



import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



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

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  pa.array(TypedSequence([1, 2, 3]				)				)
						self.assertEqual(arr.type	,pa.intaa()				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						with self.assertRaises(__lowerCamelCase				):
										lowerCAmelCase__  :       Tuple				=  pa.array(TypedSequence([1, 2, 3]				)	,type=pa.intaa()				)



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						with self.assertRaises(__lowerCamelCase				):
										lowerCAmelCase__  :       Dict				=  pa.array(TypedSequence([1, 2, 3]	,try_type=Value('''bool'''				)	,type=Value('''int64'''				)				)				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  pa.array(TypedSequence([1, 2, 3]	,type=Value('''int32'''				)				)				)
						self.assertEqual(arr.type	,pa.intaa()				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						with self.assertRaises((TypeError, pa.lib.ArrowInvalid)				):
										lowerCAmelCase__  :       Union[str, Any]				=  pa.array(TypedSequence(['''foo''', '''bar''']	,type=Value('''int64'''				)				)				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  pa.array(TypedSequence([1, 2, 3]	,try_type=Value('''int32'''				)				)				)
						self.assertEqual(arr.type	,pa.intaa()				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  pa.array(TypedSequence(['''foo''', '''bar''']	,try_type=Value('''int64'''				)				)				)
						self.assertEqual(arr.type	,pa.string()				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  pa.array(TypedSequence([[[1, 2, 3]]]	,type=ArrayaD((1, 3)	,'''int64'''				)				)				)
						self.assertEqual(arr.type	,ArrayaDExtensionType((1, 3)	,'''int64'''				)				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						with self.assertRaises((TypeError, pa.lib.ArrowInvalid)				):
										lowerCAmelCase__  :       List[str]				=  pa.array(TypedSequence(['''foo''', '''bar''']	,type=ArrayaD((1, 3)	,'''int64'''				)				)				)



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  pa.array(TypedSequence([[[1, 2, 3]]]	,try_type=ArrayaD((1, 3)	,'''int64'''				)				)				)
						self.assertEqual(arr.type	,ArrayaDExtensionType((1, 3)	,'''int64'''				)				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  pa.array(TypedSequence(['''foo''', '''bar''']	,try_type=ArrayaD((1, 3)	,'''int64'''				)				)				)
						self.assertEqual(arr.type	,pa.string()				)



		@require_pil
		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						import PIL.Image

						lowerCAmelCase__  :       List[Any]				=  PIL.Image.fromarray(np.arange(10	,dtype=np.uinta				).reshape(2	,5				)				)
						with patch(
						    '''datasets.arrow_writer.cast_to_python_objects'''	,side_effect=__lowerCamelCase				) as mock_cast_to_python_objects:
										lowerCAmelCase__  :       Union[str, Any]				=  pa.array(TypedSequence([{'''path''': None, '''bytes''': B'''image_bytes'''}, pil_image]	,type=Image()				)				)
										lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[int]				=  mock_cast_to_python_objects.call_args_list[-1]
										self.assertIn('''optimize_list_casting'''	,__lowerCamelCase				)
										self.assertFalse(kwargs['''optimize_list_casting''']				)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	int):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  pa.BufferReader(lowerCamelCase_) if isinstance(lowerCamelCase_ ,pa.Buffer) else pa.memory_map(lowerCamelCase_)
				lowerCAmelCase__  :       List[Any]				=  pa.ipc.open_stream(lowerCamelCase_)
				lowerCAmelCase__  :       pa.Table				=  f.read_all()
				assert len(pa_table.to_batches()) == expected_num_chunks
				assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
				del pa_table






@pytest.mark.parametrize('''writer_batch_size''' ,[None, 1, 10])
@pytest.mark.parametrize(
    '''fields''' ,[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	Tuple):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  pa.BufferOutputStream()
				lowerCAmelCase__  :       Any				=  pa.schema(lowerCamelCase_) if fields else None
				with ArrowWriter(stream=lowerCamelCase_ ,schema=lowerCamelCase_ ,writer_batch_size=lowerCamelCase_) as writer:
								writer.write({'''col_1''': '''foo''', '''col_2''': 1})
								writer.write({'''col_1''': '''bar''', '''col_2''': 2})
								lowerCAmelCase__		,	lowerCAmelCase__  :       Any				=  writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				if not fields:
								lowerCAmelCase__  :       int				=  {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
				assert writer._schema == pa.schema(lowerCamelCase_ ,metadata=writer._schema.metadata)
				_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1)






