
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case_      :					Dict				      =     logging.get_logger(__name__)

snake_case_      :					Tuple				      =     {
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}



def      __UpperCAmelCase    (  snake_case_							:		Dict				,					snake_case_							:		List[Any]				,					snake_case_							:		str				,					snake_case_							:		List[str]				,					snake_case_							:		Any				):



	'''simple docstring'''



	for attribute in key.split("."				):
		UpperCAmelCase:					Union[str, Any] 						=     getattr(snake_case_				,					snake_case_				)

	if weight_type is not None:
		UpperCAmelCase:					Optional[int] 						=     getattr(snake_case_				,					snake_case_				).shape
	else:
		UpperCAmelCase:					Union[str, Any] 						=     hf_pointer.shape

	assert hf_shape == value.shape, (
	    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
	    F' {value.shape} for {full_name}'
	)

	if weight_type == "weight":
		UpperCAmelCase:					List[Any] 						=     value
	elif weight_type == "weight_g":
		UpperCAmelCase:					Tuple 						=     value
	elif weight_type == "weight_v":
		UpperCAmelCase:					Optional[int] 						=     value
	elif weight_type == "bias":
		UpperCAmelCase:					Any 						=     value
	else:
		UpperCAmelCase:					List[Any] 						=     value

	logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'				)



def      __UpperCAmelCase    (  snake_case_							:		Tuple				,					snake_case_							:		Union[str, Any]				,					snake_case_							:		List[str]				):



	'''simple docstring'''



	UpperCAmelCase:					List[Any] 						=     []
	UpperCAmelCase:					List[str] 						=     fairseq_model.state_dict()

	UpperCAmelCase:					Tuple 						=     hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		UpperCAmelCase:					Optional[int] 						=     False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					hf_model.config.feat_extract_norm == "group"				,					)
			UpperCAmelCase:					Optional[Any] 						=     True
		else:
			for key, mapped_key in MAPPING.items():
				UpperCAmelCase:					Dict 						=     "sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

				if key in name or key.split("w2v_model."				)[-1] == name.split("."				)[0]:
					UpperCAmelCase:					List[str] 						=     True
					if "*" in mapped_key:
						UpperCAmelCase:					List[Any] 						=     name.split(snake_case_				)[0].split("."				)[-2]
						if not layer_index.isnumeric():
							continue
						UpperCAmelCase:					Optional[Any] 						=     mapped_key.replace("*"				,					snake_case_				)
					if "weight_g" in name:
						UpperCAmelCase:					Union[str, Any] 						=     "weight_g"
					elif "weight_v" in name:
						UpperCAmelCase:					int 						=     "weight_v"
					elif "weight" in name:
						UpperCAmelCase:					Optional[int] 						=     "weight"
					elif "bias" in name:
						UpperCAmelCase:					Optional[Any] 						=     "bias"
					else:
						UpperCAmelCase:					Optional[int] 						=     None
					set_recursively(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
				continue
		if not is_used:
			unused_weights.append(snake_case_				)

	logger.warning(F'Unused weights: {unused_weights}'				)



def      __UpperCAmelCase    (  snake_case_							:		List[Any]				,					snake_case_							:		int				,					snake_case_							:		Tuple				,					snake_case_							:		Optional[int]				,					snake_case_							:		int				):



	'''simple docstring'''



	UpperCAmelCase:					int 						=     full_name.split("conv_layers."				)[-1]
	UpperCAmelCase:					Union[str, Any] 						=     name.split("."				)
	UpperCAmelCase:					str 						=     int(items[0]				)
	UpperCAmelCase:					int 						=     int(items[1]				)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
			)
			UpperCAmelCase:					Union[str, Any] 						=     value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'				)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
			)
			UpperCAmelCase:					List[str] 						=     value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'				)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
			    " found."
			)
			UpperCAmelCase:					List[str] 						=     value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
			)
			UpperCAmelCase:					Dict 						=     value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
	else:
		unused_weights.append(snake_case_				)



def      __UpperCAmelCase    (  snake_case_							:		List[str]				,					snake_case_							:		int				):



	'''simple docstring'''



	UpperCAmelCase:					List[str] 						=     SEWDConfig()
	if is_finetuned:
		UpperCAmelCase:					Tuple 						=     model.wav_encoder.wav_model.cfg
	else:
		UpperCAmelCase:					Any 						=     model.cfg

