




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()
lowercase_		=							logging.get_logger(__name__)







def  lowerCAmelCase  (	UpperCAmelCase       )  ->List[Any]:






					"""simple docstring"""






					__magic_name__					:							List[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  lowerCAmelCase  (	)  ->Dict:






					"""simple docstring"""






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

					__magic_name__					:							int		   =							BertConfig()
					__magic_name__					:							List[Any]		   =							AlignConfig.from_text_vision_configs(
					    text_config=UpperCAmelCase,	vision_config=UpperCAmelCase,	projection_dim=640       )
					return config







def  lowerCAmelCase  (	)  ->Tuple:






					"""simple docstring"""






					__magic_name__					:							int		   =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__magic_name__					:							Dict		   =							Image.open(requests.get(UpperCAmelCase,	stream=UpperCAmelCase       ).raw       )
					return im







def  lowerCAmelCase  (	)  ->Optional[int]:






					"""simple docstring"""






					__magic_name__					:							Any		   =							EfficientNetImageProcessor(
					    do_center_crop=UpperCAmelCase,	rescale_factor=1 / 1_27.5,	rescale_offset=UpperCAmelCase,	do_normalize=UpperCAmelCase,	include_top=UpperCAmelCase,	resample=Image.BILINEAR,	)
					__magic_name__					:							str		   =							BertTokenizer.from_pretrained('''bert-base-uncased'''       )
					__magic_name__					:							Tuple		   =							64
					__magic_name__					:							int		   =							AlignProcessor(image_processor=UpperCAmelCase,	tokenizer=UpperCAmelCase       )
					return processor







def  lowerCAmelCase  (	UpperCAmelCase       )  ->str:






					"""simple docstring"""






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

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

    # BERT text encoder
					__magic_name__					:							List[str]		   =							[]
					__magic_name__					:							Dict		   =							'''tf_bert_model/bert'''
					__magic_name__					:							Any		   =							'''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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[Any]:






					"""simple docstring"""






					list(hf_params.keys()       )

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

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

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







@torch.no_grad()
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->Any:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							64
					__magic_name__					:							Any		   =							Tokenizer(UpperCAmelCase       )
					__magic_name__					:							List[str]		   =							align.Align('''efficientnet-b7''',	'''bert-base''',	640,	UpperCAmelCase,	tok.get_vocab_size()       )
					original_model.compile()
					original_model.load_weights(UpperCAmelCase       )

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

					# Load HuggingFace model
					__magic_name__					:							List[Any]		   =							get_align_config()
					__magic_name__					:							List[str]		   =							AlignModel(UpperCAmelCase       ).eval()
					__magic_name__					:							int		   =							hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print('''Converting parameters...'''       )
					__magic_name__					:							int		   =							rename_keys(UpperCAmelCase       )
					replace_params(UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )

					# Initialize processor
					__magic_name__					:							str		   =							get_processor()
					__magic_name__					:							str		   =							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__					:							Any		   =							outputs.image_embeds.detach().numpy()
					__magic_name__					:							Tuple		   =							outputs.text_embeds.detach().numpy()

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

					__magic_name__					:							Tuple		   =							original_model.image_encoder(UpperCAmelCase,	training=UpperCAmelCase       )
					__magic_name__					:							str		   =							original_model.text_encoder(UpperCAmelCase,	training=UpperCAmelCase       )

					__magic_name__					:							Optional[Any]		   =							tf.nn.la_normalize(UpperCAmelCase,	axis=-1       )
					__magic_name__					:							Optional[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__":
				lowercase_		=							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''')

				lowercase_		=							parser.parse_args()
				convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)









import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->str:






					"""simple docstring"""






					assert isinstance(UpperCAmelCase,	UpperCAmelCase       )
					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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->Any:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							tmp_path / '''cache'''
					__magic_name__					:							Tuple		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__					:							str		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase,	keep_in_memory=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )







@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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->Tuple:






					"""simple docstring"""






					__magic_name__					:							Tuple		   =							tmp_path / '''cache'''
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__magic_name__					:							List[Any]		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							Tuple		   =							features.copy() if features else default_expected_features
					__magic_name__					:							str		   =							(
					    Features({feature: Value(UpperCAmelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
					)
					__magic_name__					:							Dict		   =							CsvDatasetReader(UpperCAmelCase,	features=UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )







@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[Any]:






					"""simple docstring"""






					__magic_name__					:							str		   =							tmp_path / '''cache'''
					__magic_name__					:							List[str]		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							int		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase,	split=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )
					assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type''',	[str, list]       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->str:






					"""simple docstring"""






					if issubclass(UpperCAmelCase,	UpperCAmelCase       ):
										__magic_name__					:							str		   =							csv_path
					elif issubclass(UpperCAmelCase,	UpperCAmelCase       ):
										__magic_name__					:							str		   =							[csv_path]
					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Any		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							Tuple		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase=("train",)       )  ->Tuple:






					"""simple docstring"""






					assert isinstance(UpperCAmelCase,	UpperCAmelCase       )
					for split in splits:
										__magic_name__					:							int		   =							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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Tuple		   =							tmp_path / '''cache'''
					__magic_name__					:							int		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__					:							Optional[Any]		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase,	keep_in_memory=UpperCAmelCase       ).read()
					_check_csv_datasetdict(UpperCAmelCase,	UpperCAmelCase       )







@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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






					__magic_name__					:							Any		   =							tmp_path / '''cache'''
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__magic_name__					:							Dict		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							Dict		   =							features.copy() if features else default_expected_features
					__magic_name__					:							Any		   =							(
					    Features({feature: Value(UpperCAmelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
					)
					__magic_name__					:							List[str]		   =							CsvDatasetReader({'''train''': csv_path},	features=UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_datasetdict(UpperCAmelCase,	UpperCAmelCase       )







@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






					if split:
										__magic_name__					:							Dict		   =							{split: csv_path}
					else:
										__magic_name__					:							Any		   =							{'''train''': csv_path, '''test''': csv_path}
					__magic_name__					:							str		   =							tmp_path / '''cache'''
					__magic_name__					:							Union[str, Any]		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							int		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_datasetdict(UpperCAmelCase,	UpperCAmelCase,	splits=list(path.keys()       )       )
					assert all(dataset[split].split == split for split in path.keys()       )







def  lowerCAmelCase  (	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					with open(UpperCAmelCase,	encoding='''utf-8'''       ) as csvfile:
										yield from csv.reader(UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							tmp_path / '''cache'''
					__magic_name__					:							List[Any]		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							int		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=1       ).write()

					__magic_name__					:							Optional[int]		   =							iter_csv_file(UpperCAmelCase       )
					__magic_name__					:							Any		   =							iter_csv_file(UpperCAmelCase       )

					for rowa, rowa in zip(UpperCAmelCase,	UpperCAmelCase       ):
										assert rowa == rowa







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->Union[str, Any]:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Optional[Any]		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							List[Any]		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=2       ).write()

					__magic_name__					:							Tuple		   =							iter_csv_file(UpperCAmelCase       )
					__magic_name__					:							List[str]		   =							iter_csv_file(UpperCAmelCase       )

					for rowa, rowa in zip(UpperCAmelCase,	UpperCAmelCase       ):
										assert rowa == rowa







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Any		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							Any		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					with pytest.raises(UpperCAmelCase       ):
										CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=0       )



