





"""simple docstring"""
import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
				from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor





class      __A     :





							'''simple docstring'''

							def __init__(				self							:      Dict		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Optional[Any]=13		,       UpperCAmelCase_							:      Any=32		,       UpperCAmelCase_							:      int=2		,       UpperCAmelCase_							:      Optional[Any]=3		,       UpperCAmelCase_							:      List[str]=16		,       UpperCAmelCase_							:      str=[1, 2, 1]		,       UpperCAmelCase_							:      str=[2, 2, 4]		,       UpperCAmelCase_							:      str=2		,       UpperCAmelCase_							:      Union[str, Any]=2.0		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      List[str]=0.0		,       UpperCAmelCase_							:      int=0.0		,       UpperCAmelCase_							:      int=0.1		,       UpperCAmelCase_							:      List[Any]="gelu"		,       UpperCAmelCase_							:      List[Any]=False		,       UpperCAmelCase_							:      str=True		,       UpperCAmelCase_							:      int=0.02		,       UpperCAmelCase_							:      Dict=1E-5		,       UpperCAmelCase_							:      str=True		,       UpperCAmelCase_							:      int=None		,       UpperCAmelCase_							:      Dict=True		,       UpperCAmelCase_							:      List[Any]=10		,       UpperCAmelCase_							:      List[Any]=8		,       )					->int:



														"""simple docstring"""



														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		image_size
														snake_case_           =		patch_size
														snake_case_           =		num_channels
														snake_case_           =		embed_dim
														snake_case_           =		depths
														snake_case_           =		num_heads
														snake_case_           =		window_size
														snake_case_           =		mlp_ratio
														snake_case_           =		qkv_bias
														snake_case_           =		hidden_dropout_prob
														snake_case_           =		attention_probs_dropout_prob
														snake_case_           =		drop_path_rate
														snake_case_           =		hidden_act
														snake_case_           =		use_absolute_embeddings
														snake_case_           =		patch_norm
														snake_case_           =		layer_norm_eps
														snake_case_           =		initializer_range
														snake_case_           =		is_training
														snake_case_           =		scope
														snake_case_           =		use_labels
														snake_case_           =		type_sequence_label_size
														snake_case_           =		encoder_stride



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

														snake_case_           =		None
														if self.use_labels:
																					snake_case_           =		ids_tensor([self.batch_size]		,       self.type_sequence_label_size       )

														snake_case_           =		self.get_config()

														return config, pixel_values, labels



							def 		lowerCAmelCase       (				self							:      Any       )					->int:



														"""simple docstring"""



														return SwinvaConfig(
														    image_size=self.image_size		,       patch_size=self.patch_size		,       num_channels=self.num_channels		,       embed_dim=self.embed_dim		,       depths=self.depths		,       num_heads=self.num_heads		,       window_size=self.window_size		,       mlp_ratio=self.mlp_ratio		,       qkv_bias=self.qkv_bias		,       hidden_dropout_prob=self.hidden_dropout_prob		,       attention_probs_dropout_prob=self.attention_probs_dropout_prob		,       drop_path_rate=self.drop_path_rate		,       hidden_act=self.hidden_act		,       use_absolute_embeddings=self.use_absolute_embeddings		,       path_norm=self.patch_norm		,       layer_norm_eps=self.layer_norm_eps		,       initializer_range=self.initializer_range		,       encoder_stride=self.encoder_stride		,       )



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      str       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		SwinvaModel(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_       )

														snake_case_           =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
														snake_case_           =		int(config.embed_dim * 2 ** (len(config.depths       ) - 1)       )

														self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, expected_seq_len, expected_dim)       )



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Union[str, Any]       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		SwinvaForMaskedImageModeling(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_       )
														self.parent.assertEqual(
														    result.logits.shape		,       (self.batch_size, self.num_channels, self.image_size, self.image_size)       )

														# test greyscale images
														snake_case_           =		1
														snake_case_           =		SwinvaForMaskedImageModeling(UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()

														snake_case_           =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
														snake_case_           =		model(UpperCAmelCase_       )
														self.parent.assertEqual(result.logits.shape		,       (self.batch_size, 1, self.image_size, self.image_size)       )



							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.type_sequence_label_size
														snake_case_           =		SwinvaForImageClassification(UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_		,       labels=UpperCAmelCase_       )
														self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.type_sequence_label_size)       )



							def 		lowerCAmelCase       (				self							:      Tuple       )					->int:



														"""simple docstring"""



														snake_case_           =		self.prepare_config_and_inputs()
														snake_case_       ,						snake_case_       ,						snake_case_           =		config_and_inputs
														snake_case_           =		{"""pixel_values""": pixel_values}
														return config, inputs_dict





