





import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__  :	Union[str, Any]     =logging.get_logger(__name__)





def    _lowercase    (  _UpperCAmelCase )  ->     Any:
				lowerCamelCase		       =ConvNextVaConfig()

				if "atto" in checkpoint_url:
								lowerCamelCase		       =[2, 2, 6, 2]
								lowerCamelCase		       =[40, 80, 1_60, 3_20]
				if "femto" in checkpoint_url:
								lowerCamelCase		       =[2, 2, 6, 2]
								lowerCamelCase		       =[48, 96, 1_92, 3_84]
				if "pico" in checkpoint_url:
								lowerCamelCase		       =[2, 2, 6, 2]
								lowerCamelCase		       =[64, 1_28, 2_56, 5_12]
				if "nano" in checkpoint_url:
								lowerCamelCase		       =[2, 2, 8, 2]
								lowerCamelCase		       =[80, 1_60, 3_20, 6_40]
				if "tiny" in checkpoint_url:
								lowerCamelCase		       =[3, 3, 9, 3]
								lowerCamelCase		       =[96, 1_92, 3_84, 7_68]
				if "base" in checkpoint_url:
								lowerCamelCase		       =[3, 3, 27, 3]
								lowerCamelCase		       =[1_28, 2_56, 5_12, 10_24]
				if "large" in checkpoint_url:
								lowerCamelCase		       =[3, 3, 27, 3]
								lowerCamelCase		       =[1_92, 3_84, 7_68, 15_36]
				if "huge" in checkpoint_url:
								lowerCamelCase		       =[3, 3, 27, 3]
								lowerCamelCase		       =[3_52, 7_04, 14_08, 28_16]

				lowerCamelCase		       =10_00
				lowerCamelCase		       ="""imagenet-1k-id2label.json"""
				lowerCamelCase		       =(1, 10_00)

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

				lowerCamelCase		       =idalabel
				lowerCamelCase		       ={v: k for k, v in idalabel.items()}
				lowerCamelCase		       =hidden_sizes
				lowerCamelCase		       =depths

				return config, expected_shape





def    _lowercase    (  _UpperCAmelCase )  ->     Any:
				if "downsample_layers.0.0" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.0.0"""	,					"""embeddings.patch_embeddings""" )
				if "downsample_layers.0.1" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.0.1"""	,					"""embeddings.norm""" )  # we rename to layernorm later on
				if "downsample_layers.1.0" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.1.0"""	,					"""stages.1.downsampling_layer.0""" )
				if "downsample_layers.1.1" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.1.1"""	,					"""stages.1.downsampling_layer.1""" )
				if "downsample_layers.2.0" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.2.0"""	,					"""stages.2.downsampling_layer.0""" )
				if "downsample_layers.2.1" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.2.1"""	,					"""stages.2.downsampling_layer.1""" )
				if "downsample_layers.3.0" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.3.0"""	,					"""stages.3.downsampling_layer.0""" )
				if "downsample_layers.3.1" in name:
								lowerCamelCase		       =name.replace("""downsample_layers.3.1"""	,					"""stages.3.downsampling_layer.1""" )
				if "stages" in name and "downsampling_layer" not in name:
								# stages.0.0. for instance should be renamed to stages.0.layers.0.
								lowerCamelCase		       =name[: len("""stages.0""" )] + """.layers""" + name[len("""stages.0""" ) :]
				if "gamma" in name:
								lowerCamelCase		       =name.replace("""gamma"""	,					"""weight""" )
				if "beta" in name:
								lowerCamelCase		       =name.replace("""beta"""	,					"""bias""" )
				if "stages" in name:
								lowerCamelCase		       =name.replace("""stages"""	,					"""encoder.stages""" )
				if "norm" in name:
								lowerCamelCase		       =name.replace("""norm"""	,					"""layernorm""" )
				if "head" in name:
								lowerCamelCase		       =name.replace("""head"""	,					"""classifier""" )

				return name





def    _lowercase    (  )  ->     int:
				lowerCamelCase		       ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowerCamelCase		       =Image.open(requests.get(_UpperCAmelCase	,					stream=_UpperCAmelCase ).raw )
				return im





def    _lowercase    (  _UpperCAmelCase )  ->     Any:
				if "224" in checkpoint_url:
								lowerCamelCase		       =2_24
								lowerCamelCase		       =2_24 / 2_56
				elif "384" in checkpoint_url:
								lowerCamelCase		       =3_84
								lowerCamelCase		       =None
				else:
								lowerCamelCase		       =5_12
								lowerCamelCase		       =None

				return ConvNextImageProcessor(
				    size=_UpperCAmelCase	,					crop_pct=_UpperCAmelCase	,					image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]	,					image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]	,					resample=PILImageResampling.BICUBIC	,					)





