
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     :	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 			__A( nn.Module			):




				def __init__(     self		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      int:


								'''simple docstring'''




								__a										=    self.pool(self.model(lowerCAmelCase__				)				)
								__a										=    torch.flatten(lowerCAmelCase__		,     start_dim=2				)
								__a										=    out.transpose(1		,     2				).contiguous()
								return out  # BxNx2048







class 			__A( lowerCamelCase__			):




				def __init__(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




								__a										=    [json.loads(lowerCAmelCase__				) for l in open(lowerCAmelCase__				)]
								__a										=    os.path.dirname(lowerCAmelCase__				)
								__a										=    tokenizer
								__a										=    labels
								__a										=    len(lowerCAmelCase__				)
								__a										=    max_seq_length

								__a										=    transforms




				def __len__(     self				)    ->      Tuple:


								'''simple docstring'''




								return len(self.data				)




				def __getitem__(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']		,     add_special_tokens=lowerCAmelCase__				)				)
								__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(lowerCAmelCase__				)

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




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



def     __lowerCAmelCase			(			a__  )       ->  Dict:
				__a										=    [len(row['''sentence''']  ) for row in batch]
				__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     __lowerCAmelCase			(			)       ->  List[str]:
				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     __lowerCAmelCase			(			)       ->  Tuple:
				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]						,    ),
				    ]  )




'''simple docstring'''




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


UpperCamelCase__       :	List[Any]    = [
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]


def 				lowerCAmelCase_   (	_lowerCamelCase:   str       ,      _lowerCamelCase:   Union[str, Any]=None       ,      _lowerCamelCase:   Optional[int]=None       ,      _lowerCamelCase:   str=None					):
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		True
		while ask_again:
				__SCREAMING_SNAKE_CASE       :		Tuple           =		input(_lowerCamelCase					)
				try:
						if default is not None and len(_lowerCamelCase					) == 0:
								return default
						return convert_value(_lowerCamelCase					) if convert_value is not None else result
				except Exception:
						if error_message is not None:
								print(_lowerCamelCase					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Tuple       ,      _lowerCamelCase:   Union[str, Any]=[]       ,      _lowerCamelCase:   List[Any]=None       ,      _lowerCamelCase:   Optional[Any]=0					):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		BulletMenu(_lowerCamelCase       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		menu.run(default_choice=_lowerCamelCase					)
		return convert_value(_lowerCamelCase					) if convert_value is not None else result


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[Any]					):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		int(_lowerCamelCase					)
		return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value]					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Any					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)
		return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value]					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Tuple					):
		__SCREAMING_SNAKE_CASE       :		Tuple           =		int(_lowerCamelCase					)
		return DynamoBackend(DYNAMO_BACKENDS[value]					).value


def 				lowerCAmelCase_   (	_lowerCamelCase:   Union[str, Any]					):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		int(_lowerCamelCase					)
		return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value]					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Tuple					):
		__SCREAMING_SNAKE_CASE       :		int           =		int(_lowerCamelCase					)
		return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value]					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[Any]					):
		return {"yes": True, "no": False}[value.lower()]






class 						_UpperCamelCase  (							argparse.RawDescriptionHelpFormatter    ):


			'''simple docstring'''

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Dict							,     lowerCAmelCase__     : List[Any]							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		super()._format_usage(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Any           =		usage.replace("""<command> [<args>] """							,     """"""						)
					return usage