





import collections
import inspect
import unittest

from transformers import SwinConfig
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_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
		from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor




class 					UpperCamelCase_    :


							def __init__(							self      :int							,			__A      :Union[str, Any]							,			__A      :int=13							,			__A      :Tuple=32							,			__A      :Any=2							,			__A      :Tuple=3							,			__A      :int=16							,			__A      :int=[1, 2, 1]							,			__A      :str=[2, 2, 4]							,			__A      :Dict=2							,			__A      :List[Any]=2.0							,			__A      :Tuple=True							,			__A      :Optional[Any]=0.0							,			__A      :int=0.0							,			__A      :Dict=0.1							,			__A      :Tuple="gelu"							,			__A      :str=False							,			__A      :List[Any]=True							,			__A      :Optional[int]=0.0_2							,			__A      :Optional[int]=1E-5							,			__A      :Dict=True							,			__A      :Tuple=None							,			__A      :List[str]=True							,			__A      :Union[str, Any]=10							,			__A      :Union[str, Any]=8							,			__A      :Dict=["stage1", "stage2"]							,			__A      :List[str]=[1, 2]							,			)				->				List[Any]:



									"""simple docstring"""



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


							def        _snake_case					(							self      :Optional[int]						)				->				Tuple:



									"""simple docstring"""



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

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

									SCREAMING_SNAKE_CASE__								= self.get_config()

									return config, pixel_values, labels


							def        _snake_case					(							self      :int						)				->				int:



									"""simple docstring"""



									return SwinConfig(
									    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							,			out_features=self.out_features							,			out_indices=self.out_indices							,			)


							def        _snake_case					(							self      :int							,			__A      :Optional[int]							,			__A      :str							,			__A      :Optional[Any]						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= SwinModel(config=__A						)
									model.to(__A						)
									model.eval()
									SCREAMING_SNAKE_CASE__								= model(__A						)

									SCREAMING_SNAKE_CASE__								= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
									SCREAMING_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        _snake_case					(							self      :Union[str, Any]							,			__A      :Union[str, Any]							,			__A      :int							,			__A      :Tuple						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= SwinBackbone(config=__A						)
									model.to(__A						)
									model.eval()
									SCREAMING_SNAKE_CASE__								= model(__A						)

									# verify hidden states
									self.parent.assertEqual(len(result.feature_maps						)							,			len(config.out_features						)						)
									self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,			[self.batch_size, model.channels[0], 16, 16]						)

									# verify channels
									self.parent.assertEqual(len(model.channels						)							,			len(config.out_features						)						)

									# verify backbone works with out_features=None
									SCREAMING_SNAKE_CASE__								= None
									SCREAMING_SNAKE_CASE__								= SwinBackbone(config=__A						)
									model.to(__A						)
									model.eval()
									SCREAMING_SNAKE_CASE__								= model(__A						)

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps						)							,			1						)
									self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,			[self.batch_size, model.channels[-1], 4, 4]						)

									# verify channels
									self.parent.assertEqual(len(model.channels						)							,			1						)


							def        _snake_case					(							self      :int							,			__A      :Any							,			__A      :Any							,			__A      :int						)				->				List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= SwinForMaskedImageModeling(config=__A						)
									model.to(__A						)
									model.eval()
									SCREAMING_SNAKE_CASE__								= model(__A						)
									self.parent.assertEqual(
									    result.logits.shape							,			(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

									# test greyscale images
									SCREAMING_SNAKE_CASE__								= 1
									SCREAMING_SNAKE_CASE__								= SwinForMaskedImageModeling(__A						)
									model.to(__A						)
									model.eval()

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


							def        _snake_case					(							self      :str							,			__A      :Optional[int]							,			__A      :Tuple							,			__A      :Any						)				->				List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.type_sequence_label_size
									SCREAMING_SNAKE_CASE__								= SwinForImageClassification(__A						)
									model.to(__A						)
									model.eval()
									SCREAMING_SNAKE_CASE__								= model(__A							,			labels=__A						)
									self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)						)

									# test greyscale images
									SCREAMING_SNAKE_CASE__								= 1
									SCREAMING_SNAKE_CASE__								= SwinForImageClassification(__A						)
									model.to(__A						)
									model.eval()

									SCREAMING_SNAKE_CASE__								= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
									SCREAMING_SNAKE_CASE__								= model(__A						)
									self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)						)


							def        _snake_case					(							self      :Tuple						)				->				Tuple:



									"""simple docstring"""



