




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


a__		     =     {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    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						List[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Tuple             =      torchvision.models.resnetaaa(pretrained=lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      list(model.children()	)[:-2]
						__UpperCamelCase					:	List[str]             =      nn.Sequential(*lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Optional[Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.pool(self.model(lowerCAmelCase	)	)
						__UpperCamelCase					:	Dict             =      torch.flatten(lowerCAmelCase					,     start_dim=2	)
						__UpperCamelCase					:	Optional[int]             =      out.transpose(1					,     2	).contiguous()
						return out  # BxNx2048






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      [json.loads(lowerCAmelCase	) for l in open(lowerCAmelCase	)]
						__UpperCamelCase					:	Optional[Any]             =      os.path.dirname(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      tokenizer
						__UpperCamelCase					:	Dict             =      labels
						__UpperCamelCase					:	int             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      max_seq_length

						__UpperCamelCase					:	int             =      transforms






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







						"""simple docstring"""







						return len(self.data	)






	def __getitem__(					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]	)	->       int:







						"""simple docstring"""







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

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

						__UpperCamelCase					:	List[Any]             =      Image.open(os.path.join(self.data_dir					,     self.data[index]["""img"""]	)	).convert("""RGB"""	)
						__UpperCamelCase					:	Dict             =      self.transforms(lowerCAmelCase	)

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






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







						"""simple docstring"""







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






def 				A__ (snake_case   :		int				)     ->						Optional[int]:
					__UpperCamelCase					:	str             =      [len(row["""sentence"""]				) for row in batch]
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      len(snake_case				), max(snake_case				)

					__UpperCamelCase					:	List[str]             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)
					__UpperCamelCase					:	Tuple             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)

					for i_batch, (input_row, length) in enumerate(zip(snake_case		,	snake_case				)				):
										__UpperCamelCase					:	Optional[Any]             =      input_row["""sentence"""]
										__UpperCamelCase					:	Any             =      1

					__UpperCamelCase					:	List[Any]             =      torch.stack([row["""image"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""label"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""image_start_token"""] for row in batch]				)
					__UpperCamelCase					:	List[str]             =      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__ ()     ->						Optional[Any]:
					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__ ()     ->						Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(2_56				),
					        transforms.CenterCrop(2_24				),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46777044, 0.44531429, 0.40661017]		,	std=[0.12221994, 0.12145835, 0.14380469]		,	),
					    ]				)












import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402


a__		     =     get_tests_dir('''fixtures/dummy-config.json''')


class    SCREAMING_SNAKE_CASE_				(       unittest.TestCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						str	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      0






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







						"""simple docstring"""







						self.assertIsNotNone(transformers.models.auto.__spec__	)
						self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto"""	)	)






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      AutoConfig.from_pretrained("""bert-base-uncased"""	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)






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







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      AutoConfig.from_pretrained(lowerCAmelCase	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)






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







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      AutoConfig.from_pretrained(lowerCAmelCase	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Tuple	)	->       Any:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      AutoConfig.for_model("""roberta"""	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						int	)	->       List[Any]:







						"""simple docstring"""







						with tempfile.TemporaryDirectory() as tmp_dir:
											# This model name contains bert and roberta, but roberta ends up being picked.
											__UpperCamelCase					:	Union[str, Any]             =      os.path.join(lowerCAmelCase					,     """fake-roberta"""	)
											os.makedirs(lowerCAmelCase					,     exist_ok=lowerCAmelCase	)
											with open(os.path.join(lowerCAmelCase					,     """config.json"""	)					,     """w"""	) as f:
																f.write(json.dumps({}	)	)
											__UpperCamelCase					:	Dict             =      AutoConfig.from_pretrained(lowerCAmelCase	)
											self.assertEqual(type(lowerCAmelCase	)					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						int	)	->       str:







						"""simple docstring"""







						try:
											AutoConfig.register("""custom"""					,     lowerCAmelCase	)
											# Wrong model type will raise an error
											with self.assertRaises(lowerCAmelCase	):
																AutoConfig.register("""model"""					,     lowerCAmelCase	)
											# Trying to register something existing in the Transformers library will raise an error
											with self.assertRaises(lowerCAmelCase	):
																AutoConfig.register("""bert"""					,     lowerCAmelCase	)

											# Now that the config is registered, it can be used as any other config with the auto-API
											__UpperCamelCase					:	Optional[Any]             =      CustomConfig()
											with tempfile.TemporaryDirectory() as tmp_dir:
																config.save_pretrained(lowerCAmelCase	)
																__UpperCamelCase					:	List[str]             =      AutoConfig.from_pretrained(lowerCAmelCase	)
																self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)

						finally:
											if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]






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







						"""simple docstring"""







						with self.assertRaisesRegex(
						    lowerCAmelCase					,     """bert-base is not a local folder and is not a valid model identifier"""	):
											__UpperCamelCase					:	Tuple             =      AutoConfig.from_pretrained("""bert-base"""	)






	def 					lowerCamelCase__     (					self		:						Dict	)	->       Any:







						"""simple docstring"""







						with self.assertRaisesRegex(
						    lowerCAmelCase					,     R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""	):
											__UpperCamelCase					:	Optional[Any]             =      AutoConfig.from_pretrained(lowerCAmelCase					,     revision="""aaaaaa"""	)






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







						"""simple docstring"""







						with self.assertRaisesRegex(
						    lowerCAmelCase					,     """hf-internal-testing/no-config-test-repo does not appear to have a file named config.json."""					,     ):
											__UpperCamelCase					:	Union[str, Any]             =      AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo"""	)






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       Optional[int]:







						"""simple docstring"""







						with self.assertRaises(lowerCAmelCase	):
											__UpperCamelCase					:	str             =      AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""	)
						# If remote code is disabled, we can't load this config.
						with self.assertRaises(lowerCAmelCase	):
											__UpperCamelCase					:	Tuple             =      AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""					,     trust_remote_code=lowerCAmelCase	)

						__UpperCamelCase					:	List[str]             =      AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""					,     trust_remote_code=lowerCAmelCase	)
						self.assertEqual(config.__class__.__name__					,     """NewModelConfig"""	)

						# Test config can be reloaded.
						with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(lowerCAmelCase	)
											__UpperCamelCase					:	str             =      AutoConfig.from_pretrained(lowerCAmelCase					,     trust_remote_code=lowerCAmelCase	)
						self.assertEqual(reloaded_config.__class__.__name__					,     """NewModelConfig"""	)






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







						"""simple docstring"""







						class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



							"""simple docstring"""


							__magic_name__   :       int      	=		'new-model'

						try:
											AutoConfig.register("""new-model"""					,     lowerCAmelCase	)
											# If remote code is not set, the default is to use local
											__UpperCamelCase					:	Tuple             =      AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""	)
											self.assertEqual(config.__class__.__name__					,     """NewModelConfigLocal"""	)

											# If remote code is disabled, we load the local one.
											__UpperCamelCase					:	Any             =      AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""					,     trust_remote_code=lowerCAmelCase	)
											self.assertEqual(config.__class__.__name__					,     """NewModelConfigLocal"""	)

											# If remote is enabled, we load from the Hub
											__UpperCamelCase					:	List[Any]             =      AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""					,     trust_remote_code=lowerCAmelCase	)
											self.assertEqual(config.__class__.__name__					,     """NewModelConfig"""	)

						finally:
											if "new-model" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["new-model"]






