





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

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


_UpperCAmelCase				:       Optional[int]	   =				"""<<<<<<< This should probably be modified because it mentions: """

_UpperCAmelCase				:       List[Any]	   =				"""=======
>>>>>>>
"""

_UpperCAmelCase				:       List[str]	   =				[
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

_UpperCAmelCase				:       str	   =				[
    # (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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Any:
    return ConvertCommand(args.tfds_path						,	args.datasets_directory )


class    lowerCAmelCase							(					__UpperCamelCase			):
 @staticmethod
 def      A_	(      UpperCAmelCase  :      ArgumentParser	)			->	Tuple:
     lowerCamelCase__    :      Union[str, Any]					 =		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  :      Tuple			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      str			,     *UpperCAmelCase  :      int	)			->	Optional[Any]:
     lowerCamelCase__    :      Any					 =		get_logger('datasets-cli/converting'	)

     lowerCamelCase__    :      Optional[Any]					 =		tfds_path
     lowerCamelCase__    :      Any					 =		datasets_directory







 def      A_	(      self  :      Dict	)			->	str:
     if os.path.isdir(self._tfds_path	):
         lowerCamelCase__    :      Optional[Any]					 =		os.path.abspath(self._tfds_path	)
     elif os.path.isfile(self._tfds_path	):
         lowerCamelCase__    :      List[Any]					 =		os.path.dirname(self._tfds_path	)
     else:
         raise ValueError('--tfds_path is neither a directory nor a file. Please check path.'	)

     lowerCamelCase__    :      List[Any]					 =		os.path.abspath(self._datasets_directory	)

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

     lowerCamelCase__    :      Any					 =		[]
     lowerCamelCase__    :      int					 =		[]
     lowerCamelCase__    :      List[str]					 =		{}

     if os.path.isdir(self._tfds_path	):
         lowerCamelCase__    :      Dict					 =		os.listdir(UpperCAmelCase	)
     else:
         lowerCamelCase__    :      List[Any]					 =		[os.path.basename(self._tfds_path	)]

     for f_name in file_names:
         self._logger.info(F"""Looking at file {f_name}"""	)
         lowerCamelCase__    :      int					 =		os.path.join(UpperCAmelCase			,     UpperCAmelCase	)
         lowerCamelCase__    :      int					 =		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:
             lowerCamelCase__    :      Dict					 =		f.readlines()

         lowerCamelCase__    :      List[Any]					 =		[]
         lowerCamelCase__    :      Optional[Any]					 =		False
         lowerCamelCase__    :      Optional[Any]					 =		False
         lowerCamelCase__    :      Tuple					 =		[]
         for line in lines:
             lowerCamelCase__    :      Union[str, Any]					 =		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:
                 lowerCamelCase__    :      Tuple					 =		'import datasets\n'
             elif "import tensorflow" in out_line:
                 # order is important here
                 lowerCamelCase__    :      List[Any]					 =		''
                 continue
             elif "from absl import logging" in out_line:
                 lowerCamelCase__    :      List[Any]					 =		'from datasets import logging\n'
             elif "getLogger" in out_line:
                 lowerCamelCase__    :      Tuple					 =		out_line.replace('getLogger'			,     'get_logger'	)
             elif any(expression in out_line for expression in TO_HIGHLIGHT	):
                 lowerCamelCase__    :      str					 =		True
                 lowerCamelCase__    :      str					 =		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:
                     lowerCamelCase__    :      Union[str, Any]					 =		re.sub(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)

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

         if is_builder or "wmt" in f_name:
             # We create a new directory for each dataset
             lowerCamelCase__    :      List[Any]					 =		f_name.replace('.py'			,     ''	)
             lowerCamelCase__    :      int					 =		os.path.join(UpperCAmelCase			,     UpperCAmelCase	)
             lowerCamelCase__    :      Optional[Any]					 =		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:
             lowerCamelCase__    :      List[Any]					 =		os.path.basename(UpperCAmelCase	)
             lowerCamelCase__    :      Union[str, Any]					 =		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'."""	)












