






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 		_snake_case  (  A__					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= pa.array(TypedSequence([1, 2, 3]))
						self.assertEqual(arr.type		,					pa.intaa())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						with self.assertRaises(a):
									SCREAMING_SNAKE_CASE						= pa.array(TypedSequence([1, 2, 3])		,					type=pa.intaa())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						with self.assertRaises(a):
									SCREAMING_SNAKE_CASE						= pa.array(TypedSequence([1, 2, 3]		,					try_type=Value('bool')		,					type=Value('int64')))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= pa.array(TypedSequence([1, 2, 3]		,					type=Value('int32')))
						self.assertEqual(arr.type		,					pa.intaa())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						with self.assertRaises((TypeError, pa.lib.ArrowInvalid)):
									SCREAMING_SNAKE_CASE						= pa.array(TypedSequence(['foo', 'bar']		,					type=Value('int64')))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= pa.array(TypedSequence([1, 2, 3]		,					try_type=Value('int32')))
						self.assertEqual(arr.type		,					pa.intaa())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= pa.array(TypedSequence(['foo', 'bar']		,					try_type=Value('int64')))
						self.assertEqual(arr.type		,					pa.string())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= pa.array(TypedSequence([[[1, 2, 3]]]		,					type=ArrayaD((1, 3)		,					'int64')))
						self.assertEqual(arr.type		,					ArrayaDExtensionType((1, 3)		,					'int64'))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						with self.assertRaises((TypeError, pa.lib.ArrowInvalid)):
									SCREAMING_SNAKE_CASE						= pa.array(TypedSequence(['foo', 'bar']		,					type=ArrayaD((1, 3)		,					'int64')))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= pa.array(TypedSequence([[[1, 2, 3]]]		,					try_type=ArrayaD((1, 3)		,					'int64')))
						self.assertEqual(arr.type		,					ArrayaDExtensionType((1, 3)		,					'int64'))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= pa.array(TypedSequence(['foo', 'bar']		,					try_type=ArrayaD((1, 3)		,					'int64')))
						self.assertEqual(arr.type		,					pa.string())







			@require_pil
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						import PIL.Image

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






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= pa.BufferReader(_UpperCAmelCase) if isinstance(_UpperCAmelCase							,       pa.Buffer) else pa.memory_map(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= pa.ipc.open_stream(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= 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__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= pa.BufferOutputStream()
			SCREAMING_SNAKE_CASE						= pa.schema(_UpperCAmelCase) if fields else None
			with ArrowWriter(stream=_UpperCAmelCase							,       schema=_UpperCAmelCase							,       writer_batch_size=_UpperCAmelCase) as writer:
						writer.write({'col_1': 'foo', 'col_2': 1})
						writer.write({'col_1': 'bar', 'col_2': 2})
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						SCREAMING_SNAKE_CASE						= {'col_1': pa.string(), 'col_2': pa.intaa()}
			assert writer._schema == pa.schema(_UpperCAmelCase							,       metadata=writer._schema.metadata)
			_check_output(output.getvalue()							,       expected_num_chunks=num_examples if writer_batch_size == 1 else 1)






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= pa.BufferOutputStream()
			SCREAMING_SNAKE_CASE						= Features({'labels': ClassLabel(names=['neg', 'pos'])})
			with ArrowWriter(stream=_UpperCAmelCase							,       features=_UpperCAmelCase) as writer:
						writer.write({'labels': 0})
						writer.write({'labels': 1})
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			assert writer._schema == features.arrow_schema
			assert writer._schema.metadata == features.arrow_schema.metadata
			SCREAMING_SNAKE_CASE						= pa.BufferReader(output.getvalue())
			SCREAMING_SNAKE_CASE						= pa.ipc.open_stream(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= f.read_all()
			SCREAMING_SNAKE_CASE						= 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(_UpperCAmelCase)






@pytest.mark.parametrize('writer_batch_size'							,       [None, 1, 10])
def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= pa.BufferOutputStream()
			with ArrowWriter(
			    stream=_UpperCAmelCase							,       writer_batch_size=_UpperCAmelCase							,       hash_salt='split_name'							,       check_duplicates=_UpperCAmelCase							,       ) as writer:
						with pytest.raises(_UpperCAmelCase):
									writer.write({'col_1': 'foo', 'col_2': 1}							,       key=[1, 2])
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= writer.finalize()






