


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
  import transformers




def  a   (lowerCAmelCase__   ):
   random.seed(lowerCAmelCase__   )
   np.random.seed(lowerCAmelCase__   )
   torch.manual_seed(lowerCAmelCase__   )
   torch.cuda.manual_seed_all(lowerCAmelCase__   )
   # ^^ safe to call this function even if cuda is not available


class     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A = 0.9999  ,       __A = 0.0  ,       __A = 0  ,       __A = False  ,       __A = 1.0  ,       __A = 2 / 3  ,       __A = None  ,       __A = None  ,       **__A  ,       ):

        if isinstance(__A  ,       torch.nn.Module    ):
           __a					=  (
               """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
               """Please pass the parameters of the module instead."""
           )
           deprecate(
               """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""  ,       """1.0.0"""  ,       __A  ,       standard_warn=__A  ,       )
           __a					=  parameters.parameters()

           # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
           __a					=  True

        if kwargs.get("""max_value"""  ,       __A    ) is not None:
           __a					=  """The `max_value` argument is deprecated. Please use `decay` instead."""
           deprecate("""max_value"""  ,       """1.0.0"""  ,       __A  ,       standard_warn=__A    )
           __a					=  kwargs["""max_value"""]

        if kwargs.get("""min_value"""  ,       __A    ) is not None:
           __a					=  """The `min_value` argument is deprecated. Please use `min_decay` instead."""
           deprecate("""min_value"""  ,       """1.0.0"""  ,       __A  ,       standard_warn=__A    )
           __a					=  kwargs["""min_value"""]

        __a					=  list(__A    )
        __a					=  [p.clone().detach() for p in parameters]

        if kwargs.get("""device"""  ,       __A    ) is not None:
           __a					=  """The `device` argument is deprecated. Please use `to` instead."""
           deprecate("""device"""  ,       """1.0.0"""  ,       __A  ,       standard_warn=__A    )
           self.to(device=kwargs["""device"""]    )

        __a					=  None

        __a					=  decay
        __a					=  min_decay
        __a					=  update_after_step
        __a					=  use_ema_warmup
        __a					=  inv_gamma
        __a					=  power
        __a					=  0
        __a					=  None  # set in `step()`

        __a					=  model_cls
        __a					=  model_config


     @classmethod
     def 	snake_case_  (	cls  ,       __A  ,       __A    ):
        __a	,    __a					=  model_cls.load_config(__A  ,       return_unused_kwargs=__A    )
        __a					=  model_cls.from_pretrained(__A    )

        __a					=  cls(model.parameters()  ,       model_cls=__A  ,       model_config=model.config    )

        ema_model.load_state_dict(__A    )
        return ema_model


     def 	snake_case_  (	self  ,       __A    ):
        if self.model_cls is None:
           raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""    )

        if self.model_config is None:
           raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""    )

        __a					=  self.model_cls.from_config(self.model_config    )
        __a					=  self.state_dict()
        state_dict.pop("""shadow_params"""  ,       __A    )

        model.register_to_config(**__A    )
        self.copy_to(model.parameters()    )
        model.save_pretrained(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  max(0  ,       optimization_step - self.update_after_step - 1    )

        if step <= 0:
           return 0.0

        if self.use_ema_warmup:
           __a					=  1 - (1 + step / self.inv_gamma) ** -self.power
        else:
           __a					=  (1 + step) / (10 + step)

        __a					=  min(__A  ,       self.decay    )
        # make sure decay is not smaller than min_decay
        __a					=  max(__A  ,       self.min_decay    )
        return cur_decay_value


     @torch.no_grad()
     def 	snake_case_  (	self  ,       __A    ):
        if isinstance(__A  ,       torch.nn.Module    ):
           __a					=  (
               """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
               """Please pass the parameters of the module instead."""
           )
           deprecate(
               """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""  ,       """1.0.0"""  ,       __A  ,       standard_warn=__A  ,       )
           __a					=  parameters.parameters()

        __a					=  list(__A    )

        self.optimization_step += 1

        # Compute the decay factor for the exponential moving average.
        __a					=  self.get_decay(self.optimization_step    )
        __a					=  decay
        __a					=  1 - decay

        __a					=  contextlib.nullcontext
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
           import deepspeed

        for s_param, param in zip(self.shadow_params  ,       __A    ):
           if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
              __a					=  deepspeed.zero.GatheredParameters(__A  ,       modifier_rank=__A    )

           with context_manager():
              if param.requires_grad:
                 s_param.sub_(one_minus_decay * (s_param - param)    )
              else:
                 s_param.copy_(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  list(__A    )
        for s_param, param in zip(self.shadow_params  ,       __A    ):
           param.data.copy_(s_param.to(param.device    ).data    )


     def 	snake_case_  (	self  ,       __A=None  ,       __A=None    ):
        __a					=  [
            p.to(device=__A  ,       dtype=__A    ) if p.is_floating_point() else p.to(device=__A    )
            for p in self.shadow_params
        ]


     def 	snake_case_  (	self    ):
        return {
            "decay": self.decay,
            "min_decay": self.min_decay,
            "optimization_step": self.optimization_step,
            "update_after_step": self.update_after_step,
            "use_ema_warmup": self.use_ema_warmup,
            "inv_gamma": self.inv_gamma,
            "power": self.power,
            "shadow_params": self.shadow_params,
        }


