


'''simple docstring'''







import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


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



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Any						       =       tf.image.resize(UpperCamelCase__     ,   (346, 346)		)
				__magic_name__				:					Union[str, Any]						       =       tf.image.crop_to_bounding_box(UpperCamelCase__     ,   (346 - 289) // 2     ,   (346 - 289) // 2     ,   289     ,   289		)
				return image



def 						_UpperCamelCase							( ):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       EfficientNetConfig.from_pretrained("google/efficientnet-b7"		)
				__magic_name__				:					List[Any]						       =       289
				__magic_name__				:					List[Any]						       =       640
				__magic_name__				:					Optional[int]						       =       {"0": "LABEL_0", "1": "LABEL_1"}
				__magic_name__				:					Dict						       =       {"LABEL_0": 0, "LABEL_1": 1}
				__magic_name__				:					Optional[int]						       =       []

				__magic_name__				:					Optional[int]						       =       BertConfig()
				__magic_name__				:					Union[str, Any]						       =       AlignConfig.from_text_vision_configs(
				    text_config=UpperCamelCase__     ,   vision_config=UpperCamelCase__     ,   projection_dim=640		)
				return config



def 						_UpperCamelCase							( ):






				"""simple docstring"""



				__magic_name__				:					Any						       =       "http://images.cocodataset.org/val2017/000000039769.jpg"
				__magic_name__				:					Union[str, Any]						       =       Image.open(requests.get(UpperCamelCase__     ,   stream=UpperCamelCase__		).raw		)
				return im



def 						_UpperCamelCase							( ):






				"""simple docstring"""



				__magic_name__				:					Optional[int]						       =       EfficientNetImageProcessor(
				    do_center_crop=UpperCamelCase__     ,   rescale_factor=1 / 127.5     ,   rescale_offset=UpperCamelCase__     ,   do_normalize=UpperCamelCase__     ,   include_top=UpperCamelCase__     ,   resample=Image.BILINEAR     ,   )
				__magic_name__				:					List[Any]						       =       BertTokenizer.from_pretrained("bert-base-uncased"		)
				__magic_name__				:					Union[str, Any]						       =       64
				__magic_name__				:					List[str]						       =       AlignProcessor(image_processor=UpperCamelCase__     ,   tokenizer=UpperCamelCase__		)
				return processor



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       [v.split("_"		)[0].split("block"		)[1] for v in original_param_names if v.startswith("block"		)]
				__magic_name__				:					Tuple						       =       list(set(UpperCamelCase__		)		)
				__magic_name__				:					Union[str, Any]						       =       sorted(UpperCamelCase__		)
				__magic_name__				:					List[str]						       =       len(UpperCamelCase__		)
				__magic_name__				:					int						       =       {b: str(UpperCamelCase__		) for b, i in zip(UpperCamelCase__     ,   range(UpperCamelCase__		)		)}

				__magic_name__				:					Union[str, Any]						       =       []
				rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")		)
				rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")		)
				rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")		)
				rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")		)
				rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")		)

				for b in block_names:
								__magic_name__				:					Optional[int]						       =       block_name_mapping[b]
								rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")		)
								rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")		)
								rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")		)
								rename_keys.append(
								    (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")		)
								rename_keys.append(
								    (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")		)
								rename_keys.append(
								    (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")		)
								rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")		)
								rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")		)
								rename_keys.append(
								    (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")		)
								rename_keys.append(
								    (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")		)

								rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")		)
								rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")		)
								rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")		)
								rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")		)
								rename_keys.append(
								    (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""")		)
								rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""")		)
								rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""")		)
								rename_keys.append(
								    (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")		)
								rename_keys.append(
								    (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")		)

				__magic_name__				:					List[str]						       =       {}
				for item in rename_keys:
								if item[0] in original_param_names:
												__magic_name__				:					Optional[Any]						       =       "vision_model." + item[1]