	UpperCAmelCase:					int 						=     fs_config.conv_bias
	UpperCAmelCase:					str 						=     eval(fs_config.conv_feature_layers				)
	UpperCAmelCase:					int 						=     [x[0] for x in conv_layers]
	UpperCAmelCase:					int 						=     [x[1] for x in conv_layers]
	UpperCAmelCase:					int 						=     [x[2] for x in conv_layers]
	UpperCAmelCase:					str 						=     "gelu"
	UpperCAmelCase:					Optional[Any] 						=     "layer" if fs_config.extractor_mode == "layer_norm" else "group"
	UpperCAmelCase:					int 						=     0.0
	UpperCAmelCase:					List[Any] 						=     fs_config.activation_fn.name
	UpperCAmelCase:					Optional[int] 						=     fs_config.encoder_embed_dim
	UpperCAmelCase:					Optional[int] 						=     0.02
	UpperCAmelCase:					List[str] 						=     fs_config.encoder_ffn_embed_dim
	UpperCAmelCase:					Tuple 						=     1e-5
	UpperCAmelCase:					Tuple 						=     fs_config.encoder_layerdrop
	UpperCAmelCase:					Optional[int] 						=     fs_config.encoder_attention_heads
	UpperCAmelCase:					Optional[Any] 						=     fs_config.conv_pos_groups
	UpperCAmelCase:					Any 						=     fs_config.conv_pos
	UpperCAmelCase:					Dict 						=     len(snake_case_				)
	UpperCAmelCase:					List[str] 						=     fs_config.encoder_layers
	UpperCAmelCase:					Optional[int] 						=     fs_config.squeeze_factor
	# DeBERTa-specific parameters:
	UpperCAmelCase:					List[Any] 						=     fs_config.max_position_embeddings
	UpperCAmelCase:					Dict 						=     fs_config.position_buckets
	UpperCAmelCase:					List[str] 						=     fs_config.share_att_key
	UpperCAmelCase:					Any 						=     fs_config.relative_attention
	UpperCAmelCase:					Optional[Any] 						=     fs_config.position_biased_input
	UpperCAmelCase:					List[str] 						=     tuple(fs_config.pos_att_type.split("|"				)				)
	UpperCAmelCase:					Any 						=     fs_config.norm_rel_ebd

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		UpperCAmelCase:					Any 						=     model.cfg
		UpperCAmelCase:					Optional[int] 						=     fs_config.final_dropout
		UpperCAmelCase:					Optional[int] 						=     fs_config.layerdrop
	UpperCAmelCase:					List[str] 						=     fs_config.activation_dropout
	UpperCAmelCase:					Union[str, Any] 						=     fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	UpperCAmelCase:					Any 						=     fs_config.attention_dropout
	UpperCAmelCase:					str 						=     fs_config.dropout_input
	UpperCAmelCase:					Any 						=     fs_config.dropout
	UpperCAmelCase:					List[str] 						=     fs_config.mask_channel_length
	UpperCAmelCase:					Union[str, Any] 						=     fs_config.mask_channel_prob
	UpperCAmelCase:					List[str] 						=     fs_config.mask_length
	UpperCAmelCase:					int 						=     fs_config.mask_prob

	UpperCAmelCase:					List[str] 						=     "Wav2Vec2FeatureExtractor"
	UpperCAmelCase:					Union[str, Any] 						=     "Wav2Vec2CTCTokenizer"

	return config



@torch.no_grad()
def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		List[str]				,					snake_case_							:		Any=None				,					snake_case_							:		Optional[Any]=None				,					snake_case_							:		Any=True				):



	'''simple docstring'''



	if is_finetuned:
		UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Tuple 						=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]				,					arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
	else:
		UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

	if config_path is not None:
		UpperCAmelCase:					List[Any] 						=     SEWDConfig.from_pretrained(snake_case_				)
	else:
		UpperCAmelCase:					Optional[int] 						=     convert_config(model[0]				,					snake_case_				)
	UpperCAmelCase:					Optional[int] 						=     model[0].eval()

	UpperCAmelCase:					Optional[Any] 						=     True if config.feat_extract_norm == "layer" else False
	UpperCAmelCase:					Union[str, Any] 						=     WavaVecaFeatureExtractor(
	    feature_size=1				,					sampling_rate=1_6_0_0_0				,					padding_value=0				,					do_normalize=snake_case_				,					return_attention_mask=snake_case_				,					)