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




@require_torch
class 					UpperCamelCase_    (       UpperCamelCase__			,					UpperCamelCase__			,					unittest.TestCase      ):
							lowerCamelCase_          =   (
							    (
							        SwinModel,
							        SwinBackbone,
							        SwinForImageClassification,
							        SwinForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCamelCase_          =   (
							    {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
							    if is_torch_available()
							    else {}
							)
							lowerCamelCase_          =   True

							lowerCamelCase_          =   False
							lowerCamelCase_          =   False
							lowerCamelCase_          =   False


							def        _snake_case					(							self      :str						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= SwinModelTester(self						)
									SCREAMING_SNAKE_CASE__								= ConfigTester(self							,			config_class=__A							,			embed_dim=37						)


							def        _snake_case					(							self      :str						)				->				List[str]:



									"""simple docstring"""



									self.create_and_test_config_common_properties()
									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        _snake_case					(							self      :Dict						)				->				Optional[int]:



									"""simple docstring"""



									return


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__A						)


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



									"""simple docstring"""



									pass


							def        _snake_case					(							self      :str						)				->				Tuple:



									"""simple docstring"""



									super().test_training_gradient_checkpointing()


							def        _snake_case					(							self      :int						)				->				Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*__A						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*__A						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__A						)


							@unittest.skip(reason="""Swin does not use inputs_embeds"""						)
							def        _snake_case					(							self      :Optional[int]						)				->				Optional[Any]:



									"""simple docstring"""



									pass


							@unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""						)
							def        _snake_case					(							self      :List[str]						)				->				List[str]:



									"""simple docstring"""



									pass


							def        _snake_case					(							self      :Any						)				->				Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs_for_common()

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


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs_for_common()

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

											SCREAMING_SNAKE_CASE__								= ["""pixel_values"""]
											self.assertListEqual(arg_names[:1]							,			__A						)


							def        _snake_case					(							self      :Tuple						)				->				Optional[int]:



									"""simple docstring"""



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

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

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

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

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

											# also another +1 for reshaped_hidden_states
											SCREAMING_SNAKE_CASE__								= 1 if model_class.__name__ == """SwinBackbone""" else 2
											self.assertEqual(out_len + added_hidden_states							,			len(__A						)						)

											SCREAMING_SNAKE_CASE__								= outputs.attentions

											self.assertEqual(len(__A						)							,			__A						)

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


							def        _snake_case					(							self      :Tuple							,			__A      :Any							,			__A      :Union[str, Any]							,			__A      :str							,			__A      :Optional[int]						)				->				List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= model_class(__A						)
									model.to(__A						)
									model.eval()

									with torch.no_grad():
											SCREAMING_SNAKE_CASE__								= model(**self._prepare_for_class(__A							,			__A						)						)

									SCREAMING_SNAKE_CASE__								= outputs.hidden_states

									SCREAMING_SNAKE_CASE__								= getattr(
									    self.model_tester							,			"""expected_num_hidden_layers"""							,			len(self.model_tester.depths						) + 1						)
									self.assertEqual(len(__A						)							,			__A						)

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

									SCREAMING_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]							,			)

									if not model_class.__name__ == "SwinBackbone":
											SCREAMING_SNAKE_CASE__								= outputs.reshaped_hidden_states
											self.assertEqual(len(__A						)							,			__A						)

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


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_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:
											SCREAMING_SNAKE_CASE__								= True
											self.check_hidden_states_output(__A							,			__A							,			__A							,			__A						)

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

											self.check_hidden_states_output(__A							,			__A							,			__A							,			__A						)


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



									"""simple docstring"""



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

									SCREAMING_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)
									)
									SCREAMING_SNAKE_CASE__								= (
									    config.patch_size
									    if isinstance(config.patch_size							,			collections.abc.Iterable						)
									    else (config.patch_size, config.patch_size)
									)

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

									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE__								= True
											self.check_hidden_states_output(__A							,			__A							,			__A							,			(padded_height, padded_width)						)

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


							@slow
							def        _snake_case					(							self      :Any						)				->				List[Any]:



									"""simple docstring"""



									for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											SCREAMING_SNAKE_CASE__								= SwinModel.from_pretrained(__A						)
											self.assertIsNotNone(__A						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_SNAKE_CASE__								= _config_zero_init(__A						)
									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE__								= model_class(config=__A						)
											for name, param in model.named_parameters():
													if "embeddings" 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 					UpperCamelCase_    (       unittest.TestCase      ):


							@cached_property
							def        _snake_case					(							self      :Optional[Any]						)				->				Optional[Any]:



									"""simple docstring"""



									return (
									    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""						)
									    if is_vision_available()
									    else None
									)


							@slow
							def        _snake_case					(							self      :Dict						)				->				List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""						).to(__A						)
									SCREAMING_SNAKE_CASE__								= self.default_image_processor

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

									# forward pass
									with torch.no_grad():
											SCREAMING_SNAKE_CASE__								= model(**__A						)

									# verify the logits
									SCREAMING_SNAKE_CASE__								= torch.Size((1, 1000)						)
									self.assertEqual(outputs.logits.shape							,			__A						)
									SCREAMING_SNAKE_CASE__								= torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]						).to(__A						)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]							,			__A							,			atol=1E-4						)						)




