


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


logging.set_verbosity_info()
_UpperCAmelCase				: int							    =				logging.get_logger(__name__)

def 							__magic_name__(						lowerCamelCase):
      __lowerCAmelCase							       =				MobileNetVaConfig(layer_norm_eps=0.0_01)

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

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

      # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
      # the usual 1000. The first class (index 0) is "background".
      __lowerCAmelCase							       =				1_0_0_1
      __lowerCAmelCase							       =				'''imagenet-1k-id2label.json'''
      __lowerCAmelCase							       =				'''huggingface/label-files'''
      __lowerCAmelCase							       =				json.load(open(hf_hub_download(a_,				a_,				repo_type='''dataset'''),				'''r'''))
      __lowerCAmelCase							       =				{int(a_) + 1: v for k, v in idalabel.items()}
      __lowerCAmelCase							       =				'''background'''
      __lowerCAmelCase							       =				idalabel
      __lowerCAmelCase							       =				{v: k for k, v in idalabel.items()}

      return config

def 							__magic_name__(						):
      __lowerCAmelCase							       =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __lowerCAmelCase							       =				Image.open(requests.get(a_,				stream=a_).raw)
      return im

@torch.no_grad()
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=False):
      __lowerCAmelCase							       =				get_mobilenet_va_config(a_)

      # Load 🤗 model
      __lowerCAmelCase							       =				MobileNetVaForImageClassification(a_).eval()

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

      # Check outputs on an image, prepared by MobileNetV1ImageProcessor
      __lowerCAmelCase							       =				MobileNetVaImageProcessor(
          crop_size={'''width''': config.image_size, '''height''': config.image_size},				size={'''shortest_edge''': config.image_size + 3_2},				)
      __lowerCAmelCase							       =				image_processor(images=prepare_img(),				return_tensors='''pt''')
      __lowerCAmelCase							       =				model(**a_)
      __lowerCAmelCase							       =				outputs.logits

      assert logits.shape == (1, 1_0_0_1)

      if model_name == "mobilenet_v1_1.0_224":
            __lowerCAmelCase							       =				torch.tensor([-4.17_39, -1.12_33, 3.12_05])
      elif model_name == "mobilenet_v1_0.75_192":
            __lowerCAmelCase							       =				torch.tensor([-3.94_40, -2.31_41, -0.33_33])
      else:
            __lowerCAmelCase							       =				None

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

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

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


if __name__ == "__main__":
 _UpperCAmelCase				: str							    =				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."""
 )

 _UpperCAmelCase				: Dict							    =				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 ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase				: str							    =				{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Union[str, Any]							    =				[
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase				: int							    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

