

import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			Optional[int] )					->				str:
	A__							:   Union[str, Any]					       =		old_name

	if "patch_embed" in old_name:
		A__       ,       A__       ,       A__							:   Any					       =		old_name.split(""".""" )

		if layer == "0":
			A__							:   List[Any]					       =		old_name.replace("""0"""   ,				"""convolution1""" )
		elif layer == "1":
			A__							:   Optional[int]					       =		old_name.replace("""1"""   ,				"""batchnorm_before""" )
		elif layer == "3":
			A__							:   int					       =		old_name.replace("""3"""   ,				"""convolution2""" )
		else:
			A__							:   Dict					       =		old_name.replace("""4"""   ,				"""batchnorm_after""" )

	if "network" in old_name and re.search(r"""\d\.\d"""   ,				lowercase_ ):
		A__							:   str					       =		r"""\b\d{2}\b"""
		if bool(re.search(lowercase_   ,				lowercase_ ) ):
			A__							:   Optional[Any]					       =		re.search(r"""\d\.\d\d."""   ,				lowercase_ ).group()
		else:
			A__							:   int					       =		re.search(r"""\d\.\d."""   ,				lowercase_ ).group()
		if int(match[0] ) < 6:
			A__							:   Optional[Any]					       =		old_name.replace(lowercase_   ,				"""""" )
			A__							:   Tuple					       =		trimmed_name.replace("""network"""   ,				match[0] + """.meta4D_layers.blocks.""" + match[2:-1] )
			A__							:   int					       =		"""intermediate_stages.""" + trimmed_name
		else:
			A__							:   Dict					       =		old_name.replace(lowercase_   ,				"""""" )
			if int(match[2] ) < num_meta4D_last_stage:
				A__							:   Optional[int]					       =		trimmed_name.replace("""network"""   ,				"""meta4D_layers.blocks.""" + match[2] )
			else:
				A__							:   Optional[Any]					       =		str(int(match[2] ) - num_meta4D_last_stage )
				A__							:   Dict					       =		trimmed_name.replace("""network"""   ,				"""meta3D_layers.blocks.""" + layer_index )
				if "norm1" in old_name:
					A__							:   str					       =		trimmed_name.replace("""norm1"""   ,				"""layernorm1""" )
				elif "norm2" in old_name:
					A__							:   Optional[int]					       =		trimmed_name.replace("""norm2"""   ,				"""layernorm2""" )
				elif "fc1" in old_name:
					A__							:   List[Any]					       =		trimmed_name.replace("""fc1"""   ,				"""linear_in""" )
				elif "fc2" in old_name:
					A__							:   Optional[Any]					       =		trimmed_name.replace("""fc2"""   ,				"""linear_out""" )

			A__							:   str					       =		"""last_stage.""" + trimmed_name

	elif "network" in old_name and re.search(r""".\d."""   ,				lowercase_ ):
		A__							:   List[str]					       =		old_name.replace("""network"""   ,				"""intermediate_stages""" )

	if "fc" in new_name:
		A__							:   Optional[int]					       =		new_name.replace("""fc"""   ,				"""convolution""" )
	elif ("norm1" in new_name) and ("layernorm1" not in new_name):
		A__							:   Optional[int]					       =		new_name.replace("""norm1"""   ,				"""batchnorm_before""" )
	elif ("norm2" in new_name) and ("layernorm2" not in new_name):
		A__							:   int					       =		new_name.replace("""norm2"""   ,				"""batchnorm_after""" )
	if "proj" in new_name:
		A__							:   Tuple					       =		new_name.replace("""proj"""   ,				"""projection""" )
	if "dist_head" in new_name:
		A__							:   Optional[int]					       =		new_name.replace("""dist_head"""   ,				"""distillation_classifier""" )
	elif "head" in new_name:
		A__							:   Optional[Any]					       =		new_name.replace("""head"""   ,				"""classifier""" )
	elif "patch_embed" in new_name:
		A__							:   Optional[Any]					       =		"""efficientformer.""" + new_name
	elif new_name == "norm.weight" or new_name == "norm.bias":
		A__							:   Union[str, Any]					       =		new_name.replace("""norm"""   ,				"""layernorm""" )
		A__							:   Union[str, Any]					       =		"""efficientformer.""" + new_name
	else:
		A__							:   int					       =		"""efficientformer.encoder.""" + new_name

