
"""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      :			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      _UpperCAmelCase				(					nn.Module):
    def __init__(    self						,    snake_case_     ):
      super().__init__()
      _snake_case     :						str						=   torchvision.models.resnetaaa(pretrained=snake_case_     )
      _snake_case     :						List[str]						=   list(model.children()     )[:-2]
      _snake_case     :						List[str]						=   nn.Sequential(*snake_case_     )
      _snake_case     :						List[str]						=   nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]     )


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






class      _UpperCAmelCase				(					_snake_case):
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_     ):
      _snake_case     :						Tuple						=   [json.loads(snake_case_     ) for l in open(snake_case_     )]
      _snake_case     :						Dict						=   os.path.dirname(snake_case_     )
      _snake_case     :						List[str]						=   tokenizer
      _snake_case     :						List[Any]						=   labels
      _snake_case     :						str						=   len(snake_case_     )
      _snake_case     :						Tuple						=   max_seq_length

      _snake_case     :						str						=   transforms
    def __len__(    self     ):
      return len(self.data     )
    def __getitem__(    self						,    snake_case_     ):
      _snake_case     :						List[Any]						=   torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]						,    add_special_tokens=snake_case_     )     )
      _snake_case     :						int						=   sentence[0], sentence[1:-1], sentence[-1]
      _snake_case     :						Union[str, Any]						=   sentence[: self.max_seq_length]

      _snake_case     :						str						=   torch.zeros(self.n_classes     )
      _snake_case     :						str						=   1

      _snake_case     :						Any						=   Image.open(os.path.join(self.data_dir						,    self.data[index]["img"]     )     ).convert("RGB"     )
      _snake_case     :						List[Any]						=   self.transforms(snake_case_     )

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


    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						str						=   Counter()
      for row in self.data:
        label_freqs.update(row["label"]     )
      return label_freqs


def 				a__   (   a   :				Tuple		):

  """simple docstring"""







  _snake_case     :						Optional[Any]						=   [len(row["sentence"]		) for row in batch]
  _snake_case     :						Optional[Any]						=   len(a		), max(a		)

  _snake_case     :						Any						=   torch.zeros(a       ,     a       ,     dtype=torch.long		)
  _snake_case     :						List[Any]						=   torch.zeros(a       ,     a       ,     dtype=torch.long		)

  for i_batch, (input_row, length) in enumerate(zip(a       ,     a		)		):
    _snake_case     :						str						=   input_row["sentence"]
    _snake_case     :						List[Any]						=   1

  _snake_case     :						Tuple						=   torch.stack([row["image"] for row in batch]		)
  _snake_case     :						Dict						=   torch.stack([row["label"] for row in batch]		)
  _snake_case     :						Any						=   torch.stack([row["image_start_token"] for row in batch]		)
  _snake_case     :						str						=   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 				a__   (   ):

  """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 				a__   (   ):

  """simple docstring"""







  return transforms.Compose(
      [
          transforms.Resize(256		),
          transforms.CenterCrop(224		),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.46777044, 0.44531429, 0.40661017]       ,     std=[0.12221994, 0.12145835, 0.14380469]       ,     ),
      ]		)





"""simple docstring"""
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: int  =					"""EncodecFeatureExtractor"""
    __lowercase							: str  =					("""T5Tokenizer""", """T5TokenizerFast""")
    def __init__(    self						,    snake_case_						,    snake_case_     ):
      super().__init__(snake_case_						,    snake_case_     )
      _snake_case     :						Dict						=   self.feature_extractor
      _snake_case     :						Any						=   False
    def 					lowerCamelCase__	(    self						,    snake_case_=None						,    snake_case_=None						,    snake_case_=True     ):
      return self.tokenizer.get_decoder_prompt_ids(task=snake_case_						,    language=snake_case_						,    no_timestamps=snake_case_     )
    def __call__(    self						,    *snake_case_						,    **snake_case_     ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*snake_case_						,    **snake_case_     )

      _snake_case     :						str						=   kwargs.pop("audio"						,    snake_case_     )
      _snake_case     :						Optional[int]						=   kwargs.pop("sampling_rate"						,    snake_case_     )
      _snake_case     :						Optional[Any]						=   kwargs.pop("text"						,    snake_case_     )
      if len(snake_case_     ) > 0:
        _snake_case     :						Any						=   args[0]
        _snake_case     :						Union[str, Any]						=   args[1:]

      if audio is None and text is None:
        raise ValueError("You need to specify either an `audio` or `text` input to process."     )

      if text is not None:
        _snake_case     :						Any						=   self.tokenizer(snake_case_						,    **snake_case_     )

      if audio is not None:
        _snake_case     :						Any						=   self.feature_extractor(snake_case_						,    *snake_case_						,    sampling_rate=snake_case_						,    **snake_case_     )

      if audio is None:
        return inputs

      elif text is None:
        return audio_inputs

      else:
        _snake_case     :						str						=   audio_inputs["input_values"]
        if "padding_mask" in audio_inputs:
          _snake_case     :						List[str]						=   audio_inputs["padding_mask"]
        return inputs
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    **snake_case_     ):
      _snake_case     :						Tuple						=   kwargs.pop("audio"						,    snake_case_     )
      _snake_case     :						List[str]						=   kwargs.pop("padding_mask"						,    snake_case_     )

      if len(snake_case_     ) > 0:
        _snake_case     :						Tuple						=   args[0]
        _snake_case     :						Dict						=   args[1:]

      if audio_values is not None:
        return self._decode_audio(snake_case_						,    padding_mask=snake_case_     )
      else:
        return self.tokenizer.batch_decode(*snake_case_						,    **snake_case_     )
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    **snake_case_     ):
      return self.tokenizer.decode(*snake_case_						,    **snake_case_     )


    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = None     ):
      _snake_case     :						Optional[int]						=   to_numpy(snake_case_     )
      _snake_case			,					_snake_case			,					_snake_case     :						Tuple						=   audio_values.shape

      if padding_mask is None:
        return list(snake_case_     )

      _snake_case     :						Optional[int]						=   to_numpy(snake_case_     )

      # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
      # token (so that the generated audio values are **not** treated as padded tokens)
      _snake_case     :						Any						=   seq_len - padding_mask.shape[-1]
      _snake_case     :						Optional[Any]						=   1 - self.feature_extractor.padding_value
      _snake_case     :						Optional[int]						=   np.pad(snake_case_						,    ((0, 0), (0, difference))						,    "constant"						,    constant_values=snake_case_     )

      _snake_case     :						Any						=   audio_values.tolist()
      for i in range(snake_case_     ):
        _snake_case     :						Tuple						=   np.asarray(audio_values[i]     )[
            padding_mask[i][None, :] != self.feature_extractor.padding_value
        ]
        _snake_case     :						Tuple						=   sliced_audio.reshape(snake_case_						,    -1     )

      return audio_values

