
import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def 	a_		(	__lowercase       :     Optional[Any]	, __lowercase       :     Optional[int]			)			->  Optional[int]:
			assert isinstance(__lowercase	, __lowercase			)
			assert dataset.num_rows == 4
			assert dataset.num_columns == 3
			assert dataset.column_names == ["col_1", "col_2", "col_3"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize('keep_in_memory'	, [False, True]			)
def 	a_		(	__lowercase       :     Dict	, __lowercase       :     Union[str, Any]	, __lowercase       :     int			)			->  Union[str, Any]:
			_snake_case          =      tmp_path / 'cache'
			_snake_case          =      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						_snake_case          =      ParquetDatasetReader(__lowercase	, cache_dir=__lowercase	, keep_in_memory=__lowercase			).read()
			_check_parquet_dataset(__lowercase	, __lowercase			)






@pytest.mark.parametrize(
    'features'	, [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]	, )
def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     int	, __lowercase       :     List[str]			)			->  Optional[int]:
			_snake_case          =      tmp_path / 'cache'
			_snake_case          =      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			_snake_case          =      features.copy() if features else default_expected_features
			_snake_case          =      (
			    Features({feature: Value(__lowercase			) for feature, dtype in features.items()}			) if features is not None else None
			)
			_snake_case          =      ParquetDatasetReader(__lowercase	, features=__lowercase	, cache_dir=__lowercase			).read()
			_check_parquet_dataset(__lowercase	, __lowercase			)






@pytest.mark.parametrize('split'	, [None, NamedSplit('train'			), 'train', 'test']			)
def 	a_		(	__lowercase       :     List[Any]	, __lowercase       :     Union[str, Any]	, __lowercase       :     Optional[int]			)			->  Any:
			_snake_case          =      tmp_path / 'cache'
			_snake_case          =      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			_snake_case          =      ParquetDatasetReader(__lowercase	, cache_dir=__lowercase	, split=__lowercase			).read()
			_check_parquet_dataset(__lowercase	, __lowercase			)
			assert dataset.split == split if split else "train"






@pytest.mark.parametrize('path_type'	, [str, list]			)
def 	a_		(	__lowercase       :     Tuple	, __lowercase       :     Optional[int]	, __lowercase       :     str			)			->  int:
			if issubclass(__lowercase	, __lowercase			):
						_snake_case          =      parquet_path
			elif issubclass(__lowercase	, __lowercase			):
						_snake_case          =      [parquet_path]
			_snake_case          =      tmp_path / 'cache'
			_snake_case          =      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			_snake_case          =      ParquetDatasetReader(__lowercase	, cache_dir=__lowercase			).read()
			_check_parquet_dataset(__lowercase	, __lowercase			)






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Dict	, __lowercase       :     Dict=("train",)			)			->  str:
			assert isinstance(__lowercase	, __lowercase			)
			for split in splits:
						_snake_case          =      dataset_dict[split]
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
									assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize('keep_in_memory'	, [False, True]			)
def 	a_		(	__lowercase       :     Dict	, __lowercase       :     Tuple	, __lowercase       :     Dict			)			->  str:
			_snake_case          =      tmp_path / 'cache'
			_snake_case          =      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						_snake_case          =      ParquetDatasetReader(
						    {'train': parquet_path}	, cache_dir=__lowercase	, keep_in_memory=__lowercase			).read()
			_check_parquet_datasetdict(__lowercase	, __lowercase			)






@pytest.mark.parametrize(
    'features'	, [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]	, )
def 	a_		(	__lowercase       :     Tuple	, __lowercase       :     Tuple	, __lowercase       :     Dict			)			->  Union[str, Any]:
			_snake_case          =      tmp_path / 'cache'
			_snake_case          =      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			_snake_case          =      features.copy() if features else default_expected_features
			_snake_case          =      (
			    Features({feature: Value(__lowercase			) for feature, dtype in features.items()}			) if features is not None else None
			)
			_snake_case          =      ParquetDatasetReader({'train': parquet_path}	, features=__lowercase	, cache_dir=__lowercase			).read()
			_check_parquet_datasetdict(__lowercase	, __lowercase			)