@pytest.mark.parametrize('writer_batch_size'							,       [None, 2, 10])
def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= pa.BufferOutputStream()
			with ArrowWriter(
			    stream=_UpperCAmelCase							,       writer_batch_size=_UpperCAmelCase							,       hash_salt='split_name'							,       check_duplicates=_UpperCAmelCase							,       ) as writer:
						with pytest.raises(_UpperCAmelCase):
									writer.write({'col_1': 'foo', 'col_2': 1}							,       key=10)
									writer.write({'col_1': 'bar', 'col_2': 2}							,       key=10)
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= writer.finalize()






@pytest.mark.parametrize('writer_batch_size'							,       [None, 2, 10])
def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= pa.BufferOutputStream()
			with ArrowWriter(
			    stream=_UpperCAmelCase							,       writer_batch_size=_UpperCAmelCase							,       hash_salt='split_name'							,       check_duplicates=_UpperCAmelCase							,       ) as writer:
						writer.write({'col_1': 'foo', 'col_2': 1}							,       key=1)
						writer.write({'col_1': 'bar', 'col_2': 2}							,       key=2)
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= 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__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= pa.BufferOutputStream()
			SCREAMING_SNAKE_CASE						= pa.schema(_UpperCAmelCase) if fields else None
			with ArrowWriter(stream=_UpperCAmelCase							,       schema=_UpperCAmelCase							,       writer_batch_size=_UpperCAmelCase) as writer:
						writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]})
						writer.write_batch({'col_1': [], 'col_2': []})
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						SCREAMING_SNAKE_CASE						= {'col_1': pa.string(), 'col_2': pa.intaa()}
			assert writer._schema == pa.schema(_UpperCAmelCase							,       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__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= pa.BufferOutputStream()
			SCREAMING_SNAKE_CASE						= pa.schema(_UpperCAmelCase) if fields else None
			with ArrowWriter(stream=_UpperCAmelCase							,       schema=_UpperCAmelCase							,       writer_batch_size=_UpperCAmelCase) as writer:
						writer.write_table(pa.Table.from_pydict({'col_1': ['foo', 'bar'], 'col_2': [1, 2]}))
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						SCREAMING_SNAKE_CASE						= {'col_1': pa.string(), 'col_2': pa.intaa()}
			assert writer._schema == pa.schema(_UpperCAmelCase							,       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__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= pa.BufferOutputStream()
			SCREAMING_SNAKE_CASE						= pa.schema(_UpperCAmelCase) if fields else None
			with ArrowWriter(stream=_UpperCAmelCase							,       schema=_UpperCAmelCase							,       writer_batch_size=_UpperCAmelCase) 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]}))
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						SCREAMING_SNAKE_CASE						= {'col_1': pa.string(), 'col_2': pa.intaa()}
			assert writer._schema == pa.schema(_UpperCAmelCase							,       metadata=writer._schema.metadata)
			_check_output(output.getvalue()							,       expected_num_chunks=num_examples if writer_batch_size == 1 else 1)






def 				lowerCamelCase__				():
			with tempfile.TemporaryDirectory() as tmp_dir:
						SCREAMING_SNAKE_CASE						= {'col_1': pa.string(), 'col_2': pa.intaa()}
						SCREAMING_SNAKE_CASE						= os.path.join(_UpperCAmelCase							,       'test.arrow')
						with ArrowWriter(path=_UpperCAmelCase							,       schema=pa.schema(_UpperCAmelCase)) as writer:
									writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]})
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						assert writer._schema == pa.schema(_UpperCAmelCase							,       metadata=writer._schema.metadata)
						_check_output(_UpperCAmelCase							,       1)






def 				lowerCamelCase__				(_UpperCAmelCase):
			if pa.types.is_list(_UpperCAmelCase):
						return get_base_dtype(arr_type.value_type)
			else:
						return arr_type






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			if isinstance(lst[0]							,       _UpperCAmelCase):
						change_first_primitive_element_in_list(lst[0]							,       _UpperCAmelCase)
			else:
						SCREAMING_SNAKE_CASE						= 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__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= pa.array(TypedSequence(_UpperCAmelCase							,       optimized_int_type=_UpperCAmelCase))
			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__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# in range
			SCREAMING_SNAKE_CASE						= pa.array(OptimizedTypedSequence(_UpperCAmelCase							,       col=_UpperCAmelCase))
			assert get_base_dtype(arr.type) == expected_dtype