@require_torch
class      __A     (snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Any							      =    (
							    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
							)
							__lowercase:		Dict							      =    (
							    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__lowercase:		Union[str, Any]							      =    False
							__lowercase:		Optional[Any]							      =    False
							__lowercase:		Union[str, Any]							      =    False
							__lowercase:		str							      =    False



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		SwinvaModelTester(self       )
														snake_case_           =		ConfigTester(self		,       config_class=UpperCAmelCase_		,       embed_dim=37       )



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



														"""simple docstring"""



														self.config_tester.create_and_test_config_to_json_string()
														self.config_tester.create_and_test_config_to_json_file()
														self.config_tester.create_and_test_config_from_and_save_pretrained()
														self.config_tester.create_and_test_config_with_num_labels()
														self.config_tester.check_config_can_be_init_without_params()
														self.config_tester.check_config_arguments_init()



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Any:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase_       )



							@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""       )
							def 		lowerCAmelCase       (				self							:      Any       )					->Optional[Any]:



														"""simple docstring"""



														pass



							@unittest.skip(reason="""Swinv2 does not use inputs_embeds"""       )
							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      int       )					->List[str]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )
																					self.assertIsInstance(model.get_input_embeddings()		,       (nn.Module)       )
																					snake_case_           =		model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCAmelCase_		,       nn.Linear       )       )



							def 		lowerCAmelCase       (				self							:      int       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )
																					snake_case_           =		inspect.signature(model.forward       )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					snake_case_           =		[*signature.parameters.keys()]

																					snake_case_           =		["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      str       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()
														snake_case_           =		True

														for model_class in self.all_model_classes:
																					snake_case_           =		True
																					snake_case_           =		False
																					snake_case_           =		True
																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.eval()
																					with torch.no_grad():
																												snake_case_           =		model(**self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_       )       )
																					snake_case_           =		outputs.attentions
																					snake_case_           =		len(self.model_tester.depths       )
																					self.assertEqual(len(UpperCAmelCase_       )		,       UpperCAmelCase_       )

																					# check that output_attentions also work using config
																					del inputs_dict["output_attentions"]
																					snake_case_           =		True
																					snake_case_           =		config.window_size**2
																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.eval()
																					with torch.no_grad():
																												snake_case_           =		model(**self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_       )       )
																					snake_case_           =		outputs.attentions
																					self.assertEqual(len(UpperCAmelCase_       )		,       UpperCAmelCase_       )

																					self.assertListEqual(
																					    list(attentions[0].shape[-3:]       )		,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,       )
																					snake_case_           =		len(UpperCAmelCase_       )

																					# Check attention is always last and order is fine
																					snake_case_           =		True
																					snake_case_           =		True
																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.eval()
																					with torch.no_grad():
																												snake_case_           =		model(**self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_       )       )

																					if hasattr(self.model_tester		,       """num_hidden_states_types"""       ):
																												snake_case_           =		self.model_tester.num_hidden_states_types
																					else:
																												# also another +1 for reshaped_hidden_states
																												snake_case_           =		2
																					self.assertEqual(out_len + added_hidden_states		,       len(UpperCAmelCase_       )       )

																					snake_case_           =		outputs.attentions

																					self.assertEqual(len(UpperCAmelCase_       )		,       UpperCAmelCase_       )

																					self.assertListEqual(
																					    list(self_attentions[0].shape[-3:]       )		,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,       )



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      Dict       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		model_class(UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()

														with torch.no_grad():
																					snake_case_           =		model(**self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_       )       )

														snake_case_           =		outputs.hidden_states

														snake_case_           =		getattr(
														    self.model_tester		,       """expected_num_hidden_layers"""		,       len(self.model_tester.depths       ) + 1       )
														self.assertEqual(len(UpperCAmelCase_       )		,       UpperCAmelCase_       )

														# Swinv2 has a different seq_length
														snake_case_           =		(
														    config.patch_size
														    if isinstance(config.patch_size		,       collections.abc.Iterable       )
														    else (config.patch_size, config.patch_size)
														)

														snake_case_           =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]       )		,       [num_patches, self.model_tester.embed_dim]		,       )

														snake_case_           =		outputs.reshaped_hidden_states
														self.assertEqual(len(UpperCAmelCase_       )		,       UpperCAmelCase_       )

