



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__          =  {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__          =  ["Speech2TextTokenizer"]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__          =  ["Speech2TextFeatureExtractor"]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__          =  [
     "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFSpeech2TextForConditionalGeneration",
     "TFSpeech2TextModel",
     "TFSpeech2TextPreTrainedModel",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__          =  [
     "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Speech2TextForConditionalGeneration",
     "Speech2TextModel",
     "Speech2TextPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
 from .processing_speech_to_text import SpeechaTextProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speech_to_text import SpeechaTextTokenizer

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_speech_to_text import (
      TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSpeechaTextForConditionalGeneration,
      TFSpeechaTextModel,
      TFSpeechaTextPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text import (
      SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextForConditionalGeneration,
      SpeechaTextModel,
      SpeechaTextPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE__          =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''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


SCREAMING_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 					snake_case			(nn.Module ):



       def __init__(					self  ,UpperCAmelCase_   )	->				Dict:
           super().__init__()
           lowercase__									=    torchvision.models.resnetaaa(pretrained=UpperCAmelCase_   )
           lowercase__									=    list(model.children()   )[:-2]
           lowercase__									=    nn.Sequential(*UpperCAmelCase_   )
           lowercase__									=    nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )







       def 				_a   (					self  ,UpperCAmelCase_   )	->				str:
           # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
           lowercase__									=    self.pool(self.model(UpperCAmelCase_   )   )
           lowercase__									=    torch.flatten(UpperCAmelCase_  ,start_dim=2   )
           lowercase__									=    out.transpose(1  ,2   ).contiguous()
           return out  # BxNx2048





class 					snake_case			(UpperCamelCase ):



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

           lowercase__									=    transforms



       def __len__(					self   )	->				Optional[Any]:
           return len(self.data   )



       def __getitem__(					self  ,UpperCAmelCase_   )	->				Optional[int]:
           lowercase__									=    torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]  ,add_special_tokens=UpperCAmelCase_   )   )
           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(UpperCAmelCase_   )

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







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





def 							lowerCamelCase	(					_snake_case	:    Optional[int]							):





    '''simple docstring'''


    lowercase__									=    [len(row["sentence"]							) for row in batch]
    lowercase__							,							lowercase__									=    len(_snake_case							), max(_snake_case							)

    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)
    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)

    for i_batch, (input_row, length) in enumerate(zip(_snake_case  ,_snake_case							)							):
        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 							lowerCamelCase	(					):





    '''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 							lowerCamelCase	(					):





    '''simple docstring'''


    return transforms.Compose(
        [
            transforms.Resize(256							),
            transforms.CenterCrop(224							),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]  ,std=[0.12_221_994, 0.12_145_835, 0.14_380_469]  ,),
        ]							)




