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()
UpperCAmelCase							   =      logging.get_logger(__name__)







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Any:
     a  :    Tuple				=						MobileNetVaConfig(layer_norm_eps=0.0_01       )

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

     a  :    Any				=						re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''						,      lowerCAmelCase_       )
     if matches:
          a  :    List[Any]				=						float(matches[1]       )
          a  :    int				=						int(matches[2]       )

     if model_name.startswith('''deeplabv3_'''       ):
          a  :    Optional[Any]				=						8
          a  :    str				=						2_1
          a  :    Tuple				=						'''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".
          a  :    str				=						1_0_0_1
          a  :    Tuple				=						'''imagenet-1k-id2label.json'''

     a  :    int				=						'''huggingface/label-files'''
     a  :    Tuple				=						json.load(open(hf_hub_download(lowerCAmelCase_						,      lowerCAmelCase_						,      repo_type='''dataset'''       )						,      '''r'''       )       )

     if config.num_labels == 1_0_0_1:
          a  :    int				=						{int(lowerCAmelCase_       ) + 1: v for k, v in idalabel.items()}
          a  :    int				=						'''background'''
     else:
          a  :    List[Any]				=						{int(lowerCAmelCase_       ): v for k, v in idalabel.items()}

     a  :    str				=						idalabel
     a  :    Dict				=						{v: k for k, v in idalabel.items()}

     return config







def 	UpperCAmelCase       (		)	->	Union[str, Any]:
     a  :    List[str]				=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     a  :    Union[str, Any]				=						Image.open(requests.get(lowerCAmelCase_						,      stream=lowerCAmelCase_       ).raw       )
     return im







@torch.no_grad()
def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False       )	->	List[str]:
     a  :    Any				=						get_mobilenet_va_config(lowerCAmelCase_       )

     # Load 🤗 model
     if model_name.startswith('''deeplabv3_'''       ):
          a  :    Any				=						MobileNetVaForSemanticSegmentation(lowerCAmelCase_       ).eval()
     else:
          a  :    Optional[int]				=						MobileNetVaForImageClassification(lowerCAmelCase_       ).eval()

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

     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     a  :    Tuple				=						MobileNetVaImageProcessor(
         crop_size={'''width''': config.image_size, '''height''': config.image_size}						,      size={'''shortest_edge''': config.image_size + 3_2}						,      )
     a  :    str				=						image_processor(images=prepare_img()						,      return_tensors='''pt'''       )
     a  :    List[str]				=						model(**lowerCAmelCase_       )
     a  :    Optional[int]				=						outputs.logits

     if model_name.startswith('''deeplabv3_'''       ):
          assert logits.shape == (1, 2_1, 6_5, 6_5)

          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
               a  :    str				=						torch.tensor(
                   [
                       [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
                       [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
                       [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
                   ]       )

          else:
               raise ValueError(f"""Unknown model name: {model_name}"""       )

          assert torch.allclose(logits[0, :3, :3, :3]						,      lowerCAmelCase_						,      atol=1e-4       )
     else:
          assert logits.shape == (1, 1_0_0_1)

          if model_name == "mobilenet_v2_1.4_224":
               a  :    Dict				=						torch.tensor([0.01_81, -1.00_15, 0.46_88]       )
          elif model_name == "mobilenet_v2_1.0_224":
               a  :    str				=						torch.tensor([0.24_45, -1.19_93, 0.19_05]       )
          elif model_name == "mobilenet_v2_0.75_160":
               a  :    Union[str, Any]				=						torch.tensor([0.24_82, 0.41_36, 0.66_69]       )
          elif model_name == "mobilenet_v2_0.35_96":
               a  :    Union[str, Any]				=						torch.tensor([0.14_51, -0.46_24, 0.71_92]       )
          else:
               a  :    int				=						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...'''       )
          a  :    Union[str, Any]				=						'''google/''' + model_name
          image_processor.push_to_hub(lowerCAmelCase_       )
          model.push_to_hub(lowerCAmelCase_       )


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

  UpperCAmelCase							   =      parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						int 			=   'graphormer'
   UpperCamelCase_				:						Tuple 			=   ['past_key_values']
   def __init__(    self							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 5_12 * 9							,     lowerCAmelCase_ = 5_12 * 3							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 1_28							,     lowerCAmelCase_ = 5							,     lowerCAmelCase_ = 10_24							,     lowerCAmelCase_ = "multi_hop"							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 12							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 32							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = "gelu"							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = 8							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = True							,     lowerCAmelCase_=0							,     lowerCAmelCase_=1							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->Optional[int]:
        a  :    List[Any]				=						num_classes
        a  :    Any				=						num_atoms
        a  :    Dict				=						num_in_degree
        a  :    int				=						num_out_degree
        a  :    Dict				=						num_edges
        a  :    Tuple				=						num_spatial
        a  :    Dict				=						num_edge_dis
        a  :    Optional[int]				=						edge_type
        a  :    List[Any]				=						multi_hop_max_dist
        a  :    Optional[int]				=						spatial_pos_max
        a  :    str				=						max_nodes
        a  :    Optional[Any]				=						num_hidden_layers
        a  :    Any				=						embedding_dim
        a  :    Dict				=						embedding_dim
        a  :    Dict				=						ffn_embedding_dim
        a  :    List[Any]				=						num_attention_heads
        a  :    List[str]				=						dropout
        a  :    int				=						attention_dropout
        a  :    Optional[Any]				=						layerdrop
        a  :    str				=						encoder_normalize_before
        a  :    Optional[int]				=						pre_layernorm
        a  :    Tuple				=						apply_graphormer_init
        a  :    int				=						activation_fn
        a  :    Any				=						embed_scale
        a  :    Optional[int]				=						freeze_embeddings
        a  :    Tuple				=						num_trans_layers_to_freeze
        a  :    List[str]				=						share_input_output_embed
        a  :    List[Any]				=						traceable
        a  :    Optional[Any]				=						q_noise
        a  :    Dict				=						qn_block_size

        # These parameters are here for future extensions
        # atm, the model only supports self attention
        a  :    List[Any]				=						kdim
        a  :    List[str]				=						vdim
        a  :    int				=						self_attention
        a  :    List[Any]				=						bias

        super().__init__(
            pad_token_id=lowerCAmelCase_							,     bos_token_id=lowerCAmelCase_							,     eos_token_id=lowerCAmelCase_							,     **lowerCAmelCase_							,     )