@torch.no_grad()
def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     str:
				print("""Downloading original model from checkpoint...""" )
				# define ConvNeXTV2 configuration based on URL
				lowerCamelCase       ,     lowerCamelCase		       =get_convnextva_config(_UpperCAmelCase )
				# load original state_dict from URL
				lowerCamelCase		       =torch.hub.load_state_dict_from_url(_UpperCAmelCase )["""model"""]

				print("""Converting model parameters...""" )
				# rename keys
				for key in state_dict.copy().keys():
								lowerCamelCase		       =state_dict.pop(_UpperCAmelCase )
								lowerCamelCase		       =val
				# add prefix to all keys expect classifier head
				for key in state_dict.copy().keys():
								lowerCamelCase		       =state_dict.pop(_UpperCAmelCase )
								if not key.startswith("""classifier""" ):
												lowerCamelCase		       ="""convnextv2.""" + key
								lowerCamelCase		       =val

				# load HuggingFace model
				lowerCamelCase		       =ConvNextVaForImageClassification(_UpperCAmelCase )
				model.load_state_dict(_UpperCAmelCase )
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				lowerCamelCase		       =convert_preprocessor(_UpperCAmelCase )
				lowerCamelCase		       =preprocessor(images=prepare_img()	,					return_tensors="""pt""" )
				lowerCamelCase		       =model(**_UpperCAmelCase ).logits

				# note: the logits below were obtained without center cropping
				if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
								lowerCamelCase		       =torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
								lowerCamelCase		       =torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
								lowerCamelCase		       =torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
								lowerCamelCase		       =torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
								lowerCamelCase		       =torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6] )
				else:
								raise ValueError(F"""Unknown URL: {checkpoint_url}""" )

				assert torch.allclose(logits[0, :5]	,					_UpperCAmelCase	,					atol=1e-3 )
				assert logits.shape == expected_shape
				print("""Model outputs match the original results!""" )

				if save_model:
								print("""Saving model to local...""" )
								# Create folder to save model
								if not os.path.isdir(_UpperCAmelCase ):
												os.mkdir(_UpperCAmelCase )

								model.save_pretrained(_UpperCAmelCase )
								preprocessor.save_pretrained(_UpperCAmelCase )

				lowerCamelCase		       ="""convnextv2"""
				if "atto" in checkpoint_url:
								model_name += "-atto"
				if "femto" in checkpoint_url:
								model_name += "-femto"
				if "pico" in checkpoint_url:
								model_name += "-pico"
				if "nano" in checkpoint_url:
								model_name += "-nano"
				elif "tiny" in checkpoint_url:
								model_name += "-tiny"
				elif "base" in checkpoint_url:
								model_name += "-base"
				elif "large" in checkpoint_url:
								model_name += "-large"
				elif "huge" in checkpoint_url:
								model_name += "-huge"
				if "22k" in checkpoint_url and "1k" not in checkpoint_url:
								model_name += "-22k"
				elif "22k" in checkpoint_url and "1k" in checkpoint_url:
								model_name += "-22k-1k"
				elif "1k" in checkpoint_url:
								model_name += "-1k"
				if "224" in checkpoint_url:
								model_name += "-224"
				elif "384" in checkpoint_url:
								model_name += "-384"
				elif "512" in checkpoint_url:
								model_name += "-512"

				if push_to_hub:
								print(F"""Pushing {model_name} to the hub...""" )
								model.push_to_hub(_UpperCAmelCase )
								preprocessor.push_to_hub(_UpperCAmelCase )


if __name__ == "__main__":
							UpperCAmelCase__  :	str     =argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt''',
							    type=str,
							    help='''URL of the original ConvNeXTV2 checkpoint you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default='''model''',
							    type=str,
							    help='''Path to the output PyTorch model directory.''',
							)
							parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
							parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image preprocessor to the hub''')

							UpperCAmelCase__  :	str     =parser.parse_args()
							convert_convnextva_checkpoint(
							    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
							)








"""simple docstring"""




import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


__A     :    int    =		'none'
__A     :    List[Any]    =		'none'
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					value.split(lowercase__      )
					return len(lowercase__      ) > 1 and any((len(lowercase__      ) == 3 for x in split)      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					if isinstance(lowercase__			,							lowercase__      ):
										return value
					if isinstance(lowercase__			,							lowercase__      ):
										return float(lowercase__      )
					if not isinstance(lowercase__			,							lowercase__      ):
										raise ValueError("Argument value is not a string. Can't parse it as float"      )
					A			=					value

					try:
										# Example: 1,000.7
										if "." in sanitized and "," in sanitized:
															return float(sanitized.replace(","			,							""      )      )
										# 1,000
										if "," in sanitized and _split_thousands(","			,							lowercase__      ):
															return float(sanitized.replace(","			,							""      )      )
										# 5,5556
										if "," in sanitized and sanitized.count(","      ) == 1 and not _split_thousands(","			,							lowercase__      ):
															return float(sanitized.replace(","			,							"."      )      )
										# 0.0.0.1
										if sanitized.count("."      ) > 1:
															return float(sanitized.replace("."			,							""      )      )
										# 0,0,0,1
										if sanitized.count(","      ) > 1:
															return float(sanitized.replace(","			,							""      )      )
										return float(lowercase__      )
					except ValueError:
										# Avoid adding the sanitized value in the error message.
										raise ValueError("Unable to convert value to float"      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					if answer is None:
										return None
					try:
										A			=					convert_to_float(lowercase__      )
										if isinstance(lowercase__			,							lowercase__      ) and math.isnan(lowercase__      ):
															return None
										return value
					except ValueError:
										return answer.lower()