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






@pytest.mark.parametrize('raise_exception'							,       [False, True])
def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= str(tmp_path / 'dataset-train.arrow')
			try:
						with ArrowWriter(path=_UpperCAmelCase) 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__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= 'mock://dataset-train.arrow'
			with ArrowWriter(path=_UpperCAmelCase							,       storage_options=mockfs.storage_options) as writer:
						assert isinstance(writer._fs							,       type(_UpperCAmelCase))
						assert writer._fs.storage_options == mockfs.storage_options
						writer.write({'col_1': 'foo', 'col_2': 1})
						writer.write({'col_1': 'bar', 'col_2': 2})
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			assert mockfs.exists(_UpperCAmelCase)






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= pa.BufferOutputStream()
			with ParquetWriter(stream=_UpperCAmelCase) as writer:
						writer.write({'col_1': 'foo', 'col_2': 1})
						writer.write({'col_1': 'bar', 'col_2': 2})
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			SCREAMING_SNAKE_CASE						= pa.BufferReader(output.getvalue())
			SCREAMING_SNAKE_CASE						= pq.read_table(_UpperCAmelCase)
			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__				(_UpperCAmelCase							,       _UpperCAmelCase):
			import PIL.Image

			SCREAMING_SNAKE_CASE						= str(tmp_path / 'test_image_rgb.jpg')
			PIL.Image.fromarray(np.zeros((5, 5)							,       dtype=np.uinta)).save(_UpperCAmelCase							,       format='png')
			SCREAMING_SNAKE_CASE						= pa.BufferOutputStream()
			with ParquetWriter(
			    stream=_UpperCAmelCase							,       features=Features({'image': Image()})							,       embed_local_files=_UpperCAmelCase) as writer:
						writer.write({'image': image_path})
						writer.finalize()
			SCREAMING_SNAKE_CASE						= pa.BufferReader(output.getvalue())
			SCREAMING_SNAKE_CASE						= pq.read_table(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= pa_table.to_pydict()
			if embed_local_files:
						assert isinstance(out['image'][0]['path']							,       _UpperCAmelCase)
						with open(_UpperCAmelCase							,       '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__				():
			SCREAMING_SNAKE_CASE						= pa.schema([pa.field('col_1'							,       pa.string()							,       nullable=_UpperCAmelCase)])
			SCREAMING_SNAKE_CASE						= pa.BufferOutputStream()
			with ArrowWriter(stream=_UpperCAmelCase) as writer:
						writer._build_writer(inferred_schema=_UpperCAmelCase)
						assert writer._schema == pa.schema([pa.field('col_1'							,       pa.string())])












# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


a_		:						Dict             =    Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

a_		:						List[str]             =    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

a_		:						Union[str, Any]             =    'zero2'
a_		:						List[Any]             =    'zero3'
a_		:						List[str]             =    [ZEROa, ZEROa]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# customize the test name generator function as we want both params to appear in the sub-test
			# name, as by default it shows only the first param
			SCREAMING_SNAKE_CASE						= parameterized.to_safe_name('_'.join(str(_UpperCAmelCase) for x in param.args))
			return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
a_		:						List[Any]             =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 		_snake_case  (  A__					):






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[str]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       int:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict:
						# XXX: run_asr is premature and doesn't save any results
						# so all we check for now is that the process didn't fail
						pass






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = True		,					a = True		,					a = True		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= models[model]

						SCREAMING_SNAKE_CASE						= self.run_trainer(
						    stage=a		,					model_name=a		,					eval_steps=a		,					num_train_epochs=1		,					distributed=a		,					fpaa=a		,					)

						self.do_checks(a)

						return output_dir






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = 1		,					a = True		,					a = True		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.get_auto_remove_tmp_dir('./xxx'		,					after=a)
						SCREAMING_SNAKE_CASE						= f'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(a)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

						if fpaa:
									args.extend(['--fp16'])

						# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
						# hence the separate config files
						SCREAMING_SNAKE_CASE						= f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
						SCREAMING_SNAKE_CASE						= [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
						SCREAMING_SNAKE_CASE						= self.get_launcher(a)

						SCREAMING_SNAKE_CASE						= launcher + script + args + ds_args
						# keep for quick debug
						# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
						execute_subprocess_async(a		,					env=self.get_env())

						return output_dir







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       Optional[int]:
						# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
						# - it won't be able to handle that
						# 2. for now testing with just 2 gpus max (since some quality tests may give different
						# results with mode gpus because we use very little data)
						SCREAMING_SNAKE_CASE						= min(2		,					get_gpu_count()) if distributed else 1
						return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




