

import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


A_	:			int  	=      datasets.utils.logging.get_logger(__name__)

A_	:			Dict  	=      ['names', 'prefix']
A_	:			Any  	=      ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
A_	:			Optional[Any]  	=      ['encoding_errors', 'on_bad_lines']
A_	:			str  	=      ['date_format']



@dataclass
class    _a						(datasets.BuilderConfig    ):


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	","
					UpperCAmelCase__:       Optional[str]  							=	None
					UpperCAmelCase__:       Optional[Union[int, List[int], str]]  							=	"infer"
					UpperCAmelCase__:       Optional[List[str]]  							=	None
					UpperCAmelCase__:       Optional[List[str]]  							=	None
					UpperCAmelCase__:       Optional[Union[int, str, List[int], List[str]]]  							=	None
					UpperCAmelCase__:       Optional[Union[List[int], List[str]]]  							=	None
					UpperCAmelCase__:       Optional[str]  							=	None
					UpperCAmelCase__:       bool  							=	True
					UpperCAmelCase__:       Optional[Literal["c", "python", "pyarrow"]]  							=	None
					UpperCAmelCase__:       Dict[Union[int, str], Callable[[Any], Any]]  							=	None
					UpperCAmelCase__:       Optional[list]  							=	None
					UpperCAmelCase__:       Optional[list]  							=	None
					UpperCAmelCase__:       bool  							=	False
					UpperCAmelCase__:       Optional[Union[int, List[int]]]  							=	None
					UpperCAmelCase__:       Optional[int]  							=	None
					UpperCAmelCase__:       Optional[Union[str, List[str]]]  							=	None
					UpperCAmelCase__:       bool  							=	True
					UpperCAmelCase__:       bool  							=	True
					UpperCAmelCase__:       bool  							=	False
					UpperCAmelCase__:       bool  							=	True
					UpperCAmelCase__:       Optional[str]  							=	None
					UpperCAmelCase__:       str  							=	"."
					UpperCAmelCase__:       Optional[str]  							=	None
					UpperCAmelCase__:       str  							=	'"'
					UpperCAmelCase__:       int  							=	0
					UpperCAmelCase__:       Optional[str]  							=	None
					UpperCAmelCase__:       Optional[str]  							=	None
					UpperCAmelCase__:       Optional[str]  							=	None
					UpperCAmelCase__:       Optional[str]  							=	None
					UpperCAmelCase__:       bool  							=	True
					UpperCAmelCase__:       bool  							=	True
					UpperCAmelCase__:       int  							=	0
					UpperCAmelCase__:       bool  							=	True
					UpperCAmelCase__:       bool  							=	False
					UpperCAmelCase__:       Optional[str]  							=	None
					UpperCAmelCase__:       int  							=	1_00_00
					UpperCAmelCase__:       Optional[datasets.Features]  							=	None
					UpperCAmelCase__:       Optional[str]  							=	"strict"
					UpperCAmelCase__:       Literal["error", "warn", "skip"]  							=	"error"
					UpperCAmelCase__:       Optional[str]  							=	None






					def   __A      (			self      ):
						if self.delimiter is not None:
							A__							:   int					       =		self.delimiter
						if self.column_names is not None:
							A__							:   Any					       =		self.column_names






