






'''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


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





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






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





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

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

       UpperCamelCase_				:   int					    =							torch.zeros(self.n_classes )
       UpperCamelCase_				:   Optional[int]					    =							1

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

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





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							Counter()
       for row in self.data:
             label_freqs.update(row["""label"""] )
       return label_freqs






def 							snake_case					(     a_							: Union[str, Any]	)       ->				List[Any]:





      """simple docstring"""

      UpperCamelCase_				:   List[Any]					    =							[len(row["""sentence"""]	) for row in batch]
      UpperCamelCase_				:   Union[str, Any]					    =							len(a_	), max(a_	)

      UpperCamelCase_				:   Any					    =							torch.zeros(a_    ,   a_    ,   dtype=torch.long	)
      UpperCamelCase_				:   Optional[int]					    =							torch.zeros(a_    ,   a_    ,   dtype=torch.long	)

      for i_batch, (input_row, length) in enumerate(zip(a_    ,   a_	)	):
            UpperCamelCase_				:   Optional[int]					    =							input_row["""sentence"""]
            UpperCamelCase_				:   str					    =							1

      UpperCamelCase_				:   Dict					    =							torch.stack([row["""image"""] for row in batch]	)
      UpperCamelCase_				:   Dict					    =							torch.stack([row["""label"""] for row in batch]	)
      UpperCamelCase_				:   List[Any]					    =							torch.stack([row["""image_start_token"""] for row in batch]	)
      UpperCamelCase_				:   int					    =							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 							snake_case					(     )       ->				Tuple:





      """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 							snake_case					(     )       ->				Any:





      """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]    ,   ),
          ]	)








'''simple docstring'''

from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


UpperCamelCase							=logging.get_logger(__name__)  # pylint: disable=invalid-name



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(a_	):
                  return ext

      raise Exception(
          f"Unable to determine file format from file extension {path}. "
          f"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"	)



def 							snake_case					(     a_							: List[Any]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							pipeline(
          task=args.task    ,   model=args.model if args.model else None    ,   config=args.config    ,   tokenizer=args.tokenizer    ,   device=args.device    ,   )
      UpperCamelCase_				:   List[Any]					    =							try_infer_format_from_ext(args.input	) if args.format == """infer""" else args.format
      UpperCamelCase_				:   Tuple					    =							PipelineDataFormat.from_str(
          format=a_    ,   output_path=args.output    ,   input_path=args.input    ,   column=args.column if args.column else nlp.default_input_names    ,   overwrite=args.overwrite    ,   )
      return RunCommand(a_    ,   a_	)




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							nlp
       UpperCamelCase_				:   Optional[Any]					    =							reader
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							parser.add_parser("""run"""			,     help="""Run a pipeline through the CLI""" )
       run_parser.add_argument("""--task"""			,     choices=get_supported_tasks()			,     help="""Task to run""" )
       run_parser.add_argument("""--input"""			,     type=__lowerCAmelCase			,     help="""Path to the file to use for inference""" )
       run_parser.add_argument("""--output"""			,     type=__lowerCAmelCase			,     help="""Path to the file that will be used post to write results.""" )
       run_parser.add_argument("""--model"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model to instantiate.""" )
       run_parser.add_argument("""--config"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model's config to instantiate.""" )
       run_parser.add_argument(
           """--tokenizer"""			,     type=__lowerCAmelCase			,     help="""Name of the tokenizer to use. (default: same as the model name)""" )
       run_parser.add_argument(
           """--column"""			,     type=__lowerCAmelCase			,     help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""			,     )
       run_parser.add_argument(
           """--format"""			,     type=__lowerCAmelCase			,     default="""infer"""			,     choices=PipelineDataFormat.SUPPORTED_FORMATS			,     help="""Input format to read from"""			,     )
       run_parser.add_argument(
           """--device"""			,     type=__lowerCAmelCase			,     default=-1			,     help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""			,     )
       run_parser.add_argument("""--overwrite"""			,     action="""store_true"""			,     help="""Allow overwriting the output file.""" )
       run_parser.set_defaults(func=__lowerCAmelCase )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							self._nlp, []

       for entry in self._reader:
             UpperCamelCase_				:   List[Any]					    =							nlp(**__lowerCAmelCase ) if self._reader.is_multi_columns else nlp(__lowerCAmelCase )
             if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   outputs.append(__lowerCAmelCase )
             else:
                   outputs += output

        # Saving data
       if self._nlp.binary_output:
             UpperCamelCase_				:   int					    =							self._reader.save_binary(__lowerCAmelCase )
             logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}" )
       else:
             self._reader.save(__lowerCAmelCase )






