





'''simple docstring'''




import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
							import jax

							from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			Union[str, Any]=13      ,						lowerCamelCase_				:			Tuple=30      ,						lowerCamelCase_				:			Union[str, Any]=2      ,						lowerCamelCase_				:			List[Any]=3      ,						lowerCamelCase_				:			Dict=True      ,						lowerCamelCase_				:			Optional[int]=True      ,						lowerCamelCase_				:			List[Any]=32      ,						lowerCamelCase_				:			Any=5      ,						lowerCamelCase_				:			List[Any]=4      ,						lowerCamelCase_				:			str=37      ,						lowerCamelCase_				:			List[Any]="gelu"      ,						lowerCamelCase_				:			Union[str, Any]=0.1      ,						lowerCamelCase_				:			List[str]=0.1      ,						lowerCamelCase_				:			List[str]=10      ,						lowerCamelCase_				:			List[str]=0.02      ,						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			parent
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			batch_size
							SCREAMING_SNAKE_CASE :				str		=			image_size
							SCREAMING_SNAKE_CASE :				Dict		=			patch_size
							SCREAMING_SNAKE_CASE :				List[str]		=			num_channels
							SCREAMING_SNAKE_CASE :				List[str]		=			is_training
							SCREAMING_SNAKE_CASE :				Tuple		=			use_labels
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			hidden_size
							SCREAMING_SNAKE_CASE :				Any		=			num_hidden_layers
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_attention_heads
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			intermediate_size
							SCREAMING_SNAKE_CASE :				List[str]		=			hidden_act
							SCREAMING_SNAKE_CASE :				Any		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Optional[int]		=			attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE :				Optional[int]		=			type_sequence_label_size
							SCREAMING_SNAKE_CASE :				Any		=			initializer_range

							# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			(image_size // patch_size) ** 2
							SCREAMING_SNAKE_CASE :				Dict		=			num_patches + 1


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							SCREAMING_SNAKE_CASE :				str		=			ViTConfig(
							    image_size=self.image_size      ,						patch_size=self.patch_size      ,						num_channels=self.num_channels      ,						hidden_size=self.hidden_size      ,						num_hidden_layers=self.num_hidden_layers      ,						num_attention_heads=self.num_attention_heads      ,						intermediate_size=self.intermediate_size      ,						hidden_act=self.hidden_act      ,						hidden_dropout_prob=self.hidden_dropout_prob      ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,						is_decoder=lowerCamelCase_      ,						initializer_range=self.initializer_range      ,						)

							return config, pixel_values


					def lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			Dict      ,						lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			FlaxViTModel(config=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[str]		=			model(lowerCamelCase_						)
							# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			(self.image_size, self.image_size)
							SCREAMING_SNAKE_CASE :				Any		=			(self.patch_size, self.patch_size)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, num_patches + 1, self.hidden_size)						)


					def lowerCamelCase_			(						self				:			Union[str, Any]      ,						lowerCamelCase_				:			Tuple      ,						lowerCamelCase_				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.type_sequence_label_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			FlaxViTForImageClassification(config=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			model(lowerCamelCase_						)
							self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.type_sequence_label_size)						)

							# test greyscale images
							SCREAMING_SNAKE_CASE :				List[Any]		=			1
							SCREAMING_SNAKE_CASE :				str		=			FlaxViTForImageClassification(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
							SCREAMING_SNAKE_CASE :				List[str]		=			model(lowerCamelCase_						)



					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.prepare_config_and_inputs()
							(
							    (
							    SCREAMING_SNAKE_CASE
							),							(
							    SCREAMING_SNAKE_CASE
							),							
							) :				List[str]		=			config_and_inputs
							SCREAMING_SNAKE_CASE :				str		=			{"""pixel_values""": pixel_values}
							return config, inputs_dict







@require_flax
class      UpperCamelCase__					(					lowercase_						,					unittest.TestCase			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			FlaxViTModelTester(self						)
							SCREAMING_SNAKE_CASE :				Dict		=			ConfigTester(self      ,						config_class=lowerCamelCase_      ,						has_text_modality=lowerCamelCase_      ,						hidden_size=37						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							self.config_tester.run_common_tests()


