






'''simple docstring'''







import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


UpperCamelCase     :   int	      =		logging.get_logger(__name__)
def     A__      (					__lowerCAmelCase		:      int ,     __lowerCAmelCase		:      Union[str, Any] ,     __lowerCAmelCase		:      List[Any] ,     __lowerCAmelCase		:      Optional[Any]=False    ):
						try:
												import torch  # noqa: F401
						except ImportError:
												logger.error(
												    """Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
												    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
												    """ instructions."""    )
												raise

						if not is_sharded:
												lowerCamelCase__								=	os.path.abspath(__lowerCAmelCase    )
												logger.info(F'''Loading PyTorch weights from {pt_path}'''    )

												lowerCamelCase__								=	torch.load(__lowerCAmelCase ,     map_location="""cpu"""    )
												logger.info(F'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()    ):,} parameters.'''    )

												lowerCamelCase__								=	convert_pytorch_state_dict_to_flax(__lowerCAmelCase ,     __lowerCAmelCase    )
						else:
												# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
												lowerCamelCase__								=	convert_pytorch_sharded_state_dict_to_flax(__lowerCAmelCase ,     __lowerCAmelCase    )
						return flax_state_dict
def     A__      (					__lowerCAmelCase		:      Tuple[str] ,     __lowerCAmelCase		:      np.ndarray ,     __lowerCAmelCase		:      Dict[str, jnp.ndarray] ,     __lowerCAmelCase		:      str ,     ):

						def is_key_or_prefix_key_in_dict(__lowerCAmelCase		:      Tuple[str]    ) -> bool:
												return len(set(__lowerCAmelCase    ) & {key, (model_prefix,) + key}    ) > 0

						# layer norm
						lowerCamelCase__								=	pt_tuple_key[:-1] + ("""scale""",)
						if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowerCAmelCase    ):
												return renamed_pt_tuple_key, pt_tensor

						# batch norm layer mean
						lowerCamelCase__								=	pt_tuple_key[:-1] + ("""mean""",)
						if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowerCAmelCase    ):
												return renamed_pt_tuple_key, pt_tensor

						# batch norm layer var
						lowerCamelCase__								=	pt_tuple_key[:-1] + ("""var""",)
						if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowerCAmelCase    ):
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						lowerCamelCase__								=	pt_tuple_key[:-1] + ("""embedding""",)
						if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowerCAmelCase    ):
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						lowerCamelCase__								=	pt_tuple_key[:-1] + ("""kernel""",)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowerCAmelCase    ):
												lowerCamelCase__								=	pt_tensor.transpose(2 ,     3 ,     1 ,     0    )
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						lowerCamelCase__								=	pt_tuple_key[:-1] + ("""kernel""",)
						if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowerCAmelCase    ):
												lowerCamelCase__								=	pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						lowerCamelCase__								=	pt_tuple_key[:-1] + ("""weight""",)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						lowerCamelCase__								=	pt_tuple_key[:-1] + ("""bias""",)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
						lowerCamelCase__								=	None
						if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
												lowerCamelCase__								=	pt_tuple_key[-2] + """_g"""
						elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
												lowerCamelCase__								=	pt_tuple_key[-2] + """_v"""
						if name is not None:
												lowerCamelCase__								=	pt_tuple_key[:-3] + (name,)
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor
def     A__      (					__lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      Any    ):
						# convert pytorch tensor to numpy
						lowerCamelCase__								=	{k: v.numpy() for k, v in pt_state_dict.items()}

						lowerCamelCase__								=	flax_model.base_model_prefix

						# use params dict if the model contains batch norm layers
						if "params" in flax_model.params:
												lowerCamelCase__								=	flax_model.params["""params"""]
						else:
												lowerCamelCase__								=	flax_model.params
						lowerCamelCase__								=	flatten_dict(__lowerCAmelCase    )

						# add batch_stats keys,values to dict
						if "batch_stats" in flax_model.params:
												lowerCamelCase__								=	flatten_dict(flax_model.params["""batch_stats"""]    )
												random_flax_state_dict.update(__lowerCAmelCase    )

						lowerCamelCase__								=	{}

						lowerCamelCase__								=	(model_prefix not in flax_model_params) and (
						    model_prefix in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
						)
						lowerCamelCase__								=	(model_prefix in flax_model_params) and (
						    model_prefix not in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
						)

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												lowerCamelCase__								=	tuple(pt_key.split("""."""    )    )

												# remove base model prefix if necessary
												lowerCamelCase__								=	pt_tuple_key[0] == model_prefix
												if load_model_with_head_into_base_model and has_base_model_prefix:
																		lowerCamelCase__								=	pt_tuple_key[1:]

												# Correctly rename weight parameters
												lowerCamelCase__							,      lowerCamelCase__								=	rename_key_and_reshape_tensor(
												    __lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase    )

												# add model prefix if necessary
												lowerCamelCase__								=	(model_prefix,) + flax_key in random_flax_state_dict
												if load_base_model_into_model_with_head and require_base_model_prefix:
																		lowerCamelCase__								=	(model_prefix,) + flax_key

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																								    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''    )

        # add batch stats if the model contains batchnorm layers
												if "batch_stats" in flax_model.params:
																		if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																								lowerCamelCase__								=	jnp.asarray(__lowerCAmelCase    )
																								continue
																		# remove num_batches_tracked key
																		if "num_batches_tracked" in flax_key[-1]:
																								flax_state_dict.pop(__lowerCAmelCase ,     __lowerCAmelCase    )
																								continue

																		# also add unexpected weight so that warning is thrown
																		lowerCamelCase__								=	jnp.asarray(__lowerCAmelCase    )

												else:
																		# also add unexpected weight so that warning is thrown
																		lowerCamelCase__								=	jnp.asarray(__lowerCAmelCase    )

						return unflatten_dict(__lowerCAmelCase    )
def     A__      (					__lowerCAmelCase		:      Optional[int] ,     __lowerCAmelCase		:      List[str]    ):
						import torch

						# Load the index
						lowerCamelCase__								=	{}
						for shard_file in shard_filenames:
												# load using msgpack utils
												lowerCamelCase__								=	torch.load(__lowerCAmelCase    )
												lowerCamelCase__								=	{k: v.numpy() for k, v in pt_state_dict.items()}

												lowerCamelCase__								=	flax_model.base_model_prefix

												# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
												if "batch_stats" in flax_model.params:
																		lowerCamelCase__								=	flax_model.params["""params"""]

																		lowerCamelCase__								=	flatten_dict(__lowerCAmelCase    )
																		random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]    )    )
												else:
																		lowerCamelCase__								=	flax_model.params
																		lowerCamelCase__								=	flatten_dict(__lowerCAmelCase    )

												lowerCamelCase__								=	(model_prefix not in flax_model_params) and (
												    model_prefix in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
												)
												lowerCamelCase__								=	(model_prefix in flax_model_params) and (
												    model_prefix not in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
												)
												# Need to change some parameters name to match Flax names
												for pt_key, pt_tensor in pt_state_dict.items():
																		lowerCamelCase__								=	tuple(pt_key.split("""."""    )    )

																		# remove base model prefix if necessary
																		lowerCamelCase__								=	pt_tuple_key[0] == model_prefix
																		if load_model_with_head_into_base_model and has_base_model_prefix:
																								lowerCamelCase__								=	pt_tuple_key[1:]

																		# Correctly rename weight parameters
																		lowerCamelCase__							,      lowerCamelCase__								=	rename_key_and_reshape_tensor(
																		    __lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase    )
																		# add model prefix if necessary
																		lowerCamelCase__								=	(model_prefix,) + flax_key in random_flax_state_dict
																		if load_base_model_into_model_with_head and require_base_model_prefix:
																								lowerCamelCase__								=	(model_prefix,) + flax_key

																		if flax_key in random_flax_state_dict:
																								if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																														raise ValueError(
																														    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																														    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''    )

            # add batch stats if the model contains batchnorm layers
																		if "batch_stats" in flax_model.params:
																								if "mean" in flax_key[-1]:
																														lowerCamelCase__								=	jnp.asarray(__lowerCAmelCase    )
																														continue
																								if "var" in flax_key[-1]:
																														lowerCamelCase__								=	jnp.asarray(__lowerCAmelCase    )
																														continue
																								# remove num_batches_tracked key
																								if "num_batches_tracked" in flax_key[-1]:
																														flax_state_dict.pop(__lowerCAmelCase ,     __lowerCAmelCase    )
																														continue

																								# also add unexpected weight so that warning is thrown
																								lowerCamelCase__								=	jnp.asarray(__lowerCAmelCase    )

																		else:
																								# also add unexpected weight so that warning is thrown
																								lowerCamelCase__								=	jnp.asarray(__lowerCAmelCase    )
						return unflatten_dict(__lowerCAmelCase    )
def     A__      (					__lowerCAmelCase		:      Any ,     __lowerCAmelCase		:      Tuple    ):
						lowerCamelCase__								=	os.path.abspath(__lowerCAmelCase    )
						logger.info(F'''Loading Flax weights from {flax_checkpoint_path}'''    )

						# import correct flax class
						lowerCamelCase__								=	getattr(__lowerCAmelCase ,     """Flax""" + model.__class__.__name__    )

						# load flax weight dict
						with open(__lowerCAmelCase ,     """rb"""    ) as state_f:
												try:
																		lowerCamelCase__								=	from_bytes(__lowerCAmelCase ,     state_f.read()    )
												except UnpicklingError:
																		raise EnvironmentError(F'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. '''    )

						return load_flax_weights_in_pytorch_model(__lowerCAmelCase ,     __lowerCAmelCase    )





def     A__      (					__lowerCAmelCase		:      str ,     __lowerCAmelCase		:      Tuple    ):

						try:
												import torch  # noqa: F401
						except ImportError:
												logger.error(
												    """Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
												    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
												    """ instructions."""    )
												raise

						# check if we have bf16 weights
						lowerCamelCase__								=	flatten_dict(jax.tree_util.tree_map(lambda __lowerCAmelCase    : x.dtype == jnp.bfloataa ,     __lowerCAmelCase    )    ).values()
						if any(__lowerCAmelCase    ):
												# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
												# and bf16 is not fully supported in PT yet.
												logger.warning(
												    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
												    """before loading those in PyTorch model."""    )
												lowerCamelCase__								=	jax.tree_util.tree_map(
												    lambda __lowerCAmelCase    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params ,     __lowerCAmelCase    )

						lowerCamelCase__								=	flatten_dict(__lowerCAmelCase    )
						lowerCamelCase__								=	pt_model.state_dict()

						lowerCamelCase__								=	(pt_model.base_model_prefix in flax_state) and (
						    pt_model.base_model_prefix not in {k.split("""."""    )[0] for k in pt_model_dict.keys()}
						)
						lowerCamelCase__								=	(pt_model.base_model_prefix not in flax_state) and (
						    pt_model.base_model_prefix in {k.split("""."""    )[0] for k in pt_model_dict.keys()}
						)

						# keep track of unexpected & missing keys
						lowerCamelCase__								=	[]
						lowerCamelCase__								=	set(pt_model_dict.keys()    )

						for flax_key_tuple, flax_tensor in flax_state_dict.items():
												lowerCamelCase__								=	flax_key_tuple[0] == pt_model.base_model_prefix
												lowerCamelCase__								=	""".""".join((pt_model.base_model_prefix,) + flax_key_tuple    ) in pt_model_dict

												# adapt flax_key to prepare for loading from/to base model only
												if load_model_with_head_into_base_model and has_base_model_prefix:
																		lowerCamelCase__								=	flax_key_tuple[1:]
												elif load_base_model_into_model_with_head and require_base_model_prefix:
																		lowerCamelCase__								=	(pt_model.base_model_prefix,) + flax_key_tuple

												# rename flax weights to PyTorch format
												if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowerCAmelCase    ) not in pt_model_dict:
																		# conv layer
																		lowerCamelCase__								=	flax_key_tuple[:-1] + ("""weight""",)
																		lowerCamelCase__								=	jnp.transpose(__lowerCAmelCase ,     (3, 2, 0, 1)    )
												elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCAmelCase    ) not in pt_model_dict:
																		# linear layer
																		lowerCamelCase__								=	flax_key_tuple[:-1] + ("""weight""",)
																		lowerCamelCase__								=	flax_tensor.T
												elif flax_key_tuple[-1] in ["scale", "embedding"]:
																		lowerCamelCase__								=	flax_key_tuple[:-1] + ("""weight""",)

												# adding batch stats from flax batch norm to pt
												elif "mean" in flax_key_tuple[-1]:
																		lowerCamelCase__								=	flax_key_tuple[:-1] + ("""running_mean""",)
												elif "var" in flax_key_tuple[-1]:
																		lowerCamelCase__								=	flax_key_tuple[:-1] + ("""running_var""",)

												if "batch_stats" in flax_state:
																		lowerCamelCase__								=	""".""".join(flax_key_tuple[1:]    )  # Remove the params/batch_stats header
												else:
																		lowerCamelCase__								=	""".""".join(__lowerCAmelCase    )

												# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
												lowerCamelCase__								=	{}
												# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
												for key in pt_model_dict:
																		lowerCamelCase__								=	key.split("""."""    )
																		lowerCamelCase__								=	None
																		if key_components[-3::2] == ["parametrizations", "original0"]:
																								lowerCamelCase__								=	key_components[-2] + """_g"""
																		elif key_components[-3::2] == ["parametrizations", "original1"]:
																								lowerCamelCase__								=	key_components[-2] + """_v"""
																		if name is not None:
																								lowerCamelCase__								=	key_components[:-3] + [name]
																								lowerCamelCase__								=	""".""".join(__lowerCAmelCase    )
																								lowerCamelCase__								=	key

												if flax_key in special_pt_names:
																		lowerCamelCase__								=	special_pt_names[flax_key]

												if flax_key in pt_model_dict:
																		if flax_tensor.shape != pt_model_dict[flax_key].shape:
																								raise ValueError(
																								    F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
																								    F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''    )
																		else:
																								# add weight to pytorch dict
																								lowerCamelCase__								=	np.asarray(__lowerCAmelCase    ) if not isinstance(__lowerCAmelCase ,     np.ndarray    ) else flax_tensor
																								lowerCamelCase__								=	torch.from_numpy(__lowerCAmelCase    )
																								# remove from missing keys
																								missing_keys.remove(__lowerCAmelCase    )
												else:
																		# weight is not expected by PyTorch model
																		unexpected_keys.append(__lowerCAmelCase    )

						pt_model.load_state_dict(__lowerCAmelCase    )

						# re-transform missing_keys to list
						lowerCamelCase__								=	list(__lowerCAmelCase    )

						if len(__lowerCAmelCase    ) > 0:
												logger.warning(
												    """Some weights of the Flax model were not used when initializing the PyTorch model"""
												    F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
												    F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
												    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
												    F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
												    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
												    """ FlaxBertForSequenceClassification model)."""    )
						else:
												logger.warning(F'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''    )
						if len(__lowerCAmelCase    ) > 0:
												logger.warning(
												    F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
												    F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
												    """ use it for predictions and inference."""    )
						else:
												logger.warning(
												    F'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
												    """If your task is similar to the task the model of the checkpoint was trained on, """
												    F'''you can already use {pt_model.__class__.__name__} for predictions without further training.'''    )

						return pt_model








'''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      UpperCamelCase__					(					nn.Module			):




					"""simple docstring"""


					def __init__(						self				:			Dict      ,						lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



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



					def lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



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







class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			[json.loads(lowerCamelCase_						) for l in open(lowerCamelCase_						)]
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			os.path.dirname(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			tokenizer
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			labels
							SCREAMING_SNAKE_CASE :				str		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			max_seq_length

							SCREAMING_SNAKE_CASE :				List[Any]		=			transforms


					def __len__(						self				:			Any						):

							'''simple docstring'''



							return len(self.data						)


					def __getitem__(						self				:			Optional[int]      ,						lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]      ,						add_special_tokens=lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Tuple		=			sentence[0], sentence[1:-1], sentence[-1]
							SCREAMING_SNAKE_CASE :				int		=			sentence[: self.max_seq_length]

							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.zeros(self.n_classes						)
							SCREAMING_SNAKE_CASE :				List[str]		=			1

							SCREAMING_SNAKE_CASE :				int		=			Image.open(os.path.join(self.data_dir      ,						self.data[index]["""img"""]						)						).convert("""RGB"""						)
							SCREAMING_SNAKE_CASE :				str		=			self.transforms(lowerCamelCase_						)

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



					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



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







def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Tuple		=			[len(row["""sentence"""]	) for row in batch]
		SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Any		=			len(lowerCamelCase_	), max(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				int		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)
		SCREAMING_SNAKE_CASE :				Any		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)

		for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase_			,			lowerCamelCase_	)	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			input_row["""sentence"""]
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			1

		SCREAMING_SNAKE_CASE :				List[str]		=			torch.stack([row["""image"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				int		=			torch.stack([row["""label"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				str		=			torch.stack([row["""image_start_token"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				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 		__A		(  ):


		"""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 		__A		(  ):


		"""simple docstring"""



		return transforms.Compose(
		    [
		        transforms.Resize(2_56	),
		        transforms.CenterCrop(2_24	),
		        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]			,			),
		    ]	)



