



"""simple docstring"""







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

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


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

UpperCamelCase     =							"""=======
>>>>>>>
"""

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

UpperCamelCase     =							[
    # (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				(							UpperCAmelCase_ :       Namespace     )	-> Dict:






	"""simple docstring"""





	return ConvertCommand(args.tfds_path,	args.datasets_directory     )






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			@staticmethod
			def   snake_case__	(      SCREAMING_SNAKE_CASE__		)      ->	Union[str, Any]:
				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						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	*SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					get_logger("datasets-cli/converting"		)

				A__											=					tfds_path
				A__											=					datasets_directory







			def   snake_case__	(      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'."""		)









"""simple docstring"""







import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase     =							logging.get_logger()

def       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       str,	UpperCAmelCase_ :       LevitConfig,	UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       bool = True     )	-> int:






	"""simple docstring"""





	print(F"""Converting {name}..."""     )

	with torch.no_grad():
		if hidden_sizes == 128:
			if name[-1] == "S":
				A__											=					timm.create_model("levit_128s",	pretrained=UpperCAmelCase_     )
			else:
				A__											=					timm.create_model("levit_128",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 192:
			A__											=					timm.create_model("levit_192",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 256:
			A__											=					timm.create_model("levit_256",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 384:
			A__											=					timm.create_model("levit_384",	pretrained=UpperCAmelCase_     )

		from_model.eval()
		A__											=					LevitForImageClassificationWithTeacher(UpperCAmelCase_     ).eval()
		A__											=					OrderedDict()

		A__											=					from_model.state_dict()
		A__											=					list(from_model.state_dict().keys()     )
		A__											=					list(our_model.state_dict().keys()     )
		print(len(UpperCAmelCase_     ),	len(UpperCAmelCase_     )     )
		for i in range(len(UpperCAmelCase_     )     ):
			A__											=					weights[og_keys[i]]
		our_model.load_state_dict(UpperCAmelCase_     )

		A__											=					torch.randn((2, 3, 224, 224)     )
		A__											=					from_model(UpperCAmelCase_     )
		A__											=					our_model(UpperCAmelCase_     ).logits

	assert torch.allclose(UpperCAmelCase_,	UpperCAmelCase_     ), "The model logits don't match the original one."

	A__											=					name
	print(UpperCAmelCase_     )

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name     )
		A__											=					LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name     )

		print(F"""Pushed {checkpoint_name}"""     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       str = None,	UpperCAmelCase_ :       bool = True     )	-> Union[str, Any]:






	"""simple docstring"""





	A__											=					"imagenet-1k-id2label.json"
	A__											=					1000
	A__											=					(1, num_labels)

	A__											=					"huggingface/label-files"
	A__											=					num_labels
	A__											=					json.load(open(hf_hub_download(UpperCAmelCase_,	UpperCAmelCase_,	repo_type="dataset"     ),	"r"     )     )
	A__											=					{int(UpperCAmelCase_     ): v for k, v in idalabel.items()}

	A__											=					idalabel
	A__											=					{v: k for k, v in idalabel.items()}

	A__											=					partial(UpperCAmelCase_,	num_labels=UpperCAmelCase_,	idalabel=UpperCAmelCase_,	labelaid=UpperCAmelCase_     )

	A__											=					{
	    "levit-128S": 128,
	    "levit-128": 128,
	    "levit-192": 192,
	    "levit-256": 256,
	    "levit-384": 384,
	}

	A__											=					{
	    "levit-128S": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384],	num_attention_heads=[4, 6, 8],	depths=[2, 3, 4],	key_dim=[16, 16, 16],	drop_path_rate=0,	),
	    "levit-128": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384],	num_attention_heads=[4, 8, 12],	depths=[4, 4, 4],	key_dim=[16, 16, 16],	drop_path_rate=0,	),
	    "levit-192": ImageNetPreTrainedConfig(
	        hidden_sizes=[192, 288, 384],	num_attention_heads=[3, 5, 6],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0,	),
	    "levit-256": ImageNetPreTrainedConfig(
	        hidden_sizes=[256, 384, 512],	num_attention_heads=[4, 6, 8],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0,	),
	    "levit-384": ImageNetPreTrainedConfig(
	        hidden_sizes=[384, 512, 768],	num_attention_heads=[6, 9, 12],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0.1,	),
	}

	if model_name:
		convert_weight_and_push(
		    names_to_hidden_sizes[model_name],	UpperCAmelCase_,	names_to_config[model_name],	UpperCAmelCase_,	UpperCAmelCase_     )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name],	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
	return config, expected_shape


if __name__ == "__main__":
			UpperCamelCase     =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--model_name""",
			    default=None,
			    type=str,
			    help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""",
			    default="""levit-dump-folder/""",
			    type=Path,
			    required=False,
			    help="""Path to the output PyTorch model directory.""",
			)
			parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
			parser.add_argument(
			    """--no-push_to_hub""",
			    dest="""push_to_hub""",
			    action="""store_false""",
			    help="""Do not push model and image processor to the hub""",
			)

			UpperCamelCase     =							parser.parse_args()
			UpperCamelCase     =							args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




