import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import SwiftFormerForImageClassification, SwiftFormerModel
			from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor
class   _lowerCAmelCase		:







							def __init__(				self     :    str	,					__snake_case     :    List[Any]	,					__snake_case     :    str=13	,					__snake_case     :    str=3	,					__snake_case     :    Optional[Any]=True	,					__snake_case     :    Tuple=True	,					__snake_case     :    Any=0.1	,					__snake_case     :    str=0.1	,					__snake_case     :    Union[str, Any]=224	,					__snake_case     :    Any=1000	,					__snake_case     :    str=[3, 3, 6, 4]	,					__snake_case     :    int=[48, 56, 112, 220]	,					):
										lowerCamelCase						:int						  =     parent
										lowerCamelCase						:Tuple						  =     batch_size
										lowerCamelCase						:int						  =     num_channels
										lowerCamelCase						:Optional[int]						  =     is_training
										lowerCamelCase						:str						  =     use_labels
										lowerCamelCase						:Dict						  =     hidden_dropout_prob
										lowerCamelCase						:Any						  =     attention_probs_dropout_prob
										lowerCamelCase						:Union[str, Any]						  =     num_labels
										lowerCamelCase						:Optional[int]						  =     image_size
										lowerCamelCase						:List[Any]						  =     layer_depths
										lowerCamelCase						:List[str]						  =     embed_dims







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:List[str]						  =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

										lowerCamelCase						:int						  =     None
										if self.use_labels:
													lowerCamelCase						:int						  =     ids_tensor([self.batch_size]	,					self.num_labels		)

										lowerCamelCase						:Tuple						  =     self.get_config()

										return config, pixel_values, labels







							def 				snake_case  (				self     :    Tuple		):
										return SwiftFormerConfig(
										    depths=self.layer_depths	,					embed_dims=self.embed_dims	,					mlp_ratio=4	,					downsamples=[True, True, True, True]	,					hidden_act='''gelu'''	,					num_labels=self.num_labels	,					down_patch_size=3	,					down_stride=2	,					down_pad=1	,					drop_rate=0.0	,					drop_path_rate=0.0	,					use_layer_scale=__snake_case	,					layer_scale_init_value=1e-5	,					)







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Dict	,					__snake_case     :    List[Any]		):
										lowerCamelCase						:Optional[Any]						  =     SwiftFormerModel(config=__snake_case		)
										model.to(__snake_case		)
										model.eval()
										lowerCamelCase						:Tuple						  =     model(__snake_case		)
										self.parent.assertEqual(result.last_hidden_state.shape	,					(self.batch_size, self.embed_dims[-1], 7, 7)		)







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    Tuple	,					__snake_case     :    str	,					__snake_case     :    Dict		):
										lowerCamelCase						:Dict						  =     self.num_labels
										lowerCamelCase						:Optional[Any]						  =     SwiftFormerForImageClassification(__snake_case		)
										model.to(__snake_case		)
										model.eval()
										lowerCamelCase						:Dict						  =     model(__snake_case	,					labels=__snake_case		)
										self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.num_labels)		)

										lowerCamelCase						:Dict						  =     SwiftFormerForImageClassification(__snake_case		)
										model.to(__snake_case		)
										model.eval()

										lowerCamelCase						:Dict						  =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
										lowerCamelCase						:Optional[int]						  =     model(__snake_case		)
										self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.num_labels)		)







							def 				snake_case  (				self     :    Optional[int]		):
										((lowerCamelCase)  ,    (lowerCamelCase)  ,    (lowerCamelCase))						:str						  =     self.prepare_config_and_inputs()
										lowerCamelCase						:Tuple						  =     {'''pixel_values''': pixel_values}
										return config, inputs_dict


@require_torch
class   _lowerCAmelCase		(     lowercase__      ,   lowercase__      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase						    =     (
							    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Any						  =     SwiftFormerModelTester(self		)
										lowerCamelCase						:Union[str, Any]						  =     ConfigTester(
										    self	,					config_class=__snake_case	,					has_text_modality=__snake_case	,					hidden_size=37	,					num_attention_heads=12	,					num_hidden_layers=12	,					)







							def 				snake_case  (				self     :    Dict		):
										self.config_tester.run_common_tests()







							@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''		)
							def 				snake_case  (				self     :    List[Any]		):
										pass







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase  ,    lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:str						  =     model_class(__snake_case		)
													lowerCamelCase						:Tuple						  =     model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__snake_case	,					nn.Linear		)		)







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase  ,    lowerCamelCase						:Optional[int]						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:Any						  =     model_class(__snake_case		)
													lowerCamelCase						:Tuple						  =     inspect.signature(model.forward		)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase						:List[str]						  =     [*signature.parameters.keys()]

													lowerCamelCase						:Union[str, Any]						  =     ['''pixel_values''']
													self.assertListEqual(arg_names[:1]	,					__snake_case		)







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Any						  =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case		)







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:List[str]						  =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__snake_case		)







							@slow
							def 				snake_case  (				self     :    Optional[int]		):
										for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase						:Tuple						  =     SwiftFormerModel.from_pretrained(__snake_case		)
													self.assertIsNotNone(__snake_case		)







