





'''simple docstring'''

import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


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

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
			from test_module.custom_tokenization_fast import CustomTokenizerFast

class       UpperCAmelCase (						unittest.TestCase  ):




		def 				lowerCamelCase__   (self      :	Optional[Any]				)					-> Optional[int]:
						lowercase         = mock.Mock()
						lowercase         = 5_0_0
						lowercase         = {}
						lowercase         = HTTPError
						lowercase         = {}

						# Download this model to make sure it's in the cache.
						lowercase         = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"				)

						# Under the mock environment we get a 500 error when trying to reach the tokenizer.
						with mock.patch("requests.Session.request"							,			return_value=A__				) as mock_head:
										lowercase         = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"				)
										# This check we did call the fake head request
										mock_head.assert_called()




		@require_tokenizers
		def 				lowerCamelCase__   (self      :	Optional[int]				)					-> Tuple:
						lowercase         = mock.Mock()
						lowercase         = 5_0_0
						lowercase         = {}
						lowercase         = HTTPError
						lowercase         = {}

						# Download this model to make sure it's in the cache.
						lowercase         = GPTaTokenizerFast.from_pretrained("gpt2"				)

						# Under the mock environment we get a 500 error when trying to reach the tokenizer.
						with mock.patch("requests.Session.request"							,			return_value=A__				) as mock_head:
										lowercase         = GPTaTokenizerFast.from_pretrained("gpt2"				)
										# This check we did call the fake head request
										mock_head.assert_called()




		def 				lowerCamelCase__   (self      :	Any				)					-> Any:

						try:
										lowercase         = tempfile.mktemp()
										with open(A__							,			"wb"				) as f:
														http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"							,			A__				)

										lowercase         = AlbertTokenizer.from_pretrained(A__				)
						finally:
										os.remove(A__				)

						# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
						# the current folder and have the right name.
						if os.path.isfile("tokenizer.json"				):
										# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
										return
						try:
										with open("tokenizer.json"							,			"wb"				) as f:
														http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"							,			A__				)
										lowercase         = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"				)
										# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
										self.assertEqual(tokenizer.vocab_size							,			1_0_0_0				)
										# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

						finally:
										os.remove("tokenizer.json"				)





		def 				lowerCamelCase__   (self      :	Any				)					-> int:
						lowercase         = AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"				)




@is_staging_test
class       UpperCAmelCase (						unittest.TestCase  ):
		UpperCAmelCase					:	Optional[int] 			=		['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']




		@classmethod
		def 				lowerCamelCase__   (cls      :	Dict				)					-> int:
						lowercase         = TOKEN
						HfFolder.save_token(A__				)




		@classmethod
		def 				lowerCamelCase__   (cls      :	Dict				)					-> int:

						try:
										delete_repo(token=cls._token							,			repo_id="test-tokenizer"				)
						except HTTPError:
										pass

						try:
										delete_repo(token=cls._token							,			repo_id="valid_org/test-tokenizer-org"				)
						except HTTPError:
										pass

						try:
										delete_repo(token=cls._token							,			repo_id="test-dynamic-tokenizer"				)
						except HTTPError:
										pass




		def 				lowerCamelCase__   (self      :	Dict				)					-> Dict:

						with tempfile.TemporaryDirectory() as tmp_dir:
										lowercase         = os.path.join(A__							,			"vocab.txt"				)
										with open(A__							,			"w"							,			encoding="utf-8"				) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]				)				)
										lowercase         = BertTokenizer(A__				)

						tokenizer.push_to_hub("test-tokenizer"							,			use_auth_token=self._token				)
						lowercase         = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer'				)
						self.assertDictEqual(new_tokenizer.vocab							,			tokenizer.vocab				)

						# Reset repo
						delete_repo(token=self._token							,			repo_id="test-tokenizer"				)

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(A__							,			repo_id="test-tokenizer"							,			push_to_hub=A__							,			use_auth_token=self._token				)

						lowercase         = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer'				)
						self.assertDictEqual(new_tokenizer.vocab							,			tokenizer.vocab				)




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

						with tempfile.TemporaryDirectory() as tmp_dir:
										lowercase         = os.path.join(A__							,			"vocab.txt"				)
										with open(A__							,			"w"							,			encoding="utf-8"				) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]				)				)
										lowercase         = BertTokenizer(A__				)

						tokenizer.push_to_hub("valid_org/test-tokenizer-org"							,			use_auth_token=self._token				)
						lowercase         = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"				)
						self.assertDictEqual(new_tokenizer.vocab							,			tokenizer.vocab				)

						# Reset repo
						delete_repo(token=self._token							,			repo_id="valid_org/test-tokenizer-org"				)

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(
										    A__							,			repo_id="valid_org/test-tokenizer-org"							,			push_to_hub=A__							,			use_auth_token=self._token				)

						lowercase         = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"				)
						self.assertDictEqual(new_tokenizer.vocab							,			tokenizer.vocab				)





		@require_tokenizers
		def 				lowerCamelCase__   (self      :	Any				)					-> int:
						CustomTokenizer.register_for_auto_class()
						with tempfile.TemporaryDirectory() as tmp_dir:
										lowercase         = os.path.join(A__							,			"vocab.txt"				)
										with open(A__							,			"w"							,			encoding="utf-8"				) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]				)				)
										lowercase         = CustomTokenizer(A__				)

						# No fast custom tokenizer
						tokenizer.push_to_hub("test-dynamic-tokenizer"							,			use_auth_token=self._token				)

						lowercase         = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer'							,			trust_remote_code=A__				)
						# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
						self.assertEqual(tokenizer.__class__.__name__							,			"CustomTokenizer"				)

