




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     (		_lowerCamelCase    ):






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : str							,    lowerCamelCase_      : Union[str, Any]							,    lowerCamelCase_      : Dict=False	)    -> List[Any]:
								__magic_name__				,     __magic_name__				,     __magic_name__				:						Union[str, Any]     				=				(
								    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(lowerCamelCase_	)							,    '''wb'''	).close()
								__magic_name__				:						Dict     				=				pa.Table.from_pydict({'''filename''': [Path(lowerCamelCase_	).name] * 100}	)
								if take == -1:
													__magic_name__				:						Tuple     				=				len(lowerCamelCase_	) - skip
								if skip is not None and take is not None:
													__magic_name__				:						Tuple     				=				pa_table.slice(lowerCamelCase_							,    lowerCamelCase_	)
								return pa_table






class  lowerCamelCase     (		_lowerCamelCase    ):






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : Tuple	)    -> Dict:
								__magic_name__				:						str     				=				'''my_name'''
								__magic_name__				:						Tuple     				=				SplitInfo(name='''train'''							,    num_examples=100	)
								__magic_name__				:						Optional[Any]     				=				SplitInfo(name='''test'''							,    num_examples=100	)
								__magic_name__				:						int     				=				[train_info, test_info]
								__magic_name__				:						List[str]     				=				SplitDict()
								split_dict.add(lowerCamelCase_	)
								split_dict.add(lowerCamelCase_	)
								__magic_name__				:						Any     				=				DatasetInfo(splits=lowerCamelCase_	)

								with tempfile.TemporaryDirectory() as tmp_dir:
													__magic_name__				:						Union[str, Any]     				=				ReaderTest(lowerCamelCase_							,    lowerCamelCase_	)

													__magic_name__				:						Optional[int]     				=				'''test[:33%]'''
													__magic_name__				:						Dict     				=				Dataset(**reader.read(lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_	)	)
													self.assertEqual(dset['''filename'''][0]							,    F'''{name}-test'''	)
													self.assertEqual(dset.num_rows							,    33	)
													self.assertEqual(dset.num_columns							,    1	)

													__magic_name__				:						Union[str, Any]     				=				['''train''', '''test[:33%]''']
													__magic_name__				:						Optional[Any]     				=				[Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''	)]
													for instructions in [instructionsa, instructionsa]:
																		__magic_name__				:						List[Any]     				=				[reader.read(lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_	) for instr in instructions]
																		__magic_name__				,     __magic_name__				:						Tuple     				=				(Dataset(**lowerCamelCase_	) for dataset_kwargs in datasets_kwargs)
																		self.assertEqual(train_dset['''filename'''][0]							,    F'''{name}-train'''	)
																		self.assertEqual(train_dset.num_rows							,    100	)
																		self.assertEqual(train_dset.num_columns							,    1	)
																		self.assertIsInstance(train_dset.split							,    lowerCamelCase_	)
																		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							,    lowerCamelCase_	)
																		self.assertEqual(str(test_dset.split	)							,    '''test[:33%]'''	)
																		del train_dset, test_dset

			def 				UpperCAmelCase__							(			self      : int	)    -> Any:
								__magic_name__				:						Optional[int]     				=				'''my_name'''
								__magic_name__				:						Any     				=				SplitInfo(name='''train'''							,    num_examples=1000							,    shard_lengths=[100] * 10	)
								__magic_name__				:						int     				=				[train_info]
								__magic_name__				:						Optional[Any]     				=				SplitDict()
								split_dict.add(lowerCamelCase_	)
								__magic_name__				:						List[str]     				=				DatasetInfo(splits=lowerCamelCase_	)

								with tempfile.TemporaryDirectory() as tmp_dir:
													__magic_name__				:						Tuple     				=				ReaderTest(lowerCamelCase_							,    lowerCamelCase_	)

													__magic_name__				:						List[str]     				=				'''train[:33%]'''
													__magic_name__				:						Tuple     				=				Dataset(**reader.read(lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_	)	)
													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							,    330	)
													self.assertEqual(dset.num_columns							,    1	)


			def 				UpperCAmelCase__							(			self      : List[Any]	)    -> str:
								__magic_name__				:						Dict     				=				SplitInfo(name='''train'''							,    num_examples=100	)
								__magic_name__				:						List[Any]     				=				SplitInfo(name='''test'''							,    num_examples=100	)
								__magic_name__				:						int     				=				SplitDict()
								split_dict.add(lowerCamelCase_	)
								split_dict.add(lowerCamelCase_	)
								__magic_name__				:						List[Any]     				=				DatasetInfo(splits=lowerCamelCase_	)

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

													__magic_name__				:						Optional[int]     				=				[
													    {'''filename''': os.path.join(lowerCamelCase_							,    '''train'''	)},
													    {'''filename''': os.path.join(lowerCamelCase_							,    '''test'''	), '''skip''': 10, '''take''': 10},
													]
													__magic_name__				:						Optional[int]     				=				Dataset(**reader.read_files(lowerCamelCase_							,    original_instructions='''train+test[10:20]'''	)	)
													self.assertEqual(dset.num_rows							,    110	)
													self.assertEqual(dset.num_columns							,    1	)
													del dset