@pytest.mark.parametrize('split'	, [None, NamedSplit('train'			), 'train', 'test']			)
def 	a_		(	__lowercase       :     List[Any]	, __lowercase       :     Optional[Any]	, __lowercase       :     List[Any]			)			->  int:
			if split:
						_snake_case          =      {split: parquet_path}
			else:
						_snake_case          =      'train'
						_snake_case          =      {'train': parquet_path, 'test': parquet_path}
			_snake_case          =      tmp_path / 'cache'
			_snake_case          =      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			_snake_case          =      ParquetDatasetReader(__lowercase	, cache_dir=__lowercase			).read()
			_check_parquet_datasetdict(__lowercase	, __lowercase	, splits=list(path.keys()			)			)
			assert all(dataset[split].split == split for split in path.keys()			)






def 	a_		(	__lowercase       :     List[Any]	, __lowercase       :     Dict			)			->  int:
			_snake_case          =      ParquetDatasetWriter(__lowercase	, tmp_path / 'foo.parquet'			)
			assert writer.write() > 0
			_snake_case          =      pq.ParquetFile(tmp_path / 'foo.parquet'			)
			_snake_case          =      pf.read()
			assert dataset.data.table == output_table






def 	a_		(	__lowercase       :     Tuple	, __lowercase       :     int			)			->  int:
			_snake_case          =      str(shared_datadir / 'test_image_rgb.jpg'			)
			_snake_case          =      {'image': [image_path]}
			_snake_case          =      Features({'image': Image()}			)
			_snake_case          =      Dataset.from_dict(__lowercase	, features=__lowercase			)
			_snake_case          =      ParquetDatasetWriter(__lowercase	, tmp_path / 'foo.parquet'			)
			assert writer.write() > 0

			_snake_case          =      Dataset.from_parquet(str(tmp_path / 'foo.parquet'			)			)
			assert dataset.features == reloaded_dataset.features

			_snake_case          =      ParquetDatasetReader(str(tmp_path / 'foo.parquet'			)	, streaming=__lowercase			).read()
			assert dataset.features == reloaded_iterable_dataset.features






@pytest.mark.parametrize(
    'feature, expected'	, [
        (Features({'foo': Value('int32'			)}			), None),
        (Features({'image': Image(), 'foo': Value('int32'			)}			), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'nested': Sequence(Audio()			)}			), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]	, )
def 	a_		(	__lowercase       :     Dict	, __lowercase       :     Optional[Any]			)			->  Dict:
			assert get_writer_batch_size(__lowercase			) == expected

import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase	:    int          =      logging.get_logger(__name__)

_lowerCamelCase	:    Tuple          =      {
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

_lowerCamelCase	:    List[str]          =      {
    '''b0''': {
        '''hidden_dim''': 1_280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 224,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1_280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 240,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 1_408,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 260,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 1_536,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 300,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 1_792,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 380,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2_048,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 456,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 2_304,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 528,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 2_560,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 600,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}






def 	a_		(	__lowercase       :     int			)			->  Optional[int]:
			_snake_case          =      EfficientNetConfig()
			_snake_case          =      CONFIG_MAP[model_name]['hidden_dim']
			_snake_case          =      CONFIG_MAP[model_name]['width_coef']
			_snake_case          =      CONFIG_MAP[model_name]['depth_coef']
			_snake_case          =      CONFIG_MAP[model_name]['image_size']
			_snake_case          =      CONFIG_MAP[model_name]['dropout_rate']
			_snake_case          =      CONFIG_MAP[model_name]['dw_padding']

			_snake_case          =      'huggingface/label-files'
			_snake_case          =      'imagenet-1k-id2label.json'
			_snake_case          =      1_000
			_snake_case          =      json.load(open(hf_hub_download(__lowercase	, __lowercase	, repo_type='dataset'			)	, 'r'			)			)
			_snake_case          =      {int(__lowercase			): v for k, v in idalabel.items()}

			_snake_case          =      idalabel
			_snake_case          =      {v: k for k, v in idalabel.items()}
			return config






def 	a_		(	)			->  Any:
			_snake_case          =      'http://images.cocodataset.org/val2017/000000039769.jpg'
			_snake_case          =      Image.open(requests.get(__lowercase	, stream=__lowercase			).raw			)
			return im






def 	a_		(	__lowercase       :     Union[str, Any]			)			->  Tuple:
			_snake_case          =      CONFIG_MAP[model_name]['image_size']
			_snake_case          =      EfficientNetImageProcessor(
			    size={'height': size, 'width': size}	, image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]	, image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]	, do_center_crop=__lowercase	, )
			return preprocessor






def 	a_		(	__lowercase       :     str			)			->  List[Any]:
			_snake_case          =      [v.split('_'			)[0].split('block'			)[1] for v in original_param_names if v.startswith('block'			)]
			_snake_case          =      sorted(set(__lowercase			)			)
			_snake_case          =      len(__lowercase			)
			_snake_case          =      {b: str(__lowercase			) for b, i in zip(__lowercase	, range(__lowercase			)			)}

