




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_   		=     {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					: Tuple ,       snake_case					: int				):
          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       snake_case	(     self					: Dict ,       snake_case					: Dict				):
          # 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  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: str ,       snake_case					: Optional[Any] ,       snake_case					: str ,       snake_case					: Union[str, Any] ,       snake_case					: Dict ,       snake_case					: Tuple				):
          __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					: int				):
          return len(self.data				)







      def __getitem__(     self					: Union[str, Any] ,       snake_case					: Dict				):
          __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       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						Counter()
          for row in self.data:
              label_freqs.update(row['''label''']				)
          return label_freqs







def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase  		=						[len(row['''sentence''']      ) for row in batch]
    __UpperCamelCase		,      __UpperCamelCase  		=						len(lowercase_      ), max(lowercase_      )

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

    for i_batch, (input_row, length) in enumerate(zip(lowercase_					,     lowercase_      )      ):
        __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   (  )       ->	List[Any]:

    """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 					__SCREAMING_SNAKE_CASE   (  )       ->	Tuple:

    """simple docstring"""


    return transforms.Compose(
        [
            transforms.Resize(2_56      ),
            transforms.CenterCrop(2_24      ),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]					,     std=[0.1222_1994, 0.1214_5835, 0.1438_0469]					,     ),
        ]      )












import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
a_   		=     logging.get_logger(__name__)




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )
    if "model" in sd.keys():
        __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )['''model''']

    # pop unnecessary weights
    __UpperCamelCase  		=						[
        '''decoder.version''',
        '''decoder.output_projection.weight''',
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(lowercase_      )

    __UpperCamelCase  		=						{
        '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
        '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
        '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
        '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
    }
    for old_key, new_key in keys_to_rename.items():
        if old_key in sd:
            __UpperCamelCase  		=						sd.pop(lowercase_      )

    __UpperCamelCase  		=						list(sd.keys()      )
    for key in keys:
        if ".qkv_proj." in key:
            __UpperCamelCase  		=						sd[key]
            # We split QKV in separate Q,K,V

            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.q_proj.'''      )
            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.k_proj.'''      )
            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.v_proj.'''      )

            __UpperCamelCase  		=						value.shape[0]
            assert depth % 3 == 0
            # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
            # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
            __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						torch.split(lowercase_					,     depth // 3					,     dim=0      )

            __UpperCamelCase  		=						q
            __UpperCamelCase  		=						k
            __UpperCamelCase  		=						v
            del sd[key]

    return sd




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						load_checkpoint(lowercase_      )

    if config is not None:
        __UpperCamelCase  		=						OPTConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase  		=						OPTConfig()

    __UpperCamelCase  		=						OPTModel(lowercase_      ).half().eval()
    model.load_state_dict(lowercase_      )

    # Check results
    Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
    model.save_pretrained(lowercase_      )


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--fairseq_path",
     type=str,
     help=(
         "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
         " https://huggingface.co/models?other=opt_metasq"
     ),
 )
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
 a_   		=     parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)






