






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


lowerCamelCase : List[str]						   =       {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				:	Dict		,     __a				:	Dict							)			->  Union[str, Any]:


												"""simple docstring"""

												super().__init__()
												__lowercase      :							Tuple				=     torchvision.models.resnetaaa(pretrained=__a							)
												__lowercase      :							int				=     list(model.children()							)[:-2]
												__lowercase      :							Tuple				=     nn.Sequential(*__a							)
												__lowercase      :							Optional[Any]				=     nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]							)



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


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.pool(self.model(__a							)							)
												__lowercase      :							Optional[Any]				=     torch.flatten(__a		,     start_dim=2							)
												__lowercase      :							Dict				=     out.transpose(1		,     2							).contiguous()
												return out  # BxNx2048







class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

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


												"""simple docstring"""

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

												__lowercase      :							Optional[int]				=     transforms



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


												"""simple docstring"""

												return len(self.data							)



					def __getitem__(  self				:	Tuple		,     __a				:	int							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							int				=     torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]		,     add_special_tokens=__a							)							)
												__lowercase			,			__lowercase			,			__lowercase      :							Union[str, Any]				=     sentence[0], sentence[1:-1], sentence[-1]
												__lowercase      :							Any				=     sentence[: self.max_seq_length]

												__lowercase      :							str				=     torch.zeros(self.n_classes							)
												__lowercase      :							Optional[Any]				=     1

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

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



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     Counter()
												for row in self.data:
																			label_freqs.update(row["""label"""]							)
												return label_freqs







def       snake_case_		(     lowerCAmelCase_       :						int      ):
							__lowercase      :							List[Any]				=     [len(row["""sentence"""]      ) for row in batch]
							__lowercase			,			__lowercase      :							Union[str, Any]				=     len(lowerCAmelCase_      ), max(lowerCAmelCase_      )

							__lowercase      :							List[Any]				=     torch.zeros(lowerCAmelCase_       ,       lowerCAmelCase_       ,       dtype=torch.long      )
							__lowercase      :							Optional[Any]				=     torch.zeros(lowerCAmelCase_       ,       lowerCAmelCase_       ,       dtype=torch.long      )

							for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase_       ,       lowerCAmelCase_      )      ):
														__lowercase      :							Tuple				=     input_row["""sentence"""]
														__lowercase      :							Dict				=     1

							__lowercase      :							Dict				=     torch.stack([row["""image"""] for row in batch]      )
							__lowercase      :							Union[str, Any]				=     torch.stack([row["""label"""] for row in batch]      )
							__lowercase      :							List[Any]				=     torch.stack([row["""image_start_token"""] for row in batch]      )
							__lowercase      :							Union[str, 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       snake_case_		(     ):
							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       snake_case_		(     ):
							return transforms.Compose(
							    [
							        transforms.Resize(256      ),
							        transforms.CenterCrop(224      ),
							        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]       ,       ),
							    ]      )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase : int						   =       {
    '''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
    '''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
    '''tokenization_funnel''': ['''FunnelTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : List[str]						   =       ['''FunnelTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : Union[str, Any]						   =       [
	    '''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FunnelBaseModel''',
	    '''FunnelForMaskedLM''',
	    '''FunnelForMultipleChoice''',
	    '''FunnelForPreTraining''',
	    '''FunnelForQuestionAnswering''',
	    '''FunnelForSequenceClassification''',
	    '''FunnelForTokenClassification''',
	    '''FunnelModel''',
	    '''FunnelPreTrainedModel''',
	    '''load_tf_weights_in_funnel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : str						   =       [
	    '''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFFunnelBaseModel''',
	    '''TFFunnelForMaskedLM''',
	    '''TFFunnelForMultipleChoice''',
	    '''TFFunnelForPreTraining''',
	    '''TFFunnelForQuestionAnswering''',
	    '''TFFunnelForSequenceClassification''',
	    '''TFFunnelForTokenClassification''',
	    '''TFFunnelModel''',
	    '''TFFunnelPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
	from .tokenization_funnel import FunnelTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_funnel_fast import FunnelTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_funnel import (
		    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FunnelBaseModel,
		    FunnelForMaskedLM,
		    FunnelForMultipleChoice,
		    FunnelForPreTraining,
		    FunnelForQuestionAnswering,
		    FunnelForSequenceClassification,
		    FunnelForTokenClassification,
		    FunnelModel,
		    FunnelPreTrainedModel,
		    load_tf_weights_in_funnel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_funnel import (
		    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFunnelBaseModel,
		    TFFunnelForMaskedLM,
		    TFFunnelForMultipleChoice,
		    TFFunnelForPreTraining,
		    TFFunnelForQuestionAnswering,
		    TFFunnelForSequenceClassification,
		    TFFunnelForTokenClassification,
		    TFFunnelModel,
		    TFFunnelPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase : Any						   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)