"""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   UpperCAmelCase					(	UpperCamelCase__				):






       """simple docstring"""





       return ConvertCommand(args.tfds_path       ,    args.datasets_directory				)


class     UpperCamelCase__(			__lowerCAmelCase ):





      @staticmethod
      def 				snake_case__	(       __UpperCAmelCase     )					->				Any:
             A__	     =							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		,__UpperCAmelCase		,__UpperCAmelCase		,*__UpperCAmelCase     )					->				Any:
             A__	     =							get_logger('datasets-cli/converting'     )

             A__	     =							tfds_path
             A__	     =							datasets_directory





      def 				snake_case__	(       self     )					->				List[str]:
             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(lowerCAmelCase_     )
             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(lowerCAmelCase_		,lowerCAmelCase_     )
                    A__	     =							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:
                           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 __UpperCAmelCase     : 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:
                                         A__	     =							re.sub(lowerCAmelCase_		,lowerCAmelCase_		,lowerCAmelCase_     )

                # 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]+)'		,lowerCAmelCase_     )
                                  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(lowerCAmelCase_     )

                    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(lowerCAmelCase_		,lowerCAmelCase_     )
                           A__	     =							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:
                           A__	     =							os.path.basename(lowerCAmelCase_     )
                           A__	     =							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\'.'''     )






"""simple docstring"""




from manim import *
class     UpperCamelCase__(			__A ):





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							Rectangle(height=0.5		,width=0.5     )
													A__	     =							Rectangle(height=0.4_6		,width=0.4_6     ).set_stroke(width=0     )

													A__	     =							Rectangle(height=0.2_5		,width=0.2_5     )

													A__	     =							[mem.copy() for i in range(6     )]
													A__	     =							[mem.copy() for i in range(6     )]
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							VGroup(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							Text('CPU'		,font_size=24     )
													A__	     =							Group(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0.5		,aligned_edge=__UpperCAmelCase     )
													cpu.move_to([-2.5, -0.5, 0]     )
													self.add(__UpperCAmelCase     )

													A__	     =							[mem.copy() for i in range(4     )]
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							Text('GPU'		,font_size=24     )
													A__	     =							Group(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0.5		,aligned_edge=__UpperCAmelCase     )
													gpu.move_to([-1, -1, 0]     )
													self.add(__UpperCAmelCase     )

													A__	     =							[mem.copy() for i in range(6     )]
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )

													A__	     =							Text('Model'		,font_size=24     )
													A__	     =							Group(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0.5		,aligned_edge=__UpperCAmelCase     )
													model.move_to([3, -1.0, 0]     )
													self.add(__UpperCAmelCase     )

													A__	     =							[]
													A__	     =							[]

													for i, rect in enumerate(__UpperCAmelCase     ):
																				A__	     =							fill.copy().set_fill(__UpperCAmelCase		,opacity=0.8     )
																				target.move_to(__UpperCAmelCase     )
																				model_arr.append(__UpperCAmelCase     )

																				A__	     =							Rectangle(height=0.4_6		,width=0.4_6     ).set_stroke(width=0.0     ).set_fill(__UpperCAmelCase		,opacity=0.8     )
																				cpu_target.move_to(cpu_left_col_base[i]     )
																				model_cpu_arr.append(__UpperCAmelCase     )

													self.add(*__UpperCAmelCase		,*__UpperCAmelCase     )

													A__	     =							[meta_mem.copy() for i in range(6     )]
													A__	     =							[meta_mem.copy() for i in range(6     )]
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							VGroup(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							Text('Disk'		,font_size=24     )
													A__	     =							Group(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0.5		,aligned_edge=__UpperCAmelCase     )
													disk.move_to([-4, -1.2_5, 0]     )
													self.add(__UpperCAmelCase		,__UpperCAmelCase     )

													A__	     =							Square(side_length=2.2     )
													key.move_to([-5, 2, 0]     )

													A__	     =							MarkupText(
													    f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''		,font_size=18		,)

													key_text.move_to([-5, 2.4, 0]     )

													self.add(__UpperCAmelCase		,__UpperCAmelCase     )

													A__	     =							MarkupText(
													    f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''		,font_size=18		,)

													blue_text.next_to(__UpperCAmelCase		,DOWN * 2.4		,aligned_edge=key_text.get_left()     )
													self.add(__UpperCAmelCase     )

													A__	     =							MarkupText(
													    f'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.'''		,font_size=24		,)
													step_a.move_to([2, 2, 0]     )

													self.play(Write(__UpperCAmelCase     )     )

													A__	     =							Square(0.3     )
													input.set_fill(__UpperCAmelCase		,opacity=1.0     )
													input.set_stroke(width=0.0     )
													input.next_to(model_base[0]		,__UpperCAmelCase		,buff=0.5     )

													self.play(Write(__UpperCAmelCase     )     )

													input.generate_target()
													input.target.next_to(model_arr[0]		,direction=__UpperCAmelCase		,buff=0.0_2     )
													self.play(MoveToTarget(__UpperCAmelCase     )     )

													self.play(FadeOut(__UpperCAmelCase     )     )

													A__	     =							Arrow(start=__UpperCAmelCase		,end=__UpperCAmelCase		,color=__UpperCAmelCase		,buff=0.5     )
													a.next_to(model_arr[0].get_left()		,__UpperCAmelCase		,buff=0.2     )

													model_cpu_arr[0].generate_target()
													model_cpu_arr[0].target.move_to(gpu_rect[0]     )

													A__	     =							MarkupText(
													    f'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'''		,font_size=24		,)
													step_a.move_to([2, 2, 0]     )

													self.play(Write(__UpperCAmelCase		,run_time=3     )     )

													A__	     =							{'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.0_2}

													self.play(
													    Write(__UpperCAmelCase     )		,Circumscribe(model_arr[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(model_cpu_arr[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(gpu_rect[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,)
													self.play(MoveToTarget(model_cpu_arr[0]     )     )

													A__	     =							a.copy()
													for i in range(6     ):
																				a_c.next_to(model_arr[i].get_right() + 0.0_2		,__UpperCAmelCase		,buff=0.2     )

																				input.generate_target()
																				input.target.move_to(model_arr[i].get_right() + 0.0_2     )

																				A__	     =							AnimationGroup(
																				    FadeOut(__UpperCAmelCase		,run_time=0.5     )		,MoveToTarget(__UpperCAmelCase		,run_time=0.5     )		,FadeIn(__UpperCAmelCase		,run_time=0.5     )		,lag_ratio=0.2     )

																				self.play(__UpperCAmelCase     )

																				model_cpu_arr[i].generate_target()
																				model_cpu_arr[i].target.move_to(cpu_left_col_base[i]     )

																				if i < 5:
																											model_cpu_arr[i + 1].generate_target()
																											model_cpu_arr[i + 1].target.move_to(gpu_rect[0]     )
																											if i >= 1:
																																		A__	     =							0.7

																											self.play(
																											    Circumscribe(model_arr[i]		,**__UpperCAmelCase     )		,Circumscribe(cpu_left_col_base[i]		,**__UpperCAmelCase     )		,Circumscribe(cpu_left_col_base[i + 1]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(gpu_rect[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(model_arr[i + 1]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,)
																											if i < 1:
																																		self.play(
																																		    MoveToTarget(model_cpu_arr[i]     )		,MoveToTarget(model_cpu_arr[i + 1]     )		,)
																											else:
																																		self.play(
																																		    MoveToTarget(model_cpu_arr[i]		,run_time=0.7     )		,MoveToTarget(model_cpu_arr[i + 1]		,run_time=0.7     )		,)
																				else:
																											model_cpu_arr[i].generate_target()
																											model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]     )
																											input.generate_target()
																											input.target.next_to(model_arr[-1].get_right()		,RIGHT + 0.0_2		,buff=0.2     )

																											self.play(
																											    Circumscribe(model_arr[-1]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(cpu_left_col_base[-1]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(gpu_rect[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,)

																											self.play(MoveToTarget(model_cpu_arr[i]     )     )

																				A__	     =							a_c
																				A__	     =							a_c.copy()

													input.generate_target()
													input.target.next_to(model_base[-1]		,RIGHT + 0.0_2		,buff=0.5     )
													self.play(
													    FadeOut(__UpperCAmelCase     )		,FadeOut(__UpperCAmelCase		,run_time=0.5     )		,)

													A__	     =							MarkupText(f'''Inference on a model too large for GPU memory\nis successfully completed.'''		,font_size=24     )
													step_a.move_to([2, 2, 0]     )

													self.play(Write(__UpperCAmelCase		,run_time=3     )		,MoveToTarget(__UpperCAmelCase     )     )

													self.wait()





