


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


__snake_case            =   {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							:		Union[str, Any]       ,       __magic_name__							:		List[Any]						):







      """simple docstring"""







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





   def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		Any						):







      """simple docstring"""







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




class    UpperCAmelCase					(       __snake_case    ):




   def __init__(						self							:		Union[str, Any]       ,       __magic_name__							:		Any       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Any       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Dict						):







      """simple docstring"""







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

      UpperCamelCase         =					transforms




   def __len__(						self							:		Optional[int]						):







      """simple docstring"""







      return len(self.data						)




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







      """simple docstring"""







      UpperCamelCase         =					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]       ,       add_special_tokens=__magic_name__						)						)
      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(__magic_name__						)

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





   def   lowerCamelCase_					(						self							:		int						):







      """simple docstring"""







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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





   """simple docstring"""




   UpperCamelCase         =					[len(row["""sentence"""]	) for row in batch]
   UpperCamelCase  ,  UpperCamelCase         =					len(SCREAMING_SNAKE_CASE_	), max(SCREAMING_SNAKE_CASE_	)

   UpperCamelCase         =					torch.zeros(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  dtype=torch.long	)
   UpperCamelCase         =					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_	)	):
      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 							_lowercase    (		):





   """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 							_lowercase    (		):





   """simple docstring"""




   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 json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= XLMTokenizer
			lowercase						= False




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						UpperCamelCase         =					[
						    """l""",
						    """o""",
						    """w""",
						    """e""",
						    """r""",
						    """s""",
						    """t""",
						    """i""",
						    """d""",
						    """n""",
						    """w</w>""",
						    """r</w>""",
						    """t</w>""",
						    """lo""",
						    """low""",
						    """er</w>""",
						    """low</w>""",
						    """lowest</w>""",
						    """newer</w>""",
						    """wider</w>""",
						    """<unk>""",
						]
						UpperCamelCase         =					dict(zip(__magic_name__       ,       range(len(__magic_name__						)						)						)						)
						UpperCamelCase         =					["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""vocab_file"""]						)
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""merges_file"""]						)
						with open(self.vocab_file       ,       """w"""						) as fp:
									fp.write(json.dumps(__magic_name__						)						)
						with open(self.merges_file       ,       """w"""						) as fp:
									fp.write("""\n""".join(__magic_name__						)						)




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







						"""simple docstring"""







						UpperCamelCase         =					"""lower newer"""
						UpperCamelCase         =					"""lower newer"""
						return input_text, output_text




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer(self.vocab_file       ,       self.merges_file						)

						UpperCamelCase         =					"""lower"""
						UpperCamelCase         =					["""low""", """er</w>"""]
						UpperCamelCase         =					tokenizer.tokenize(__magic_name__						)
						self.assertListEqual(__magic_name__       ,       __magic_name__						)

						UpperCamelCase         =					tokens + ["""<unk>"""]
						UpperCamelCase         =					[1_4, 1_5, 2_0]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__						)       ,       __magic_name__						)





			@slow
			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""						)

						UpperCamelCase         =					tokenizer.encode("""sequence builders"""       ,       add_special_tokens=__magic_name__						)
						UpperCamelCase         =					tokenizer.encode("""multi-sequence build"""       ,       add_special_tokens=__magic_name__						)

						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__						)
						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__       ,       __magic_name__						)

						assert encoded_sentence == [0] + text + [1]
						assert encoded_pair == [0] + text + [1] + text_a + [1]




