




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 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						int										=MobileNetVaConfig(layer_norm_eps=0.0_0_1 )

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

				__magic_name__						:						Optional[Any]										=re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""						,  lowerCamelCase )
				if matches:
								__magic_name__						:						Dict										=float(matches[1] )
								__magic_name__						:						Tuple										=int(matches[2] )

				# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
				# the usual 1000. The first class (index 0) is "background".
				__magic_name__						:						str										=1001
				__magic_name__						:						Union[str, Any]										="""imagenet-1k-id2label.json"""
				__magic_name__						:						List[str]										="""huggingface/label-files"""
				__magic_name__						:						Optional[Any]										=json.load(open(hf_hub_download(lowerCamelCase						,  lowerCamelCase						,  repo_type="""dataset""" )						,  """r""" ) )
				__magic_name__						:						List[str]										={int(lowerCamelCase ) + 1: v for k, v in idalabel.items()}
				__magic_name__						:						Tuple										="""background"""
				__magic_name__						:						List[Any]										=idalabel
				__magic_name__						:						Optional[Any]										={v: k for k, v in idalabel.items()}

				return config




def 		lowerCAmelCase_ (						):
				__magic_name__						:						Dict										="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__magic_name__						:						Any										=Image.open(requests.get(lowerCamelCase						,  stream=lowerCamelCase ).raw )
				return im




@torch.no_grad()
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__						:						List[str]										=get_mobilenet_va_config(lowerCamelCase )

				# Load 🤗 model
				__magic_name__						:						List[str]										=MobileNetVaForImageClassification(lowerCamelCase ).eval()

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

				# Check outputs on an image, prepared by MobileNetV1ImageProcessor
				__magic_name__						:						Optional[int]										=MobileNetVaImageProcessor(
				    crop_size={"""width""": config.image_size, """height""": config.image_size}						,  size={"""shortest_edge""": config.image_size + 32}						,  )
				__magic_name__						:						Any										=image_processor(images=prepare_img()						,  return_tensors="""pt""" )
				__magic_name__						:						Tuple										=model(**lowerCamelCase )
				__magic_name__						:						Dict										=outputs.logits

				assert logits.shape == (1, 1001)

				if model_name == "mobilenet_v1_1.0_224":
								__magic_name__						:						Union[str, Any]										=torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] )
				elif model_name == "mobilenet_v1_0.75_192":
								__magic_name__						:						List[Any]										=torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3] )
				else:
								__magic_name__						:						Union[str, Any]										=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...""" )
								__magic_name__						:						Tuple										="""google/""" + model_name
								image_processor.push_to_hub(lowerCamelCase )
								model.push_to_hub(lowerCamelCase )


if __name__ == "__main__":
							UpperCAmelCase_		:					int          =  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_		:					Tuple          =  parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_		:					Tuple          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """xlm-roberta-xl"""

						def __init__(		self :Dict     ,			__snake_case :Optional[Any]=25_08_80     ,			__snake_case :List[Any]=25_60     ,			__snake_case :Optional[Any]=36     ,			__snake_case :Any=32     ,			__snake_case :int=1_02_40     ,			__snake_case :List[Any]="gelu"     ,			__snake_case :Union[str, Any]=0.1     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :str=5_14     ,			__snake_case :Union[str, Any]=1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :str=1E-05     ,			__snake_case :str=1     ,			__snake_case :int=0     ,			__snake_case :Tuple=2     ,			__snake_case :Optional[int]="absolute"     ,			__snake_case :str=True     ,			__snake_case :Any=None     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			**__snake_case			)
										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						List[str]										=hidden_size
										__magic_name__						:						Union[str, Any]										=num_hidden_layers
										__magic_name__						:						Any										=num_attention_heads
										__magic_name__						:						Any										=hidden_act
										__magic_name__						:						List[str]										=intermediate_size
										__magic_name__						:						Any										=hidden_dropout_prob
										__magic_name__						:						Union[str, Any]										=attention_probs_dropout_prob
										__magic_name__						:						Any										=max_position_embeddings
										__magic_name__						:						Any										=type_vocab_size
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						Optional[int]										=layer_norm_eps
										__magic_name__						:						Dict										=position_embedding_type
										__magic_name__						:						Any										=use_cache
										__magic_name__						:						Dict										=classifier_dropout




class 		__A   (      UpperCamelCase__      ):

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										if self.task == "multiple-choice":
														__magic_name__						:						str										={0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
														__magic_name__						:						Optional[Any]										={0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]			)