    # BERT text encoder
				__magic_name__				:					List[Any]						       =       []
				__magic_name__				:					Dict						       =       "tf_bert_model/bert"
				__magic_name__				:					Optional[int]						       =       "text_model"
				for i in range(12		):
								rename_keys.append(
								    (
								        F"""{old}/encoder/layer_._{i}/attention/self/query/kernel:0""",
								        F"""{new}.encoder.layer.{i}.attention.self.query.weight""",
								    )		)
								rename_keys.append(
								    (
								        F"""{old}/encoder/layer_._{i}/attention/self/query/bias:0""",
								        F"""{new}.encoder.layer.{i}.attention.self.query.bias""",
								    )		)
								rename_keys.append(
								    (
								        F"""{old}/encoder/layer_._{i}/attention/self/key/kernel:0""",
								        F"""{new}.encoder.layer.{i}.attention.self.key.weight""",
								    )		)
								rename_keys.append(
								    (
								        F"""{old}/encoder/layer_._{i}/attention/self/key/bias:0""",
								        F"""{new}.encoder.layer.{i}.attention.self.key.bias""",
								    )		)
								rename_keys.append(
								    (
								        F"""{old}/encoder/layer_._{i}/attention/self/value/kernel:0""",
								        F"""{new}.encoder.layer.{i}.attention.self.value.weight""",
								    )		)
								rename_keys.append(
								    (
								        F"""{old}/encoder/layer_._{i}/attention/self/value/bias:0""",
								        F"""{new}.encoder.layer.{i}.attention.self.value.bias""",
								    )		)
								rename_keys.append(
								    (
								        F"""{old}/encoder/layer_._{i}/attention/output/dense/kernel:0""",
								        F"""{new}.encoder.layer.{i}.attention.output.dense.weight""",
								    )		)
								rename_keys.append(
								    (
								        F"""{old}/encoder/layer_._{i}/attention/output/dense/bias:0""",
								        F"""{new}.encoder.layer.{i}.attention.output.dense.bias""",
								    )		)
								rename_keys.append(
								    (
								        F"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0""",
								        F"""{new}.encoder.layer.{i}.attention.output.LayerNorm.weight""",
								    )		)
								rename_keys.append(
								    (
								        F"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0""",
								        F"""{new}.encoder.layer.{i}.attention.output.LayerNorm.bias""",
								    )		)
								rename_keys.append(
								    (
								        F"""{old}/encoder/layer_._{i}/intermediate/dense/kernel:0""",
								        F"""{new}.encoder.layer.{i}.intermediate.dense.weight""",
								    )		)
								rename_keys.append(
								    (
								        F"""{old}/encoder/layer_._{i}/intermediate/dense/bias:0""",
								        F"""{new}.encoder.layer.{i}.intermediate.dense.bias""",
								    )		)
								rename_keys.append(
								    (F"""{old}/encoder/layer_._{i}/output/dense/kernel:0""", F"""{new}.encoder.layer.{i}.output.dense.weight""")		)
								rename_keys.append(
								    (F"""{old}/encoder/layer_._{i}/output/dense/bias:0""", F"""{new}.encoder.layer.{i}.output.dense.bias""")		)
								rename_keys.append(
								    (F"""{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0""", F"""{new}.encoder.layer.{i}.output.LayerNorm.weight""")		)
								rename_keys.append(
								    (F"""{old}/encoder/layer_._{i}/output/LayerNorm/beta:0""", F"""{new}.encoder.layer.{i}.output.LayerNorm.bias""")		)

				rename_keys.append((F"""{old}/embeddings/word_embeddings/weight:0""", F"""{new}.embeddings.word_embeddings.weight""")		)
				rename_keys.append(
				    (F"""{old}/embeddings/position_embeddings/embeddings:0""", F"""{new}.embeddings.position_embeddings.weight""")		)
				rename_keys.append(
				    (F"""{old}/embeddings/token_type_embeddings/embeddings:0""", F"""{new}.embeddings.token_type_embeddings.weight""")		)
				rename_keys.append((F"""{old}/embeddings/LayerNorm/gamma:0""", F"""{new}.embeddings.LayerNorm.weight""")		)
				rename_keys.append((F"""{old}/embeddings/LayerNorm/beta:0""", F"""{new}.embeddings.LayerNorm.bias""")		)

