


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_   :				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 				SCREAMING_SNAKE_CASE__						(       nn.Module    ):
      def __init__( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : str  )	->    Optional[Any]:
             super().__init__()
             a_      :	Any							=						torchvision.models.resnetaaa(pretrained=SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						list(model.children()  )[:-2]
             a_      :	Any							=						nn.Sequential(*SCREAMING_SNAKE_CASE__  )
             a_      :	List[str]							=						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )

      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : int  )	->    Tuple:
             # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
             a_      :	Union[str, Any]							=						self.pool(self.model(SCREAMING_SNAKE_CASE__  )  )
             a_      :	int							=						torch.flatten(SCREAMING_SNAKE_CASE__  ,							start_dim=2  )
             a_      :	Optional[int]							=						out.transpose(1  ,							2  ).contiguous()
             return out  # BxNx2048
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      def __init__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : Tuple  )	->    int:
             a_      :	Dict							=						[json.loads(SCREAMING_SNAKE_CASE__  ) for l in open(SCREAMING_SNAKE_CASE__  )]
             a_      :	Optional[Any]							=						os.path.dirname(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						tokenizer
             a_      :	Tuple							=						labels
             a_      :	List[Any]							=						len(SCREAMING_SNAKE_CASE__  )
             a_      :	Dict							=						max_seq_length

             a_      :	int							=						transforms
      def __len__( self       : Tuple  )	->    Tuple:
             return len(self.data  )
      def __getitem__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Dict  )	->    Union[str, Any]:
             a_      :	List[Any]							=						torch.LongTensor(self.tokenizer.encode(self.data[index]['text']  ,							add_special_tokens=SCREAMING_SNAKE_CASE__  )  )
             a_      :	List[str]							=						sentence[0], sentence[1:-1], sentence[-1]
             a_      :	Optional[int]							=						sentence[: self.max_seq_length]

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

             a_      :	Optional[Any]							=						Image.open(os.path.join(self.data_dir  ,							self.data[index]['img']  )  ).convert('RGB'  )
             a_      :	List[str]							=						self.transforms(SCREAMING_SNAKE_CASE__  )

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

      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    int:
             a_      :	Dict							=						Counter()
             for row in self.data:
                    label_freqs.update(row['label']  )
             return label_freqs







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Tuple							=						[len(row['sentence']       ) for row in batch]
       a_      :	Optional[int]							=						len(__A       ), max(__A       )

       a_      :	Union[str, Any]							=						torch.zeros(__A						,					__A						,					dtype=torch.long       )
       a_      :	Optional[Any]							=						torch.zeros(__A						,					__A						,					dtype=torch.long       )

       for i_batch, (input_row, length) in enumerate(zip(__A						,					__A       )       ):
              a_      :	Tuple							=						input_row['sentence']
              a_      :	List[str]							=						1

       a_      :	Dict							=						torch.stack([row['image'] for row in batch]       )
       a_      :	Optional[int]							=						torch.stack([row['label'] for row in batch]       )
       a_      :	int							=						torch.stack([row['image_start_token'] for row in batch]       )
       a_      :	List[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  SCREAMING_SNAKE_CASE_		(    )			->	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_		(    )			->	Optional[int]:

       """simple docstring"""

       return transforms.Compose(
           [
               transforms.Resize(2_56       ),
               transforms.CenterCrop(2_24       ),
               transforms.ToTensor(),
               transforms.Normalize(
                   mean=[0.46777044, 0.44531429, 0.40661017]						,					std=[0.12221994, 0.12145835, 0.14380469]						,					),
           ]       )






import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCAmelCase_   :				Optional[int]						      = [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any       )			->	Union[str, Any]:

       """simple docstring"""

       for pegasus_name, hf_name in PATTERNS:
              a_      :	Union[str, Any]							=						k.replace(__A						,					__A       )
       return k







def  SCREAMING_SNAKE_CASE_		(    __A				:    dict						,					__A				:    dict       )			->	PegasusForConditionalGeneration:

       """simple docstring"""

       a_      :	int							=						DEFAULTS.copy()
       cfg_kwargs.update(__A       )
       a_      :	List[str]							=						PegasusConfig(**__A       )
       a_      :	List[Any]							=						PegasusForConditionalGeneration(__A       )
       a_      :	List[str]							=						torch_model.model.state_dict()
       a_      :	Optional[int]							=						{}
       for k, v in tf_weights.items():
              a_      :	str							=						rename_state_dict_key(__A       )
              if new_k not in sd:
                     raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""       )

              if "dense" in k or "proj" in new_k:
                     a_      :	Union[str, Any]							=						v.T
              a_      :	List[str]							=						torch.tensor(__A						,					dtype=sd[new_k].dtype       )
              assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
       # make sure embedding.padding_idx is respected
       a_      :	int							=						torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]       )
       a_      :	Optional[Any]							=						mapping['shared.weight']
       a_      :	Any							=						mapping['shared.weight']
       a_      :	Optional[int]							=						{k: torch.zeros_like(__A       ) for k, v in sd.items() if k.endswith('bias'       ) and k not in mapping}
       mapping.update(**__A       )
       a_							,	a_      :	Optional[Any]							=						torch_model.model.load_state_dict(__A						,					strict=__A       )
       a_      :	Optional[Any]							=						[
           k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
       ]
       assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
       assert extra == [], F"""no matches found for the following tf keys {extra}"""
       return torch_model







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict="./ckpt/aeslc/model.ckpt-32000"       )			->	Dict:

       """simple docstring"""

       a_      :	List[Any]							=						tf.train.list_variables(__A       )
       a_      :	Optional[Any]							=						{}
       a_      :	int							=						['Adafactor', 'global_step']
       for name, shape in tqdm(__A						,					desc='converting tf checkpoint to dict'       ):
              a_      :	Optional[int]							=						any(pat in name for pat in ignore_name       )
              if skip_key:
                     continue
              a_      :	List[str]							=						tf.train.load_variable(__A						,					__A       )
              a_      :	Optional[Any]							=						array
       return tf_weights







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Any							=						Path(__A       ).parent.name
       a_      :	Dict							=						task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
       a_      :	Union[str, Any]							=						PegasusTokenizer.from_pretrained('sshleifer/pegasus'						,					model_max_length=__A       )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(__A       )

       # convert model
       a_      :	List[Any]							=						get_tf_weights_as_numpy(__A       )
       a_      :	List[str]							=						task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              a_      :	Union[str, Any]							=						task_specific_params
       a_      :	Optional[int]							=						convert_pegasus(__A						,					__A       )
       torch_model.save_pretrained(__A       )
       a_      :	Dict							=						torch_model.state_dict()
       sd.pop('model.decoder.embed_positions.weight'       )
       sd.pop('model.encoder.embed_positions.weight'       )
       torch.save(__A						,					Path(__A       ) / 'pytorch_model.bin'       )


if __name__ == "__main__":
   UpperCAmelCase_   :				Dict						      = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
   parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
   UpperCAmelCase_   :				str						      = parser.parse_args()
   if args.save_dir is None:
      UpperCAmelCase_   :				str						      = Path(args.tf_ckpt_path).parent.name
      UpperCAmelCase_   :				Optional[int]						      = os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