@require_torch
class 					UpperCamelCase_    (       unittest.TestCase			,					UpperCamelCase__      ):
							lowerCamelCase_          =   (SwinBackbone,) if is_torch_available() else ()
							lowerCamelCase_          =   SwinConfig


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= SwinModelTester(self						)






import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
	from tests.trainer.test_trainer import (  # noqa
	    RegressionModelConfig,
	    RegressionPreTrainedModel,
	    get_regression_trainer,
	)


set_seed(42)

_lowerCamelCase							=     get_tests_dir('fixtures')
_lowerCamelCase							=     os.path.join(dirname(get_tests_dir()))
_lowerCamelCase							=     dirname(os.path.abspath(__file__))

# default torch.distributed port
_lowerCamelCase							=     '10999'

_lowerCamelCase							=     't5-small'

# *** Working Models ***
_lowerCamelCase							=     'hf-internal-testing/tiny-albert'
_lowerCamelCase							=     'sshleifer/bart-tiny-random'
_lowerCamelCase							=     'hf-internal-testing/tiny-bert'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-bigbird_pegasus'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-big_bird'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-blenderbot'
_lowerCamelCase							=     'bigscience/bigscience-small-testing'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-deberta'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-deberta-v2'
_lowerCamelCase							=     'sshleifer/tiny-distilbert-base-cased'
_lowerCamelCase							=     'hf-internal-testing/tiny-electra'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-flaubert'
_lowerCamelCase							=     'stas/tiny-wmt19-en-de'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-funnel'
_lowerCamelCase							=     'sshleifer/tiny-gpt2'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-gptj'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-gpt_neo'
_lowerCamelCase							=     'hf-internal-testing/tiny-layoutlm'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-led'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-longformer'
_lowerCamelCase							=     'stas/tiny-m2m_100'  # hf tiny model is unsuitable
_lowerCamelCase							=     'sshleifer/tiny-marian-en-de'
_lowerCamelCase							=     'sshleifer/tiny-mbart'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-mobilebert'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-mpnet'
_lowerCamelCase							=     'stas/pegasus-cnn_dailymail-tiny-random'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-prophetnet'
_lowerCamelCase							=     'sshleifer/tiny-distilroberta-base'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-squeezebert'
_lowerCamelCase							=     'patrickvonplaten/t5-tiny-random'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-t5-v1.1'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-vit'
_lowerCamelCase							=     'hf-internal-testing/tiny-xlm-roberta'
_lowerCamelCase							=     'sshleifer/tiny-xlnet-base-cased'


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_lowerCamelCase							=     'hf-internal-testing/tiny-random-mt5'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-camembert'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-openai-gpt'

# missing tokenizer files
_lowerCamelCase							=     'hf-internal-testing/tiny-random-convbert'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-layoutlmv2'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-hubert'

# issues with tokenizer
_lowerCamelCase							=     'hf-internal-testing/tiny-random-ctrl'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-transfo-xl'  # same as ctrl

# other issues with tiny models
_lowerCamelCase							=     'hf-internal-testing/tiny-random-ibert'  # multiple issues with either mlm/qa/clas
_lowerCamelCase							=     'hf-internal-testing/tiny-random-reformer'  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
_lowerCamelCase							=     'hf-internal-testing/tiny-random-dpr'
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_lowerCamelCase							=     'hf-internal-testing/tiny-random-rag'
# - "rag" research_projects
_lowerCamelCase							=     ''
# - "luke" Entities classes - no plan to make such example
_lowerCamelCase							=     'hf-internal-testing/tiny-random-lxmert'
# - "lxmert" doesn't work with run_qa.py
_lowerCamelCase							=     'hf-internal-testing/tiny-random-clip'
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
_lowerCamelCase							=     'hf-internal-testing/tiny-random-speech_to_text'
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
_lowerCamelCase							=     'hf-internal-testing/tiny-random-tapas'






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Optional[Any]=False      ):
		# 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
		SCREAMING_SNAKE_CASE__								= os.environ.get("""DS_TEST_PORT"""   ,				UpperCamelCase__      )
		return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()






def 			SCREAMING_SNAKE_CASE__							(    ):
		SCREAMING_SNAKE_CASE__								= f'''{FIXTURE_DIRECTORY}/tests_samples'''
		SCREAMING_SNAKE_CASE__								= f'''{data_dir_samples}/wmt_en_ro'''
		SCREAMING_SNAKE_CASE__								= f'''{data_dir_samples}/xsum'''
		SCREAMING_SNAKE_CASE__								= """
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        """.split()

