




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      			=						{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       a					(       nn.Module      ):

     '''simple docstring'''





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




     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      Optional[Any]    ):
         # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
         UpperCAmelCase_										=					self.pool(self.model(__snake_case    )    )
         UpperCAmelCase_										=					torch.flatten(__snake_case ,   start_dim=2    )
         UpperCAmelCase_										=					out.transpose(1 ,   2    ).contiguous()
         return out  # BxNx2048



class       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      Union[str, Any] ,   __snake_case       :      int ,   __snake_case       :      Optional[int] ,   __snake_case       :      int ,   __snake_case       :      Optional[Any] ,   __snake_case       :      List[str]    ):
         UpperCAmelCase_										=					[json.loads(__snake_case    ) for l in open(__snake_case    )]
         UpperCAmelCase_										=					os.path.dirname(__snake_case    )
         UpperCAmelCase_										=					tokenizer
         UpperCAmelCase_										=					labels
         UpperCAmelCase_										=					len(__snake_case    )
         UpperCAmelCase_										=					max_seq_length

         UpperCAmelCase_										=					transforms
     def __len__(     self       :      Tuple    ):
         return len(self.data    )
     def __getitem__(     self       :      str ,   __snake_case       :      List[str]    ):
         UpperCAmelCase_										=					torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''] ,   add_special_tokens=__snake_case    )    )
         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(__snake_case    )

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




     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					Counter()
         for row in self.data:
             label_freqs.update(row['''label''']    )
         return label_freqs




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					[len(row['''sentence''']				) for row in batch]
    UpperCAmelCase_			,							UpperCAmelCase_										=					len(__UpperCamelCase				), max(__UpperCamelCase				)

    UpperCAmelCase_										=					torch.zeros(__UpperCamelCase	,					__UpperCamelCase	,					dtype=torch.long				)
    UpperCAmelCase_										=					torch.zeros(__UpperCamelCase	,					__UpperCamelCase	,					dtype=torch.long				)

    for i_batch, (input_row, length) in enumerate(zip(__UpperCamelCase	,					__UpperCamelCase				)				):
        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  SCREAMING_SNAKE_CASE     (	)  ->							Optional[int]:
    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  SCREAMING_SNAKE_CASE     (	)  ->							Optional[Any]:
    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]	,					),
        ]				)











from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowerCamelCase      			=						logging.get_logger(__name__)



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  ['input_values', 'padding_mask']
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      int = 1 ,   __snake_case       :      int = 2_40_00 ,   __snake_case       :      float = 0.0 ,   __snake_case       :      float = None ,   __snake_case       :      float = None ,   **__snake_case       :      Dict ,   ):
         super().__init__(feature_size=__snake_case ,   sampling_rate=__snake_case ,   padding_value=__snake_case ,   **__snake_case    )
         UpperCAmelCase_										=					chunk_length_s
         UpperCAmelCase_										=					overlap
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         if self.chunk_length_s is None:
             return None
         else:
             return int(self.chunk_length_s * self.sampling_rate    )
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         if self.chunk_length_s is None or self.overlap is None:
             return None
         else:
             return max(1 ,   int((1.0 - self.overlap) * self.chunk_length    )    )




     def __call__(     self       :      List[str] ,   __snake_case       :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,   __snake_case       :      Optional[Union[bool, str, PaddingStrategy]] = None ,   __snake_case       :      Optional[bool] = False ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[Union[str, TensorType]] = None ,   __snake_case       :      Optional[int] = None ,   ):
         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
                     F' {self.sampling_rate} and not {sampling_rate}.'    )
         else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''    )

         if padding and truncation:
             raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''    )
         elif padding is None:
             # by default let's pad the inputs
             UpperCAmelCase_										=					True

         UpperCAmelCase_										=					bool(
             isinstance(__snake_case ,   (list, tuple)    ) and (isinstance(raw_audio[0] ,   (np.ndarray, tuple, list)    ))    )

         if is_batched:
             UpperCAmelCase_										=					[np.asarray(__snake_case ,   dtype=np.floataa    ).T for audio in raw_audio]
         elif not is_batched and not isinstance(__snake_case ,   np.ndarray    ):
             UpperCAmelCase_										=					np.asarray(__snake_case ,   dtype=np.floataa    )
         elif isinstance(__snake_case ,   np.ndarray    ) and raw_audio.dtype is np.dtype(np.floataa    ):
             UpperCAmelCase_										=					raw_audio.astype(np.floataa    )

         # always return batch
         if not is_batched:
             UpperCAmelCase_										=					[np.asarray(__snake_case    ).T]

         # verify inputs are valid
         for idx, example in enumerate(__snake_case    ):
             if example.ndim > 2:
                 raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'    )
             if self.feature_size == 1 and example.ndim != 1:
                 raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'    )
             if self.feature_size == 2 and example.shape[-1] != 2:
                 raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'    )

         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					BatchFeature({'''input_values''': raw_audio}    )
         if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
             if truncation:
                 UpperCAmelCase_										=					min(array.shape[0] for array in raw_audio    )
                 UpperCAmelCase_										=					int(np.floor(max_length / self.chunk_stride    )    )
                 UpperCAmelCase_										=					(nb_step - 1) * self.chunk_stride + self.chunk_length
             elif padding:
                 UpperCAmelCase_										=					max(array.shape[0] for array in raw_audio    )
                 UpperCAmelCase_										=					int(np.ceil(max_length / self.chunk_stride    )    )
                 UpperCAmelCase_										=					(nb_step - 1) * self.chunk_stride + self.chunk_length
                 UpperCAmelCase_										=					'''max_length'''
             else:
                 UpperCAmelCase_										=					input_values

        # normal padding on batch
         if padded_inputs is None:
             UpperCAmelCase_										=					self.pad(
                 __snake_case ,   max_length=__snake_case ,   truncation=__snake_case ,   padding=__snake_case ,   return_attention_mask=__snake_case ,   )
             if padding:
                 UpperCAmelCase_										=					padded_inputs.pop('''attention_mask'''    )

         UpperCAmelCase_										=					[]
         for example in padded_inputs.pop('''input_values'''    ):
             if self.feature_size == 1:
                 UpperCAmelCase_										=					example[..., None]
             input_values.append(example.T    )

         UpperCAmelCase_										=					input_values
         if return_tensors is not None:
             UpperCAmelCase_										=					padded_inputs.convert_to_tensors(__snake_case    )

         return padded_inputs





