






"""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()
_UpperCamelCase				:    Any     =						logging.get_logger(__name__)




def 			snake_case  (A_      :Dict      ):







  '''simple docstring'''

  a     :			str				=   MobileNetVaConfig(layer_norm_eps=0.001      )

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

  a     :			Tuple				=   re.match(R'^.*mobilenet_v2_([^_]*)_([^_]*)$'	,			SCREAMING_SNAKE_CASE__      )
  if matches:
    a     :			List[str]				=   float(matches[1]      )
    a     :			str				=   int(matches[2]      )

  if model_name.startswith('deeplabv3_'      ):
    a     :			Tuple				=   8
    a     :			Tuple				=   2_1
    a     :			Optional[Any]				=   '''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     :			List[str]				=   1_0_0_1
    a     :			Tuple				=   '''imagenet-1k-id2label.json'''

  a     :			Optional[Any]				=   '''huggingface/label-files'''
  a     :			Union[str, Any]				=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__	,			SCREAMING_SNAKE_CASE__	,			repo_type='dataset'      )	,			'r'      )      )

  if config.num_labels == 1_0_0_1:
    a     :			Optional[int]				=   {int(SCREAMING_SNAKE_CASE__      ) + 1: v for k, v in idalabel.items()}
    a     :			int				=   '''background'''
  else:
    a     :			List[str]				=   {int(SCREAMING_SNAKE_CASE__      ): v for k, v in idalabel.items()}

  a     :			str				=   idalabel
  a     :			Optional[Any]				=   {v: k for k, v in idalabel.items()}

  return config




def 			snake_case  ():







  '''simple docstring'''

  a     :			Union[str, Any]				=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  a     :			Tuple				=   Image.open(requests.get(SCREAMING_SNAKE_CASE__	,			stream=SCREAMING_SNAKE_CASE__      ).raw      )
  return im




@torch.no_grad()
def 			snake_case  (A_      :str	,			A_      :Tuple	,			A_      :List[str]	,			A_      :Optional[int]=False      ):







  '''simple docstring'''

  a     :			List[str]				=   get_mobilenet_va_config(SCREAMING_SNAKE_CASE__      )

  # Load 🤗 model
  if model_name.startswith('deeplabv3_'      ):
    a     :			Optional[int]				=   MobileNetVaForSemanticSegmentation(SCREAMING_SNAKE_CASE__      ).eval()
  else:
    a     :			Tuple				=   MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE__      ).eval()

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

  # Check outputs on an image, prepared by MobileNetV2ImageProcessor
  a     :			Dict				=   MobileNetVaImageProcessor(
      crop_size={'width': config.image_size, 'height': config.image_size}	,			size={'shortest_edge': config.image_size + 3_2}	,			)
  a     :			Any				=   image_processor(images=prepare_img()	,			return_tensors='pt'      )
  a     :			str				=   model(**SCREAMING_SNAKE_CASE__      )
  a     :			str				=   outputs.logits

  if model_name.startswith('deeplabv3_'      ):
    assert logits.shape == (1, 2_1, 6_5, 6_5)

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      a     :			Optional[int]				=   torch.tensor(
          [
              [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
              [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
              [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
          ]      )

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

    assert torch.allclose(logits[0, :3, :3, :3]	,			SCREAMING_SNAKE_CASE__	,			atol=1E-4      )
  else:
    assert logits.shape == (1, 1_0_0_1)

    if model_name == "mobilenet_v2_1.4_224":
      a     :			Dict				=   torch.tensor([0.0181, -1.0015, 0.4688]      )
    elif model_name == "mobilenet_v2_1.0_224":
      a     :			str				=   torch.tensor([0.2445, -1.1993, 0.1905]      )
    elif model_name == "mobilenet_v2_0.75_160":
      a     :			Optional[int]				=   torch.tensor([0.2482, 0.4136, 0.6669]      )
    elif model_name == "mobilenet_v2_0.35_96":
      a     :			Optional[int]				=   torch.tensor([0.1451, -0.4624, 0.7192]      )
    else:
      a     :			Dict				=   None

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

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

  if push_to_hub:
    print('Pushing to the hub...'      )
    a     :			Dict				=   '''google/''' + model_name
    image_processor.push_to_hub(SCREAMING_SNAKE_CASE__      )
    model.push_to_hub(SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
   _UpperCamelCase				:    str     =						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.'
   )

   _UpperCamelCase				:    List[str]     =						parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )






'''simple docstring'''
#
# Sort large text files in a minimum amount of memory
#
import argparse
import os






class      snake_case__    :







		"""simple docstring"""

		lowerCamelCase							=		"""block_{0}.dat"""



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






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		filename
				snake_case   :    Any			       =		[]



		def 	lowerCAmelCase							(	self							:       Any   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       str	)		->       Dict:






				"""simple docstring"""







				snake_case   :    str			       =		self.BLOCK_FILENAME_FORMAT.format(UpperCamelCase__	)
				with open(UpperCamelCase__   ,     '''w'''	) as file:
						file.write(UpperCamelCase__	)
				self.block_filenames.append(UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Optional[int]:






				"""simple docstring"""







				return self.block_filenames



		def 	lowerCAmelCase							(	self							:       Optional[int]   ,     UpperCamelCase__							:       Union[str, Any]   ,     UpperCamelCase__							:       Optional[Any]=None	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		0
				with open(self.filename	) as file:
						while True:
								snake_case   :    List[str]			       =		file.readlines(UpperCamelCase__	)

								if lines == []:
										break

								if sort_key is None:
										lines.sort()
								else:
										lines.sort(key=UpperCamelCase__	)

								self.write_block(''''''.join(UpperCamelCase__	)   ,     UpperCamelCase__	)
								i += 1



		def 	lowerCAmelCase							(	self							:       Dict	)		->       Optional[Any]:






				"""simple docstring"""







				map(os.remove   ,     self.block_filenames	)







class      snake_case__    :







		"""simple docstring"""

		def 	lowerCAmelCase							(	self							:       str   ,     UpperCamelCase__							:       Optional[int]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    str			       =		-1
				snake_case   :    Optional[Any]			       =		None

				for i in range(len(UpperCamelCase__	)	):
						if min_str is None or choices[i] < min_str:
								snake_case   :    Union[str, Any]			       =		i

				return min_index







class      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       str   ,     UpperCamelCase__							:       Tuple	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Dict			       =		files
				snake_case   :    Optional[Any]			       =		set()
				snake_case   :    Tuple			       =		len(UpperCamelCase__	)
				snake_case   :    List[Any]			       =		{i: None for i in range(self.num_buffers	)}



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       int:






				"""simple docstring"""







				return {
				    i: self.buffers[i] for i in range(self.num_buffers	) if i not in self.empty
				}



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       int:






				"""simple docstring"""







				for i in range(self.num_buffers	):
						if self.buffers[i] is None and i not in self.empty:
								snake_case   :    Optional[Any]			       =		self.files[i].readline()

								if self.buffers[i] == "":
										self.empty.add(UpperCamelCase__	)
										self.files[i].close()

				if len(self.empty	) == self.num_buffers:
						return False

				return True



		def 	lowerCAmelCase							(	self							:       Optional[Any]   ,     UpperCamelCase__							:       Any	)		->       str:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.buffers[index]
				snake_case   :    Optional[int]			       =		None

				return value







class      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       List[Any]   ,     UpperCamelCase__							:       Optional[Any]	)		->       Optional[Any]:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		merge_strategy



		def 	lowerCAmelCase							(	self							:       str   ,     UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       Dict	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		FilesArray(self.get_file_handles(UpperCamelCase__   ,     UpperCamelCase__	)	)
				with open(UpperCamelCase__   ,     '''w'''   ,     UpperCamelCase__	) as outfile:
						while buffers.refresh():
								snake_case   :    List[Any]			       =		self.merge_strategy.select(buffers.get_dict()	)
								outfile.write(buffers.unshift(UpperCamelCase__	)	)



		def 	lowerCAmelCase							(	self							:       List[str]   ,     UpperCamelCase__							:       Tuple   ,     UpperCamelCase__							:       str	)		->       int:






				"""simple docstring"""







				snake_case   :    str			       =		{}

				for i in range(len(UpperCamelCase__	)	):
						snake_case   :    Any			       =		open(filenames[i]   ,     '''r'''   ,     UpperCamelCase__	)  # noqa: UP015

				return files







class      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       Any   ,     UpperCamelCase__							:       Any	)		->       Optional[Any]:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		block_size



		def 	lowerCAmelCase							(	self							:       Dict   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       Tuple=None	)		->       Optional[Any]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		self.get_number_blocks(UpperCamelCase__   ,     self.block_size	)
				snake_case   :    Optional[Any]			       =		FileSplitter(UpperCamelCase__	)
				splitter.split(self.block_size   ,     UpperCamelCase__	)

				snake_case   :    Optional[Any]			       =		FileMerger(NWayMerge()	)
				snake_case   :    Any			       =		self.block_size / (num_blocks + 1)
				merger.merge(splitter.get_block_filenames()   ,     filename + '''.out'''   ,     UpperCamelCase__	)

				splitter.cleanup()



		def 	lowerCAmelCase							(	self							:       Optional[Any]   ,     UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       Dict	)		->       List[Any]:






				"""simple docstring"""







				return (os.stat(UpperCamelCase__	).st_size / block_size) + 1







def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				List[Any]:


		'''simple docstring'''


		if string[-1].lower() == "k":
				return int(string[:-1]       ) * 1024
		elif string[-1].lower() == "m":
				return int(string[:-1]       ) * 1024 * 1024
		elif string[-1].lower() == "g":
				return int(string[:-1]       ) * 1024 * 1024 * 1024
		else:
				return int(SCREAMING_SNAKE_CASE__       )




def 					_UpperCamelCase		(    )   ->				Optional[int]:


		'''simple docstring'''


		snake_case   :    Tuple			       =		argparse.ArgumentParser()
		parser.add_argument(
		    '''-m'''      ,    '''--mem'''      ,    help='''amount of memory to use for sorting'''      ,    default='''100M'''       )
		parser.add_argument(
		    '''filename'''      ,    metavar='''<filename>'''      ,    nargs=1      ,    help='''name of file to sort'''       )
		snake_case   :    Any			       =		parser.parse_args()

		snake_case   :    int			       =		ExternalSort(parse_memory(args.mem       )       )
		sorter.sort(args.filename[0]       )


if __name__ == "__main__":
						main()


