



"""simple docstring"""


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 			_a :
			"""simple docstring"""



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Tuple=1_3				,   __UpperCamelCase     :    Optional[Any]=3				,   __UpperCamelCase     :    List[Any]=True				,   __UpperCamelCase     :    Union[str, Any]=True				,   __UpperCamelCase     :    Optional[Any]=0.1				,   __UpperCamelCase     :    Union[str, Any]=0.1				,   __UpperCamelCase     :    Dict=2_2_4				,   __UpperCamelCase     :    Union[str, Any]=1_0_0_0				,   __UpperCamelCase     :    Optional[Any]=[3, 3, 6, 4]				,   __UpperCamelCase     :    Optional[int]=[4_8, 5_6, 1_1_2, 2_2_0]				,   )->Union[str, Any]:
							_UpperCAmelCase					=    parent
							_UpperCAmelCase					=    batch_size
							_UpperCAmelCase					=    num_channels
							_UpperCAmelCase					=    is_training
							_UpperCAmelCase					=    use_labels
							_UpperCAmelCase					=    hidden_dropout_prob
							_UpperCAmelCase					=    attention_probs_dropout_prob
							_UpperCAmelCase					=    num_labels
							_UpperCAmelCase					=    image_size
							_UpperCAmelCase					=    layer_depths
							_UpperCAmelCase					=    embed_dims



			def  lowercase__						(		self     :    List[Any]   )->int:
							_UpperCAmelCase					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

							_UpperCAmelCase					=    self.get_config()

							return config, pixel_values, labels



			def  lowercase__						(		self     :    int   )->Any:
							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=__UpperCamelCase				,   layer_scale_init_value=1e-5				,   )



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    List[Any]   )->List[Any]:
							_UpperCAmelCase					=    SwiftFormerModel(config=__UpperCamelCase   )
							model.to(__UpperCamelCase   )
							model.eval()
							_UpperCAmelCase					=    model(__UpperCamelCase   )
							self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.embed_dims[-1], 7, 7)   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Any   )->str:
							_UpperCAmelCase					=    self.num_labels
							_UpperCAmelCase					=    SwiftFormerForImageClassification(__UpperCamelCase   )
							model.to(__UpperCamelCase   )
							model.eval()
							_UpperCAmelCase					=    model(__UpperCamelCase				,   labels=__UpperCamelCase   )
							self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)   )

							_UpperCAmelCase					=    SwiftFormerForImageClassification(__UpperCamelCase   )
							model.to(__UpperCamelCase   )
							model.eval()

							_UpperCAmelCase					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
							_UpperCAmelCase					=    model(__UpperCamelCase   )
							self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)   )



			def  lowercase__						(		self     :    Any   )->Dict:
							((_UpperCAmelCase)		,						(_UpperCAmelCase)		,						(_UpperCAmelCase))					=    self.prepare_config_and_inputs()
							_UpperCAmelCase					=    {'''pixel_values''': pixel_values}
							return config, inputs_dict



@require_torch
class 			_a (					lowerCAmelCase			,							lowerCAmelCase			,							unittest.TestCase):
			"""simple docstring"""
			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  lowercase__						(		self     :    List[Any]   )->List[str]:
							_UpperCAmelCase					=    SwiftFormerModelTester(self   )
							_UpperCAmelCase					=    ConfigTester(
							    self				,   config_class=__UpperCamelCase				,   has_text_modality=__UpperCamelCase				,   hidden_size=3_7				,   num_attention_heads=1_2				,   num_hidden_layers=1_2				,   )



			def  lowercase__						(		self     :    Any   )->Optional[Any]:
							self.config_tester.run_common_tests()



			@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''   )
			def  lowercase__						(		self     :    int   )->Tuple:
							pass



			def  lowercase__						(		self     :    Optional[int]   )->Union[str, Any]:
							_UpperCAmelCase		,						_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											_UpperCAmelCase					=    model_class(__UpperCamelCase   )
											_UpperCAmelCase					=    model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(__UpperCamelCase				,   nn.Linear   )   )



			def  lowercase__						(		self     :    Dict   )->List[str]:
							_UpperCAmelCase		,						_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