				rename_keys.append((F"""{old}/pooler/dense/kernel:0""", F"""{new}.pooler.dense.weight""")		)
				rename_keys.append((F"""{old}/pooler/dense/bias:0""", F"""{new}.pooler.dense.bias""")		)
				rename_keys.append(("dense/kernel:0", "text_projection.weight")		)
				rename_keys.append(("dense/bias:0", "text_projection.bias")		)
				rename_keys.append(("dense/bias:0", "text_projection.bias")		)
				rename_keys.append(("temperature:0", "temperature")		)

				for item in rename_keys:
								if item[0] in original_param_names:
												__magic_name__				:					Dict						       =       item[1]
				return key_mapping



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				list(hf_params.keys()		)

				for key, value in tf_params.items():
								if key not in key_mapping:
												continue

								__magic_name__				:					Optional[Any]						       =       key_mapping[key]
								if "_conv" in key and "kernel" in key:
												__magic_name__				:					Dict						       =       torch.from_numpy(UpperCamelCase__		).permute(3     ,   2     ,   0     ,   1		)
								elif "embeddings" in key:
												__magic_name__				:					List[str]						       =       torch.from_numpy(UpperCamelCase__		)
								elif "depthwise_kernel" in key:
												__magic_name__				:					Tuple						       =       torch.from_numpy(UpperCamelCase__		).permute(2     ,   3     ,   0     ,   1		)
								elif "kernel" in key:
												__magic_name__				:					Optional[Any]						       =       torch.from_numpy(np.transpose(UpperCamelCase__		)		)
								elif "temperature" in key:
												__magic_name__				:					int						       =       value
								elif "bn/gamma" or "bn/beta" in key:
												__magic_name__				:					int						       =       torch.from_numpy(np.transpose(UpperCamelCase__		)		).squeeze()
								else:
												__magic_name__				:					Union[str, Any]						       =       torch.from_numpy(UpperCamelCase__		)

								# Replace HF parameters with original TF model parameters
								hf_params[hf_key].copy_(UpperCamelCase__		)



@torch.no_grad()
def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Dict						       =       64
				__magic_name__				:					Any						       =       Tokenizer(UpperCamelCase__		)
				__magic_name__				:					List[Any]						       =       align.Align("efficientnet-b7"     ,   "bert-base"     ,   640     ,   UpperCamelCase__     ,   tok.get_vocab_size()		)
				original_model.compile()
				original_model.load_weights(UpperCamelCase__		)

				__magic_name__				:					str						       =       original_model.trainable_variables
				__magic_name__				:					Optional[Any]						       =       original_model.non_trainable_variables
				__magic_name__				:					Union[str, Any]						       =       {param.name: param.numpy() for param in tf_params}
				for param in tf_non_train_params:
								__magic_name__				:					Optional[int]						       =       param.numpy()
				__magic_name__				:					Optional[int]						       =       list(tf_params.keys()		)

				# Load HuggingFace model
				__magic_name__				:					Optional[Any]						       =       get_align_config()
				__magic_name__				:					Optional[Any]						       =       AlignModel(UpperCamelCase__		).eval()
				__magic_name__				:					Optional[int]						       =       hf_model.state_dict()

				# Create src-to-dst parameter name mapping dictionary
				print("Converting parameters..."		)
				__magic_name__				:					Optional[Any]						       =       rename_keys(UpperCamelCase__		)
				replace_params(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		)

				# Initialize processor
				__magic_name__				:					str						       =       get_processor()
				__magic_name__				:					Tuple						       =       processor(
				    images=prepare_img()     ,   text="A picture of a cat"     ,   padding="max_length"     ,   max_length=64     ,   return_tensors="pt"		)