							@unittest.skip(reason='''SwiftFormer does not output attentions'''		)
							def 				snake_case  (				self     :    Tuple		):
										pass







							def 				snake_case  (				self     :    int		):
										def check_hidden_states_output(__snake_case     :    Any	,					__snake_case     :    str	,					__snake_case     :    Optional[int]		):
													lowerCamelCase						:List[Any]						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()

													with torch.no_grad():
																lowerCamelCase						:Tuple						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)

													lowerCamelCase						:List[str]						  =     outputs.hidden_states

													lowerCamelCase						:List[str]						  =     8
													self.assertEqual(len(__snake_case		)	,					__snake_case		)  # TODO

													# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
													# with the width and height being successively divided by 2, after every 2 blocks
													for i in range(len(__snake_case		)		):
																self.assertEqual(
																    hidden_states[i].shape	,					torch.Size(
																        [
																            self.model_tester.batch_size,
																            self.model_tester.embed_dims[i // 2],
																            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																        ]		)	,					)

										lowerCamelCase  ,    lowerCamelCase						:Optional[int]						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:Optional[Any]						  =     True
													check_hidden_states_output(__snake_case	,					__snake_case	,					__snake_case		)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCamelCase						:int						  =     True

													check_hidden_states_output(__snake_case	,					__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]		):
										def _config_zero_init(__snake_case     :    Union[str, Any]		):
													lowerCamelCase						:List[str]						  =     copy.deepcopy(__snake_case		)
													for key in configs_no_init.__dict__.keys():
																if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																			setattr(__snake_case	,					__snake_case	,					1e-1_0		)
																if isinstance(getattr(__snake_case	,					__snake_case	,					__snake_case		)	,					__snake_case		):
																			lowerCamelCase						:List[Any]						  =     _config_zero_init(getattr(__snake_case	,					__snake_case		)		)
																			setattr(__snake_case	,					__snake_case	,					__snake_case		)
													return configs_no_init

										lowerCamelCase  ,    lowerCamelCase						:List[str]						  =     self.model_tester.prepare_config_and_inputs_for_common()

										lowerCamelCase						:List[str]						  =     _config_zero_init(__snake_case		)
										for model_class in self.all_model_classes:
													lowerCamelCase						:Optional[int]						  =     model_class(config=__snake_case		)
													for name, param in model.named_parameters():
																if param.requires_grad:
																			self.assertIn(
																			    ((param.data.mean() * 1e9) / 1e9).round().item()	,					[0.0, 1.0]	,					msg=F"Parameter {name} of model {model_class} seems not properly initialized"	,					)







							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
							def 				snake_case  (				self     :    int		):
										pass





def  _lowerCamelCase					(   ):
			lowerCamelCase						:int						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
			return image


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







							@cached_property
							def 				snake_case  (				self     :    Union[str, Any]		):
										return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''		) if is_vision_available() else None







							@slow
							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Optional[Any]						  =     SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''		).to(__snake_case		)

										lowerCamelCase						:Tuple						  =     self.default_image_processor
										lowerCamelCase						:List[str]						  =     prepare_img()
										lowerCamelCase						:Any						  =     image_processor(images=__snake_case	,					return_tensors='''pt'''		).to(__snake_case		)

										# forward pass
										with torch.no_grad():
													lowerCamelCase						:str						  =     model(**__snake_case		)

										# verify the logits
										lowerCamelCase						:Tuple						  =     torch.Size((1, 1000)		)
										self.assertEqual(outputs.logits.shape	,					__snake_case		)

										lowerCamelCase						:str						  =     torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]		).to(__snake_case		)
										self.assertTrue(torch.allclose(outputs.logits[0, :3]	,					__snake_case	,					atol=1e-4		)		)




import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

A__           =							[
    """cross_validation.py""",
    """gradient_accumulation.py""",
    """local_sgd.py""",
    """multi_process_metrics.py""",
    """memory.py""",
    """automatic_gradient_accumulation.py""",
    """fsdp_with_peak_mem_tracking.py""",
    """deepspeed_with_config_support.py""",
    """megatron_lm_gpt_pretraining.py""",
]
class   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    Any	,					__snake_case     :    str	,					__snake_case     :    bool	,					__snake_case     :    str = None	,					__snake_case     :    list = None		):
										lowerCamelCase						:Tuple						  =     None
										lowerCamelCase						:Tuple						  =     os.path.abspath(os.path.join('''examples'''	,					'''by_feature'''		)		)
										lowerCamelCase						:Optional[int]						  =     os.path.abspath('''examples'''		)
										for item in os.listdir(__snake_case		):
													if item not in EXCLUDE_EXAMPLES:
																lowerCamelCase						:Optional[int]						  =     os.path.join(__snake_case	,					__snake_case		)
																if os.path.isfile(__snake_case		) and ".py" in item_path:
																			with self.subTest(
																			    tested_script=__snake_case	,					feature_script=__snake_case	,					tested_section='''main()''' if parser_only else '''training_function()'''	,					):
																						lowerCamelCase						:Union[str, Any]						  =     compare_against_test(
																						    os.path.join(__snake_case	,					__snake_case		)	,					__snake_case	,					__snake_case	,					__snake_case		)
																						lowerCamelCase						:int						  =     '''\n'''.join(__snake_case		)
																						if special_strings is not None:
																									for string in special_strings:
																												lowerCamelCase						:int						  =     diff.replace(__snake_case	,					''''''		)
																						self.assertEqual(__snake_case	,					''''''		)







