



'''simple docstring'''


import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


lowercase_      :				Any   					=    {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}




class      __UpperCamelCase       (nn.Module			):







	def __init__(    self ,				_lowerCAmelCase						)    ->    Any:
								'''simple docstring'''






								super().__init__()
								lowercase        =							torchvision.models.resnetaaa(pretrained=__lowercase						)
								lowercase        =							list(model.children()						)[:-2]
								lowercase        =							nn.Sequential(*__lowercase						)
								lowercase        =							nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]						)







	def   _a   (    self ,				_lowerCAmelCase						)    ->    int:
								'''simple docstring'''






								lowercase        =							self.pool(self.model(__lowercase						)						)
								lowercase        =							torch.flatten(__lowercase ,				start_dim=2						)
								lowercase        =							out.transpose(1 ,				2						).contiguous()
								return out  # BxNx2048




class      __UpperCamelCase       (lowercase__			):







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    str:
								'''simple docstring'''






								lowercase        =							[json.loads(__lowercase						) for l in open(__lowercase						)]
								lowercase        =							os.path.dirname(__lowercase						)
								lowercase        =							tokenizer
								lowercase        =							labels
								lowercase        =							len(__lowercase						)
								lowercase        =							max_seq_length

								lowercase        =							transforms







	def __len__(    self						)    ->    Any:
								'''simple docstring'''






								return len(self.data						)







	def __getitem__(    self ,				_lowerCAmelCase						)    ->    List[Any]:
								'''simple docstring'''






								lowercase        =							torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,				add_special_tokens=__lowercase						)						)
								lowercase     ,   lowercase     ,   lowercase        =							sentence[0], sentence[1:-1], sentence[-1]
								lowercase        =							sentence[: self.max_seq_length]

								lowercase        =							torch.zeros(self.n_classes						)
								lowercase        =							1

								lowercase        =							Image.open(os.path.join(self.data_dir ,				self.data[index]["""img"""]						)						).convert("""RGB"""						)
								lowercase        =							self.transforms(__lowercase						)

								return {
								    "image_start_token": start_token,
								    "image_end_token": end_token,
								    "sentence": sentence,
								    "image": image,
								    "label": label,
								}







	def   _a   (    self						)    ->    Dict:
								'''simple docstring'''






								lowercase        =							Counter()
								for row in self.data:
															label_freqs.update(row["""label"""]						)
								return label_freqs







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict						):
							lowercase        =							[len(row["""sentence"""]						) for row in batch]
							lowercase     ,   lowercase        =							len(SCREAMING_SNAKE_CASE_						), max(SCREAMING_SNAKE_CASE_						)

							lowercase        =							torch.zeros(SCREAMING_SNAKE_CASE_ ,  SCREAMING_SNAKE_CASE_ ,  dtype=torch.long						)
							lowercase        =							torch.zeros(SCREAMING_SNAKE_CASE_ ,  SCREAMING_SNAKE_CASE_ ,  dtype=torch.long						)

							for i_batch, (input_row, length) in enumerate(zip(SCREAMING_SNAKE_CASE_ ,  SCREAMING_SNAKE_CASE_						)						):
														lowercase        =							input_row["""sentence"""]
														lowercase        =							1

							lowercase        =							torch.stack([row["""image"""] for row in batch]						)
							lowercase        =							torch.stack([row["""label"""] for row in batch]						)
							lowercase        =							torch.stack([row["""image_start_token"""] for row in batch]						)
							lowercase        =							torch.stack([row["""image_end_token"""] for row in batch]						)

							return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def      SCREAMING_SNAKE_CASE				(				):
							return [
							    "Crime",
							    "Drama",
							    "Thriller",
							    "Action",
							    "Comedy",
							    "Romance",
							    "Documentary",
							    "Short",
							    "Mystery",
							    "History",
							    "Family",
							    "Adventure",
							    "Fantasy",
							    "Sci-Fi",
							    "Western",
							    "Horror",
							    "Sport",
							    "War",
							    "Music",
							    "Musical",
							    "Animation",
							    "Biography",
							    "Film-Noir",
							]







def      SCREAMING_SNAKE_CASE				(				):
							return transforms.Compose(
							    [
							        transforms.Resize(256						),
							        transforms.CenterCrop(224						),
							        transforms.ToTensor(),
							        transforms.Normalize(
							            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017] ,  std=[0.12_221_994, 0.12_145_835, 0.14_380_469] ,  ),
							    ]						)







