'''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


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


       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  Dict:
        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
        lowercase__				:    Dict  =					self.pool(self.model(__lowerCAmelCase ) )
        lowercase__				:    int  =					torch.flatten(__lowerCAmelCase	,   start_dim=2 )
        lowercase__				:    Union[str, Any]  =					out.transpose(1	,   2 ).contiguous()
        return out  # BxNx2048



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



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

        lowercase__				:    Dict  =					transforms

       def __len__(						self )  ->  Dict:
        return len(self.data )

       def __getitem__(						self	,   __lowerCAmelCase )  ->  Union[str, Any]:
        lowercase__				:    Tuple  =					torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']	,   add_special_tokens=__lowerCAmelCase ) )
        lowercase__ ,							lowercase__ ,							lowercase__				:    Any  =					sentence[0], sentence[1:-1], sentence[-1]
        lowercase__				:    Dict  =					sentence[: self.max_seq_length]

        lowercase__				:    Any  =					torch.zeros(self.n_classes )
        lowercase__				:    Dict  =					1

        lowercase__				:    Optional[int]  =					Image.open(os.path.join(self.data_dir	,   self.data[index]['''img'''] ) ).convert('''RGB''' )
        lowercase__				:    Union[str, Any]  =					self.transforms(__lowerCAmelCase )

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


       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Dict  =					Counter()
        for row in self.data:
         label_freqs.update(row['''label'''] )
        return label_freqs


def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    Tuple  =					[len(row['''sentence''']     ) for row in batch]
 lowercase__ ,							lowercase__				:    Any  =					len(UpperCAmelCase     ), max(UpperCAmelCase     )

 lowercase__				:    str  =					torch.zeros(UpperCAmelCase		,     UpperCAmelCase		,     dtype=torch.long     )
 lowercase__				:    Dict  =					torch.zeros(UpperCAmelCase		,     UpperCAmelCase		,     dtype=torch.long     )

 for i_batch, (input_row, length) in enumerate(zip(UpperCAmelCase		,     UpperCAmelCase     )     ):
  lowercase__				:    Tuple  =					input_row['''sentence''']
  lowercase__				:    List[str]  =					1

 lowercase__				:    Dict  =					torch.stack([row['''image'''] for row in batch]     )
 lowercase__				:    int  =					torch.stack([row['''label'''] for row in batch]     )
 lowercase__				:    Optional[int]  =					torch.stack([row['''image_start_token'''] for row in batch]     )
 lowercase__				:    Optional[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 			__UpperCamelCase       (   ):
 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       (   ):
 return transforms.Compose(
     [
         transforms.Resize(256     ),
         transforms.CenterCrop(224     ),
         transforms.ToTensor(),
         transforms.Normalize(
             mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]		,     std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]		,     ),
     ]     )


'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import tensorflow as tf

  from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' )
        lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''google/mt5-small''' )

        lowercase__				:    Any  =					tokenizer('''Hello there'''	,   return_tensors='''tf''' ).input_ids
        lowercase__				:    List[Any]  =					tokenizer('''Hi I am'''	,   return_tensors='''tf''' ).input_ids

        lowercase__				:    str  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase ).loss
        lowercase__				:    List[str]  =					-tf.math.reduce_mean(__lowerCAmelCase ).numpy()

        lowercase__				:    str  =					-2_1.2_2_8_1_6_8
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )

