

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

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


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

_lowerCAmelCase   :     Optional[Any]    							=     "=======\n>>>>>>>\n"

_lowerCAmelCase   :     Union[str, Any]    							=     [
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

_lowerCAmelCase   :     Dict    							=     [
    # (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 			UpperCamelCase_(				_snake_case  :    str      ):




			"""simple docstring"""






			return ConvertCommand(args.tfds_path			,	args.datasets_directory      )






class       __magic_name__       (     _UpperCamelCase ):





			@staticmethod
			def 						__magic_name__       (						__snake_case   )  ->					str:




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





			def __init__(						self   ,    __snake_case   ,    __snake_case   ,    *__snake_case   )  ->					Optional[int]:




						'''simple docstring'''
						__a     =get_logger('datasets-cli/converting'   )

						__a     =tfds_path
						__a     =datasets_directory





			def 						__magic_name__       (						self   )  ->					Dict:




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

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

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

						__a     =[]
						__a     =[]
						__a     ={}

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

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

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

									with open(__a   ,    encoding='utf-8'   ) as f:
												__a     =f.readlines()

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

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

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

									if needs_manual_update:
												with_manual_update.append(__a   )

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

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













"""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 GLPNImageProcessor



class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def __init__(		self :							Any       , __a :							Any       , __a :							Optional[Any]=7       , __a :							Tuple=3       , __a :							int=18       , __a :							Optional[Any]=30       , __a :							Optional[Any]=400       , __a :							List[str]=True       , __a :							Any=32       , __a :							Tuple=True       , )		->					List[str]:
        _UpperCamelCase   :						List[str]    =							parent
        _UpperCamelCase   :						int    =							batch_size
        _UpperCamelCase   :						List[str]    =							num_channels
        _UpperCamelCase   :						Union[str, Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							min_resolution
        _UpperCamelCase   :						Union[str, Any]    =							max_resolution
        _UpperCamelCase   :						Optional[Any]    =							do_resize
        _UpperCamelCase   :						str    =							size_divisor
        _UpperCamelCase   :						Optional[Any]    =							do_rescale


  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        return {
            "do_resize": self.do_resize,
            "size_divisor": self.size_divisor,
            "do_rescale": self.do_rescale,
        }





@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Any						  =       GLPNImageProcessor if is_vision_available() else None

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Any:
        _UpperCamelCase   :						Optional[Any]    =							GLPNImageProcessingTester(self						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Dict:
        return self.image_processor_tester.prepare_image_processor_dict()

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        _UpperCamelCase   :						int    =							self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(__a       , "do_resize"						)						)
        self.assertTrue(hasattr(__a       , "size_divisor"						)						)
        self.assertTrue(hasattr(__a       , "resample"						)						)
        self.assertTrue(hasattr(__a       , "do_rescale"						)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					List[Any]:
        pass

  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					Optional[Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[Any]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PIL images
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , Image.Image						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Dict:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , numpify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , np.ndarray						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Dict    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Union[str, Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        _UpperCamelCase   :						Dict    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , torchify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , torch.Tensor						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)