					@property
					def   __A      (			self      ):
						A__							:   Union[str, Any]					       =		{
						    """sep""": self.sep,
						    """header""": self.header,
						    """names""": self.names,
						    """index_col""": self.index_col,
						    """usecols""": self.usecols,
						    """prefix""": self.prefix,
						    """mangle_dupe_cols""": self.mangle_dupe_cols,
						    """engine""": self.engine,
						    """converters""": self.converters,
						    """true_values""": self.true_values,
						    """false_values""": self.false_values,
						    """skipinitialspace""": self.skipinitialspace,
						    """skiprows""": self.skiprows,
						    """nrows""": self.nrows,
						    """na_values""": self.na_values,
						    """keep_default_na""": self.keep_default_na,
						    """na_filter""": self.na_filter,
						    """verbose""": self.verbose,
						    """skip_blank_lines""": self.skip_blank_lines,
						    """thousands""": self.thousands,
						    """decimal""": self.decimal,
						    """lineterminator""": self.lineterminator,
						    """quotechar""": self.quotechar,
						    """quoting""": self.quoting,
						    """escapechar""": self.escapechar,
						    """comment""": self.comment,
						    """encoding""": self.encoding,
						    """dialect""": self.dialect,
						    """error_bad_lines""": self.error_bad_lines,
						    """warn_bad_lines""": self.warn_bad_lines,
						    """skipfooter""": self.skipfooter,
						    """doublequote""": self.doublequote,
						    """memory_map""": self.memory_map,
						    """float_precision""": self.float_precision,
						    """chunksize""": self.chunksize,
						    """encoding_errors""": self.encoding_errors,
						    """on_bad_lines""": self.on_bad_lines,
						    """date_format""": self.date_format,
						}

						# some kwargs must not be passed if they don't have a default value
						# some others are deprecated and we can also not pass them if they are the default value
						for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
							if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()   ,	A__      ):
								del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
						if not (datasets.config.PANDAS_VERSION.major >= 2):
							for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
								del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
						if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
							for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
								del pd_read_csv_kwargs[pd_read_csv_parameter]

						return pd_read_csv_kwargs



class    _a						(datasets.ArrowBasedBuilder    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[int]  							=	CsvConfig






					def   __A      (			self      ):
						return datasets.DatasetInfo(features=self.config.features      )






					def   __A      (			self   ,	A__      ):
						if not self.config.data_files:
							raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""      )
						A__							:   Tuple					       =		dl_manager.download_and_extract(self.config.data_files      )
						if isinstance(A__   ,	(str, list, tuple)      ):
							A__							:   Any					       =		data_files
							if isinstance(A__   ,	A__      ):
								A__							:   int					       =		[files]
							A__							:   str					       =		[dl_manager.iter_files(A__      ) for file in files]
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,	gen_kwargs={"""files""": files}      )]
						A__							:   Any					       =		[]
						for split_name, files in data_files.items():
							if isinstance(A__   ,	A__      ):
								A__							:   Dict					       =		[files]
							A__							:   Tuple					       =		[dl_manager.iter_files(A__      ) for file in files]
							splits.append(datasets.SplitGenerator(name=A__   ,	gen_kwargs={"""files""": files}      )      )
						return splits






					def   __A      (			self   ,	A__      ):
						if self.config.features is not None:
							A__							:   Optional[int]					       =		self.config.features.arrow_schema
							if all(not require_storage_cast(A__      ) for feature in self.config.features.values()      ):
								# cheaper cast
								A__							:   str					       =		pa.Table.from_arrays([pa_table[field.name] for field in schema]   ,	schema=A__      )
							else:
								# more expensive cast; allows str <-> int/float or str to Audio for example
								A__							:   Dict					       =		table_cast(A__   ,	A__      )
						return pa_table






					def   __A      (			self   ,	A__      ):
						A__							:   int					       =		self.config.features.arrow_schema if self.config.features else None
						# dtype allows reading an int column as str
						A__							:   int					       =		(
						    {
						        name: dtype.to_pandas_dtype() if not require_storage_cast(A__      ) else object
						        for name, dtype, feature in zip(schema.names   ,	schema.types   ,	self.config.features.values()      )
						    }
						    if schema is not None
						    else None
						)
						for file_idx, file in enumerate(itertools.chain.from_iterable(A__      )      ):
							A__							:   Optional[Any]					       =		pd.read_csv(A__   ,	iterator=A__   ,	dtype=A__   ,	**self.config.pd_read_csv_kwargs      )
							try:
								for batch_idx, df in enumerate(A__      ):
									A__							:   int					       =		pa.Table.from_pandas(A__      )
									# Uncomment for debugging (will print the Arrow table size and elements)
									# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
									# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
									yield (file_idx, batch_idx), self._cast_table(A__      )
							except ValueError as e:
								logger.error(F"""Failed to read file '{file}' with error {type(A__      )}: {e}"""      )
								raise






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,
	)