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  [param.detach().cpu().clone() for param in parameters]


     def 	snake_case_  (	self  ,       __A    ):
        if self.temp_stored_params is None:
           raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""    )
        for c_param, param in zip(self.temp_stored_params  ,       __A    ):
           param.data.copy_(c_param.data    )

        # Better memory-wise.
        __a					=  None


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  copy.deepcopy(__A    )

        __a					=  state_dict.get("""decay"""  ,       self.decay    )
        if self.decay < 0.0 or self.decay > 1.0:
           raise ValueError("""Decay must be between 0 and 1"""    )

        __a					=  state_dict.get("""min_decay"""  ,       self.min_decay    )
        if not isinstance(self.min_decay  ,       __A    ):
           raise ValueError("""Invalid min_decay"""    )

        __a					=  state_dict.get("""optimization_step"""  ,       self.optimization_step    )
        if not isinstance(self.optimization_step  ,       __A    ):
           raise ValueError("""Invalid optimization_step"""    )

        __a					=  state_dict.get("""update_after_step"""  ,       self.update_after_step    )
        if not isinstance(self.update_after_step  ,       __A    ):
           raise ValueError("""Invalid update_after_step"""    )

        __a					=  state_dict.get("""use_ema_warmup"""  ,       self.use_ema_warmup    )
        if not isinstance(self.use_ema_warmup  ,       __A    ):
           raise ValueError("""Invalid use_ema_warmup"""    )

        __a					=  state_dict.get("""inv_gamma"""  ,       self.inv_gamma    )
        if not isinstance(self.inv_gamma  ,       (float, int)    ):
           raise ValueError("""Invalid inv_gamma"""    )

        __a					=  state_dict.get("""power"""  ,       self.power    )
        if not isinstance(self.power  ,       (float, int)    ):
           raise ValueError("""Invalid power"""    )

        __a					=  state_dict.get("""shadow_params"""  ,       __A    )
        if shadow_params is not None:
           __a					=  shadow_params
           if not isinstance(self.shadow_params  ,       __A    ):
              raise ValueError("""shadow_params must be a list"""    )
           if not all(isinstance(__A  ,       torch.Tensor    ) for p in self.shadow_params    ):
              raise ValueError("""shadow_params must all be Tensors"""    )







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


SCREAMING_SNAKE_CASE							    =							{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     __UpperCAmelCase							(       nn.Module							):


     """simple docstring"""


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


     def 	snake_case_  (	self  ,       __A    ):
        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
        __a					=  self.pool(self.model(__A    )    )
        __a					=  torch.flatten(__A  ,       start_dim=2    )
        __a					=  out.transpose(1  ,       2    ).contiguous()
        return out  # BxNx2048







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        __a					=  [json.loads(__A    ) for l in open(__A    )]
        __a					=  os.path.dirname(__A    )
        __a					=  tokenizer
        __a					=  labels
        __a					=  len(__A    )
        __a					=  max_seq_length

        __a					=  transforms


     def __len__(	self    ):
        return len(self.data    )


     def __getitem__(	self  ,       __A    ):
        __a					=  torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]  ,       add_special_tokens=__A    )    )
        __a	,    __a	,    __a					=  sentence[0], sentence[1:-1], sentence[-1]
        __a					=  sentence[: self.max_seq_length]

        __a					=  torch.zeros(self.n_classes    )
        __a					=  1

        __a					=  Image.open(os.path.join(self.data_dir  ,       self.data[index]["""img"""]    )    ).convert("""RGB"""    )
        __a					=  self.transforms(__A    )

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


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







def  a   (lowerCAmelCase__   ):
   __a					=  [len(row["""sentence"""]   ) for row in batch]
   __a	,    __a					=  len(lowerCAmelCase__   ), max(lowerCAmelCase__   )

   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )
   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )

   for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase__		,				lowerCAmelCase__   )   ):
      __a					=  input_row["""sentence"""]
      __a					=  1

   __a					=  torch.stack([row["""image"""] for row in batch]   )
   __a					=  torch.stack([row["""label"""] for row in batch]   )
   __a					=  torch.stack([row["""image_start_token"""] for row in batch]   )
   __a					=  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  a   ():
   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  a   ():
   return transforms.Compose(
       [
           transforms.Resize(256   ),
           transforms.CenterCrop(224   ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]		,				std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]		,				),
       ]   )