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				str		=			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			model_class(lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Optional[int]		=			inspect.signature(model.__call__						)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE :				Tuple		=			[*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE :				str		=			["""pixel_values"""]
									self.assertListEqual(arg_names[:1]      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__						):
											SCREAMING_SNAKE_CASE :				List[str]		=			self._prepare_for_class(lowerCamelCase_      ,						lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Optional[int]		=			model_class(lowerCamelCase_						)

											@jax.jit
											def model_jitted(lowerCamelCase_				:			Any      ,						**lowerCamelCase_				:			Optional[Any]						):
													return model(pixel_values=lowerCamelCase_      ,						**lowerCamelCase_						)

											with self.subTest("""JIT Enabled"""						):
													SCREAMING_SNAKE_CASE :				Optional[int]		=			model_jitted(**lowerCamelCase_						).to_tuple()

											with self.subTest("""JIT Disabled"""						):
													with jax.disable_jit():
															SCREAMING_SNAKE_CASE :				Optional[int]		=			model_jitted(**lowerCamelCase_						).to_tuple()

											self.assertEqual(len(lowerCamelCase_						)      ,						len(lowerCamelCase_						)						)
											for jitted_output, output in zip(lowerCamelCase_      ,						lowerCamelCase_						):
													self.assertEqual(jitted_output.shape      ,						output.shape						)



					@slow
					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE :				List[Any]		=			model_class_name.from_pretrained("""google/vit-base-patch16-224"""						)
									SCREAMING_SNAKE_CASE :				Tuple		=			model(np.ones((1, 3, 2_24, 2_24)						)						)
									self.assertIsNotNone(lowerCamelCase_						)










'''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


__UpperCAmelCase            =       {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			):




					"""simple docstring"""


					def __init__(						self				:			Dict      ,						lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



							super().__init__()
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torchvision.models.resnetaaa(pretrained=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			list(model.children()						)[:-2]
							SCREAMING_SNAKE_CASE :				Any		=			nn.Sequential(*lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]						)



					def lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			self.pool(self.model(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				int		=			torch.flatten(lowerCamelCase_      ,						start_dim=2						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			out.transpose(1      ,						2						).contiguous()
							return out  # BxNx2048







class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			[json.loads(lowerCamelCase_						) for l in open(lowerCamelCase_						)]
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			os.path.dirname(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			tokenizer
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			labels
							SCREAMING_SNAKE_CASE :				str		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			max_seq_length

							SCREAMING_SNAKE_CASE :				List[Any]		=			transforms


					def __len__(						self				:			Any						):

							'''simple docstring'''



							return len(self.data						)


					def __getitem__(						self				:			Optional[int]      ,						lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]      ,						add_special_tokens=lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Tuple		=			sentence[0], sentence[1:-1], sentence[-1]
							SCREAMING_SNAKE_CASE :				int		=			sentence[: self.max_seq_length]

							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.zeros(self.n_classes						)
							SCREAMING_SNAKE_CASE :				List[str]		=			1

							SCREAMING_SNAKE_CASE :				int		=			Image.open(os.path.join(self.data_dir      ,						self.data[index]["""img"""]						)						).convert("""RGB"""						)
							SCREAMING_SNAKE_CASE :				str		=			self.transforms(lowerCamelCase_						)

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



					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



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







def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Tuple		=			[len(row["""sentence"""]	) for row in batch]
		SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Any		=			len(lowerCamelCase_	), max(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				int		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)
		SCREAMING_SNAKE_CASE :				Any		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)

		for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase_			,			lowerCamelCase_	)	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			input_row["""sentence"""]
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			1

		SCREAMING_SNAKE_CASE :				List[str]		=			torch.stack([row["""image"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				int		=			torch.stack([row["""label"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				str		=			torch.stack([row["""image_start_token"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				Any		=			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 		__A		(  ):


		"""simple docstring"""



		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 		__A		(  ):


		"""simple docstring"""



		return transforms.Compose(
		    [
		        transforms.Resize(2_56	),
		        transforms.CenterCrop(2_24	),
		        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]			,			),
		    ]	)