	if is_finetuned:
		if dict_path:
			UpperCAmelCase:					Any 						=     Dictionary.load(snake_case_				)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			UpperCAmelCase:					Any 						=     target_dict.pad_index
			UpperCAmelCase:					Optional[Any] 						=     target_dict.bos_index
			UpperCAmelCase:					Any 						=     target_dict.pad_index
			UpperCAmelCase:					Union[str, Any] 						=     target_dict.bos_index
			UpperCAmelCase:					List[str] 						=     target_dict.eos_index
			UpperCAmelCase:					Optional[int] 						=     len(target_dict.symbols				)
			UpperCAmelCase:					List[str] 						=     os.path.join(snake_case_				,					"vocab.json"				)
			if not os.path.isdir(snake_case_				):
				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(snake_case_				)				)
				return
			os.makedirs(snake_case_				,					exist_ok=snake_case_				)
			with open(snake_case_				,					"w"				,					encoding="utf-8"				) as vocab_handle:
				json.dump(target_dict.indices				,					snake_case_				)
			UpperCAmelCase:					Optional[int] 						=     WavaVecaCTCTokenizer(
			    snake_case_				,					unk_token=target_dict.unk_word				,					pad_token=target_dict.pad_word				,					bos_token=target_dict.bos_word				,					eos_token=target_dict.eos_word				,					word_delimiter_token="|"				,					do_lower_case=snake_case_				,					)
			UpperCAmelCase:					List[Any] 						=     WavaVecaProcessor(feature_extractor=snake_case_				,					tokenizer=snake_case_				)
			processor.save_pretrained(snake_case_				)

		UpperCAmelCase:					str 						=     SEWDForCTC(snake_case_				)
	else:
		UpperCAmelCase:					Optional[Any] 						=     SEWDModel(snake_case_				)
		feature_extractor.save_pretrained(snake_case_				)

	recursively_load_weights(snake_case_				,					snake_case_				,					snake_case_				)

	hf_model.save_pretrained(snake_case_				)


if __name__ == "__main__":
	snake_case_      :					Optional[int]				      =     argparse.ArgumentParser()
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
	parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
	parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
	parser.add_argument(
	    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
	)
	snake_case_      :					Optional[Any]				      =     parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)


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 		__lowerCamelCase     (   lowercase		):



							def     A__ (     self	,				__snake_case	,				__snake_case=False							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					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(__snake_case							)	,				"wb"							).close()
								UpperCAmelCase:					Any 						=     pa.Table.from_pydict({"filename": [Path(__snake_case							).name] * 1_0_0}							)
								if take == -1:
									UpperCAmelCase:					List[Any] 						=     len(__snake_case							) - skip
								if skip is not None and take is not None:
									UpperCAmelCase:					Optional[int] 						=     pa_table.slice(__snake_case	,				__snake_case							)
								return pa_table





class 		__lowerCamelCase     (   lowercase		):



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     "my_name"
								UpperCAmelCase:					str 						=     SplitInfo(name="train"	,				num_examples=1_0_0							)
								UpperCAmelCase:					Optional[int] 						=     SplitInfo(name="test"	,				num_examples=1_0_0							)
								UpperCAmelCase:					Optional[int] 						=     [train_info, test_info]
								UpperCAmelCase:					Tuple 						=     SplitDict()
								split_dict.add(__snake_case							)
								split_dict.add(__snake_case							)
								UpperCAmelCase:					Optional[int] 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					int 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					int 						=     "test[:33%]"
									UpperCAmelCase:					Dict 						=     Dataset(**reader.read(__snake_case	,				__snake_case	,				__snake_case							)							)
									self.assertEqual(dset["filename"][0]	,				F'{name}-test'							)
									self.assertEqual(dset.num_rows	,				3_3							)
									self.assertEqual(dset.num_columns	,				1							)

									UpperCAmelCase:					int 						=     ["train", "test[:33%]"]
									UpperCAmelCase:					str 						=     [Split.TRAIN, ReadInstruction.from_spec("test[:33%]"							)]
									for instructions in [instructionsa, instructionsa]:
										UpperCAmelCase:					List[str] 						=     [reader.read(__snake_case	,				__snake_case	,				__snake_case							) for instr in instructions]
										UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     (Dataset(**__snake_case							) for dataset_kwargs in datasets_kwargs)
										self.assertEqual(train_dset["filename"][0]	,				F'{name}-train'							)
										self.assertEqual(train_dset.num_rows	,				1_0_0							)
										self.assertEqual(train_dset.num_columns	,				1							)
										self.assertIsInstance(train_dset.split	,				__snake_case							)
										self.assertEqual(str(train_dset.split							)	,				"train"							)
										self.assertEqual(test_dset["filename"][0]	,				F'{name}-test'							)
										self.assertEqual(test_dset.num_rows	,				3_3							)
										self.assertEqual(test_dset.num_columns	,				1							)
										self.assertIsInstance(test_dset.split	,				__snake_case							)
										self.assertEqual(str(test_dset.split							)	,				"test[:33%]"							)
										del train_dset, test_dset



