



"""simple docstring"""







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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
A     =      logging.get_logger(__name__)

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				Optional[int]:
							A    =       MobileNetVaConfig(layer_norm_eps=0.0_01      )

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

							A    =       re.match(R'^.*mobilenet_v2_([^_]*)_([^_]*)$'					,   lowerCamelCase__      )
							if matches:
														A    =       float(matches[1]      )
														A    =       int(matches[2]      )

							if model_name.startswith('deeplabv3_'      ):
														A    =       8
														A    =       21
														A    =       'pascal-voc-id2label.json'
							else:
														# The TensorFlow version of MobileNetV2 predicts 1001 classes instead
														# of the usual 1000. The first class (index 0) is "background".
														A    =       1001
														A    =       'imagenet-1k-id2label.json'

							A    =       'huggingface/label-files'
							A    =       json.load(open(hf_hub_download(lowerCamelCase__					,   lowerCamelCase__					,   repo_type='dataset'      )					,   'r'      )      )

							if config.num_labels == 1001:
														A    =       {int(lowerCamelCase__      ) + 1: v for k, v in idalabel.items()}
														A    =       'background'
							else:
														A    =       {int(lowerCamelCase__      ): v for k, v in idalabel.items()}

							A    =       idalabel
							A    =       {v: k for k, v in idalabel.items()}

							return config

def 	lowerCAmelCase__      (  )			->				Union[str, Any]:
							A    =       'http://images.cocodataset.org/val2017/000000039769.jpg'
							A    =       Image.open(requests.get(lowerCamelCase__					,   stream=lowerCamelCase__      ).raw      )
							return im

@torch.no_grad()
def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__=False      )			->				Optional[int]:
							A    =       get_mobilenet_va_config(lowerCamelCase__      )

							# Load 🤗 model
							if model_name.startswith('deeplabv3_'      ):
														A    =       MobileNetVaForSemanticSegmentation(lowerCamelCase__      ).eval()
							else:
														A    =       MobileNetVaForImageClassification(lowerCamelCase__      ).eval()

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

							# Check outputs on an image, prepared by MobileNetV2ImageProcessor
							A    =       MobileNetVaImageProcessor(
							    crop_size={'width': config.image_size, 'height': config.image_size}					,   size={'shortest_edge': config.image_size + 32}					,   )
							A    =       image_processor(images=prepare_img()					,   return_tensors='pt'      )
							A    =       model(**lowerCamelCase__      )
							A    =       outputs.logits

							if model_name.startswith('deeplabv3_'      ):
														assert logits.shape == (1, 21, 65, 65)

														if model_name == "deeplabv3_mobilenet_v2_1.0_513":
																					A    =       torch.tensor(
																					    [
																					        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
																					        [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
																					        [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
																					    ]      )

														else:
																					raise ValueError(f"""Unknown model name: {model_name}"""      )

														assert torch.allclose(logits[0, :3, :3, :3]					,   lowerCamelCase__					,   atol=1E-4      )
							else:
														assert logits.shape == (1, 1001)

														if model_name == "mobilenet_v2_1.4_224":
																					A    =       torch.tensor([0.01_81, -1.00_15, 0.46_88]      )
														elif model_name == "mobilenet_v2_1.0_224":
																					A    =       torch.tensor([0.24_45, -1.19_93, 0.19_05]      )
														elif model_name == "mobilenet_v2_0.75_160":
																					A    =       torch.tensor([0.24_82, 0.41_36, 0.66_69]      )
														elif model_name == "mobilenet_v2_0.35_96":
																					A    =       torch.tensor([0.14_51, -0.46_24, 0.71_92]      )
														else:
																					A    =       None

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

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

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


if __name__ == "__main__":
							A     =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='mobilenet_v2_1.0_224',
							    type=str,
							    help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<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.'
							)

							A     =      parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)








"""simple docstring"""







import os

from ...utils import logging
from .utils import DataProcessor, InputExample


A     =      logging.get_logger(__name__)






class   UpperCAmelCase__		(   UpperCamelCase  ):



		def __init__(			self	:     List[str]   ,				snake_case	:     List[Any]   ,				snake_case	:     str=None					)      ->    Tuple:






									'''simple docstring'''

									A    =       language
									A    =       train_language



		def        A_		(			self	:     Optional[int]   ,				snake_case	:     Any					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       self.language if self.train_language is None else self.train_language
									A    =       self._read_tsv(os.path.join(snake_case   ,				f"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""					)					)
									A    =       []
									for i, line in enumerate(snake_case					):
																if i == 0:
																							continue
																A    =       f"""train-{i}"""
																A    =       line[0]
																A    =       line[1]
																A    =       'contradiction' if line[2] == 'contradictory' else line[2]
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_a} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_b} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training label {label} is not a string"""					)
																examples.append(InputExample(guid=snake_case   ,				text_a=snake_case   ,				text_b=snake_case   ,				label=snake_case					)					)
									return examples



		def        A_		(			self	:     int   ,				snake_case	:     int					)      ->    Optional[int]:






									'''simple docstring'''

									A    =       self._read_tsv(os.path.join(snake_case   ,				'XNLI-1.0/xnli.test.tsv'					)					)
									A    =       []
									for i, line in enumerate(snake_case					):
																if i == 0:
																							continue
																A    =       line[0]
																if language != self.language:
																							continue
																A    =       f"""test-{i}"""
																A    =       line[6]
																A    =       line[7]
																A    =       line[1]
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_a} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_b} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training label {label} is not a string"""					)
																examples.append(InputExample(guid=snake_case   ,				text_a=snake_case   ,				text_b=snake_case   ,				label=snake_case					)					)
									return examples



		def        A_		(			self	:     Any					)      ->    List[Any]:






									'''simple docstring'''

									return ["contradiction", "entailment", "neutral"]



A     =      {
    'xnli': XnliProcessor,
}

A     =      {
    'xnli': 'classification',
}

A     =      {
    'xnli': 3,
}