						# Fast and slow custom tokenizer
						CustomTokenizerFast.register_for_auto_class()
						with tempfile.TemporaryDirectory() as tmp_dir:
										lowercase         = os.path.join(A__							,			"vocab.txt"				)
										with open(A__							,			"w"							,			encoding="utf-8"				) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]				)				)

										lowercase         = BertTokenizerFast.from_pretrained(A__				)
										bert_tokenizer.save_pretrained(A__				)
										lowercase         = CustomTokenizerFast.from_pretrained(A__				)

						tokenizer.push_to_hub("test-dynamic-tokenizer"							,			use_auth_token=self._token				)

						lowercase         = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer'							,			trust_remote_code=A__				)
						# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
						self.assertEqual(tokenizer.__class__.__name__							,			"CustomTokenizerFast"				)
						lowercase         = AutoTokenizer.from_pretrained(
						    f'{USER}/test-dynamic-tokenizer'							,			use_fast=A__							,			trust_remote_code=A__				)
						# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
						self.assertEqual(tokenizer.__class__.__name__							,			"CustomTokenizer"				)




class       UpperCAmelCase (						unittest.TestCase  ):




		def 				lowerCamelCase__   (self      :	List[str]				)					-> int:
						lowercase         = Trie()
						trie.add("Hello 友達"				)
						self.assertEqual(trie.data							,			{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}}				)
						trie.add("Hello"				)
						trie.data
						self.assertEqual(trie.data							,			{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}}				)




		def 				lowerCamelCase__   (self      :	int				)					-> str:
						lowercase         = Trie()
						self.assertEqual(trie.split("[CLS] This is a extra_id_100"				)							,			["[CLS] This is a extra_id_100"]				)
						trie.add("[CLS]"				)
						trie.add("extra_id_1"				)
						trie.add("extra_id_100"				)
						self.assertEqual(trie.split("[CLS] This is a extra_id_100"				)							,			["[CLS]", " This is a ", "extra_id_100"]				)




		def 				lowerCamelCase__   (self      :	str				)					-> Tuple:
						lowercase         = Trie()
						trie.add("A"				)
						self.assertEqual(trie.split("ABC"				)							,			["A", "BC"]				)
						self.assertEqual(trie.split("BCA"				)							,			["BC", "A"]				)




		def 				lowerCamelCase__   (self      :	int				)					-> Dict:
						lowercase         = Trie()
						trie.add("TOKEN]"				)
						trie.add("[SPECIAL_TOKEN]"				)
						self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"				)							,			["This is something ", "[SPECIAL_TOKEN]"]				)




		def 				lowerCamelCase__   (self      :	str				)					-> Tuple:
						lowercase         = Trie()
						trie.add("A"				)
						trie.add("P"				)
						trie.add("[SPECIAL_TOKEN]"				)
						self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"				)							,			["This is something ", "[SPECIAL_TOKEN]"]				)




		def 				lowerCamelCase__   (self      :	int				)					-> Optional[Any]:
						lowercase         = Trie()
						trie.add("AB"				)
						trie.add("B"				)
						trie.add("C"				)
						self.assertEqual(trie.split("ABC"				)							,			["AB", "C"]				)




		def 				lowerCamelCase__   (self      :	Union[str, Any]				)					-> List[str]:
						lowercase         = Trie()
						trie.add("ABC"				)
						trie.add("B"				)
						trie.add("CD"				)
						self.assertEqual(trie.split("ABCD"				)							,			["ABC", "D"]				)





		def 				lowerCamelCase__   (self      :	Any				)					-> Union[str, Any]:
						lowercase         = Trie()
						lowercase         = trie.cut_text("ABC"							,			[0, 0, 2, 1, 2, 3]				)
						self.assertEqual(A__							,			["AB", "C"]				)











'''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


__lowerCamelCase : 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      :	Tuple							,			A__      :	Any				)					-> str:
						super().__init__()
						lowercase         = torchvision.models.resnetaaa(pretrained=A__				)
						lowercase         = list(model.children()				)[:-2]
						lowercase         = nn.Sequential(*A__				)
						lowercase         = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]				)





		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Optional[Any]				)					-> Optional[int]:
						# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
						lowercase         = self.pool(self.model(A__				)				)
						lowercase         = torch.flatten(A__							,			start_dim=2				)
						lowercase         = out.transpose(1							,			2				).contiguous()
						return out  # BxNx2048




class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	int							,			A__      :	Optional[int]							,			A__      :	Optional[Any]							,			A__      :	List[Any]							,			A__      :	Tuple							,			A__      :	Optional[int]				)					-> Union[str, Any]:
						lowercase         = [json.loads(A__				) for l in open(A__				)]
						lowercase         = os.path.dirname(A__				)
						lowercase         = tokenizer
						lowercase         = labels
						lowercase         = len(A__				)
						lowercase         = max_seq_length

						lowercase         = transforms




		def __len__(self      :	List[str]				)					-> Dict:
						return len(self.data				)




		def __getitem__(self      :	int							,			A__      :	Union[str, Any]				)					-> List[str]:
						lowercase         = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]							,			add_special_tokens=A__				)				)
						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(A__				)

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





		def 				UpperCAmelCase__   (self      :	str				)					-> str:
						lowercase         = Counter()
						for row in self.data:
										label_freqs.update(row["label"]				)
						return label_freqs







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = [len(row["sentence"]							) for row in batch]
				lowercase   ,							lowercase         = len(lowerCAmelCase_							), max(lowerCAmelCase_							)

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

				for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase_		,							lowerCAmelCase_							)							):
								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 							UpperCAmelCase_    (	):

				"""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 							UpperCAmelCase_    (	):

				"""simple docstring"""






				return transforms.Compose(
				    [
				        transforms.Resize(256							),
				        transforms.CenterCrop(224							),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]		,							std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]		,							),
				    ]							)