							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     "my_name"
								UpperCAmelCase:					List[Any] 						=     SplitInfo(name="train"	,				num_examples=1_0_0_0	,				shard_lengths=[1_0_0] * 1_0							)
								UpperCAmelCase:					List[Any] 						=     [train_info]
								UpperCAmelCase:					Dict 						=     SplitDict()
								split_dict.add(__snake_case							)
								UpperCAmelCase:					int 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					Optional[Any] 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					Optional[Any] 						=     "train[:33%]"
									UpperCAmelCase:					Tuple 						=     Dataset(**reader.read(__snake_case	,				__snake_case	,				__snake_case							)							)
									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_3_0							)
									self.assertEqual(dset.num_columns	,				1							)




							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     SplitInfo(name="train"	,				num_examples=1_0_0							)
								UpperCAmelCase:					int 						=     SplitInfo(name="test"	,				num_examples=1_0_0							)
								UpperCAmelCase:					List[Any] 						=     SplitDict()
								split_dict.add(__snake_case							)
								split_dict.add(__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					List[Any] 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					List[str] 						=     [
									    {"filename": os.path.join(__snake_case	,				"train"							)},
									    {"filename": os.path.join(__snake_case	,				"test"							), "skip": 1_0, "take": 1_0},
									]
									UpperCAmelCase:					Tuple 						=     Dataset(**reader.read_files(__snake_case	,				original_instructions="train+test[10:20]"							)							)
									self.assertEqual(dset.num_rows	,				1_1_0							)
									self.assertEqual(dset.num_columns	,				1							)
									del dset



@pytest.mark.parametrize("in_memory"				,					[False, True]				)
def      __UpperCAmelCase    (  snake_case_							:		str				,					snake_case_							:		str				,					snake_case_							:		Any				):



	'''simple docstring'''



	UpperCAmelCase:					Union[str, Any] 						=     arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase:					int 						=     ArrowReader.read_table(snake_case_				,					in_memory=snake_case_				)
	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      __UpperCAmelCase    (  snake_case_							:		List[str]				,					snake_case_							:		Optional[int]				,					snake_case_							:		int				):



	'''simple docstring'''



	UpperCAmelCase:					List[Any] 						=     arrow_file
	UpperCAmelCase:					Tuple 						=     ArrowReader(""				,					snake_case_				)
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase:					Any 						=     reader.read_files([{"filename": filename}]				,					in_memory=snake_case_				)
	assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
	UpperCAmelCase:					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      __UpperCAmelCase    (  ):



	'''simple docstring'''



	assert ReadInstruction("train"				,					to=1_0				,					unit="abs"				).to_spec() == "train[:10]"
	assert ReadInstruction("train"				,					from_=-8_0				,					to=1_0				,					unit="%"				).to_spec() == "train[-80%:10%]"

	UpperCAmelCase:					str 						=     "train+test"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_test

	UpperCAmelCase:					List[Any] 						=     "train[2:10]"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_abs

	UpperCAmelCase:					Tuple 						=     "train[15%:-20%]"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_pct

	UpperCAmelCase:					Dict 						=     "train[:10%](closest)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == "train[:10%]"

	UpperCAmelCase:					List[str] 						=     "train[:10%](pct1_dropremainder)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_pct_rounding

	UpperCAmelCase:					Any 						=     "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_test_pct_rounding



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					Dict 						=     "dummy"
	UpperCAmelCase:					int 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				)]
	UpperCAmelCase:					List[str] 						=     "train[:33%]"
	UpperCAmelCase:					Optional[int] 						=     "arrow"
	UpperCAmelCase:					List[Any] 						=     "prefix"

	UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
	assert isinstance(snake_case_				,					snake_case_				)
	assert file_instructions.num_examples == 3_3
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(snake_case_				,					F'{name}-train.arrow'				), "skip": 0, "take": 3_3}
	]

	UpperCAmelCase:					int 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				,					shard_lengths=[1_0] * 1_0				)]
	UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
	assert isinstance(snake_case_				,					snake_case_				)
	assert file_instructions.num_examples == 3_3
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00000-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00001-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00002-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00003-of-00010.arrow'				), "skip": 0, "take": 3},
	]



@pytest.mark.parametrize("name, expected_exception"				,					[(None, TypeError), ("", ValueError)]				)
def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		Dict				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[int] 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				)]
	UpperCAmelCase:					Union[str, Any] 						=     "train"
	UpperCAmelCase:					List[str] 						=     "arrow"
	UpperCAmelCase:					Any 						=     "prefix_path"
	with pytest.raises(snake_case_				):
		UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