														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		reshaped_hidden_states[0].shape
														snake_case_           =		(
														    reshaped_hidden_states[0].view(UpperCAmelCase_		,       UpperCAmelCase_		,       height * width       ).permute(0		,       2		,       1       )
														)
														self.assertListEqual(
														    list(reshaped_hidden_states.shape[-2:]       )		,       [num_patches, self.model_tester.embed_dim]		,       )



							def 		lowerCAmelCase       (				self							:      Any       )					->List[str]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

														snake_case_           =		(
														    self.model_tester.image_size
														    if isinstance(self.model_tester.image_size		,       collections.abc.Iterable       )
														    else (self.model_tester.image_size, self.model_tester.image_size)
														)

														for model_class in self.all_model_classes:
																					snake_case_           =		True
																					self.check_hidden_states_output(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					snake_case_           =		True

																					self.check_hidden_states_output(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()
														snake_case_           =		3

														snake_case_           =		(
														    self.model_tester.image_size
														    if isinstance(self.model_tester.image_size		,       collections.abc.Iterable       )
														    else (self.model_tester.image_size, self.model_tester.image_size)
														)
														snake_case_           =		(
														    config.patch_size
														    if isinstance(config.patch_size		,       collections.abc.Iterable       )
														    else (config.patch_size, config.patch_size)
														)

														snake_case_           =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
														snake_case_           =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

														for model_class in self.all_model_classes:
																					snake_case_           =		True
																					self.check_hidden_states_output(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       (padded_height, padded_width)       )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					snake_case_           =		True
																					self.check_hidden_states_output(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       (padded_height, padded_width)       )



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



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_       )



							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					snake_case_           =		SwinvaModel.from_pretrained(UpperCAmelCase_       )
																					self.assertIsNotNone(UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Tuple:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

														snake_case_           =		_config_zero_init(UpperCAmelCase_       )
														for model_class in self.all_model_classes:
																					snake_case_           =		model_class(config=UpperCAmelCase_       )
																					for name, param in model.named_parameters():
																												if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																																			self.assertIn(
																																			    ((param.data.mean() * 1E9).round() / 1E9).item()		,       [0.0, 1.0]		,       msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""		,       )





@require_vision
@require_torch
class      __A     (unittest.TestCase):





							'''simple docstring'''

							@cached_property
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Tuple:



														"""simple docstring"""



														return (
														    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""       )
														    if is_vision_available()
														    else None
														)



							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""       ).to(
														    UpperCAmelCase_       )
														snake_case_           =		self.default_image_processor

														snake_case_           =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
														snake_case_           =		image_processor(images=UpperCAmelCase_		,       return_tensors="""pt"""       ).to(UpperCAmelCase_       )

														# forward pass
														with torch.no_grad():
																					snake_case_           =		model(**UpperCAmelCase_       )

														# verify the logits
														snake_case_           =		torch.Size((1, 1_000)       )
														self.assertEqual(outputs.logits.shape		,       UpperCAmelCase_       )
														snake_case_           =		torch.tensor([-0.3_947, -0.4_306, 0.0_026]       ).to(UpperCAmelCase_       )
														self.assertTrue(torch.allclose(outputs.logits[0, :3]		,       UpperCAmelCase_		,       atol=1E-4       )       )







"""simple docstring"""
# 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


__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    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)

__SCREAMING_SNAKE_CASE							:    Dict		=    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

__SCREAMING_SNAKE_CASE							:    Dict		=    'zero2'
__SCREAMING_SNAKE_CASE							:    List[Any]		=    'zero3'
__SCREAMING_SNAKE_CASE							:    int		=    [ZEROa, ZEROa]







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Union[str, Any]:
							# 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
							snake_case_           =		parameterized.to_safe_name("""_""".join(str(_SCREAMING_SNAKE_CASE				) for x in param.args				)				)
							return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
__SCREAMING_SNAKE_CASE							:    Dict		=    list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class      __A     (snake_case__):





							'''simple docstring'''

							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Union[str, Any]       )					->Any:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							@require_torch_multi_gpu
							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Optional[Any]       )					->Optional[Any]:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Optional[int]       )					->List[str]:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							@require_torch_multi_gpu
							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[Any]       )					->Optional[int]:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      int = 10		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		models[model]

														snake_case_           =		self.run_trainer(
														    stage=UpperCAmelCase_		,       model_name=UpperCAmelCase_		,       eval_steps=UpperCAmelCase_		,       num_train_epochs=1		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )

														self.do_checks(UpperCAmelCase_       )

														return output_dir



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      int = 10		,       UpperCAmelCase_							:      int = 1		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.get_auto_remove_tmp_dir("""./xxx"""		,       after=UpperCAmelCase_       )
														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(UpperCAmelCase_       )}
            --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
														snake_case_           =		F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
														snake_case_           =		[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
														snake_case_           =		self.get_launcher(UpperCAmelCase_       )

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

														return output_dir



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Any=False       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		min(2		,       get_gpu_count()       ) if distributed else 1
														return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()



