'''simple docstring'''




import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


__UpperCAmelCase     							=logging.get_logger(__name__)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    None:
    __lowerCamelCase		=  nn.ModuleList([src_layers[i] for i in layers_to_copy]				)
    assert len(UpperCamelCase__				) == len(UpperCamelCase__				), f"""{len(UpperCamelCase__				)} != {len(UpperCamelCase__				)}"""
    dest_layers.load_state_dict(layers_to_copy.state_dict()				)


__UpperCAmelCase     							={
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    1_2: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 1_1],
        4: [0, 4, 8, 1_1],
        6: [0, 2, 4, 7, 9, 1_1],
        9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
        1_2: list(range(1_2)),
    },
    1_6: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 1_5],
        3: [0, 8, 1_5],
        4: [0, 5, 1_0, 1_5],
        6: [0, 3, 6, 9, 1_2, 1_5],
        8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
        9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
        1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
        1_6: list(range(1_6)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__UpperCAmelCase     							={
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
    1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Union[str, Any]:
    try:
        __lowerCamelCase		=  LAYERS_TO_COPY[n_teacher][n_student]
        return val
    except KeyError:
        if n_student != n_teacher:
            warnings.warn(
                f"""no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"""
                f""" {n_student}"""				)
        return list(range(UpperCamelCase__				)				)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    List[int]:
    if n_student > n_teacher:
        raise ValueError(f"""Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}"""				)
    elif n_teacher == n_student:
        return list(range(UpperCamelCase__				)				)
    elif n_student == 1:
        return [n_teacher - 1]
    else:
        return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__ = "student"				,  UpperCamelCase__ = None				,  UpperCamelCase__ = None				,  UpperCamelCase__=False				,  UpperCamelCase__=None				,  UpperCamelCase__=None				,  **UpperCamelCase__				,  )	->    Tuple[PreTrainedModel, List[int], List[int]]:
    __lowerCamelCase		=  '''encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'''
    assert (e is not None) or (d is not None), _msg
    if isinstance(UpperCamelCase__				,  UpperCamelCase__				):
        AutoTokenizer.from_pretrained(UpperCamelCase__				).save_pretrained(UpperCamelCase__				)  # purely for convenience
        __lowerCamelCase		=  AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__				).eval()
    else:
        assert isinstance(UpperCamelCase__				,  UpperCamelCase__				), f"""teacher must be a model or string got type {type(UpperCamelCase__				)}"""
    __lowerCamelCase		=  teacher.config.to_diff_dict()

    try:
        __lowerCamelCase	,  __lowerCamelCase		=  teacher.config.encoder_layers, teacher.config.decoder_layers
        if e is None:
            __lowerCamelCase		=  teacher_e
        if d is None:
            __lowerCamelCase		=  teacher_d
        init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}				)
    except AttributeError:  # T5
        if hasattr(teacher.config				,  '''num_encoder_layers'''				):
            __lowerCamelCase	,  __lowerCamelCase		=  teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
        else:
            __lowerCamelCase	,  __lowerCamelCase		=  teacher.config.num_layers, teacher.config.num_decoder_layers
        if e is None:
            __lowerCamelCase		=  teacher_e
        if d is None:
            __lowerCamelCase		=  teacher_d
        if hasattr(teacher.config				,  '''num_encoder_layers'''				):
            init_kwargs.update({'''num_encoder_layers''': e, '''num_decoder_layers''': d}				)
        else:
            init_kwargs.update({'''num_layers''': e, '''num_decoder_layers''': d}				)

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
    init_kwargs.update(UpperCamelCase__				)

    # Copy weights
    __lowerCamelCase		=  teacher.config_class(**UpperCamelCase__				)
    __lowerCamelCase		=  AutoModelForSeqaSeqLM.from_config(UpperCamelCase__				)
    # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
    __lowerCamelCase		=  student.load_state_dict(teacher.state_dict()				,  strict=UpperCamelCase__				)
    assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

    if copy_first_teacher_layers:  # Our copying is done. We just log and save
        __lowerCamelCase	,  __lowerCamelCase		=  list(range(UpperCamelCase__				)				), list(range(UpperCamelCase__				)				)
        logger.info(
            f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"""
            f""" {save_path}"""				)
        student.save_pretrained(UpperCamelCase__				)
        return student, e_layers_to_copy, d_layers_to_copy

    # Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
    if e_layers_to_copy is None:
        __lowerCamelCase		=  pick_layers_to_copy(UpperCamelCase__				,  UpperCamelCase__				)
    if d_layers_to_copy is None:
        __lowerCamelCase		=  pick_layers_to_copy(UpperCamelCase__				,  UpperCamelCase__				)

    try:
        if hasattr(
            UpperCamelCase__				,  '''prophetnet'''				):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
            copy_layers(teacher.prophetnet.encoder.layers				,  student.prophetnet.encoder.layers				,  UpperCamelCase__				)
            copy_layers(teacher.prophetnet.decoder.layers				,  student.prophetnet.decoder.layers				,  UpperCamelCase__				)
        else:
            copy_layers(teacher.model.encoder.layers				,  student.model.encoder.layers				,  UpperCamelCase__				)
            copy_layers(teacher.model.decoder.layers				,  student.model.decoder.layers				,  UpperCamelCase__				)
    except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
        copy_layers(teacher.encoder.block				,  student.encoder.block				,  UpperCamelCase__				)
        copy_layers(teacher.decoder.block				,  student.decoder.block				,  UpperCamelCase__				)
    logger.info(
        f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""				)
    __lowerCamelCase		=  {
        '''teacher_type''': teacher.config.model_type,
        '''copied_encoder_layers''': e_layers_to_copy,
        '''copied_decoder_layers''': d_layers_to_copy,
    }
    student.save_pretrained(UpperCamelCase__				)
    # Save information about copying for easier reproducibility

    return student, e_layers_to_copy, d_layers_to_copy


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




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 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		return ConvertCommand(args.tfds_path						,				args.datasets_directory     )


class        _a							(			UpperCamelCase__			):

							@staticmethod
							def       lowerCamelCase_ (							UpperCamelCase_:	ArgumentParser							)			->   int:





									"""simple docstring"""







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

							def __init__(							self:	int    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str    ,						*UpperCamelCase_:	Optional[int]							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      get_logger('''datasets-cli/converting'''							)

									lowercase__            =      tfds_path
									lowercase__            =      datasets_directory


							def       lowerCamelCase_ (							self:	int							)			->   Optional[int]:





									"""simple docstring"""







									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(UpperCamelCase_							)
									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(UpperCamelCase_    ,						UpperCamelCase_							)
											lowercase__            =      os.path.join(UpperCamelCase_    ,						UpperCamelCase_							)

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

											with open(UpperCamelCase_    ,						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 UpperCamelCase_							: e in out_line    ,						UpperCamelCase_							)							)
															out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(UpperCamelCase_							) + '''\n'''							)
															out_lines.append(UpperCamelCase_							)
															out_lines.append(UpperCamelCase_							)
															continue
													else:
															for pattern, replacement in TO_CONVERT:
																	lowercase__            =      re.sub(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)

                # 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]+)'''    ,						UpperCamelCase_							)
															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(UpperCamelCase_							)

											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(UpperCamelCase_    ,						UpperCamelCase_							)
													lowercase__            =      os.path.join(UpperCamelCase_    ,						UpperCamelCase_							)
													os.makedirs(UpperCamelCase_    ,						exist_ok=UpperCamelCase_							)
													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(UpperCamelCase_							)

											if needs_manual_update:
													with_manual_update.append(UpperCamelCase_							)

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

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


