




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()
UpperCAmelCase__						:       str							=    logging.get_logger(__name__)


def 		_lowerCamelCase				(					UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  _lowercase: int						     =    tf.image.resize(UpperCamelCase					,     (346, 346)							)
  _lowercase: Dict						     =    tf.image.crop_to_bounding_box(UpperCamelCase					,     (346 - 289) // 2					,     (346 - 289) // 2					,     289					,     289							)
  return image


def 		_lowerCamelCase				(					)			->		List[Any]:





  """simple docstring"""





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

  _lowercase: str						     =    BertConfig()
  _lowercase: List[str]						     =    AlignConfig.from_text_vision_configs(
      text_config=UpperCamelCase					,     vision_config=UpperCamelCase					,     projection_dim=640							)
  return config


def 		_lowerCamelCase				(					)			->		List[str]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase: List[Any]						     =    Image.open(requests.get(UpperCamelCase					,     stream=UpperCamelCase							).raw							)
  return im


def 		_lowerCamelCase				(					)			->		Optional[Any]:





  """simple docstring"""





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


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Any:





  """simple docstring"""





  _lowercase: int						     =    [v.split('_'							)[0].split('block'							)[1] for v in original_param_names if v.startswith('block'							)]
  _lowercase: Dict						     =    list(set(UpperCamelCase							)							)
  _lowercase: Tuple						     =    sorted(UpperCamelCase							)
  _lowercase: Dict						     =    len(UpperCamelCase							)
  _lowercase: List[str]						     =    {b: str(UpperCamelCase							) for b, i in zip(UpperCamelCase					,     range(UpperCamelCase							)							)}

  _lowercase: int						     =    []
  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:
    _lowercase: Any						     =    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')							)

  _lowercase: List[str]						     =    {}
  for item in rename_keys:
    if item[0] in original_param_names:
      _lowercase: Dict						     =    'vision_model.' + item[1]

    # BERT text encoder
  _lowercase: Tuple						     =    []
  _lowercase: Optional[int]						     =    'tf_bert_model/bert'
  _lowercase: List[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:
      _lowercase: Dict						     =    item[1]
  return key_mapping


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Any:





  """simple docstring"""





  list(hf_params.keys()							)

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

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





  """simple docstring"""





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

  _lowercase: Any						     =    original_model.trainable_variables
  _lowercase: str						     =    original_model.non_trainable_variables
  _lowercase: str						     =    {param.name: param.numpy() for param in tf_params}
  for param in tf_non_train_params:
    _lowercase: List[Any]						     =    param.numpy()
  _lowercase: Any						     =    list(tf_params.keys()							)

  # Load HuggingFace model
  _lowercase: Union[str, Any]						     =    get_align_config()
  _lowercase: Optional[int]						     =    AlignModel(UpperCamelCase							).eval()
  _lowercase: Optional[Any]						     =    hf_model.state_dict()

  # Create src-to-dst parameter name mapping dictionary
  print('Converting parameters...'							)
  _lowercase: List[str]						     =    rename_keys(UpperCamelCase							)
  replace_params(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)

  # Initialize processor
  _lowercase: int						     =    get_processor()
  _lowercase: 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():
    _lowercase: Union[str, Any]						     =    hf_model(**UpperCamelCase							)

  _lowercase: Union[str, Any]						     =    outputs.image_embeds.detach().numpy()
  _lowercase: Dict						     =    outputs.text_embeds.detach().numpy()

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

  _lowercase: Tuple						     =    original_model.image_encoder(UpperCamelCase					,     training=UpperCamelCase							)
  _lowercase: str						     =    original_model.text_encoder(UpperCamelCase					,     training=UpperCamelCase							)

  _lowercase: Dict						     =    tf.nn.la_normalize(UpperCamelCase					,     axis=-1							)
  _lowercase: str						     =    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__":
   UpperCAmelCase__						:       str							=    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')

   UpperCAmelCase__						:       Dict							=    parser.parse_args()
   convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

class      UpperCAmelCase						(			A   ):