'''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      __UpperCamelCase       :







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=13 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=1000 ,				_lowerCAmelCase=[3, 3, 6, 4] ,				_lowerCAmelCase=[48, 56, 112, 220] ,				)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							parent
							lowercase        =							batch_size
							lowercase        =							num_channels
							lowercase        =							is_training
							lowercase        =							use_labels
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							num_labels
							lowercase        =							image_size
							lowercase        =							layer_depths
							lowercase        =							embed_dims







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






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

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

							lowercase        =							self.get_config()

							return config, pixel_values, labels







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							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=_lowerCAmelCase ,				layer_scale_init_value=1E-5 ,				)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							SwiftFormerModel(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							lowercase        =							model(_lowerCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.embed_dims[-1], 7, 7)						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.num_labels
							lowercase        =							SwiftFormerForImageClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							lowercase        =							model(_lowerCAmelCase ,				labels=_lowerCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)						)

							lowercase        =							SwiftFormerForImageClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()

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







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






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




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

	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							SwiftFormerModelTester(self						)
							lowercase        =							ConfigTester(
							    self ,				config_class=_lowerCAmelCase ,				has_text_modality=_lowerCAmelCase ,				hidden_size=37 ,				num_attention_heads=12 ,				num_hidden_layers=12 ,				)







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							self.config_tester.run_common_tests()







	@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""						)
	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

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







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

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

													lowercase        =							["""pixel_values"""]
													self.assertListEqual(arg_names[:1] ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowerCAmelCase						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)







	@slow
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase        =							SwiftFormerModel.from_pretrained(_lowerCAmelCase						)
													self.assertIsNotNone(_lowerCAmelCase						)







	@unittest.skip(reason="""SwiftFormer does not output attentions"""						)
	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							def check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						):
													lowercase        =							model_class(_lowerCAmelCase						)
													model.to(_lowerCAmelCase						)
													model.eval()

													with torch.no_grad():
																			lowercase        =							model(**self._prepare_for_class(_lowerCAmelCase ,				_lowerCAmelCase						)						)

													lowercase        =							outputs.hidden_states

													lowercase        =							8
													self.assertEqual(len(_lowerCAmelCase						) ,				_lowerCAmelCase						)  # 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(_lowerCAmelCase						)						):
																			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),
																			        ]						) ,				)

							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase        =							True
													check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)

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

													check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							def _config_zero_init(_lowerCAmelCase						):
													lowercase        =							copy.deepcopy(_lowerCAmelCase						)
													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(_lowerCAmelCase ,				_lowerCAmelCase ,				1E-10						)
																			if isinstance(getattr(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						) ,				_lowerCAmelCase						):
																									lowercase        =							_config_zero_init(getattr(_lowerCAmelCase ,				_lowerCAmelCase						)						)
																									setattr(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)
													return configs_no_init

							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							lowercase        =							_config_zero_init(_lowerCAmelCase						)
							for model_class in self.all_model_classes:
													lowercase        =							model_class(config=_lowerCAmelCase						)
													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   _a   (    self						)    ->    Any:
							'''simple docstring'''






							pass







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						return image




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







	@cached_property
	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""						) if is_vision_available() else None







	@slow
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""						).to(_lowerCAmelCase						)

							lowercase        =							self.default_image_processor
							lowercase        =							prepare_img()
							lowercase        =							image_processor(images=_lowerCAmelCase ,				return_tensors="""pt"""						).to(_lowerCAmelCase						)

							# forward pass
							with torch.no_grad():
													lowercase        =							model(**_lowerCAmelCase						)

							# verify the logits
							lowercase        =							torch.Size((1, 1000)						)
							self.assertEqual(outputs.logits.shape ,				_lowerCAmelCase						)

							lowercase        =							torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]						).to(_lowerCAmelCase						)
							self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				_lowerCAmelCase ,				atol=1E-4						)						)


