





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


SCREAMING_SNAKE_CASE_   :      Optional[int]			=   {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 		snake_case_   (							nn.Module						):







	'''simple docstring'''




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

			'''simple docstring'''




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






	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     Optional[int]   )       -> Tuple:

			'''simple docstring'''




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





class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




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

			'''simple docstring'''




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

			__lowercase											=     transforms




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

			'''simple docstring'''




			return len(self.data   )




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

			'''simple docstring'''




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

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

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

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






	def    UpperCAmelCase			(						self							:     str   )       -> int:

			'''simple docstring'''




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





def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		__lowercase											=     [len(row['sentence']							) for row in batch]
		__lowercase						,      __lowercase											=     len(snake_case							), max(snake_case							)

		__lowercase											=     torch.zeros(snake_case						,   snake_case						,   dtype=torch.long							)
		__lowercase											=     torch.zeros(snake_case						,   snake_case						,   dtype=torch.long							)

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

		__lowercase											=     torch.stack([row['image'] for row in batch]							)
		__lowercase											=     torch.stack([row['label'] for row in batch]							)
		__lowercase											=     torch.stack([row['image_start_token'] for row in batch]							)
		__lowercase											=     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  SCREAMING_SNAKE_CASE				(      )		->    int:
		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  SCREAMING_SNAKE_CASE				(      )		->    Optional[Any]:
		return transforms.Compose(
		    [
		        transforms.Resize(256							),
		        transforms.CenterCrop(224							),
		        transforms.ToTensor(),
		        transforms.Normalize(
		            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]						,   std=[0.1222_1994, 0.1214_5835, 0.1438_0469]						,   ),
		    ]							)











import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      str			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     MobileNetVaConfig(layer_norm_eps=0.001							)

		if "_quant" in model_name:
				raise ValueError('Quantized models are not supported.'							)

		__lowercase											=     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,   snake_case							)
		if matches:
				__lowercase											=     float(matches[1]							)
				__lowercase											=     int(matches[2]							)

		# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
		# the usual 1000. The first class (index 0) is "background".
		__lowercase											=     1_001
		__lowercase											=     'imagenet-1k-id2label.json'
		__lowercase											=     'huggingface/label-files'
		__lowercase											=     json.load(open(hf_hub_download(snake_case						,   snake_case						,   repo_type='dataset'							)						,   'r'							)							)
		__lowercase											=     {int(snake_case							) + 1: v for k, v in idalabel.items()}
		__lowercase											=     'background'
		__lowercase											=     idalabel
		__lowercase											=     {v: k for k, v in idalabel.items()}

		return config



def  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     get_mobilenet_va_config(snake_case							)

		# Load 🤗 model
		__lowercase											=     MobileNetVaForImageClassification(snake_case							).eval()

		# Load weights from TensorFlow checkpoint
		load_tf_weights_in_mobilenet_va(snake_case						,   snake_case						,   snake_case							)

		# Check outputs on an image, prepared by MobileNetV1ImageProcessor
		__lowercase											=     MobileNetVaImageProcessor(
		    crop_size={'width': config.image_size, 'height': config.image_size}						,   size={'shortest_edge': config.image_size + 32}						,   )
		__lowercase											=     image_processor(images=prepare_img()						,   return_tensors='pt'							)
		__lowercase											=     model(**snake_case							)
		__lowercase											=     outputs.logits

		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v1_1.0_224":
				__lowercase											=     torch.tensor([-4.1739, -1.1233, 3.1205]							)
		elif model_name == "mobilenet_v1_0.75_192":
				__lowercase											=     torch.tensor([-3.9440, -2.3141, -0.3333]							)
		else:
				__lowercase											=     None

		if expected_logits is not None:
				assert torch.allclose(logits[0, :3]						,   snake_case						,   atol=1E-4							)

		Path(snake_case							).mkdir(exist_ok=snake_case							)
		print(F"Saving model {model_name} to {pytorch_dump_folder_path}"							)
		model.save_pretrained(snake_case							)
		print(F"Saving image processor to {pytorch_dump_folder_path}"							)
		image_processor.save_pretrained(snake_case							)

		if push_to_hub:
				print('Pushing to the hub...'							)
				__lowercase											=     'google/' + model_name
				image_processor.push_to_hub(snake_case							)
				model.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''mobilenet_v1_1.0_224''',
		    type=str,
		    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
		)
		parser.add_argument(
		    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)

		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