	return new_name

def 						UpperCamelCase (lowercase_:			Optional[Any]   ,				lowercase_:			Union[str, Any] )					->				Tuple:
	for key in checkpoint.copy().keys():
		A__							:   List[Any]					       =		checkpoint.pop(lowercase_ )
		A__							:   Dict					       =		val

	return checkpoint

def 						UpperCamelCase ()					->				Optional[int]:
	A__							:   List[str]					       =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	A__							:   List[str]					       =		Image.open(requests.get(lowercase_   ,				stream=lowercase_ ).raw )

	return image

def 						UpperCamelCase (lowercase_:			Path   ,				lowercase_:			Path   ,				lowercase_:			Path   ,				lowercase_:			bool )					->				Tuple:
	A__							:   Any					       =		torch.load(lowercase_   ,				map_location="""cpu""" )["""model"""]
	A__							:   List[Any]					       =		EfficientFormerConfig.from_json_file(lowercase_ )
	A__							:   Any					       =		EfficientFormerForImageClassificationWithTeacher(lowercase_ )
	A__							:   List[str]					       =		"""_""".join(checkpoint_path.split("""/""" )[-1].split(""".""" )[0].split("""_""" )[:-1] )

	A__							:   Union[str, Any]					       =		config.depths[-1] - config.num_metaad_blocks + 1
	A__							:   Any					       =		convert_torch_checkpoint(lowercase_   ,				lowercase_ )

	model.load_state_dict(lowercase_ )
	model.eval()

	A__							:   Tuple					       =		{
	    """bilinear""": PILImageResampling.BILINEAR,
	    """bicubic""": PILImageResampling.BICUBIC,
	    """nearest""": PILImageResampling.NEAREST,
	}

	# prepare image
	A__							:   Optional[int]					       =		prepare_img()
	A__							:   Optional[Any]					       =		256
	A__							:   str					       =		224
	A__							:   List[str]					       =		EfficientFormerImageProcessor(
	    size={"""shortest_edge""": image_size}   ,				crop_size={"""height""": crop_size, """width""": crop_size}   ,				resample=pillow_resamplings["""bicubic"""]   ,				)
	A__							:   Tuple					       =		processor(images=lowercase_   ,				return_tensors="""pt""" ).pixel_values

	# original processing pipeline
	A__							:   List[Any]					       =		Compose(
	    [
	        Resize(lowercase_   ,				interpolation=pillow_resamplings["""bicubic"""] ),
	        CenterCrop(lowercase_ ),
	        ToTensor(),
	        Normalize(lowercase_   ,				lowercase_ ),
	    ] )
	A__							:   Any					       =		image_transforms(lowercase_ ).unsqueeze(0 )

	assert torch.allclose(lowercase_   ,				lowercase_ )

	A__							:   Optional[int]					       =		model(lowercase_ )
	A__							:   List[str]					       =		outputs.logits

	A__							:   Tuple					       =		(1, 1000)

	if "l1" in model_name:
		A__							:   List[str]					       =		torch.Tensor(
		    [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328] )
		assert torch.allclose(logits[0, :10]   ,				lowercase_   ,				atol=1E-3 )
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		A__							:   Any					       =		torch.Tensor(
		    [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127] )
		assert torch.allclose(logits[0, :10]   ,				lowercase_   ,				atol=1E-3 )
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		A__							:   Union[str, Any]					       =		torch.Tensor(
		    [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878] )
		assert logits.shape == expected_shape
	else:
		raise ValueError(
		    f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7""" )

	# Save Checkpoints
	Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
	model.save_pretrained(lowercase_ )
	print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
	processor.save_pretrained(lowercase_ )
	print(f"""Processor successfuly saved at {pytorch_dump_path}""" )

	if push_to_hub:
		print("""Pushing model to the hub...""" )

		model.push_to_hub(
		    repo_id=f"""Bearnardd/{pytorch_dump_path}"""   ,				commit_message="""Add model"""   ,				use_temp_dir=lowercase_   ,				)
		processor.push_to_hub(
		    repo_id=f"""Bearnardd/{pytorch_dump_path}"""   ,				commit_message="""Add image processor"""   ,				use_temp_dir=lowercase_   ,				)


if __name__ == "__main__":
	A_	:			List[str]  	=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--pytorch_model_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to EfficientFormer pytorch checkpoint.',
	)
	parser.add_argument(
	    '--config_file',
	    default=None,
	    type=str,
	    required=True,
	    help='The json file for EfficientFormer model config.',
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)