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

											_UpperCAmelCase					=    ['''pixel_values''']
											self.assertListEqual(arg_names[:1]				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[Any]   )->Optional[Any]:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCamelCase   )



			def  lowercase__						(		self     :    str   )->Optional[int]:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase   )



			@slow
			def  lowercase__						(		self     :    List[str]   )->List[Any]:
							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_UpperCAmelCase					=    SwiftFormerModel.from_pretrained(__UpperCamelCase   )
											self.assertIsNotNone(__UpperCamelCase   )



			@unittest.skip(reason='''SwiftFormer does not output attentions'''   )
			def  lowercase__						(		self     :    Union[str, Any]   )->Optional[Any]:
							pass



			def  lowercase__						(		self     :    Dict   )->List[Any]:
							def check_hidden_states_output(__UpperCamelCase     :    str				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    List[Any]   ):
											_UpperCAmelCase					=    model_class(__UpperCamelCase   )
											model.to(__UpperCamelCase   )
											model.eval()

											with torch.no_grad():
															_UpperCAmelCase					=    model(**self._prepare_for_class(__UpperCamelCase				,   __UpperCamelCase   )   )

											_UpperCAmelCase					=    outputs.hidden_states

											_UpperCAmelCase					=    8
											self.assertEqual(len(__UpperCamelCase   )				,   __UpperCamelCase   )  # 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(__UpperCamelCase   )   ):
															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),
															        ]   )				,   )

							_UpperCAmelCase		,						_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											_UpperCAmelCase					=    True
											check_hidden_states_output(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )

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

											check_hidden_states_output(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[Any]   )->Dict:
							def _config_zero_init(__UpperCamelCase     :    Dict   ):
											_UpperCAmelCase					=    copy.deepcopy(__UpperCamelCase   )
											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(__UpperCamelCase				,   __UpperCamelCase				,   1e-10   )
															if isinstance(getattr(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )				,   __UpperCamelCase   ):
																			_UpperCAmelCase					=    _config_zero_init(getattr(__UpperCamelCase				,   __UpperCamelCase   )   )
																			setattr(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
											return configs_no_init

							_UpperCAmelCase		,						_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCAmelCase					=    _config_zero_init(__UpperCamelCase   )
							for model_class in self.all_model_classes:
											_UpperCAmelCase					=    model_class(config=__UpperCamelCase   )
											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  lowercase__						(		self     :    Union[str, Any]   )->Union[str, Any]:
							pass







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
				return image
@require_torch
@require_vision
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			@cached_property
			def  lowercase__						(		self     :    Optional[int]   )->Dict:
							return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''   ) if is_vision_available() else None



			@slow
			def  lowercase__						(		self     :    Optional[int]   )->Tuple:
							_UpperCAmelCase					=    SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''   ).to(__UpperCamelCase   )

							_UpperCAmelCase					=    self.default_image_processor
							_UpperCAmelCase					=    prepare_img()
							_UpperCAmelCase					=    image_processor(images=__UpperCamelCase				,   return_tensors='''pt'''   ).to(__UpperCamelCase   )

							# forward pass
							with torch.no_grad():
											_UpperCAmelCase					=    model(**__UpperCamelCase   )

							# verify the logits
							_UpperCAmelCase					=    torch.Size((1, 1_0_0_0)   )
							self.assertEqual(outputs.logits.shape				,   __UpperCamelCase   )

							_UpperCAmelCase					=    torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]]   ).to(__UpperCamelCase   )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]				,   __UpperCamelCase				,   atol=1e-4   )   )








"""simple docstring"""


import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    filter(lambda _SCREAMING_SNAKE_CASE				: p.requires_grad					,     model.parameters()				)
				_UpperCAmelCase					=    sum([np.prod(p.size()				) for p in model_parameters]				)
				return params


