



"""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


__lowerCAmelCase							:       Dict          =       {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  _lowerCAmelCase     (					nn.Module		):





		"""simple docstring"""






		def __init__(       self				,     _lowercase )			->    int:







								'''simple docstring'''

								super().__init__()
								snake_case_ :			int            =      torchvision.models.resnetaaa(pretrained=_lowercase )
								snake_case_ :			Tuple            =      list(model.children() )[:-2]
								snake_case_ :			str            =      nn.Sequential(*_lowercase )
								snake_case_ :			Union[str, Any]            =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    str:







								'''simple docstring'''

								snake_case_ :			List[str]            =      self.pool(self.model(_lowercase ) )
								snake_case_ :			Any            =      torch.flatten(_lowercase				,     start_dim=2 )
								snake_case_ :			str            =      out.transpose(1				,     2 ).contiguous()
								return out  # BxNx2048




class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		def __init__(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    int:







								'''simple docstring'''

								snake_case_ :			Any            =      [json.loads(_lowercase ) for l in open(_lowercase )]
								snake_case_ :			List[str]            =      os.path.dirname(_lowercase )
								snake_case_ :			str            =      tokenizer
								snake_case_ :			Dict            =      labels
								snake_case_ :			Optional[int]            =      len(_lowercase )
								snake_case_ :			Any            =      max_seq_length

								snake_case_ :			Union[str, Any]            =      transforms





		def __len__(       self )			->    Union[str, Any]:







								'''simple docstring'''

								return len(self.data )





		def __getitem__(       self				,     _lowercase )			->    Union[str, Any]:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]				,     add_special_tokens=_lowercase ) )
								snake_case_      ,		snake_case_      ,		snake_case_ :			List[str]            =      sentence[0], sentence[1:-1], sentence[-1]
								snake_case_ :			Any            =      sentence[: self.max_seq_length]

								snake_case_ :			Dict            =      torch.zeros(self.n_classes )
								snake_case_ :			Dict            =      1

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

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





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

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




def 						__lowerCAmelCase    (		__UpperCamelCase   :      int     ):


						'''simple docstring'''






						snake_case_ :			Tuple            =      [len(row["""sentence"""]     ) for row in batch]
						snake_case_      ,		snake_case_ :			str            =      len(__UpperCamelCase     ), max(__UpperCamelCase     )

						snake_case_ :			Optional[int]            =      torch.zeros(__UpperCamelCase       ,			__UpperCamelCase       ,			dtype=torch.long     )
						snake_case_ :			Any            =      torch.zeros(__UpperCamelCase       ,			__UpperCamelCase       ,			dtype=torch.long     )

						for i_batch, (input_row, length) in enumerate(zip(__UpperCamelCase       ,			__UpperCamelCase     )     ):
												snake_case_ :			Optional[int]            =      input_row["""sentence"""]
												snake_case_ :			Any            =      1

						snake_case_ :			str            =      torch.stack([row["""image"""] for row in batch]     )
						snake_case_ :			List[Any]            =      torch.stack([row["""label"""] for row in batch]     )
						snake_case_ :			Dict            =      torch.stack([row["""image_start_token"""] for row in batch]     )
						snake_case_ :			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 						__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(2_5_6     ),
						        transforms.CenterCrop(2_2_4     ),
						        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 dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
    import tensorflow as tf


__A			:					Dict						=			logging.get_logger(__name__)





@dataclass
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      [
       "no_inference",
       "no_cuda",
       "no_tpu",
       "no_speed",
       "no_memory",
       "no_env_print",
       "no_multi_process",
   ]





   def __init__(       self					:			List[Any]     , **__lowerCamelCase					:			Any			):
       for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
               SCREAMING_SNAKE_CASE										=   deprecated_arg[3:]
               SCREAMING_SNAKE_CASE										=   not kwargs.pop(__lowerCamelCase			)
               logger.warning(
                   f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
                   f" {positive_arg}={kwargs[positive_arg]}"			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("tpu_name"     , self.tpu_name			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("device_idx"     , self.device_idx			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("eager_mode"     , self.eager_mode			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("use_xla"     , self.use_xla			)
       super().__init__(**__lowerCamelCase			)

   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Name of TPU"}					,       )
   lowerCamelCase__				=      field(
       default=0					,       metadata={"help": "CPU / GPU device index. Defaults to 0."}					,       )
   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Benchmark models in eager model."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
       }					,       )





   @cached_property
   def      _snake_case		(       self					:			Optional[int]			):
       requires_backends(self     , ["tf"]			)
       SCREAMING_SNAKE_CASE										=   None
       if self.tpu:
           try:
               if self.tpu_name:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
               else:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
               SCREAMING_SNAKE_CASE										=   None
       return tpu





   @cached_property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       if self.is_tpu:
           tf.config.experimental_connect_to_cluster(self._setup_tpu			)
           tf.tpu.experimental.initialize_tpu_system(self._setup_tpu			)

           SCREAMING_SNAKE_CASE										=   tf.distribute.TPUStrategy(self._setup_tpu			)
       else:
           # currently no multi gpu is allowed
           if self.is_gpu:
               # TODO: Currently only single GPU is supported
               tf.config.set_visible_devices(self.gpu_list[self.device_idx]     , "GPU"			)
               SCREAMING_SNAKE_CASE										=   tf.distribute.OneDeviceStrategy(device=f"/gpu:{self.device_idx}"			)
           else:
               tf.config.set_visible_devices([]     , "GPU"			)  # disable GPU
               SCREAMING_SNAKE_CASE										=   tf.distribute.OneDeviceStrategy(device=f"/cpu:{self.device_idx}"			)

       return strategy





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       return self._setup_tpu is not None





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       requires_backends(self     , ["tf"]			)
       return self._setup_strategy





   @property
   def      _snake_case		(       self					:			List[str]			):
       requires_backends(self     , ["tf"]			)
       return tf.config.list_physical_devices("GPU"			)





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       if self.cuda:
           return len(self.gpu_list			)
       return 0





   @property
   def      _snake_case		(       self					:			Dict			):
       return self.n_gpu > 0