		# try to cover as many models as possible once (it's enough to run on one task per model)
		# but need a tiny model for each
		#
		# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
		SCREAMING_SNAKE_CASE__								= {
		    """trans""": [
		        """bart""",
		        """fsmt""",
		        """m2m_100""",
		        """marian""",
		        """mbart""",
		        """t5""",
		        """t5_v1""",
		        # "mt5", missing model files
		    ],
		    """sum""": [
		        """pegasus""",
		    ],
		    """clm""": [
		        """big_bird""",
		        """bigbird_pegasus""",
		        """blenderbot""",
		        """bloom""",
		        """gpt2""",
		        """gpt_neo""",
		        """gptj""",
		        """xlm-roberta""",
		        """prophetnet""",
		        # "camembert", missing model files
		    ],
		    """mlm""": [
		        """albert""",
		        """deberta""",
		        """deberta-v2""",
		        """distilbert""",
		        """electra""",
		        """flaubert""",
		        """funnel""",
		        """layoutlm""",
		        # "reformer", # multiple issues with either mlm/qa/clas
		    ],
		    """qa""": [
		        """led""",
		        """longformer""",
		        """mobilebert""",
		        """mpnet""",
		        """roberta""",
		        """squeezebert""",
		        # "convbert", # missing tokenizer files
		        # "layoutlmv2", missing model files
		    ],
		    """clas""": [
		        """bert""",
		        """xlnet""",
		        # "hubert", # missing tokenizer files
		        # "ibert", # multiple issues with either mlm/qa/clas
		        # "transfo-xl", # tokenizer issues as ctrl
		        # "ctrl", # tokenizer issues
		        # "openai-gpt", missing model files
		        # "tapas", multiple issues
		    ],
		    """img_clas""": [
		        """vit""",
		    ],
		}

		SCREAMING_SNAKE_CASE__								= f'''{ROOT_DIRECTORY}/examples/pytorch'''

		SCREAMING_SNAKE_CASE__								= {
		    """trans""": f'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
		    """sum""": f'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
		    """clm""": f'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
		    """mlm""": f'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
		    """qa""": f'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
		    """clas""": f'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
		    """img_clas""": f'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
		}

		SCREAMING_SNAKE_CASE__								= get_launcher(distributed=UpperCamelCase__      )

		SCREAMING_SNAKE_CASE__								= {}
		for task, args in tasks.items():
				SCREAMING_SNAKE_CASE__								= args.split()
				for model in tasksamodels[task]:
						SCREAMING_SNAKE_CASE__								= globals()[f'''{model.upper().replace('-'   ,				'_'      )}_TINY''']
						SCREAMING_SNAKE_CASE__								= f'''--model_name_or_path {model_name}'''.split()
						SCREAMING_SNAKE_CASE__								= launcher + args + args_model + args_main

						# # generation special case
						# if task == "gen":
						#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
						#     args_model += f"--model_type {model}".split()
						#     cmds[f"{task}_{model}"] = launcher + args + args_model
						# else:

		return cmds


_lowerCamelCase							=     make_task_cmds()

_lowerCamelCase							=     'zero2'
_lowerCamelCase							=     'zero3'

_lowerCamelCase							=     [ZEROa, ZEROa]






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							str   ,				UpperCamelCase__:							Tuple      ):
		# 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
_lowerCamelCase							=     list(itertools.product(stages, task_cmds.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class 					UpperCamelCase_    (       UpperCamelCase__      ):


							def        _snake_case					(							self      :Tuple							,			__A      :int							,			__A      :str						)				->				List[Any]:



									"""simple docstring"""



									if task not in task_cmds:
											raise ValueError(f'''don\'t know of task {task}, have {task_cmds.keys()}'''						)

									SCREAMING_SNAKE_CASE__								= task_cmds[task]
									SCREAMING_SNAKE_CASE__								= f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

									SCREAMING_SNAKE_CASE__								= self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__								= f'''--output_dir {output_dir}'''.split()

									cmd += args_ds + args_out

									return cmd, output_dir


							@parameterized.expand(__A							,			name_func=__A						)
							def        _snake_case					(							self      :List[Any]							,			__A      :Tuple							,			__A      :int						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.get_task_cmd(__A							,			__A						)

									# 1. generate the checkpoint
									cmd += "--save_steps 1".split()
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
									execute_subprocess_async(__A							,			env=self.get_env()						)

									# 2. test that the fp32 weights get reconsolidated
									SCREAMING_SNAKE_CASE__								= f'''{output_dir}/checkpoint-1'''
									SCREAMING_SNAKE_CASE__								= f'''{chkpt_dir}/out.bin'''
									SCREAMING_SNAKE_CASE__								= f'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
									subprocess.check_call(__A							,			shell=__A						)
									assert os.path.exists(__A						), f'''{recovered_model_path} was not found'''

									# possibly could also test that the resulting saved model is usable but given that we use
									# random models we won't know if it's any good