



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     :							Union[str, 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  __magic_name__   (							nn.Module						):






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







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







		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		UpperCamelCase__      :     Union[str, Any]      )				->					Dict:
								'''simple docstring'''







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





class  __magic_name__   (							A__						):






		def __init__(						self      :     str				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Dict      )				->					int:
								'''simple docstring'''







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

								UpperCAmelCase         =     transforms






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







								return len(self.data      )






		def __getitem__(						self      :     int				,		UpperCamelCase__      :     Dict      )				->					Tuple:
								'''simple docstring'''







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

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

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

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







		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Dict:
								'''simple docstring'''







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

def    lowerCamelCase_(lowerCamelCase_    ) ->       List[Any]:
						UpperCAmelCase         =     [len(row["sentence"]    ) for row in batch]
						UpperCAmelCase       ,		UpperCAmelCase         =     len(lowerCamelCase_    ), max(lowerCamelCase_    )

						UpperCAmelCase         =     torch.zeros(lowerCamelCase_    ,   lowerCamelCase_    ,   dtype=torch.long    )
						UpperCAmelCase         =     torch.zeros(lowerCamelCase_    ,   lowerCamelCase_    ,   dtype=torch.long    )

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

						UpperCAmelCase         =     torch.stack([row["image"] for row in batch]    )
						UpperCAmelCase         =     torch.stack([row["label"] for row in batch]    )
						UpperCAmelCase         =     torch.stack([row["image_start_token"] for row in batch]    )
						UpperCAmelCase         =     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    lowerCamelCase_() ->       Dict:
						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    lowerCamelCase_() ->       List[Any]:
						return transforms.Compose(
						    [
						        transforms.Resize(256    ),
						        transforms.CenterCrop(224    ),
						        transforms.ToTensor(),
						        transforms.Normalize(
						            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]    ,   std=[0.1222_1994, 0.1214_5835, 0.1438_0469]    ,   ),
						    ]    )








import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin





@require_jieba
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Optional[Any]          =CpmAntTokenizer
		lowercase      :       Dict          =False






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







								super().setUp()

								UpperCAmelCase         =     [
								    "<d>",
								    "</d>",
								    "<s>",
								    "</s>",
								    "</_>",
								    "<unk>",
								    "<pad>",
								    "</n>",
								    "我",
								    "是",
								    "C",
								    "P",
								    "M",
								    "A",
								    "n",
								    "t",
								]
								UpperCAmelCase         =     os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["vocab_file"]      )
								with open(self.vocab_file				,		"w"				,		encoding="utf-8"      ) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )







		@tooslow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"      )
								UpperCAmelCase         =     "今天天气真好！"
								UpperCAmelCase         =     ["今天", "天气", "真", "好", "！"]
								UpperCAmelCase         =     tokenizer.tokenize(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )
								UpperCAmelCase         =     "今天天气真好！"
								UpperCAmelCase         =     [tokenizer.bos_token] + tokens

								UpperCAmelCase         =     [6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__      )				,		UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.decode(UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )