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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		42


class    lowerCAmelCase							(					__UpperCamelCase,  __UpperCamelCase			):
 @register_to_config
 def __init__(      self  :      List[str]			,     UpperCAmelCase  :      int = 65536			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      int = 2			,     UpperCAmelCase  :      int = 2			,     UpperCAmelCase  :      int = 0			,     UpperCAmelCase  :      str = "fourier"			,     UpperCAmelCase  :      bool = True			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      float = 0.0			,     UpperCAmelCase  :      Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,     UpperCAmelCase  :      Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,     UpperCAmelCase  :      Tuple[str] = "UNetMidBlock1D"			,     UpperCAmelCase  :      str = None			,     UpperCAmelCase  :      Tuple[int] = (32, 32, 64)			,     UpperCAmelCase  :      str = None			,     UpperCAmelCase  :      int = 8			,     UpperCAmelCase  :      int = 1			,     UpperCAmelCase  :      bool = False			,     )			->	List[Any]:
     super().__init__()
     lowerCamelCase__    :      Optional[int]					 =		sample_size

     # time
     if time_embedding_type == "fourier":
         lowerCamelCase__    :      Optional[Any]					 =		GaussianFourierProjection(
             embedding_size=8			,     set_W_to_weight=UpperCAmelCase			,     log=UpperCAmelCase			,     flip_sin_to_cos=UpperCAmelCase	)
         lowerCamelCase__    :      Any					 =		2 * block_out_channels[0]
     elif time_embedding_type == "positional":
         lowerCamelCase__    :      List[Any]					 =		Timesteps(
             block_out_channels[0]			,     flip_sin_to_cos=UpperCAmelCase			,     downscale_freq_shift=UpperCAmelCase	)
         lowerCamelCase__    :      Dict					 =		block_out_channels[0]

     if use_timestep_embedding:
         lowerCamelCase__    :      str					 =		block_out_channels[0] * 4
         lowerCamelCase__    :      List[Any]					 =		TimestepEmbedding(
             in_channels=UpperCAmelCase			,     time_embed_dim=UpperCAmelCase			,     act_fn=UpperCAmelCase			,     out_dim=block_out_channels[0]			,     )

     lowerCamelCase__    :      Any					 =		nn.ModuleList([]	)
     lowerCamelCase__    :      Tuple					 =		None
     lowerCamelCase__    :      List[str]					 =		nn.ModuleList([]	)
     lowerCamelCase__    :      Optional[int]					 =		None

     # down
     lowerCamelCase__    :      Optional[int]					 =		in_channels
     for i, down_block_type in enumerate(UpperCAmelCase	):
         lowerCamelCase__    :      Union[str, Any]					 =		output_channel
         lowerCamelCase__    :      Tuple					 =		block_out_channels[i]

         if i == 0:
             input_channel += extra_in_channels

         lowerCamelCase__    :      Union[str, Any]					 =		i == len(UpperCAmelCase	) - 1

         lowerCamelCase__    :      Optional[int]					 =		get_down_block(
             UpperCAmelCase			,     num_layers=UpperCAmelCase			,     in_channels=UpperCAmelCase			,     out_channels=UpperCAmelCase			,     temb_channels=block_out_channels[0]			,     add_downsample=not is_final_block or downsample_each_block			,     )
         self.down_blocks.append(UpperCAmelCase	)

     # mid
     lowerCamelCase__    :      Optional[int]					 =		get_mid_block(
         UpperCAmelCase			,     in_channels=block_out_channels[-1]			,     mid_channels=block_out_channels[-1]			,     out_channels=block_out_channels[-1]			,     embed_dim=block_out_channels[0]			,     num_layers=UpperCAmelCase			,     add_downsample=UpperCAmelCase			,     )

