






"""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()
lowerCAmelCase_	:    List[str]            =      logging.get_logger(__name__)






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			MobileNetVaConfig(layer_norm_eps=0.0_01		)

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

       UpperCAmelCase	    =			re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$"""	,      lowerCAmelCase		)
       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	    =			1001
       UpperCAmelCase	    =			"""imagenet-1k-id2label.json"""
       UpperCAmelCase	    =			"""huggingface/label-files"""
       UpperCAmelCase	    =			json.load(open(hf_hub_download(lowerCAmelCase	,      lowerCAmelCase	,      repo_type="""dataset"""		)	,      """r"""		)		)
       UpperCAmelCase	    =			{int(lowerCAmelCase		) + 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(lowerCAmelCase	,      stream=lowerCAmelCase		).raw		)
       return im






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=False		):







       '''simple docstring'''
       UpperCAmelCase	    =			get_mobilenet_va_config(lowerCAmelCase		)

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

       assert logits.shape == (1, 1001)

       if model_name == "mobilenet_v1_1.0_224":
              UpperCAmelCase	    =			torch.tensor([-4.17_39, -1.12_33, 3.12_05]		)
       elif model_name == "mobilenet_v1_0.75_192":
              UpperCAmelCase	    =			torch.tensor([-3.94_40, -2.31_41, -0.33_33]		)
       else:
              UpperCAmelCase	    =			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..."""		)
              UpperCAmelCase	    =			"""google/""" + model_name
              image_processor.push_to_hub(lowerCAmelCase		)
              model.push_to_hub(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    List[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.'''
 )

 lowerCAmelCase_	:    Optional[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 collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_	:    Tuple            =      logging.get_logger(__name__)

lowerCAmelCase_	:    List[str]            =      {
    '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json''',
    '''google/bigbird-roberta-large''': '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json''',
    '''google/bigbird-base-trivia-itc''': '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json''',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Any				=	'big_bird'



  def __init__(      self ,		snake_case__=5_03_58 ,		snake_case__=7_68 ,		snake_case__=12 ,		snake_case__=12 ,		snake_case__=30_72 ,		snake_case__="gelu_new" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=40_96 ,		snake_case__=2 ,		snake_case__=0.02 ,		snake_case__=1e-12 ,		snake_case__=True ,		snake_case__=0 ,		snake_case__=1 ,		snake_case__=2 ,		snake_case__=66 ,		snake_case__="block_sparse" ,		snake_case__=True ,		snake_case__=False ,		snake_case__=64 ,		snake_case__=3 ,		snake_case__=None ,		**snake_case__ ,		)			->			List[str]:



         """simple docstring"""





         super().__init__(
             pad_token_id=snake_case__ ,		bos_token_id=snake_case__ ,		eos_token_id=snake_case__ ,		sep_token_id=snake_case__ ,		**snake_case__ ,		)

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

         UpperCAmelCase	    =			rescale_embeddings
         UpperCAmelCase	    =			attention_type
         UpperCAmelCase	    =			use_bias
         UpperCAmelCase	    =			block_size
         UpperCAmelCase	    =			num_random_blocks
         UpperCAmelCase	    =			classifier_dropout





class   UpperCamelCase_ (       a_  ):



  @property
  def 		UpperCamelCase_				(      self				)			->			Mapping[str, Mapping[int, str]]:



         """simple docstring"""





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