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       Optional[Any]=False     )->			List[str]:
     _lowercase     ,					_lowercase     ,					_lowercase: Dict						     =    (
         filename_skip_take['filename'],
         filename_skip_take['skip'] if 'skip' in filename_skip_take else None,
         filename_skip_take['take'] if 'take' in filename_skip_take else None,
     )
     open(os.path.join(UpperCamelCase_     )						,					'wb'     ).close()
     _lowercase: int						     =    pa.Table.from_pydict({'filename': [Path(UpperCamelCase_     ).name] * 1_00}     )
     if take == -1:
       _lowercase: int						     =    len(UpperCamelCase_     ) - skip
     if skip is not None and take is not None:
       _lowercase: Any						     =    pa_table.slice(UpperCamelCase_						,					UpperCamelCase_     )
     return pa_table

class      UpperCAmelCase						(			A   ):






   def  _a						(	self   :       Any     )->			Dict:
     _lowercase: str						     =    'my_name'
     _lowercase: Optional[int]						     =    SplitInfo(name='train'						,					num_examples=1_00     )
     _lowercase: Dict						     =    SplitInfo(name='test'						,					num_examples=1_00     )
     _lowercase: Any						     =    [train_info, test_info]
     _lowercase: Any						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     split_dict.add(UpperCamelCase_     )
     _lowercase: Tuple						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: List[str]						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Dict						     =    'test[:33%]'
       _lowercase: Optional[Any]						     =    Dataset(**reader.read(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )     )
       self.assertEqual(dset['filename'][0]						,					f'{name}-test'     )
       self.assertEqual(dset.num_rows						,					33     )
       self.assertEqual(dset.num_columns						,					1     )

       _lowercase: Tuple						     =    ['train', 'test[:33%]']
       _lowercase: Union[str, Any]						     =    [Split.TRAIN, ReadInstruction.from_spec('test[:33%]'     )]
       for instructions in [instructionsa, instructionsa]:
         _lowercase: int						     =    [reader.read(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ) for instr in instructions]
         _lowercase     ,					_lowercase: Dict						     =    (Dataset(**UpperCamelCase_     ) for dataset_kwargs in datasets_kwargs)
         self.assertEqual(train_dset['filename'][0]						,					f'{name}-train'     )
         self.assertEqual(train_dset.num_rows						,					1_00     )
         self.assertEqual(train_dset.num_columns						,					1     )
         self.assertIsInstance(train_dset.split						,					UpperCamelCase_     )
         self.assertEqual(str(train_dset.split     )						,					'train'     )
         self.assertEqual(test_dset['filename'][0]						,					f'{name}-test'     )
         self.assertEqual(test_dset.num_rows						,					33     )
         self.assertEqual(test_dset.num_columns						,					1     )
         self.assertIsInstance(test_dset.split						,					UpperCamelCase_     )
         self.assertEqual(str(test_dset.split     )						,					'test[:33%]'     )
         del train_dset, test_dset






   def  _a						(	self   :       Any     )->			List[str]:
     _lowercase: int						     =    'my_name'
     _lowercase: List[str]						     =    SplitInfo(name='train'						,					num_examples=10_00						,					shard_lengths=[1_00] * 10     )
     _lowercase: Optional[Any]						     =    [train_info]
     _lowercase: Optional[int]						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     _lowercase: Optional[Any]						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: Optional[int]						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Any						     =    'train[:33%]'
       _lowercase: List[Any]						     =    Dataset(**reader.read(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )     )
       self.assertEqual(dset['filename'][0]						,					f'{name}-train-00000-of-00010'     )
       self.assertEqual(dset['filename'][-1]						,					f'{name}-train-00003-of-00010'     )
       self.assertEqual(dset.num_rows						,					3_30     )
       self.assertEqual(dset.num_columns						,					1     )






