






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__     :      Optional[Any]             =			logging.get_logger(__name__)






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      MobileNetVaConfig(layer_norm_eps=0.0_01    )

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

				lowercase__       			=      re.match(r'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''		,       lowerCamelCase_    )
				if matches:
								lowercase__       			=      float(matches[1]    )
								lowercase__       			=      int(matches[2]    )

				if model_name.startswith('''deeplabv3_'''    ):
								lowercase__       			=      8
								lowercase__       			=      21
								lowercase__       			=      '''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".
								lowercase__       			=      1001
								lowercase__       			=      '''imagenet-1k-id2label.json'''

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

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

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

				return config






def   a      (						):






				'''simple docstring'''
				lowercase__       			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowercase__       			=      Image.open(requests.get(lowerCamelCase_		,       stream=lowerCamelCase_    ).raw    )
				return im






@torch.no_grad()
def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_=False    ):






				'''simple docstring'''
				lowercase__       			=      get_mobilenet_va_config(lowerCamelCase_    )

				# Load 🤗 model
				if model_name.startswith('''deeplabv3_'''    ):
								lowercase__       			=      MobileNetVaForSemanticSegmentation(lowerCamelCase_    ).eval()
				else:
								lowercase__       			=      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
				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(**lowerCamelCase_    )
				lowercase__       			=      outputs.logits

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

								if model_name == "deeplabv3_mobilenet_v2_1.0_513":
												lowercase__       			=      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":
												lowercase__       			=      torch.tensor([0.01_81, -1.00_15, 0.46_88]    )
								elif model_name == "mobilenet_v2_1.0_224":
												lowercase__       			=      torch.tensor([0.24_45, -1.19_93, 0.19_05]    )
								elif model_name == "mobilenet_v2_0.75_160":
												lowercase__       			=      torch.tensor([0.24_82, 0.41_36, 0.66_69]    )
								elif model_name == "mobilenet_v2_0.35_96":
												lowercase__       			=      torch.tensor([0.14_51, -0.46_24, 0.71_92]    )
								else:
												lowercase__       			=      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...'''    )
								lowercase__       			=      '''google/''' + model_name
								image_processor.push_to_hub(lowerCamelCase_    )
								model.push_to_hub(lowerCamelCase_    )


if __name__ == "__main__":
							A__     :      Optional[int]             =			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__     :      int             =			parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)












import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin
class 				_UpperCAmelCase (	unittest.TestCase		,A__     ):

				"""simple docstring"""




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								lowercase__       			=      load_tool('''document-question-answering'''  )
								self.tool.setup()
								lowercase__       			=      load_tool('''document-question-answering''',  remote=lowerCamelCase  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      load_dataset('''hf-internal-testing/example-documents''',  split='''test'''  )
								lowercase__       			=      dataset[0]['''image''']

								lowercase__       			=      self.tool(lowerCamelCase,  '''When is the coffee break?'''  )
								self.assertEqual(lowerCamelCase,  '''11-14 to 11:39 a.m.'''  )




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      load_dataset('''hf-internal-testing/example-documents''',  split='''test'''  )
								lowercase__       			=      dataset[0]['''image''']

								lowercase__       			=      self.remote_tool(lowerCamelCase,  '''When is the coffee break?'''  )
								self.assertEqual(lowerCamelCase,  '''11-14 to 11:39 a.m.'''  )




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      load_dataset('''hf-internal-testing/example-documents''',  split='''test'''  )
								lowercase__       			=      dataset[0]['''image''']

								self.tool(document=lowerCamelCase,  question='''When is the coffee break?'''  )




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      load_dataset('''hf-internal-testing/example-documents''',  split='''test'''  )
								lowercase__       			=      dataset[0]['''image''']

								lowercase__       			=      self.remote_tool(document=lowerCamelCase,  question='''When is the coffee break?'''  )
								self.assertEqual(lowerCamelCase,  '''11-14 to 11:39 a.m.'''  )