__A     :    List[str]    =		{
    'text': lambda x: x,
    'real': convert_to_float,
}





class 			__UpperCamelCase (   enum.Enum						):
		SCREAMING_SNAKE_CASE								=    0
		SCREAMING_SNAKE_CASE								=    1
		SCREAMING_SNAKE_CASE								=    2
		SCREAMING_SNAKE_CASE								=    3
		SCREAMING_SNAKE_CASE								=    4
		SCREAMING_SNAKE_CASE								=    5





class 			__UpperCamelCase (   enum.Enum						):
		SCREAMING_SNAKE_CASE								=    0
		SCREAMING_SNAKE_CASE								=    1
		SCREAMING_SNAKE_CASE								=    2





@dataclasses.dataclass
class 			__UpperCamelCase :
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42

__A     :    int    =		re.compile(R'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					return list(_TOKENIZER.findall(x.lower()      )      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					if operator == _Operator.EQUALS:
										return src == tgt
					elif operator == _Operator.GREATER:
										return src > tgt
					elif operator == _Operator.LESSER:
										return src < tgt
					raise ValueError(F"""Unknown operator: {operator}"""      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					table["types"]
					return _TYPE_CONVERTER[types[column]](lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					return isinstance(lowercase__			,							lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					for cond in conditions:
										A			=					row[cond.column]

										A			=					_parse_value(lowercase__			,							cond.column			,							cond.cmp_value      )

										if _is_string(lowercase__      ) and _is_string(lowercase__      ):
															A			=					_normalize_for_match(lowercase__      )
															A			=					_normalize_for_match(lowercase__      )

										if not isinstance(lowercase__			,							type(lowercase__      )      ):
															raise ValueError("Type difference {} != {}".format(type(lowercase__      )			,							type(lowercase__      )      )      )

										if not _compare(cond.operator			,							lowercase__			,							lowercase__      ):
															return False
					return True
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					if not answer_coordinates:
										if aggregation_op == _Aggregation.COUNT:
															return 0.0
										else:
															return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
					if aggregation_op == _Aggregation.COUNT:
										return float(len(lowercase__      )      )

					# If we have just one answer, if float returns it or try a conversion.
					A			=					[table["rows"][i][j] for (i, j) in answer_coordinates]
					if len(lowercase__      ) == 1:
										try:
															return convert_to_float(values[0]      )
										except ValueError as e:
															if aggregation_op != _Aggregation.NONE:
																				raise e

					if aggregation_op == _Aggregation.NONE:
										return None

					# Other aggregation only support numeric values. Bail out if we have strings.
					if not all((isinstance(lowercase__			,							(int, float)      ) for v in values)      ):
										return None

					if aggregation_op == _Aggregation.SUM:
										return float(sum(lowercase__      )      )
					elif aggregation_op == _Aggregation.AVERAGE:
										return sum(lowercase__      ) / len(lowercase__      )
					else:
										raise ValueError(F"""Unknown aggregation: {aggregation_op}"""      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					sql_query["agg"]
					if aggregation_op_index >= 3:
										A			=					_Aggregation(lowercase__      )
					else:
										A			=					_Aggregation.NONE

					A			=					sql_query["sel"]
					A			=					[
					    _Condition(lowercase__			,							_Operator(lowercase__      )			,							lowercase__      )
					    for column, operator, cmp_value in zip(
					        sql_query["conds"]["column_index"]			,							sql_query["conds"]["operator_index"]			,							sql_query["conds"]["condition"]      )
					]

					A			=					[]
					for row in range(len(table["rows"]      )      ):
										if _respect_conditions(lowercase__			,							table["rows"][row]			,							lowercase__      ):
															indices.append((row, target_column)      )

					if not indices:
										return [], aggregation_op

					if len(lowercase__      ) == 1:
										return indices, aggregation_op

					# Parsing of MIN/MAX.
					if aggregation_op_index in (1, 2):
										A			=					{2: min, 1: max}
										A			=					[(table["rows"][i][j], index) for index, (i, j) in enumerate(lowercase__      )]
										A			=					functools.reduce(operators[sql_query["agg"]]			,							lowercase__      )

										A			=					[indices[reduced[1]]]
										return ret, _Aggregation.NONE

					return indices, aggregation_op
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					if float_answer is not None:
										return [str(lowercase__      )]
					return [str(table["real_rows"][r][c]      ) for r, c in answer_coordinates]
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			,    A			=					_get_answer_coordinates(lowercase__			,							lowercase__      )
					A			=					_get_float_answer(lowercase__			,							lowercase__			,							lowercase__      )
					A			=					_get_answer_text(lowercase__			,							lowercase__			,							lowercase__      )
					# keep the original data the same with TaPas
					if len(lowercase__      ) == 0:
										A			=					[EMPTY_ANSWER]
					return answer_text





