'''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()
__snake_case       :   Optional[int]  		=    logging.get_logger(__name__)



def lowerCamelCase__						(       A_       ):
 UpperCAmelCase_				       =					MobileNetVaConfig(layer_norm_eps=0.001       )

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

 UpperCAmelCase_				       =					re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$"       ,			_UpperCamelCase       )
 if matches:
  UpperCAmelCase_				       =					float(matches[1]       )
  UpperCAmelCase_				       =					int(matches[2]       )

 if model_name.startswith("deeplabv3_"       ):
  UpperCAmelCase_				       =					8
  UpperCAmelCase_				       =					21
  UpperCAmelCase_				       =					"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".
  UpperCAmelCase_				       =					1_001
  UpperCAmelCase_				       =					"imagenet-1k-id2label.json"

 UpperCAmelCase_				       =					"huggingface/label-files"
 UpperCAmelCase_				       =					json.load(open(hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase       ,			repo_type="dataset"       )       ,			"r"       )       )

 if config.num_labels == 1_001:
  UpperCAmelCase_				       =					{int(_UpperCamelCase       ) + 1: v for k, v in idalabel.items()}
  UpperCAmelCase_				       =					"background"
 else:
  UpperCAmelCase_				       =					{int(_UpperCamelCase       ): v for k, v in idalabel.items()}

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

 return config



def lowerCamelCase__						(       ):
 UpperCAmelCase_				       =					"http://images.cocodataset.org/val2017/000000039769.jpg"
 UpperCAmelCase_				       =					Image.open(requests.get(_UpperCamelCase       ,			stream=_UpperCamelCase       ).raw       )
 return im



@torch.no_grad()
def lowerCamelCase__						(       A_       ,			A_       ,			A_       ,			A_=False       ):
 UpperCAmelCase_				       =					get_mobilenet_va_config(_UpperCamelCase       )

 # Load 🤗 model
 if model_name.startswith("deeplabv3_"       ):
  UpperCAmelCase_				       =					MobileNetVaForSemanticSegmentation(_UpperCamelCase       ).eval()
 else:
  UpperCAmelCase_				       =					MobileNetVaForImageClassification(_UpperCamelCase       ).eval()

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

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

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

  if model_name == "deeplabv3_mobilenet_v2_1.0_513":
   UpperCAmelCase_				       =					torch.tensor(
       [
           [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
           [[-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]       ,			_UpperCamelCase       ,			atol=1e-4       )
 else:
  assert logits.shape == (1, 1_001)

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

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

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

 if push_to_hub:
  print("Pushing to the hub..."       )
  UpperCAmelCase_				       =					"google/" + model_name
  image_processor.push_to_hub(_UpperCamelCase       )
  model.push_to_hub(_UpperCamelCase       )


if __name__ == "__main__":
      __snake_case       :   Dict  		=    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.'''
      )

      __snake_case       :   Union[str, Any]  		=    parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )
'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case       :   Optional[int]  		=    {
    '''configuration_gptsan_japanese''': ['''GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTSanJapaneseConfig'''],
    '''tokenization_gptsan_japanese''': ['''GPTSanJapaneseTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   List[Any]  		=    [
          '''GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''GPTSanJapaneseForConditionalGeneration''',
          '''GPTSanJapaneseModel''',
          '''GPTSanJapanesePreTrainedModel''',
      ]
      __snake_case       :   Optional[int]  		=    [
          '''GPTSanJapaneseTokenizer''',
      ]


if TYPE_CHECKING:
      from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
      from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gptsan_japanese import (
                GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTSanJapaneseForConditionalGeneration,
                GPTSanJapaneseModel,
                GPTSanJapanesePreTrainedModel,
            )
            from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
      import sys

      __snake_case       :   Tuple  		=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