				# HF model inference
				hf_model.eval()
				with torch.no_grad():
								__magic_name__				:					str						       =       hf_model(**UpperCamelCase__		)

				__magic_name__				:					Optional[int]						       =       outputs.image_embeds.detach().numpy()
				__magic_name__				:					Any						       =       outputs.text_embeds.detach().numpy()

				# Original model inference
				__magic_name__				:					List[Any]						       =       False
				__magic_name__				:					Any						       =       EfficientNetImageProcessor(
				    do_center_crop=UpperCamelCase__     ,   do_rescale=UpperCamelCase__     ,   do_normalize=UpperCamelCase__     ,   include_top=UpperCamelCase__     ,   resample=Image.BILINEAR     ,   )
				__magic_name__				:					Union[str, Any]						       =       tf_image_processor(images=prepare_img()     ,   return_tensors="tf"     ,   data_format="channels_last"		)["pixel_values"]
				__magic_name__				:					Union[str, Any]						       =       tok(tf.constant(["A picture of a cat"]		)		)

				__magic_name__				:					Dict						       =       original_model.image_encoder(UpperCamelCase__     ,   training=UpperCamelCase__		)
				__magic_name__				:					Optional[int]						       =       original_model.text_encoder(UpperCamelCase__     ,   training=UpperCamelCase__		)

				__magic_name__				:					Any						       =       tf.nn.la_normalize(UpperCamelCase__     ,   axis=-1		)
				__magic_name__				:					List[Any]						       =       tf.nn.la_normalize(UpperCamelCase__     ,   axis=-1		)

				# Check whether original and HF model outputs match  -> np.allclose
				if not np.allclose(UpperCamelCase__     ,   UpperCamelCase__     ,   atol=1E-3		):
								raise ValueError("The predicted image features are not the same."		)
				if not np.allclose(UpperCamelCase__     ,   UpperCamelCase__     ,   atol=1E-3		):
								raise ValueError("The predicted text features are not the same."		)
				print("Model outputs match!"		)

				if save_model:
								# Create folder to save model
								if not os.path.isdir(UpperCamelCase__		):
												os.mkdir(UpperCamelCase__		)
								# Save converted model and image processor
								hf_model.save_pretrained(UpperCamelCase__		)
								processor.save_pretrained(UpperCamelCase__		)

				if push_to_hub:
								# Push model and image processor to hub
								print("Pushing converted ALIGN to the hub..."		)
								processor.push_to_hub("align-base"		)
								hf_model.push_to_hub("align-base"		)


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE       :   Tuple        = argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--checkpoint_path",
		    default="./weights/model-weights",
		    type=str,
		    help="Path to the pretrained TF ALIGN checkpoint.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path",
		    default="hf_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 processor to the hub")

		_SCREAMING_SNAKE_CASE       :   Optional[Any]        = parser.parse_args()
		convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)



'''simple docstring'''







import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput






@dataclass
class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						__snake_case   =      42
						__snake_case   =      None
						__snake_case   =      None






class 		_snake_case    (				unittest.TestCase		):






						'''simple docstring'''







						def lowerCAmelCase__ (  self:	List[str]     )							->	str:
										__magic_name__				:					List[Any]						       =       ModelOutputTest(a=30     )
										self.assertEqual(x.a      ,							30     )
										self.assertIsNone(x.b     )
										self.assertIsNone(x.c     )
										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					Optional[int]						       =       x.d







						def lowerCAmelCase__ (  self:	Dict     )							->	Dict:
										__magic_name__				:					List[Any]						       =       ModelOutputTest(a=30      ,							b=10     )
										self.assertEqual(x[0]      ,							30     )
										self.assertEqual(x[1]      ,							10     )
										self.assertEqual(x[:2]      ,							(30, 10)     )
										self.assertEqual(x[:]      ,							(30, 10)     )