     # up
     lowerCamelCase__    :      Optional[int]					 =		list(reversed(UpperCAmelCase	)	)
     lowerCamelCase__    :      Optional[int]					 =		reversed_block_out_channels[0]
     if out_block_type is None:
         lowerCamelCase__    :      List[str]					 =		out_channels
     else:
         lowerCamelCase__    :      Any					 =		block_out_channels[0]

     for i, up_block_type in enumerate(UpperCAmelCase	):
         lowerCamelCase__    :      Tuple					 =		output_channel
         lowerCamelCase__    :      Union[str, Any]					 =		(
             reversed_block_out_channels[i + 1] if i < len(UpperCAmelCase	) - 1 else final_upsample_channels
         )

         lowerCamelCase__    :      List[str]					 =		i == len(UpperCAmelCase	) - 1

         lowerCamelCase__    :      Dict					 =		get_up_block(
             UpperCAmelCase			,     num_layers=UpperCAmelCase			,     in_channels=UpperCAmelCase			,     out_channels=UpperCAmelCase			,     temb_channels=block_out_channels[0]			,     add_upsample=not is_final_block			,     )
         self.up_blocks.append(UpperCAmelCase	)
         lowerCamelCase__    :      int					 =		output_channel

     # out
     lowerCamelCase__    :      int					 =		norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,     32	)
     lowerCamelCase__    :      List[Any]					 =		get_out_block(
         out_block_type=UpperCAmelCase			,     num_groups_out=UpperCAmelCase			,     embed_dim=block_out_channels[0]			,     out_channels=UpperCAmelCase			,     act_fn=UpperCAmelCase			,     fc_dim=block_out_channels[-1] // 4			,     )







 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      torch.FloatTensor			,     UpperCAmelCase  :      Union[torch.Tensor, float, int]			,     UpperCAmelCase  :      bool = True			,     )			->	Union[UNetaDOutput, Tuple]:
     lowerCamelCase__    :      Optional[Any]					 =		timestep
     if not torch.is_tensor(UpperCAmelCase	):
         lowerCamelCase__    :      Optional[int]					 =		torch.tensor([timesteps]			,     dtype=torch.long			,     device=sample.device	)
     elif torch.is_tensor(UpperCAmelCase	) and len(timesteps.shape	) == 0:
         lowerCamelCase__    :      List[str]					 =		timesteps[None].to(sample.device	)

     lowerCamelCase__    :      Optional[int]					 =		self.time_proj(UpperCAmelCase	)
     if self.config.use_timestep_embedding:
         lowerCamelCase__    :      str					 =		self.time_mlp(UpperCAmelCase	)
     else:
         lowerCamelCase__    :      List[str]					 =		timestep_embed[..., None]
         lowerCamelCase__    :      str					 =		timestep_embed.repeat([1, 1, sample.shape[2]]	).to(sample.dtype	)
         lowerCamelCase__    :      str					 =		timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])	)

     # 2. down
     lowerCamelCase__    :      str					 =		()
     for downsample_block in self.down_blocks:
         lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		downsample_block(hidden_states=UpperCAmelCase			,     temb=UpperCAmelCase	)
         down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
         lowerCamelCase__    :      Optional[Any]					 =		self.mid_block(UpperCAmelCase			,     UpperCAmelCase	)

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks	):
         lowerCamelCase__    :      Dict					 =		down_block_res_samples[-1:]
         lowerCamelCase__    :      Optional[Any]					 =		down_block_res_samples[:-1]
         lowerCamelCase__    :      Any					 =		upsample_block(UpperCAmelCase			,     res_hidden_states_tuple=UpperCAmelCase			,     temb=UpperCAmelCase	)

     # 5. post-process
     if self.out_block:
         lowerCamelCase__    :      Any					 =		self.out_block(UpperCAmelCase			,     UpperCAmelCase	)

     if not return_dict:
         return (sample,)

     return UNetaDOutput(sample=UpperCAmelCase	)