			_snake_case          =      []
			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:
						_snake_case          =      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''')			)

			rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')			)
			rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')			)
			rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')			)
			rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')			)
			rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')			)

			_snake_case          =      {}
			for item in rename_keys:
						if item[0] in original_param_names:
									_snake_case          =      'efficientnet.' + item[1]

			_snake_case          =      'classifier.weight'
			_snake_case          =      'classifier.bias'
			return key_mapping






def 	a_		(	__lowercase       :     Any	, __lowercase       :     Any	, __lowercase       :     Any			)			->  Optional[Any]:
			for key, value in tf_params.items():
						if "normalization" in key:
									continue

						_snake_case          =      key_mapping[key]
						if "_conv" in key and "kernel" in key:
									_snake_case          =      torch.from_numpy(__lowercase			).permute(3	, 2	, 0	, 1			)
						elif "depthwise_kernel" in key:
									_snake_case          =      torch.from_numpy(__lowercase			).permute(2	, 3	, 0	, 1			)
						elif "kernel" in key:
									_snake_case          =      torch.from_numpy(np.transpose(__lowercase			)			)
						else:
									_snake_case          =      torch.from_numpy(__lowercase			)

						# Replace HF parameters with original TF model parameters
						assert hf_params[hf_key].shape == new_hf_value.shape
						hf_params[hf_key].copy_(__lowercase			)






@torch.no_grad()
def 	a_		(	__lowercase       :     List[Any]	, __lowercase       :     Any	, __lowercase       :     int	, __lowercase       :     str			)			->  Dict:
			_snake_case          =      model_classes[model_name](
			    include_top=__lowercase	, weights='imagenet'	, input_tensor=__lowercase	, input_shape=__lowercase	, pooling=__lowercase	, classes=1_000	, classifier_activation='softmax'	, )

			_snake_case          =      original_model.trainable_variables
			_snake_case          =      original_model.non_trainable_variables
			_snake_case          =      {param.name: param.numpy() for param in tf_params}
			for param in tf_non_train_params:
						_snake_case          =      param.numpy()
			_snake_case          =      list(tf_params.keys()			)

			# Load HuggingFace model
			_snake_case          =      get_efficientnet_config(__lowercase			)
			_snake_case          =      EfficientNetForImageClassification(__lowercase			).eval()
			_snake_case          =      hf_model.state_dict()

			# Create src-to-dst parameter name mapping dictionary
			print('Converting parameters...'			)
			_snake_case          =      rename_keys(__lowercase			)
			replace_params(__lowercase	, __lowercase	, __lowercase			)

			# Initialize preprocessor and preprocess input image
			_snake_case          =      convert_image_processor(__lowercase			)
			_snake_case          =      preprocessor(images=prepare_img()	, return_tensors='pt'			)

			# HF model inference
			hf_model.eval()
			with torch.no_grad():
						_snake_case          =      hf_model(**__lowercase			)
			_snake_case          =      outputs.logits.detach().numpy()

			# Original model inference
			_snake_case          =      False
			_snake_case          =      CONFIG_MAP[model_name]['image_size']
			_snake_case          =      prepare_img().resize((image_size, image_size)	, resample=PIL.Image.NEAREST			)
			_snake_case          =      image.img_to_array(__lowercase			)
			_snake_case          =      np.expand_dims(__lowercase	, axis=0			)
			_snake_case          =      original_model.predict(__lowercase			)

			# Check whether original and HF model outputs match  -> np.allclose
			assert np.allclose(__lowercase	, __lowercase	, atol=1E-3			), "The predicted logits are not the same."
			print('Model outputs match!'			)

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

			if push_to_hub:
						# Push model and image processor to hub
						print(f'''Pushing converted {model_name} to the hub...'''			)
						_snake_case          =      f'''efficientnet-{model_name}'''
						preprocessor.push_to_hub(__lowercase			)
						hf_model.push_to_hub(__lowercase			)


if __name__ == "__main__":
			_lowerCamelCase	:    int          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''b0''',
			    type=str,
			    help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
			)
			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''')

			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)