


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


def    lowerCamelCase__   ( A     :    List[str]  ):




						'''simple docstring'''






						UpperCAmelCase         =			MobileNetVaConfig(layer_norm_eps=0.001  )

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

						UpperCAmelCase         =			re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''   ,     A  )
						if matches:
												UpperCAmelCase         =			float(matches[1]  )
												UpperCAmelCase         =			int(matches[2]  )

						# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
						# the usual 1000. The first class (index 0) is "background".
						UpperCAmelCase         =			10_01
						UpperCAmelCase         =			'''imagenet-1k-id2label.json'''
						UpperCAmelCase         =			'''huggingface/label-files'''
						UpperCAmelCase         =			json.load(open(hf_hub_download(A   ,     A   ,     repo_type='''dataset'''  )   ,     '''r'''  )  )
						UpperCAmelCase         =			{int(A  ) + 1: v for k, v in idalabel.items()}
						UpperCAmelCase         =			'''background'''
						UpperCAmelCase         =			idalabel
						UpperCAmelCase         =			{v: k for k, v in idalabel.items()}

						return config


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						UpperCAmelCase         =			Image.open(requests.get(A   ,     stream=A  ).raw  )
						return im


@torch.no_grad()
def    lowerCamelCase__   ( A     :    str   ,     A     :    str   ,     A     :    str   ,     A     :    Union[str, Any]=False  ):




						'''simple docstring'''






						UpperCAmelCase         =			get_mobilenet_va_config(A  )

						# Load 🤗 model
						UpperCAmelCase         =			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
						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(**A  )
						UpperCAmelCase         =			outputs.logits

						assert logits.shape == (1, 10_01)

						if model_name == "mobilenet_v1_1.0_224":
												UpperCAmelCase         =			torch.tensor([-4.1_739, -1.1_233, 3.1_205]  )
						elif model_name == "mobilenet_v1_0.75_192":
												UpperCAmelCase         =			torch.tensor([-3.9_440, -2.3_141, -0.3_333]  )
						else:
												UpperCAmelCase         =			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...'''  )
												UpperCAmelCase         =			'''google/''' + model_name
												image_processor.push_to_hub(A  )
												model.push_to_hub(A  )


if __name__ == "__main__":
	_lowercase    :    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."""
	)

	_lowercase    :    Optional[int]			     =  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


_lowercase    :    Any			     =  logging.get_logger(__name__)

_lowercase    :    Dict			     =  {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Optional[int]							= "dpr"




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				Any=30522   ,					lowerCAmelCase    :				List[str]=768   ,					lowerCAmelCase    :				Union[str, Any]=12   ,					lowerCAmelCase    :				Tuple=12   ,					lowerCAmelCase    :				Optional[int]=3072   ,					lowerCAmelCase    :				Optional[int]="gelu"   ,					lowerCAmelCase    :				Optional[Any]=0.1   ,					lowerCAmelCase    :				Any=0.1   ,					lowerCAmelCase    :				Optional[Any]=512   ,					lowerCAmelCase    :				List[Any]=2   ,					lowerCAmelCase    :				Tuple=0.02   ,					lowerCAmelCase    :				str=1E-12   ,					lowerCAmelCase    :				Optional[Any]=0   ,					lowerCAmelCase    :				Tuple="absolute"   ,					lowerCAmelCase    :				int = 0   ,					**lowerCAmelCase    :				Union[str, Any]   ,					)->  Optional[int]:

										"""simple docstring"""




										super().__init__(pad_token_id=lowerCAmelCase   ,					**lowerCAmelCase   )

										UpperCAmelCase         =			vocab_size
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_hidden_layers
										UpperCAmelCase         =			num_attention_heads
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			intermediate_size
										UpperCAmelCase         =			hidden_dropout_prob
										UpperCAmelCase         =			attention_probs_dropout_prob
										UpperCAmelCase         =			max_position_embeddings
										UpperCAmelCase         =			type_vocab_size
										UpperCAmelCase         =			initializer_range
										UpperCAmelCase         =			layer_norm_eps
										UpperCAmelCase         =			projection_dim
										UpperCAmelCase         =			position_embedding_type