   def  _a						(	self   :       List[Any]     )->			Dict:
     _lowercase: Any						     =    SplitInfo(name='train'						,					num_examples=1_00     )
     _lowercase: List[Any]						     =    SplitInfo(name='test'						,					num_examples=1_00     )
     _lowercase: List[Any]						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     split_dict.add(UpperCamelCase_     )
     _lowercase: Union[str, Any]						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: Dict						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: List[Any]						     =    [
           {'filename': os.path.join(UpperCamelCase_						,					'train'     )},
           {'filename': os.path.join(UpperCamelCase_						,					'test'     ), 'skip': 10, 'take': 10},
       ]
       _lowercase: Tuple						     =    Dataset(**reader.read_files(UpperCamelCase_						,					original_instructions='train+test[10:20]'     )     )
       self.assertEqual(dset.num_rows						,					1_10     )
       self.assertEqual(dset.num_columns						,					1     )
       del dset


@pytest.mark.parametrize('in_memory'					,     [False, True]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _lowercase: List[Any]						     =    ArrowReader.read_table(UpperCamelCase					,     in_memory=UpperCamelCase							)
  assert table.shape == dataset.data.shape
  assert set(table.column_names							) == set(dataset.data.column_names							)
  assert dict(table.to_pydict()							) == dict(dataset.data.to_pydict()							)  # to_pydict returns OrderedDict


@pytest.mark.parametrize('in_memory'					,     [False, True]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





  _lowercase: Any						     =    arrow_file
  _lowercase: Union[str, Any]						     =    ArrowReader(''					,     UpperCamelCase							)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _lowercase: Tuple						     =    reader.read_files([{'filename': filename}]					,     in_memory=UpperCamelCase							)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  _lowercase: Any						     =    dataset_kwargs['arrow_table']
  assert table.shape == dataset.data.shape
  assert set(table.column_names							) == set(dataset.data.column_names							)
  assert dict(table.to_pydict()							) == dict(dataset.data.to_pydict()							)  # to_pydict returns OrderedDict


def 		_lowerCamelCase				(					)			->		int:





  """simple docstring"""





  assert ReadInstruction('train'					,     to=10					,     unit='abs'							).to_spec() == "train[:10]"
  assert ReadInstruction('train'					,     from_=-80					,     to=10					,     unit='%'							).to_spec() == "train[-80%:10%]"

  _lowercase: List[str]						     =    'train+test'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_test

  _lowercase: Union[str, Any]						     =    'train[2:10]'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_abs

  _lowercase: Any						     =    'train[15%:-20%]'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_pct

  _lowercase: List[Any]						     =    'train[:10%](closest)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == "train[:10%]"

  _lowercase: int						     =    'train[:10%](pct1_dropremainder)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_pct_rounding

  _lowercase: int						     =    'train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_test_pct_rounding


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





  """simple docstring"""





  _lowercase: int						     =    'dummy'
  _lowercase: Any						     =    [SplitInfo(name='train'					,     num_examples=100							)]
  _lowercase: Any						     =    'train[:33%]'
  _lowercase: Optional[int]						     =    'arrow'
  _lowercase: Optional[int]						     =    'prefix'

  _lowercase: Optional[int]						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
  assert isinstance(UpperCamelCase					,     UpperCamelCase							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train.arrow'							), "skip": 0, "take": 33}
  ]

  _lowercase: int						     =    [SplitInfo(name='train'					,     num_examples=100					,     shard_lengths=[10] * 10							)]
  _lowercase: Dict						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
  assert isinstance(UpperCamelCase					,     UpperCamelCase							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00000-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00001-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00002-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00003-of-00010.arrow'							), "skip": 0, "take": 3},
  ]





@pytest.mark.parametrize('name, expected_exception'					,     [(None, TypeError), ('', ValueError)]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Tuple:





  """simple docstring"""





  _lowercase: List[str]						     =    [SplitInfo(name='train'					,     num_examples=100							)]
  _lowercase: List[Any]						     =    'train'
  _lowercase: List[str]						     =    'arrow'
  _lowercase: Optional[Any]						     =    'prefix_path'
  with pytest.raises(UpperCamelCase							):
    _lowercase: List[str]						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