	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',
	)
	parser.set_defaults(push_to_hub=True)

	A_	:			List[Any]  	=      parser.parse_args()
	convert_efficientformer_checkpoint(
	    checkpoint_path=args.pytorch_model_path,
	    efficientformer_config_file=args.config_file,
	    pytorch_dump_path=args.pytorch_dump_path,
	    push_to_hub=args.push_to_hub,
	)






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

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


A_	:			Optional[int]  	=      '<<<<<<< This should probably be modified because it mentions: '

A_	:			int  	=      '=======\n>>>>>>>\n'

A_	:			Optional[Any]  	=      [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

A_	:			List[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 						UpperCamelCase (lowercase_:			Namespace )					->				str:
	return ConvertCommand(args.tfds_path   ,				args.datasets_directory )



class    _a						(__magic_name__    ):


					'''simple docstring'''






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






					def __init__(			self   ,	A__   ,	A__   ,	*A__      ):
						A__							:   Optional[Any]					       =		get_logger("""datasets-cli/converting"""      )

						A__							:   Optional[Any]					       =		tfds_path
						A__							:   Dict					       =		datasets_directory






					def   __A      (			self      ):
						if os.path.isdir(self._tfds_path      ):
							A__							:   Any					       =		os.path.abspath(self._tfds_path      )
						elif os.path.isfile(self._tfds_path      ):
							A__							:   int					       =		os.path.dirname(self._tfds_path      )
						else:
							raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""      )

						A__							:   Optional[int]					       =		os.path.abspath(self._datasets_directory      )

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

						A__							:   Optional[Any]					       =		[]
						A__							:   List[Any]					       =		[]
						A__							:   List[Any]					       =		{}

						if os.path.isdir(self._tfds_path      ):
							A__							:   List[str]					       =		os.listdir(A__      )
						else:
							A__							:   Optional[Any]					       =		[os.path.basename(self._tfds_path      )]

						for f_name in file_names:
							self._logger.info(F"""Looking at file {f_name}"""      )
							A__							:   Any					       =		os.path.join(A__   ,	A__      )
							A__							:   Optional[Any]					       =		os.path.join(A__   ,	A__      )

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

							with open(A__   ,	encoding="""utf-8"""      ) as f:
								A__							:   Dict					       =		f.readlines()

							A__							:   Tuple					       =		[]
							A__							:   str					       =		False
							A__							:   Tuple					       =		False
							A__							:   Optional[Any]					       =		[]
							for line in lines:
								A__							:   Optional[int]					       =		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__							:   Dict					       =		"""import datasets\n"""
								elif "import tensorflow" in out_line:
									# order is important here
									A__							:   Any					       =		""""""
									continue
								elif "from absl import logging" in out_line:
									A__							:   int					       =		"""from datasets import logging\n"""
								elif "getLogger" in out_line:
									A__							:   List[str]					       =		out_line.replace("""getLogger"""   ,	"""get_logger"""      )
								elif any(expression in out_line for expression in TO_HIGHLIGHT      ):
									A__							:   Optional[Any]					       =		True
									A__							:   int					       =		list(filter(lambda A__      : e in out_line   ,	A__      )      )
									out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(A__      ) + """\n"""      )
									out_lines.append(A__      )
									out_lines.append(A__      )
									continue
								else:
									for pattern, replacement in TO_CONVERT:
										A__							:   List[str]					       =		re.sub(A__   ,	A__   ,	A__      )

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

							if is_builder or "wmt" in f_name:
								# We create a new directory for each dataset
								A__							:   Tuple					       =		f_name.replace(""".py"""   ,	""""""      )
								A__							:   Optional[int]					       =		os.path.join(A__   ,	A__      )
								A__							:   Optional[Any]					       =		os.path.join(A__   ,	A__      )
								os.makedirs(A__   ,	exist_ok=A__      )
								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(A__      )

							if needs_manual_update:
								with_manual_update.append(A__      )

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

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