@pytest.mark.parametrize('''in_memory'''				,[False, True]					)
def 		lowercase__ (		__A:						List[str]				,__A:						Tuple				,__A:						Any					):





					'''simple docstring'''






					__magic_name__				:						Any     				=				arrow_file
					with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__				:						Optional[Any]     				=				ArrowReader.read_table(__A				,in_memory=__A					)
					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 		lowercase__ (		__A:						Tuple				,__A:						Optional[Any]				,__A:						List[str]					):





					'''simple docstring'''






					__magic_name__				:						List[str]     				=				arrow_file
					__magic_name__				:						Any     				=				ArrowReader(''''''				,__A					)
					with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__				:						str     				=				reader.read_files([{'''filename''': filename}]				,in_memory=__A					)
					assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
					__magic_name__				:						List[str]     				=				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 		lowercase__ (		):





					'''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%]"

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

					__magic_name__				:						Dict     				=				'''train[2:10]'''
					assert ReadInstruction.from_spec(__A					).to_spec() == spec_train_abs

					__magic_name__				:						List[str]     				=				'''train[15%:-20%]'''
					assert ReadInstruction.from_spec(__A					).to_spec() == spec_train_pct

					__magic_name__				:						str     				=				'''train[:10%](closest)'''
					assert ReadInstruction.from_spec(__A					).to_spec() == "train[:10%]"

					__magic_name__				:						Tuple     				=				'''train[:10%](pct1_dropremainder)'''
					assert ReadInstruction.from_spec(__A					).to_spec() == spec_train_pct_rounding

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







def 		lowercase__ (		):





					'''simple docstring'''






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

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

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







@pytest.mark.parametrize('''name, expected_exception'''				,[(None, TypeError), ('''''', ValueError)]					)
def 		lowercase__ (		__A:						Optional[Any]				,__A:						Any					):





					'''simple docstring'''






					__magic_name__				:						List[str]     				=				[SplitInfo(name='''train'''				,num_examples=1_0_0					)]
					__magic_name__				:						List[str]     				=				'''train'''
					__magic_name__				:						Optional[int]     				=				'''arrow'''
					__magic_name__				:						Any     				=				'''prefix_path'''
					with pytest.raises(__A					):
										__magic_name__				:						Optional[int]     				=				make_file_instructions(__A				,__A				,__A				,__A				,__A					)









import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


__lowerCamelCase :    Any      						=      get_logger(__name__)







def 		lowercase__ (		__A:						str				,__A:						str					):





					'''simple docstring'''






					if text.startswith(__A					):
										return text[len(__A					) :]
					return text  # or whatever







def 		lowercase__ (		__A:						List[str]					):





					'''simple docstring'''






					return {remove_prefix(__A				,'''model.'''					): v for k, v in sd.items()}







def 		lowercase__ (		__A:						List[Dict[str, torch.Tensor]]					):





					'''simple docstring'''






					__magic_name__				:						List[Any]     				=				{}
					for k in state_dicts[0].keys():
										__magic_name__				:						str     				=				[sd[k] for sd in state_dicts]
										__magic_name__				:						List[Any]     				=				sum(__A					) / len(__A					)
										assert isinstance(__A				,torch.Tensor					)
										__magic_name__				:						Any     				=				new_t
					return new_sd







def 		lowercase__ (		__A:						str				,__A:						str				,__A:						str					):





					'''simple docstring'''






					__magic_name__				:						List[Any]     				=				AutoModelForSeqaSeqLM.from_pretrained(__A					)
					if os.path.isfile(__A					):
										__magic_name__				:						str     				=				[pl_ckpt_path]
					else:
										assert os.path.isdir(__A					)
										__magic_name__				:						List[str]     				=				list(Path(__A					).glob('''*.ckpt'''					)					)
										assert ckpt_files, F'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

					if len(__A					) > 1:
										logger.info(F'''averaging the weights of {ckpt_files}'''					)

					__magic_name__				:						Union[str, Any]     				=				[sanitize(torch.load(__A				,map_location='''cpu'''					)['''state_dict''']					) for x in ckpt_files]
					__magic_name__				:						Dict     				=				average_state_dicts(__A					)

					__magic_name__				,     __magic_name__				:						Union[str, Any]     				=				hf_model.load_state_dict(__A				,strict=__A					)
					assert not missing, F'''missing keys: {missing}'''
					hf_model.save_pretrained(__A					)
					try:
										__magic_name__				:						Optional[Any]     				=				AutoTokenizer.from_pretrained(__A					)
										tok.save_pretrained(__A					)
					except Exception:
										pass
										# dont copy tokenizer if cant


if __name__ == "__main__":
	fire.Fire(convert_pl_to_hf)



