



import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


lowercase_								=     {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class 				_snake_case			(					nn.Module):




    def __init__(						self					:						List[Any],					__lowercase					:						List[Any]						):
       super().__init__()
       lowercase__					  =				torchvision.models.resnetaaa(pretrained=__lowercase						)
       lowercase__					  =				list(model.children()						)[:-2]
       lowercase__					  =				nn.Sequential(*__lowercase						)
       lowercase__					  =				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]						)




    def 							A__      (						self					:						Union[str, Any],					__lowercase					:						Dict						):
       # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
       lowercase__					  =				self.pool(self.model(__lowercase						)						)
       lowercase__					  =				torch.flatten(__lowercase,					start_dim=2						)
       lowercase__					  =				out.transpose(1,					2						).contiguous()
       return out  # BxNx2048
class 				_snake_case			(					lowercase__):




    def __init__(						self					:						Any,					__lowercase					:						List[Any],					__lowercase					:						Optional[Any],					__lowercase					:						Tuple,					__lowercase					:						Optional[Any],					__lowercase					:						Optional[int]						):
       lowercase__					  =				[json.loads(__lowercase						) for l in open(__lowercase						)]
       lowercase__					  =				os.path.dirname(__lowercase						)
       lowercase__					  =				tokenizer
       lowercase__					  =				labels
       lowercase__					  =				len(__lowercase						)
       lowercase__					  =				max_seq_length

       lowercase__					  =				transforms




    def __len__(						self					:						str						):
       return len(self.data						)




    def __getitem__(						self					:						List[str],					__lowercase					:						Tuple						):
       lowercase__					  =				torch.LongTensor(self.tokenizer.encode(self.data[index]["text"],					add_special_tokens=__lowercase						)						)
       lowercase__  ,       lowercase__  ,       lowercase__					  =				sentence[0], sentence[1:-1], sentence[-1]
       lowercase__					  =				sentence[: self.max_seq_length]

       lowercase__					  =				torch.zeros(self.n_classes						)
       lowercase__					  =				1

       lowercase__					  =				Image.open(os.path.join(self.data_dir,					self.data[index]["img"]						)						).convert("RGB"						)
       lowercase__					  =				self.transforms(__lowercase						)

       return {
           "image_start_token": start_token,
           "image_end_token": end_token,
           "sentence": sentence,
           "image": image,
           "label": label,
       }




    def 							A__      (						self					:						Any						):
       lowercase__					  =				Counter()
       for row in self.data:
          label_freqs.update(row["label"]						)
       return label_freqs



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
   lowercase__					  =				[len(row["sentence"]  ) for row in batch]
   lowercase__  ,       lowercase__					  =				len(SCREAMING_SNAKE_CASE_  ), max(SCREAMING_SNAKE_CASE_  )

   lowercase__					  =				torch.zeros(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  dtype=torch.long  )
   lowercase__					  =				torch.zeros(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  dtype=torch.long  )

   for i_batch, (input_row, length) in enumerate(zip(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )  ):
      lowercase__					  =				input_row["sentence"]
      lowercase__					  =				1

   lowercase__					  =				torch.stack([row["image"] for row in batch]  )
   lowercase__					  =				torch.stack([row["label"] for row in batch]  )
   lowercase__					  =				torch.stack([row["image_start_token"] for row in batch]  )
   lowercase__					  =				torch.stack([row["image_end_token"] for row in batch]  )

   return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor



def __lowerCAmelCase (     ):
   return [
       "Crime",
       "Drama",
       "Thriller",
       "Action",
       "Comedy",
       "Romance",
       "Documentary",
       "Short",
       "Mystery",
       "History",
       "Family",
       "Adventure",
       "Fantasy",
       "Sci-Fi",
       "Western",
       "Horror",
       "Sport",
       "War",
       "Music",
       "Musical",
       "Animation",
       "Biography",
       "Film-Noir",
   ]




def __lowerCAmelCase (     ):
   return transforms.Compose(
       [
           transforms.Resize(256  ),
           transforms.CenterCrop(224  ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]						,  std=[0.1222_1994, 0.1214_5835, 0.1438_0469]						,  ),
       ]  )









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

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


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

lowercase_								=     """=======
>>>>>>>
"""

lowercase_								=     [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

lowercase_								=     [
    # (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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			return ConvertCommand(args.tfds_path						,  args.datasets_directory  )




class 				_snake_case			(					lowercase__):




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




				def __init__(						self					:						Tuple,					__lowercase					:						str,					__lowercase					:						str,					*__lowercase					:						Tuple						):
							lowercase__					  =				get_logger("datasets-cli/converting"						)

							lowercase__					  =				tfds_path
							lowercase__					  =				datasets_directory




				def 							A__      (						self					:						Any						):
							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(__lowercase						)
							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(__lowercase,					__lowercase						)
										lowercase__					  =				os.path.join(__lowercase,					__lowercase						)

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

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

                # 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]+)",					__lowercase						)
																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(__lowercase						)

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

										if needs_manual_update:
													with_manual_update.append(__lowercase						)

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

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