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  pa.BufferOutputStream()
				lowerCAmelCase__  :       Union[str, Any]				=  Features({'''labels''': ClassLabel(names=['''neg''', '''pos'''])})
				with ArrowWriter(stream=lowerCamelCase_ ,features=lowerCamelCase_) as writer:
								writer.write({'''labels''': 0})
								writer.write({'''labels''': 1})
								lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[Any]				=  writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				assert writer._schema == features.arrow_schema
				assert writer._schema.metadata == features.arrow_schema.metadata
				lowerCAmelCase__  :       Tuple				=  pa.BufferReader(output.getvalue())
				lowerCAmelCase__  :       Optional[Any]				=  pa.ipc.open_stream(lowerCamelCase_)
				lowerCAmelCase__  :       pa.Table				=  f.read_all()
				lowerCAmelCase__  :       Any				=  pa_table.schema
				assert pa_table.num_rows == 2
				assert schema == features.arrow_schema
				assert schema.metadata == features.arrow_schema.metadata
				assert features == Features.from_arrow_schema(lowerCamelCase_)






@pytest.mark.parametrize('''writer_batch_size''' ,[None, 1, 10])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  pa.BufferOutputStream()
				with ArrowWriter(
				    stream=lowerCamelCase_ ,writer_batch_size=lowerCamelCase_ ,hash_salt='''split_name''' ,check_duplicates=lowerCamelCase_ ,) as writer:
								with pytest.raises(lowerCamelCase_):
												writer.write({'''col_1''': '''foo''', '''col_2''': 1} ,key=[1, 2])
												lowerCAmelCase__		,	lowerCAmelCase__  :       Union[str, Any]				=  writer.finalize()






@pytest.mark.parametrize('''writer_batch_size''' ,[None, 2, 10])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  pa.BufferOutputStream()
				with ArrowWriter(
				    stream=lowerCamelCase_ ,writer_batch_size=lowerCamelCase_ ,hash_salt='''split_name''' ,check_duplicates=lowerCamelCase_ ,) as writer:
								with pytest.raises(lowerCamelCase_):
												writer.write({'''col_1''': '''foo''', '''col_2''': 1} ,key=10)
												writer.write({'''col_1''': '''bar''', '''col_2''': 2} ,key=10)
												lowerCAmelCase__		,	lowerCAmelCase__  :       Union[str, Any]				=  writer.finalize()






@pytest.mark.parametrize('''writer_batch_size''' ,[None, 2, 10])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  pa.BufferOutputStream()
				with ArrowWriter(
				    stream=lowerCamelCase_ ,writer_batch_size=lowerCamelCase_ ,hash_salt='''split_name''' ,check_duplicates=lowerCamelCase_ ,) as writer:
								writer.write({'''col_1''': '''foo''', '''col_2''': 1} ,key=1)
								writer.write({'''col_1''': '''bar''', '''col_2''': 2} ,key=2)
								lowerCAmelCase__		,	lowerCAmelCase__  :       int				=  writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1)






@pytest.mark.parametrize('''writer_batch_size''' ,[None, 1, 10])
@pytest.mark.parametrize(
    '''fields''' ,[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	str ,lowerCamelCase_	:	Any):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  pa.BufferOutputStream()
				lowerCAmelCase__  :       int				=  pa.schema(lowerCamelCase_) if fields else None
				with ArrowWriter(stream=lowerCamelCase_ ,schema=lowerCamelCase_ ,writer_batch_size=lowerCamelCase_) as writer:
								writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]})
								writer.write_batch({'''col_1''': [], '''col_2''': []})
								lowerCAmelCase__		,	lowerCAmelCase__  :       int				=  writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				if not fields:
								lowerCAmelCase__  :       List[Any]				=  {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
				assert writer._schema == pa.schema(lowerCamelCase_ ,metadata=writer._schema.metadata)
				_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1)






@pytest.mark.parametrize('''writer_batch_size''' ,[None, 1, 10])
@pytest.mark.parametrize(
    '''fields''' ,[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  pa.BufferOutputStream()
				lowerCAmelCase__  :       Union[str, Any]				=  pa.schema(lowerCamelCase_) if fields else None
				with ArrowWriter(stream=lowerCamelCase_ ,schema=lowerCamelCase_ ,writer_batch_size=lowerCamelCase_) as writer:
								writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}))
								lowerCAmelCase__		,	lowerCAmelCase__  :       int				=  writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				if not fields:
								lowerCAmelCase__  :       Dict				=  {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
				assert writer._schema == pa.schema(lowerCamelCase_ ,metadata=writer._schema.metadata)
				_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1)






@pytest.mark.parametrize('''writer_batch_size''' ,[None, 1, 10])
@pytest.mark.parametrize(
    '''fields''' ,[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Tuple):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  pa.BufferOutputStream()
				lowerCAmelCase__  :       Union[str, Any]				=  pa.schema(lowerCamelCase_) if fields else None
				with ArrowWriter(stream=lowerCamelCase_ ,schema=lowerCamelCase_ ,writer_batch_size=lowerCamelCase_) as writer:
								writer.write_row(pa.Table.from_pydict({'''col_1''': ['''foo'''], '''col_2''': [1]}))
								writer.write_row(pa.Table.from_pydict({'''col_1''': ['''bar'''], '''col_2''': [2]}))
								lowerCAmelCase__		,	lowerCAmelCase__  :       Union[str, Any]				=  writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				if not fields:
								lowerCAmelCase__  :       Tuple				=  {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
				assert writer._schema == pa.schema(lowerCamelCase_ ,metadata=writer._schema.metadata)
				_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1)






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				with tempfile.TemporaryDirectory() as tmp_dir:
								lowerCAmelCase__  :       Optional[int]				=  {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
								lowerCAmelCase__  :       Tuple				=  os.path.join(lowerCamelCase_ ,'''test.arrow''')
								with ArrowWriter(path=lowerCamelCase_ ,schema=pa.schema(lowerCamelCase_)) as writer:
												writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]})
												lowerCAmelCase__		,	lowerCAmelCase__  :       List[Any]				=  writer.finalize()
								assert num_examples == 2
								assert num_bytes > 0
								assert writer._schema == pa.schema(lowerCamelCase_ ,metadata=writer._schema.metadata)
								_check_output(lowerCamelCase_ ,1)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				if pa.types.is_list(lowerCamelCase_):
								return get_base_dtype(arr_type.value_type)
				else:
								return arr_type






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict ,lowerCamelCase_	:	List[str]):





				'''simple docstring'''

				if isinstance(lst[0] ,lowerCamelCase_):
								change_first_primitive_element_in_list(lst[0] ,lowerCamelCase_)
				else:
								lowerCAmelCase__  :       List[str]				=  value






@pytest.mark.parametrize('''optimized_int_type, expected_dtype''' ,[(None, pa.intaa()), (Value('''int32'''), pa.intaa())])
@pytest.mark.parametrize('''sequence''' ,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	int ,lowerCamelCase_	:	Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  pa.array(TypedSequence(lowerCamelCase_ ,optimized_int_type=lowerCamelCase_))
				assert get_base_dtype(arr.type) == expected_dtype






@pytest.mark.parametrize(
    '''col, expected_dtype''' ,[
        ('''attention_mask''', pa.inta()),
        ('''special_tokens_mask''', pa.inta()),
        ('''token_type_ids''', pa.inta()),
        ('''input_ids''', pa.intaa()),
        ('''other''', pa.intaa()),
    ] ,)
@pytest.mark.parametrize('''sequence''' ,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[Any]				=  pa.array(OptimizedTypedSequence(lowerCamelCase_ ,col=lowerCamelCase_))
				assert get_base_dtype(arr.type) == expected_dtype

				# not in range
				if col != "other":
								# avoids errors due to in-place modifications
								lowerCAmelCase__  :       Union[str, Any]				=  copy.deepcopy(lowerCamelCase_)
								lowerCAmelCase__  :       Any				=  np.iinfo(expected_dtype.to_pandas_dtype()).max + 1
								change_first_primitive_element_in_list(lowerCamelCase_ ,lowerCamelCase_)
								lowerCAmelCase__  :       Tuple				=  pa.array(OptimizedTypedSequence(lowerCamelCase_ ,col=lowerCamelCase_))
								assert get_base_dtype(arr.type) == pa.intaa()






@pytest.mark.parametrize('''raise_exception''' ,[False, True])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Dict):





