

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

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


UpperCamelCase__  :      Union[str, Any]			=    "<<<<<<< This should probably be modified because it mentions: "

UpperCamelCase__  :      int			=    "=======\n>>>>>>>\n"

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

UpperCamelCase__  :      Tuple			=    [
    # (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       (  _SCREAMING_SNAKE_CASE					:		Namespace       ):





    """simple docstring"""


    return ConvertCommand(args.tfds_path ,   args.datasets_directory       )





class       __snake_case  (    __lowerCAmelCase       ):

  @staticmethod
  def 						lowerCAmelCase__     (					_A):
      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						,		_A						,		_A						,		*_A):
      SCREAMING_SNAKE_CASE_									=						get_logger('datasets-cli/converting')

      SCREAMING_SNAKE_CASE_									=						tfds_path
      SCREAMING_SNAKE_CASE_									=						datasets_directory


  def 						lowerCAmelCase__     (					self):
      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 _A: 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\'.""")








from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	torch.FloatTensor






class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__       ):

  @register_to_config
  def __init__(					self						,		_A = 3						,		_A = 3						,		_A = ("DownEncoderBlock2D",)						,		_A = ("UpDecoderBlock2D",)						,		_A = (64,)						,		_A = 1						,		_A = "silu"						,		_A = 3						,		_A = 32						,		_A = 256						,		_A = 32						,		_A = None						,		_A = 0.1_8_2_1_5						,		_A = "group"						,		):
      super().__init__()

      # pass init params to Encoder
      SCREAMING_SNAKE_CASE_									=						Encoder(
          in_channels=_A						,		out_channels=_A						,		down_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		double_z=_A						,		)

      SCREAMING_SNAKE_CASE_									=						vq_embed_dim if vq_embed_dim is not None else latent_channels

      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)
      SCREAMING_SNAKE_CASE_									=						VectorQuantizer(_A						,		_A						,		beta=0.2_5						,		remap=_A						,		sane_index_shape=_A)
      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)

      # pass init params to Decoder
      SCREAMING_SNAKE_CASE_									=						Decoder(
          in_channels=_A						,		out_channels=_A						,		up_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		norm_type=_A						,		)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						self.encoder(_A)
      SCREAMING_SNAKE_CASE_									=						self.quant_conv(_A)

      if not return_dict:
          return (h,)

      return VQEncoderOutput(latents=_A)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = False						,		_A = True):
      # also go through quantization layer
      if not force_not_quantize:
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.quantize(_A)
      else:
          SCREAMING_SNAKE_CASE_									=						h
      SCREAMING_SNAKE_CASE_									=						self.post_quant_conv(_A)
      SCREAMING_SNAKE_CASE_									=						self.decoder(_A						,		quant if self.config.norm_type == 'spatial' else None)

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						sample
      SCREAMING_SNAKE_CASE_									=						self.encode(_A).latents
      SCREAMING_SNAKE_CASE_									=						self.decode(_A).sample

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