										__magic_name__				:					str						       =       ModelOutputTest(a=30      ,							c=10     )
										self.assertEqual(x[0]      ,							30     )
										self.assertEqual(x[1]      ,							10     )
										self.assertEqual(x[:2]      ,							(30, 10)     )
										self.assertEqual(x[:]      ,							(30, 10)     )







						def lowerCAmelCase__ (  self:	List[Any]     )							->	Optional[int]:
										__magic_name__				:					Optional[Any]						       =       ModelOutputTest(a=30      ,							b=10     )
										self.assertEqual(x["a"]      ,							30     )
										self.assertEqual(x["b"]      ,							10     )
										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					str						       =       x["c"]

										__magic_name__				:					Optional[Any]						       =       ModelOutputTest(a=30      ,							c=10     )
										self.assertEqual(x["a"]      ,							30     )
										self.assertEqual(x["c"]      ,							10     )
										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					Union[str, Any]						       =       x["b"]







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Any:
										__magic_name__				:					Any						       =       ModelOutputTest(a=30     )
										self.assertEqual(list(x.keys()     )      ,							["a"]     )
										self.assertEqual(list(x.values()     )      ,							[30]     )
										self.assertEqual(list(x.items()     )      ,							[("a", 30)]     )
										self.assertEqual(list(__UpperCamelCase     )      ,							["a"]     )

										__magic_name__				:					Dict						       =       ModelOutputTest(a=30      ,							b=10     )
										self.assertEqual(list(x.keys()     )      ,							["a", "b"]     )
										self.assertEqual(list(x.values()     )      ,							[30, 10]     )
										self.assertEqual(list(x.items()     )      ,							[("a", 30), ("b", 10)]     )
										self.assertEqual(list(__UpperCamelCase     )      ,							["a", "b"]     )

										__magic_name__				:					int						       =       ModelOutputTest(a=30      ,							c=10     )
										self.assertEqual(list(x.keys()     )      ,							["a", "c"]     )
										self.assertEqual(list(x.values()     )      ,							[30, 10]     )
										self.assertEqual(list(x.items()     )      ,							[("a", 30), ("c", 10)]     )
										self.assertEqual(list(__UpperCamelCase     )      ,							["a", "c"]     )

										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					str						       =       x.update({"d": 20}     )
										with self.assertRaises(__UpperCamelCase     ):
														del x["a"]
										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					List[str]						       =       x.pop("a"     )
										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					Optional[Any]						       =       x.setdefault("d"      ,							32     )







						def lowerCAmelCase__ (  self:	Dict     )							->	int:
										__magic_name__				:					Any						       =       ModelOutputTest(a=30     )
										__magic_name__				:					Tuple						       =       10
										self.assertEqual(x.a      ,							10     )
										self.assertEqual(x["a"]      ,							10     )







						def lowerCAmelCase__ (  self:	Dict     )							->	Union[str, Any]:
										__magic_name__				:					Tuple						       =       ModelOutputTest(a=30     )
										__magic_name__				:					Union[str, Any]						       =       10
										self.assertEqual(x.a      ,							10     )
										self.assertEqual(x["a"]      ,							10     )







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Optional[int]:
										__magic_name__				:					Tuple						       =       ModelOutputTest({"a": 30, "b": 10}     )
										self.assertEqual(list(x.keys()     )      ,							["a", "b"]     )
										self.assertEqual(x.a      ,							30     )
										self.assertEqual(x.b      ,							10     )







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Optional[Any]:
										__magic_name__				:					List[str]						       =       ModelOutputTest([("a", 30), ("b", 10)]     )
										self.assertEqual(list(x.keys()     )      ,							["a", "b"]     )
										self.assertEqual(x.a      ,							30     )
										self.assertEqual(x.b      ,							10     )

										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					Any						       =       ModelOutputTest([("a", 30), (10, 10)]     )

										__magic_name__				:					Any						       =       ModelOutputTest(a=(30, 30)     )
										self.assertEqual(list(x.keys()     )      ,							["a"]     )
										self.assertEqual(x.a      ,							(30, 30)     )