




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()
snake_case_ : Optional[int]    		=       logging.get_logger(__name__)


def    A						(__A   :    List[str]       )   ->      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_          =							1001
     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    A						()   ->      Union[str, Any]:







     """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    A						(__A   :    Optional[Any]   ,				__A   :    List[Any]   ,				__A   :    Any   ,				__A   :    Union[str, Any]=False       )   ->      Optional[Any]:







     """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, 1001)

     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__":
    snake_case_ : Dict    		=       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."
    )

    snake_case_ : Optional[Any]    		=       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


snake_case_ : Any    		=       logging.get_logger(__name__)

snake_case_ : Optional[Any]    		=       {
    "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
    "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}







class __snake_case			(			a		):
   UpperCAmelCase__  :    Optional[Any]          =  '''falcon'''
   UpperCAmelCase__  :    List[Any]          =  ['''past_key_values''']







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						List[str]=65024					,						_snake_case		:						int=4544					,						_snake_case		:						int=32					,						_snake_case		:						Any=71					,						_snake_case		:						int=1e-5					,						_snake_case		:						Dict=0.0_2					,						_snake_case		:						int=True					,						_snake_case		:						List[Any]=0.0					,						_snake_case		:						Tuple=0.0					,						_snake_case		:						int=None					,						_snake_case		:						Tuple=False					,						_snake_case		:						Any=False					,						_snake_case		:						str=True					,						_snake_case		:						Any=True					,						_snake_case		:						List[str]=False					,						_snake_case		:						Tuple=11					,						_snake_case		:						Dict=11					,						**_snake_case		:						Optional[int]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							vocab_size
        # Backward compatibility with n_embed kwarg
        UpperCAmelCase_          =							kwargs.pop('''n_embed'''					,						_snake_case)
        UpperCAmelCase_          =							hidden_size if n_embed is None else n_embed
        UpperCAmelCase_          =							num_hidden_layers
        UpperCAmelCase_          =							num_attention_heads
        UpperCAmelCase_          =							layer_norm_epsilon
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							use_cache
        UpperCAmelCase_          =							hidden_dropout
        UpperCAmelCase_          =							attention_dropout

        UpperCAmelCase_          =							bos_token_id
        UpperCAmelCase_          =							eos_token_id
        UpperCAmelCase_          =							num_attention_heads if num_kv_heads is None else num_kv_heads
        UpperCAmelCase_          =							alibi
        UpperCAmelCase_          =							new_decoder_architecture
        UpperCAmelCase_          =							multi_query  # Ignored when new_decoder_architecture is True
        UpperCAmelCase_          =							parallel_attn
        UpperCAmelCase_          =							bias

        super().__init__(bos_token_id=_snake_case					,						eos_token_id=_snake_case					,						**_snake_case)







   @property
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        return self.hidden_size // self.num_attention_heads







   @property
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        return not self.alibi






