






'''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      :     Dict       	=logging.get_logger(__name__)





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			List[str]:
							lowerCamelCase__   :      List[str]			    =			MobileNetVaConfig(layer_norm_eps=0.001 )

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

							lowerCamelCase__   :      Tuple			    =			re.match(r"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""							,     UpperCamelCase )
							if matches:
														lowerCamelCase__   :      Dict			    =			float(matches[1] )
														lowerCamelCase__   :      str			    =			int(matches[2] )

							if model_name.startswith("""deeplabv3_""" ):
														lowerCamelCase__   :      Optional[Any]			    =			8
														lowerCamelCase__   :      Tuple			    =			21
														lowerCamelCase__   :      str			    =			"""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".
														lowerCamelCase__   :      Union[str, Any]			    =			1001
														lowerCamelCase__   :      int			    =			"""imagenet-1k-id2label.json"""

							lowerCamelCase__   :      Union[str, Any]			    =			"""huggingface/label-files"""
							lowerCamelCase__   :      int			    =			json.load(open(hf_hub_download(UpperCamelCase							,     UpperCamelCase							,     repo_type="""dataset""" )							,     """r""" ) )

							if config.num_labels == 1001:
														lowerCamelCase__   :      List[Any]			    =			{int(UpperCamelCase ) + 1: v for k, v in idalabel.items()}
														lowerCamelCase__   :      Optional[Any]			    =			"""background"""
							else:
														lowerCamelCase__   :      Any			    =			{int(UpperCamelCase ): v for k, v in idalabel.items()}

							lowerCamelCase__   :      Optional[int]			    =			idalabel
							lowerCamelCase__   :      Dict			    =			{v: k for k, v in idalabel.items()}

							return config





def 					SCREAMING_SNAKE_CASE_			() ->			str:
							lowerCamelCase__   :      str			    =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase__   :      Optional[Any]			    =			Image.open(requests.get(UpperCamelCase							,     stream=UpperCamelCase ).raw )
							return im





@torch.no_grad()
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase=False ) ->			str:
							lowerCamelCase__   :      Union[str, Any]			    =			get_mobilenet_va_config(UpperCamelCase )

							# Load 🤗 model
							if model_name.startswith("""deeplabv3_""" ):
														lowerCamelCase__   :      int			    =			MobileNetVaForSemanticSegmentation(UpperCamelCase ).eval()
							else:
														lowerCamelCase__   :      Union[str, Any]			    =			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
							lowerCamelCase__   :      Union[str, Any]			    =			MobileNetVaImageProcessor(
							    crop_size={"""width""": config.image_size, """height""": config.image_size}							,     size={"""shortest_edge""": config.image_size + 32}							,     )
							lowerCamelCase__   :      Union[str, Any]			    =			image_processor(images=prepare_img()							,     return_tensors="""pt""" )
							lowerCamelCase__   :      Optional[Any]			    =			model(**UpperCamelCase )
							lowerCamelCase__   :      Any			    =			outputs.logits

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

														if model_name == "deeplabv3_mobilenet_v2_1.0_513":
																					lowerCamelCase__   :      Dict			    =			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]							,     UpperCamelCase							,     atol=1E-4 )
							else:
														assert logits.shape == (1, 1001)

														if model_name == "mobilenet_v2_1.4_224":
																					lowerCamelCase__   :      Optional[int]			    =			torch.tensor([0.0181, -1.0015, 0.4688] )
														elif model_name == "mobilenet_v2_1.0_224":
																					lowerCamelCase__   :      Dict			    =			torch.tensor([0.2445, -1.1993, 0.1905] )
														elif model_name == "mobilenet_v2_0.75_160":
																					lowerCamelCase__   :      Any			    =			torch.tensor([0.2482, 0.4136, 0.6669] )
														elif model_name == "mobilenet_v2_0.35_96":
																					lowerCamelCase__   :      Tuple			    =			torch.tensor([0.1451, -0.4624, 0.7192] )
														else:
																					lowerCamelCase__   :      Optional[Any]			    =			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...""" )
														lowerCamelCase__   :      int			    =			"""google/""" + model_name
														image_processor.push_to_hub(UpperCamelCase )
														model.push_to_hub(UpperCamelCase )


if __name__ == "__main__":
			_A      :     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      :     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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_A      :     Union[str, Any]       	=logging.get_logger(__name__)

_A      :     Tuple       	={
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class _lowercase      (							_lowercase						):
							a							 =			"""splinter"""




							def __init__(				self:							List[str]     ,				UpperCamelCase__:							List[Any]=30_522     ,				UpperCamelCase__:							Union[str, Any]=768     ,				UpperCamelCase__:							str=12     ,				UpperCamelCase__:							Dict=12     ,				UpperCamelCase__:							Tuple=3_072     ,				UpperCamelCase__:							Optional[int]="gelu"     ,				UpperCamelCase__:							Optional[int]=0.1     ,				UpperCamelCase__:							str=0.1     ,				UpperCamelCase__:							List[Any]=512     ,				UpperCamelCase__:							List[str]=2     ,				UpperCamelCase__:							Any=0.02     ,				UpperCamelCase__:							Optional[Any]=1e-12     ,				UpperCamelCase__:							Dict=True     ,				UpperCamelCase__:							str=0     ,				UpperCamelCase__:							int=104     ,				**UpperCamelCase__:							Any     ,				):
														super().__init__(pad_token_id=UpperCamelCase__     ,				**UpperCamelCase__	)

														lowerCamelCase__   :      Union[str, Any]			    =			vocab_size
														lowerCamelCase__   :      str			    =			max_position_embeddings
														lowerCamelCase__   :      Tuple			    =			hidden_size
														lowerCamelCase__   :      Dict			    =			num_hidden_layers
														lowerCamelCase__   :      Tuple			    =			num_attention_heads
														lowerCamelCase__   :      Tuple			    =			intermediate_size
														lowerCamelCase__   :      Any			    =			hidden_act
														lowerCamelCase__   :      int			    =			hidden_dropout_prob
														lowerCamelCase__   :      List[str]			    =			attention_probs_dropout_prob
														lowerCamelCase__   :      Dict			    =			initializer_range
														lowerCamelCase__   :      List[Any]			    =			type_vocab_size
														lowerCamelCase__   :      Tuple			    =			layer_norm_eps
														lowerCamelCase__   :      Optional[Any]			    =			use_cache
														lowerCamelCase__   :      Optional[Any]			    =			question_token_id