__A   :							Dict										=   logging.getLogger(__name__)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				if metric == "rouge2":
								_UpperCAmelCase					=    '''{val_avg_rouge2:.4f}-{step_count}'''
				elif metric == "bleu":
								_UpperCAmelCase					=    '''{val_avg_bleu:.4f}-{step_count}'''
				elif metric == "em":
								_UpperCAmelCase					=    '''{val_avg_em:.4f}-{step_count}'''
				else:
								raise NotImplementedError(
								    f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
								    ''' function.'''				)

				_UpperCAmelCase					=    ModelCheckpoint(
				    dirpath=_SCREAMING_SNAKE_CASE					,     filename=_SCREAMING_SNAKE_CASE					,     monitor=f'val_{metric}'					,     mode='''max'''					,     save_top_k=3					,     every_n_epochs=1					,     )
				return checkpoint_callback







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						List[str]				):






				'''simple docstring'''



				return EarlyStopping(
				    monitor=f'val_{metric}'					,     mode='''min''' if '''loss''' in metric else '''max'''					,     patience=_SCREAMING_SNAKE_CASE					,     verbose=_SCREAMING_SNAKE_CASE					,     )
class 			_a (					pl.Callback):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    str   )->Tuple:
							_UpperCAmelCase					=    {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
							pl_module.logger.log_metrics(__UpperCamelCase   )



			@rank_zero_only
			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    pl.Trainer				,   __UpperCamelCase     :    pl.LightningModule				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Tuple=True   )->None:
							logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'   )
							_UpperCAmelCase					=    trainer.callback_metrics
							trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']}   )
							# Log results
							_UpperCAmelCase					=    Path(pl_module.hparams.output_dir   )
							if type_path == "test":
											_UpperCAmelCase					=    od / '''test_results.txt'''
											_UpperCAmelCase					=    od / '''test_generations.txt'''
							else:
											# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
											# If people want this it will be easy enough to add back.
											_UpperCAmelCase					=    od / F'{type_path}_results/{trainer.global_step:05d}.txt'
											_UpperCAmelCase					=    od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
											results_file.parent.mkdir(exist_ok=__UpperCamelCase   )
											generations_file.parent.mkdir(exist_ok=__UpperCamelCase   )
							with open(__UpperCamelCase				,   '''a+'''   ) as writer:
											for key in sorted(__UpperCamelCase   ):
															if key in ["log", "progress_bar", "preds"]:
																			continue
															_UpperCAmelCase					=    metrics[key]
															if isinstance(__UpperCamelCase				,   torch.Tensor   ):
																			_UpperCAmelCase					=    val.item()
															_UpperCAmelCase					=    F'{key}: {val:.6f}\n'
															writer.write(__UpperCamelCase   )

							if not save_generations:
											return

							if "preds" in metrics:
											_UpperCAmelCase					=    '''\n'''.join(metrics['''preds''']   )
											generations_file.open('''w+'''   ).write(__UpperCamelCase   )



			@rank_zero_only
			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Dict   )->Union[str, Any]:
							try:
											_UpperCAmelCase					=    pl_module.model.model.num_parameters()
							except AttributeError:
											_UpperCAmelCase					=    pl_module.model.num_parameters()

							_UpperCAmelCase					=    count_trainable_parameters(__UpperCamelCase   )
							# mp stands for million parameters
							trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6}   )



			@rank_zero_only
			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    pl.Trainer				,   __UpperCamelCase     :    pl.LightningModule   )->List[Any]:
							save_json(pl_module.metrics				,   pl_module.metrics_save_path   )
							return self._write_logs(__UpperCamelCase				,   __UpperCamelCase				,   '''test'''   )



			@rank_zero_only
			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    pl.Trainer				,   __UpperCamelCase     :    List[str]   )->Optional[Any]:
							save_json(pl_module.metrics				,   pl_module.metrics_save_path   )
							# Uncommenting this will save val generations
							# return self._write_logs(trainer, pl_module, "valid")