							def 				snake_case  (				self     :    Dict		):
										self.one_complete_example('''complete_nlp_example.py'''	,					__snake_case		)
										self.one_complete_example('''complete_nlp_example.py'''	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Union[str, Any]						  =     os.path.abspath(os.path.join('''examples'''	,					'''cv_example.py'''		)		)
										lowerCamelCase						:Optional[int]						  =     [
										    ''' ''' * 16 + '''{\n\n''',
										    ''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
										    ''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
										    ''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
										    ''' ''' * 20 + '''"epoch": epoch,\n\n''',
										    ''' ''' * 16 + '''},\n\n''',
										    ''' ''' * 16 + '''step=epoch,\n''',
										    ''' ''' * 12,
										    ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
										]
										self.one_complete_example('''complete_cv_example.py'''	,					__snake_case	,					__snake_case	,					__snake_case		)
										self.one_complete_example('''complete_cv_example.py'''	,					__snake_case	,					__snake_case	,					__snake_case		)


@mock.patch.dict(os.environ      ,   {'TESTING_MOCKED_DATALOADERS': '1'}				)
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     False







							@classmethod
							def 				snake_case  (				cls     :    Optional[Any]		):
										super().setUpClass()
										lowerCamelCase						:Any						  =     tempfile.mkdtemp()
										lowerCamelCase						:Optional[int]						  =     os.path.join(cls._tmpdir	,					'''default_config.yml'''		)

										write_basic_config(save_location=cls.configPath		)
										lowerCamelCase						:List[str]						  =     ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]







							@classmethod
							def 				snake_case  (				cls     :    Dict		):
										super().tearDownClass()
										shutil.rmtree(cls._tmpdir		)







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:Any						  =     F"\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        ".split()
										run_command(self._launch_args + testargs		)
										self.assertTrue(os.path.exists(os.path.join(self.tmpdir	,					'''epoch_0'''		)		)		)







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:Tuple						  =     F"\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        ".split()
										lowerCamelCase						:List[Any]						  =     run_command(self._launch_args + testargs		)
										self.assertTrue(os.path.exists(os.path.join(self.tmpdir	,					'''step_2'''		)		)		)







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:Dict						  =     F"\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir	,					'epoch_0'		)}\n        ".split()
										lowerCamelCase						:Dict						  =     run_command(self._launch_args + testargs	,					return_stdout=__snake_case		)
										self.assertNotIn('''epoch 0:'''	,					__snake_case		)
										self.assertIn('''epoch 1:'''	,					__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:List[Any]						  =     F"\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir	,					'step_2'		)}\n        ".split()
										lowerCamelCase						:Optional[int]						  =     run_command(self._launch_args + testargs	,					return_stdout=__snake_case		)
										if torch.cuda.is_available():
													lowerCamelCase						:Union[str, Any]						  =     torch.cuda.device_count()
										else:
													lowerCamelCase						:Dict						  =     1
										if num_processes > 1:
													self.assertNotIn('''epoch 0:'''	,					__snake_case		)
													self.assertIn('''epoch 1:'''	,					__snake_case		)
										else:
													self.assertIn('''epoch 0:'''	,					__snake_case		)
													self.assertIn('''epoch 1:'''	,					__snake_case		)







							@slow
							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:Tuple						  =     '''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
										with mock.patch.dict(os.environ	,					{'''TESTING_MOCKED_DATALOADERS''': '''0'''}		):
													lowerCamelCase						:Dict						  =     run_command(self._launch_args + testargs	,					return_stdout=__snake_case		)
													lowerCamelCase						:Tuple						  =     re.findall('''({.+})'''	,					__snake_case		)
													lowerCamelCase						:Optional[Any]						  =     [r for r in results if '''accuracy''' in r][-1]
													lowerCamelCase						:List[str]						  =     ast.literal_eval(__snake_case		)
													self.assertGreaterEqual(results['''accuracy''']	,					0.7_5		)







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:Dict						  =     ['''examples/by_feature/multi_process_metrics.py''']
										run_command(self._launch_args + testargs		)







							@require_trackers
							@mock.patch.dict(os.environ	,					{'''WANDB_MODE''': '''offline'''}		)
							def 				snake_case  (				self     :    Any		):
										with tempfile.TemporaryDirectory() as tmpdir:
													lowerCamelCase						:Tuple						  =     F"\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            ".split()
													run_command(self._launch_args + testargs		)
													self.assertTrue(os.path.exists(os.path.join(__snake_case	,					'''tracking'''		)		)		)







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Tuple						  =     ['''examples/by_feature/gradient_accumulation.py''']
										run_command(self._launch_args + testargs		)







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:int						  =     ['''examples/by_feature/local_sgd.py''']
										run_command(self._launch_args + testargs		)



