercase__					  =				json.loads(f.read()						)

       lowercase__					  =				{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

       lowercase__					  =				pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

       # encode them
       lowercase__					  =				ConditionalDetrImageProcessor(format="coco_panoptic"						)
       lowercase__					  =				image_processing(images=__lowercase,					annotations=__lowercase,					masks_path=__lowercase,					return_tensors="pt"						)

       # verify pixel values
       lowercase__					  =				torch.Size([1, 3, 800, 1066]						)
       self.assertEqual(encoding["pixel_values"].shape,					__lowercase						)

       lowercase__					  =				torch.tensor([0.2796, 0.3138, 0.3481]						)
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3],					__lowercase,					atol=1e-4						)						)

       # verify area
       lowercase__					  =				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"],					__lowercase						)						)
       # verify boxes
       lowercase__					  =				torch.Size([6, 4]						)
       self.assertEqual(encoding["labels"][0]["boxes"].shape,					__lowercase						)
       lowercase__					  =				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0],					__lowercase,					atol=1e-3						)						)
       # verify image_id
       lowercase__					  =				torch.tensor([3_9769]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"],					__lowercase						)						)
       # verify is_crowd
       lowercase__					  =				torch.tensor([0, 0, 0, 0, 0, 0]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"],					__lowercase						)						)
       # verify class_labels
       lowercase__					  =				torch.tensor([17, 17, 63, 75, 75, 93]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"],					__lowercase						)						)
       # verify masks
       lowercase__					  =				82_2873
       self.assertEqual(encoding["labels"][0]["masks"].sum().item(),					__lowercase						)
       # verify orig_size
       lowercase__					  =				torch.tensor([480, 640]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"],					__lowercase						)						)
       # verify size
       lowercase__					  =				torch.tensor([800, 1066]						)
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"],					__lowercase						)						)









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

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


lowercase_								=     """<<<<<<< This should probably be modified because it mentions: """

lowercase_								=     """=======
>>>>>>>
"""

lowercase_								=     [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

lowercase_								=     [
    # (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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			return ConvertCommand(args.tfds_path						,  args.datasets_directory  )




class 				_snake_case			(					lowercase__):




				@staticmethod
				def 							A__      (						__lowercase					:						ArgumentParser						):
							lowercase__					  =				parser.add_parser(
							    "convert",					help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.",					)
							train_parser.add_argument(
							    "--tfds_path",					type=__lowercase,					required=__lowercase,					help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.",					)
							train_parser.add_argument(
							    "--datasets_directory",					type=__lowercase,					required=__lowercase,					help="Path to the HuggingFace Datasets folder."						)
							train_parser.set_defaults(func=__lowercase						)




				def __init__(						self					:						Tuple,					__lowercase					:						str,					__lowercase					:						str,					*__lowercase					:						Tuple						):
							lowercase__					  =				get_logger("datasets-cli/converting"						)

							lowercase__					  =				tfds_path
							lowercase__					  =				datasets_directory




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

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

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

							lowercase__					  =				[]
							lowercase__					  =				[]
							lowercase__					  =				{}

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

							for f_name in file_names:
										self._logger.info(F'''Looking at file {f_name}'''						)
										lowercase__					  =				os.path.join(__lowercase,					__lowercase						)
										lowercase__					  =				os.path.join(__lowercase,					__lowercase						)

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

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

										lowercase__					  =				[]
										lowercase__					  =				False
										lowercase__					  =				False
										lowercase__					  =				[]
										for line in lines:
													lowercase__					  =				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:
																lowercase__					  =				"import datasets\n"
													elif "import tensorflow" in out_line:
																# order is important here
																lowercase__					  =				""
																continue
													elif "from absl import logging" in out_line:
																lowercase__					  =				"from datasets import logging\n"
													elif "getLogger" in out_line:
																lowercase__					  =				out_line.replace("getLogger",					"get_logger"						)
													elif any(expression in out_line for expression in TO_HIGHLIGHT						):
																lowercase__					  =				True
																lowercase__					  =				list(filter(lambda __lowercase						: e in out_line,					__lowercase						)						)
																out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__lowercase						) + "\n"						)
																out_lines.append(__lowercase						)
																out_lines.append(__lowercase						)
																continue
													else:
																for pattern, replacement in TO_CONVERT:
																			lowercase__					  =				re.sub(__lowercase,					__lowercase,					__lowercase						)

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

										if is_builder or "wmt" in f_name:
													# We create a new directory for each dataset
													lowercase__					  =				f_name.replace(".py",					""						)
													lowercase__					  =				os.path.join(__lowercase,					__lowercase						)
													lowercase__					  =				os.path.join(__lowercase,					__lowercase						)
													os.makedirs(__lowercase,					exist_ok=__lowercase						)
													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(__lowercase						)

										if needs_manual_update:
													with_manual_update.append(__lowercase						)

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

							for utils_file in utils_files:
										try:
													lowercase__					  =				os.path.basename(__lowercase						)
													lowercase__					  =				imports_to_builder_map[f_name.replace(".py",					""						)]
													self._logger.info(F'''Moving {dest_folder} to {utils_file}'''						)
													shutil.copy(__lowercase,					__lowercase						)
										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\'.'''						)