				'''simple docstring'''

				lowerCAmelCase__  :       str				=  str(tmp_path / '''dataset-train.arrow''')
				try:
								with ArrowWriter(path=lowerCamelCase_) as writer:
												if raise_exception:
																raise pa.lib.ArrowInvalid()
												else:
																writer.stream.close()
				except pa.lib.ArrowInvalid:
								pass
				finally:
								assert writer.stream.closed






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  '''mock://dataset-train.arrow'''
				with ArrowWriter(path=lowerCamelCase_ ,storage_options=mockfs.storage_options) as writer:
								assert isinstance(writer._fs ,type(lowerCamelCase_))
								assert writer._fs.storage_options == mockfs.storage_options
								writer.write({'''col_1''': '''foo''', '''col_2''': 1})
								writer.write({'''col_1''': '''bar''', '''col_2''': 2})
								lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[Any]				=  writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				assert mockfs.exists(lowerCamelCase_)






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[Any]				=  pa.BufferOutputStream()
				with ParquetWriter(stream=lowerCamelCase_) as writer:
								writer.write({'''col_1''': '''foo''', '''col_2''': 1})
								writer.write({'''col_1''': '''bar''', '''col_2''': 2})
								lowerCAmelCase__		,	lowerCAmelCase__  :       List[Any]				=  writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				lowerCAmelCase__  :       List[Any]				=  pa.BufferReader(output.getvalue())
				lowerCAmelCase__  :       pa.Table				=  pq.read_table(lowerCamelCase_)
				assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}






@require_pil
@pytest.mark.parametrize('''embed_local_files''' ,[False, True])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	List[str]):





				'''simple docstring'''

				import PIL.Image

				lowerCAmelCase__  :       Optional[int]				=  str(tmp_path / '''test_image_rgb.jpg''')
				PIL.Image.fromarray(np.zeros((5, 5) ,dtype=np.uinta)).save(lowerCamelCase_ ,format='''png''')
				lowerCAmelCase__  :       Union[str, Any]				=  pa.BufferOutputStream()
				with ParquetWriter(
				    stream=lowerCamelCase_ ,features=Features({'''image''': Image()}) ,embed_local_files=lowerCamelCase_) as writer:
								writer.write({'''image''': image_path})
								writer.finalize()
				lowerCAmelCase__  :       Dict				=  pa.BufferReader(output.getvalue())
				lowerCAmelCase__  :       pa.Table				=  pq.read_table(lowerCamelCase_)
				lowerCAmelCase__  :       List[Any]				=  pa_table.to_pydict()
				if embed_local_files:
								assert isinstance(out['''image'''][0]['''path'''] ,lowerCamelCase_)
								with open(lowerCamelCase_ ,'''rb''') as f:
												assert out["image"][0]["bytes"] == f.read()
				else:
								assert out["image"][0]["path"] == image_path
								assert out["image"][0]["bytes"] is None






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  pa.schema([pa.field('''col_1''' ,pa.string() ,nullable=lowerCamelCase_)])
				lowerCAmelCase__  :       List[str]				=  pa.BufferOutputStream()
				with ArrowWriter(stream=lowerCamelCase_) as writer:
								writer._build_writer(inferred_schema=lowerCamelCase_)
								assert writer._schema == pa.schema([pa.field('''col_1''' ,pa.string())])






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__snake_case       :	List[str]            ='tiny-wmt19-en-ru'

# Build

# borrowed from a test
__snake_case       :	Any            =[
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
__snake_case       :	Union[str, Any]            =dict(zip(vocab, range(len(vocab))))
__snake_case       :	List[Any]            =['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case       :	Optional[Any]            =Path(tmpdirname)
							__snake_case       :	int            =build_dir / VOCAB_FILES_NAMES['src_vocab_file']
							__snake_case       :	str            =build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
							__snake_case       :	Optional[int]            =build_dir / VOCAB_FILES_NAMES['merges_file']
							with open(src_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(tgt_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(merges_file, 'w') as fp:
														fp.write('\n'.join(merges))

							__snake_case       :	Union[str, Any]            =FSMTTokenizer(
							    langs=['en', 'ru'],
							    src_vocab_size=len(vocab),
							    tgt_vocab_size=len(vocab),
							    src_vocab_file=src_vocab_file,
							    tgt_vocab_file=tgt_vocab_file,
							    merges_file=merges_file,
							)

__snake_case       :	Dict            =FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__snake_case       :	Optional[Any]            =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__snake_case       :	Dict            =tokenizer(['Making tiny model'], return_tensors='pt')
__snake_case       :	Dict            =tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru

