




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


a__		     =     logging.get_logger(__name__)





def 				A__ (snake_case   :		List[Any]		,	snake_case   :		List[str]		,	snake_case   :		Optional[int]		,	snake_case   :		Union[str, Any]=False				)     ->						Optional[int]:
					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:
										__UpperCamelCase					:	Dict             =      os.path.abspath(snake_case				)
										logger.info(F'''Loading PyTorch weights from {pt_path}'''				)

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

										__UpperCamelCase					:	str             =      convert_pytorch_state_dict_to_flax(snake_case		,	snake_case				)
					else:
										# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
										__UpperCamelCase					:	List[Any]             =      convert_pytorch_sharded_state_dict_to_flax(snake_case		,	snake_case				)
					return flax_state_dict





def 				A__ (snake_case   :		Tuple[str]		,	snake_case   :		np.ndarray		,	snake_case   :		Dict[str, jnp.ndarray]		,	snake_case   :		str		,	)     ->						(Tuple[str], np.ndarray):

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

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

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

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

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

					# conv layer
					__UpperCamelCase					:	Optional[Any]             =      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(snake_case				):
										__UpperCamelCase					:	List[str]             =      pt_tensor.transpose(2		,	3		,	1		,	0				)
										return renamed_pt_tuple_key, pt_tensor

					# linear layer
					__UpperCamelCase					:	Optional[int]             =      pt_tuple_key[:-1] + ("""kernel""",)
					if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(snake_case				):
										__UpperCamelCase					:	Union[str, Any]             =      pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

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

					# old PyTorch layer norm bias
					__UpperCamelCase					:	Optional[Any]             =      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
					__UpperCamelCase					:	int             =      None
					if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
										__UpperCamelCase					:	Tuple             =      pt_tuple_key[-2] + """_g"""
					elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
										__UpperCamelCase					:	Optional[Any]             =      pt_tuple_key[-2] + """_v"""
					if name is not None:
										__UpperCamelCase					:	List[str]             =      pt_tuple_key[:-3] + (name,)
										return renamed_pt_tuple_key, pt_tensor

					return pt_tuple_key, pt_tensor





def 				A__ (snake_case   :		Dict		,	snake_case   :		Any				)     ->						Union[str, Any]:
					# convert pytorch tensor to numpy
					__UpperCamelCase					:	Optional[Any]             =      {k: v.numpy() for k, v in pt_state_dict.items()}

					__UpperCamelCase					:	int             =      flax_model.base_model_prefix

					# use params dict if the model contains batch norm layers
					if "params" in flax_model.params:
										__UpperCamelCase					:	Union[str, Any]             =      flax_model.params["""params"""]
					else:
										__UpperCamelCase					:	List[Any]             =      flax_model.params
					__UpperCamelCase					:	Tuple             =      flatten_dict(snake_case				)

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

					__UpperCamelCase					:	Tuple             =      {}

					__UpperCamelCase					:	int             =      (model_prefix not in flax_model_params) and (
					    model_prefix in {k.split("""."""				)[0] for k in pt_state_dict.keys()}
					)
					__UpperCamelCase					:	Dict             =      (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():
										__UpperCamelCase					:	Dict             =      tuple(pt_key.split("""."""				)				)

										# remove base model prefix if necessary
										__UpperCamelCase					:	Dict             =      pt_tuple_key[0] == model_prefix
										if load_model_with_head_into_base_model and has_base_model_prefix:
															__UpperCamelCase					:	Optional[Any]             =      pt_tuple_key[1:]

										# Correctly rename weight parameters
										__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      rename_key_and_reshape_tensor(
										    snake_case		,	snake_case		,	snake_case		,	snake_case				)

										# add model prefix if necessary
										__UpperCamelCase					:	str             =      (model_prefix,) + flax_key in random_flax_state_dict
										if load_base_model_into_model_with_head and require_base_model_prefix:
															__UpperCamelCase					:	Dict             =      (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]:
																				__UpperCamelCase					:	Union[str, Any]             =      jnp.asarray(snake_case				)
																				continue
															# remove num_batches_tracked key
															if "num_batches_tracked" in flax_key[-1]:
																				flax_state_dict.pop(snake_case		,	snake_case				)
																				continue

															# also add unexpected weight so that warning is thrown
															__UpperCamelCase					:	List[Any]             =      jnp.asarray(snake_case				)

										else:
															# also add unexpected weight so that warning is thrown
															__UpperCamelCase					:	List[str]             =      jnp.asarray(snake_case				)

					return unflatten_dict(snake_case				)





def 				A__ (snake_case   :		Dict		,	snake_case   :		Optional[int]				)     ->						Dict:
					import torch

					# Load the index
					__UpperCamelCase					:	str             =      {}
					for shard_file in shard_filenames:
										# load using msgpack utils
										__UpperCamelCase					:	Any             =      torch.load(snake_case				)
										__UpperCamelCase					:	Union[str, Any]             =      {k: v.numpy() for k, v in pt_state_dict.items()}

										__UpperCamelCase					:	List[Any]             =      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:
															__UpperCamelCase					:	int             =      flax_model.params["""params"""]

															__UpperCamelCase					:	List[Any]             =      flatten_dict(snake_case				)
															random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]				)				)
										else:
															__UpperCamelCase					:	Optional[int]             =      flax_model.params
															__UpperCamelCase					:	str             =      flatten_dict(snake_case				)

										__UpperCamelCase					:	Tuple             =      (model_prefix not in flax_model_params) and (
										    model_prefix in {k.split("""."""				)[0] for k in pt_state_dict.keys()}
										)
										__UpperCamelCase					:	int             =      (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():
															__UpperCamelCase					:	Any             =      tuple(pt_key.split("""."""				)				)

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

															# Correctly rename weight parameters
															__UpperCamelCase      ,			__UpperCamelCase					:	Optional[Any]             =      rename_key_and_reshape_tensor(
															    snake_case		,	snake_case		,	snake_case		,	snake_case				)
															# add model prefix if necessary
															__UpperCamelCase					:	int             =      (model_prefix,) + flax_key in random_flax_state_dict
															if load_base_model_into_model_with_head and require_base_model_prefix:
																				__UpperCamelCase					:	List[Any]             =      (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]:
																									__UpperCamelCase					:	Any             =      jnp.asarray(snake_case				)
																									continue
																				if "var" in flax_key[-1]:
																									__UpperCamelCase					:	Union[str, Any]             =      jnp.asarray(snake_case				)
																									continue
																				# remove num_batches_tracked key
																				if "num_batches_tracked" in flax_key[-1]:
																									flax_state_dict.pop(snake_case		,	snake_case				)
																									continue

																				# also add unexpected weight so that warning is thrown
																				__UpperCamelCase					:	List[Any]             =      jnp.asarray(snake_case				)

															else:
																				# also add unexpected weight so that warning is thrown
																				__UpperCamelCase					:	int             =      jnp.asarray(snake_case				)
					return unflatten_dict(snake_case				)





def 				A__ (snake_case   :		str		,	snake_case   :		Any				)     ->						Optional[int]:
					__UpperCamelCase					:	Tuple             =      os.path.abspath(snake_case				)
					logger.info(F'''Loading Flax weights from {flax_checkpoint_path}'''				)

					# import correct flax class
					__UpperCamelCase					:	str             =      getattr(snake_case		,	"""Flax""" + model.__class__.__name__				)

					# load flax weight dict
					with open(snake_case		,	"""rb"""				) as state_f:
										try:
															__UpperCamelCase					:	List[Any]             =      from_bytes(snake_case		,	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(snake_case		,	snake_case				)







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

					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
					__UpperCamelCase					:	Any             =      flatten_dict(jax.tree_util.tree_map(lambda snake_case				: x.dtype == jnp.bfloataa		,	snake_case				)				).values()
					if any(snake_case				):
										# 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."""				)
										__UpperCamelCase					:	Tuple             =      jax.tree_util.tree_map(
										    lambda snake_case				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params		,	snake_case				)

					__UpperCamelCase					:	Optional[Any]             =      flatten_dict(snake_case				)
					__UpperCamelCase					:	str             =      pt_model.state_dict()

					__UpperCamelCase					:	Union[str, Any]             =      (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()}
					)
					__UpperCamelCase					:	Dict             =      (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
					__UpperCamelCase					:	Dict             =      []
					__UpperCamelCase					:	Optional[Any]             =      set(pt_model_dict.keys()				)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										__UpperCamelCase					:	List[str]             =      flax_key_tuple[0] == pt_model.base_model_prefix
										__UpperCamelCase					:	int             =      """.""".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:
															__UpperCamelCase					:	int             =      flax_key_tuple[1:]
										elif load_base_model_into_model_with_head and require_base_model_prefix:
															__UpperCamelCase					:	List[Any]             =      (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(snake_case				) not in pt_model_dict:
															# conv layer
															__UpperCamelCase					:	Union[str, Any]             =      flax_key_tuple[:-1] + ("""weight""",)
															__UpperCamelCase					:	int             =      jnp.transpose(snake_case		,	(3, 2, 0, 1)				)
										elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case				) not in pt_model_dict:
															# linear layer
															__UpperCamelCase					:	Tuple             =      flax_key_tuple[:-1] + ("""weight""",)
															__UpperCamelCase					:	Tuple             =      flax_tensor.T
										elif flax_key_tuple[-1] in ["scale", "embedding"]:
															__UpperCamelCase					:	Optional[int]             =      flax_key_tuple[:-1] + ("""weight""",)

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

										if "batch_stats" in flax_state:
															__UpperCamelCase					:	Dict             =      """.""".join(flax_key_tuple[1:]				)  # Remove the params/batch_stats header
										else:
															__UpperCamelCase					:	Optional[int]             =      """.""".join(snake_case				)

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

										if flax_key in special_pt_names:
															__UpperCamelCase					:	int             =      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
																				__UpperCamelCase					:	List[str]             =      np.asarray(snake_case				) if not isinstance(snake_case		,	np.ndarray				) else flax_tensor
																				__UpperCamelCase					:	Optional[int]             =      torch.from_numpy(snake_case				)
																				# remove from missing keys
																				missing_keys.remove(snake_case				)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(snake_case				)

					pt_model.load_state_dict(snake_case				)

					# re-transform missing_keys to list
					__UpperCamelCase					:	List[Any]             =      list(snake_case				)

					if len(snake_case				) > 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(snake_case				) > 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












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    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						List[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Tuple             =      torchvision.models.resnetaaa(pretrained=lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      list(model.children()	)[:-2]
						__UpperCamelCase					:	List[str]             =      nn.Sequential(*lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Optional[Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.pool(self.model(lowerCAmelCase	)	)
						__UpperCamelCase					:	Dict             =      torch.flatten(lowerCAmelCase					,     start_dim=2	)
						__UpperCamelCase					:	Optional[int]             =      out.transpose(1					,     2	).contiguous()
						return out  # BxNx2048






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      [json.loads(lowerCAmelCase	) for l in open(lowerCAmelCase	)]
						__UpperCamelCase					:	Optional[Any]             =      os.path.dirname(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      tokenizer
						__UpperCamelCase					:	Dict             =      labels
						__UpperCamelCase					:	int             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      max_seq_length

						__UpperCamelCase					:	int             =      transforms






	def __len__(					self		:						Union[str, Any]	)	->       List[Any]:







						"""simple docstring"""







						return len(self.data	)






	def __getitem__(					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]	)	->       int:







						"""simple docstring"""







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

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

						__UpperCamelCase					:	List[Any]             =      Image.open(os.path.join(self.data_dir					,     self.data[index]["""img"""]	)	).convert("""RGB"""	)
						__UpperCamelCase					:	Dict             =      self.transforms(lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Optional[int]:







						"""simple docstring"""







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






def 				A__ (snake_case   :		int				)     ->						Optional[int]:
					__UpperCamelCase					:	str             =      [len(row["""sentence"""]				) for row in batch]
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      len(snake_case				), max(snake_case				)

					__UpperCamelCase					:	List[str]             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)
					__UpperCamelCase					:	Tuple             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)

					for i_batch, (input_row, length) in enumerate(zip(snake_case		,	snake_case				)				):
										__UpperCamelCase					:	Optional[Any]             =      input_row["""sentence"""]
										__UpperCamelCase					:	Any             =      1

					__UpperCamelCase					:	List[Any]             =      torch.stack([row["""image"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""label"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""image_start_token"""] for row in batch]				)
					__UpperCamelCase					:	List[str]             =      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__ ()     ->						Optional[Any]:
					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__ ()     ->						Union[str, Any]:
					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]		,	),
					    ]				)






