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

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


SCREAMING_SNAKE_CASE			:						int				  =		"""<<<<<<< This should probably be modified because it mentions: """

SCREAMING_SNAKE_CASE			:						Tuple				  =		"""=======
>>>>>>>
"""

SCREAMING_SNAKE_CASE			:						List[Any]				  =		[
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

SCREAMING_SNAKE_CASE			:						Any				  =		[
    # (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		(      _A       ):



 """simple docstring"""


 return ConvertCommand(args.tfds_path				,					args.datasets_directory       )






class  A_			(							a_			):

  @staticmethod
  def 		_UpperCAmelCase (		__SCREAMING_SNAKE_CASE			:   ArgumentParser							):
   __a							=	parser.add_parser(
       "convert"  ,				help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."  ,				)
   train_parser.add_argument(
       "--tfds_path"  ,				type=__SCREAMING_SNAKE_CASE  ,				required=__SCREAMING_SNAKE_CASE  ,				help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."  ,				)
   train_parser.add_argument(
       "--datasets_directory"  ,				type=__SCREAMING_SNAKE_CASE  ,				required=__SCREAMING_SNAKE_CASE  ,				help="Path to the HuggingFace Datasets folder."							)
   train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE							)

  def __init__(		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   str  ,				*__SCREAMING_SNAKE_CASE			:   int							):
   __a							=	get_logger("datasets-cli/converting"							)

   __a							=	tfds_path
   __a							=	datasets_directory

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   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(__SCREAMING_SNAKE_CASE							)
   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(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

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

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

                # 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]+)"  ,				__SCREAMING_SNAKE_CASE							)
      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(__SCREAMING_SNAKE_CASE							)

    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(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
     __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
     os.makedirs(__SCREAMING_SNAKE_CASE  ,				exist_ok=__SCREAMING_SNAKE_CASE							)
     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(__SCREAMING_SNAKE_CASE							)

    if needs_manual_update:
     with_manual_update.append(__SCREAMING_SNAKE_CASE							)

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

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



import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE			:						Dict				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Tuple				  =		{
    """google/owlvit-base-patch32""": """https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json""",
    """google/owlvit-base-patch16""": """https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json""",
    """google/owlvit-large-patch14""": """https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json""",
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """owlvit_text_model"""

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=4_94_08  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=5_12  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=20_48  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=12  ,				__SCREAMING_SNAKE_CASE			:   Tuple=8  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=16  ,				__SCREAMING_SNAKE_CASE			:   Tuple="quick_gelu"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Any=0.0  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.02  ,				__SCREAMING_SNAKE_CASE			:   int=1.0  ,				__SCREAMING_SNAKE_CASE			:   int=0  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4_94_06  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4_94_07  ,				**__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   __a							=	vocab_size
   __a							=	hidden_size
   __a							=	intermediate_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	max_position_embeddings
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	attention_dropout
   __a							=	initializer_range
   __a							=	initializer_factor

  @classmethod
  def 		_UpperCAmelCase (		cls			:   int  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # get the text config dict if we are loading from OwlViTConfig
   if config_dict.get("model_type"							) == "owlvit":
    __a							=	config_dict["text_config"]

   if "model_type" in config_dict and hasattr(cls  ,				"model_type"							) and config_dict["model_type"] != cls.model_type:
    logger.warning(
        f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
        f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """owlvit_vision_model"""

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Any=30_72  ,				__SCREAMING_SNAKE_CASE			:   str=12  ,				__SCREAMING_SNAKE_CASE			:   int=12  ,				__SCREAMING_SNAKE_CASE			:   str=3  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=7_68  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=32  ,				__SCREAMING_SNAKE_CASE			:   Dict="quick_gelu"  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   int=0.0  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Dict=1.0  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	hidden_size
   __a							=	intermediate_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	num_channels
   __a							=	image_size
   __a							=	patch_size
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	attention_dropout
   __a							=	initializer_range
   __a							=	initializer_factor

  @classmethod
  def 		_UpperCAmelCase (		cls			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   Tuple							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # get the vision config dict if we are loading from OwlViTConfig
   if config_dict.get("model_type"							) == "owlvit":
    __a							=	config_dict["vision_config"]

   if "model_type" in config_dict and hasattr(cls  ,				"model_type"							) and config_dict["model_type"] != cls.model_type:
    logger.warning(
        f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
        f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """owlvit"""
  _SCREAMING_SNAKE_CASE     					=      True

  def __init__(		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				__SCREAMING_SNAKE_CASE			:   str=5_12  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=2.65_92  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   if text_config is None:
    __a							=	{}
    logger.info("text_config is None. Initializing the OwlViTTextConfig with default values."							)

   if vision_config is None:
    __a							=	{}
    logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values."							)

   __a							=	OwlViTTextConfig(**__SCREAMING_SNAKE_CASE							)
   __a							=	OwlViTVisionConfig(**__SCREAMING_SNAKE_CASE							)

   __a							=	projection_dim
   __a							=	logit_scale_init_value
   __a							=	return_dict
   __a							=	1.0

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   List[str]							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if "model_type" in config_dict and hasattr(cls  ,				"model_type"							) and config_dict["model_type"] != cls.model_type:
    logger.warning(
        f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
        f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  @classmethod
  def 		_UpperCAmelCase (		cls			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				**__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   __a							=	{}
   __a							=	text_config
   __a							=	vision_config

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	copy.deepcopy(self.__dict__							)
   __a							=	self.text_config.to_dict()
   __a							=	self.vision_config.to_dict()
   __a							=	self.__class__.model_type
   return output
class  A_			(							a_			):

  @property
  def 		_UpperCAmelCase (		self			:   Any							):
   return OrderedDict(
       [
           ("input_ids", {0: "batch", 1: "sequence"}),
           ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ("attention_mask", {0: "batch", 1: "sequence"}),
       ]							)

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   return OrderedDict(
       [
           ("logits_per_image", {0: "batch"}),
           ("logits_per_text", {0: "batch"}),
           ("text_embeds", {0: "batch"}),
           ("image_embeds", {0: "batch"}),
       ]							)

  @property
  def 		_UpperCAmelCase (		self			:   str							):
   return 1E-4

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   "ProcessorMixin"  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   Optional["TensorType"] = None  ,				):
   __a							=	super().generate_dummy_inputs(
       processor.tokenizer  ,				batch_size=__SCREAMING_SNAKE_CASE  ,				seq_length=__SCREAMING_SNAKE_CASE  ,				framework=__SCREAMING_SNAKE_CASE							)
   __a							=	super().generate_dummy_inputs(
       processor.image_processor  ,				batch_size=__SCREAMING_SNAKE_CASE  ,				framework=__SCREAMING_SNAKE_CASE							)
   return {**text_input_dict, **image_input_dict}

  @property
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   return 14


