


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


_lowercase    :    Optional[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  UpperCamelCase__(     nn.Module ):




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

										"""simple docstring"""




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





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

										"""simple docstring"""




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







class  UpperCamelCase__(     __snake_case ):




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

										"""simple docstring"""




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

										UpperCAmelCase         =			transforms




				def __len__(	self    :				Dict   )->  List[str]:

										"""simple docstring"""




										return len(self.data   )




				def __getitem__(	self    :				Dict   ,					lowerCAmelCase    :				Dict   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']   ,					add_special_tokens=__UpperCamelCase   )   )
										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			sentence[0], sentence[1:-1], sentence[-1]
										UpperCAmelCase         =			sentence[: self.max_seq_length]

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

										UpperCAmelCase         =			Image.open(os.path.join(self.data_dir   ,					self.data[index]['''img''']   )   ).convert('''RGB'''   )
										UpperCAmelCase         =			self.transforms(__UpperCamelCase   )

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





				def   a__(	self    :				Dict   )->  Union[str, Any]:

										"""simple docstring"""




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







def    lowerCamelCase__   ( A     :    str  ):




						'''simple docstring'''






						UpperCAmelCase         =			[len(row['''sentence''']  ) for row in batch]
						UpperCAmelCase       ,       UpperCAmelCase         =			len(lowercase__  ), max(lowercase__  )

						UpperCAmelCase         =			torch.zeros(lowercase__   ,     lowercase__   ,     dtype=torch.long  )
						UpperCAmelCase         =			torch.zeros(lowercase__   ,     lowercase__   ,     dtype=torch.long  )

						for i_batch, (input_row, length) in enumerate(zip(lowercase__   ,     lowercase__  )  ):
												UpperCAmelCase         =			input_row['''sentence''']
												UpperCAmelCase         =			1

						UpperCAmelCase         =			torch.stack([row['''image'''] for row in batch]  )
						UpperCAmelCase         =			torch.stack([row['''label'''] for row in batch]  )
						UpperCAmelCase         =			torch.stack([row['''image_start_token'''] for row in batch]  )
						UpperCAmelCase         =			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__   ( ):




						'''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    lowerCamelCase__   ( ):




						'''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]   ,     ),
						    ]  )









'''simple docstring'''



# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


_lowercase    :    Union[str, Any]			     =  """3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
	import torch

	print("""Torch version:""", torch.__version__)
	print("""Cuda available:""", torch.cuda.is_available())
	print("""Cuda version:""", torch.version.cuda)
	print("""CuDNN version:""", torch.backends.cudnn.version())
	print("""Number of GPUs available:""", torch.cuda.device_count())
	print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
	print("""Torch version:""", None)

try:
	import deepspeed

	print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
	print("""DeepSpeed version:""", None)

try:
	import tensorflow as tf

	print("""TensorFlow version:""", tf.__version__)
	print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
	print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
	print("""TensorFlow version:""", None)





