






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__						:Union[str, Any]          =			logging.get_logger(__name__)
def     __lowercase  (_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				MobileNetVaConfig(layer_norm_eps=0.001     )

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

     __lowerCamelCase						:       Dict				   =				re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$""",						_lowercase     )
     if matches:
          __lowerCamelCase						:       int				   =				float(matches[1]     )
          __lowerCamelCase						:       str				   =				int(matches[2]     )

     # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
     # the usual 1000. The first class (index 0) is "background".
     __lowerCamelCase						:       Any				   =				1_001
     __lowerCamelCase						:       Union[str, Any]				   =				"""imagenet-1k-id2label.json"""
     __lowerCamelCase						:       Tuple				   =				"""huggingface/label-files"""
     __lowerCamelCase						:       Optional[Any]				   =				json.load(open(hf_hub_download(_lowercase,						_lowercase,						repo_type="""dataset"""     ),						"""r"""     )     )
     __lowerCamelCase						:       Tuple				   =				{int(_lowercase     ) + 1: v for k, v in idalabel.items()}
     __lowerCamelCase						:       List[str]				   =				"""background"""
     __lowerCamelCase						:       str				   =				idalabel
     __lowerCamelCase						:       Dict				   =				{v: k for k, v in idalabel.items()}

     return config
def     __lowercase  ()   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     __lowerCamelCase						:       List[Any]				   =				Image.open(requests.get(_lowercase,						stream=_lowercase     ).raw     )
     return im
@torch.no_grad()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase=False     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				get_mobilenet_va_config(_lowercase     )

     # Load 🤗 model
     __lowerCamelCase						:       int				   =				MobileNetVaForImageClassification(_lowercase     ).eval()

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

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

     assert logits.shape == (1, 1_001)

     if model_name == "mobilenet_v1_1.0_224":
          __lowerCamelCase						:       Optional[int]				   =				torch.tensor([-4.1739, -1.1233, 3.1205]     )
     elif model_name == "mobilenet_v1_0.75_192":
          __lowerCamelCase						:       List[str]				   =				torch.tensor([-3.9440, -2.3141, -0.3333]     )
     else:
          __lowerCamelCase						:       Tuple				   =				None

     if expected_logits is not None:
          assert torch.allclose(logits[0, :3],						_lowercase,						atol=1e-4     )

     Path(_lowercase     ).mkdir(exist_ok=_lowercase     )
     print(f"Saving model {model_name} to {pytorch_dump_folder_path}"     )
     model.save_pretrained(_lowercase     )
     print(f"Saving image processor to {pytorch_dump_folder_path}"     )
     image_processor.save_pretrained(_lowercase     )

     if push_to_hub:
          print("""Pushing to the hub..."""     )
          __lowerCamelCase						:       Union[str, Any]				   =				"""google/""" + model_name
          image_processor.push_to_hub(_lowercase     )
          model.push_to_hub(_lowercase     )


if __name__ == "__main__":
   UpperCAmelCase__						:Optional[Any]          =			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__						:str          =			parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )











'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

UpperCAmelCase__						:List[Any]          =			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Dict   		=			'sew-d'




       def __init__(					self      : Tuple	,    A__      : Optional[int]=32	,    A__      : Optional[int]=768	,    A__      : Any=12	,    A__      : List[str]=12	,    A__      : List[Any]=3072	,    A__      : str=2	,    A__      : Dict=512	,    A__      : Optional[Any]=256	,    A__      : Optional[Any]=True	,    A__      : Any=True	,    A__      : List[str]=("p2c", "c2p")	,    A__      : List[Any]="layer_norm"	,    A__      : Union[str, Any]="gelu_python"	,    A__      : int=0.1	,    A__      : Dict=0.1	,    A__      : int=0.1	,    A__      : Dict=0.0	,    A__      : Optional[Any]=0.1	,    A__      : Dict=0.02	,    A__      : Dict=1e-7	,    A__      : List[Any]=1e-5	,    A__      : Any="group"	,    A__      : Any="gelu"	,    A__      : Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)	,    A__      : Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)	,    A__      : str=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)	,    A__      : Optional[Any]=False	,    A__      : Any=128	,    A__      : Optional[Any]=16	,    A__      : Union[str, Any]=True	,    A__      : Any=0.05	,    A__      : List[str]=10	,    A__      : Union[str, Any]=2	,    A__      : Dict=0.0	,    A__      : str=10	,    A__      : Tuple=0	,    A__      : Any="mean"	,    A__      : Optional[int]=False	,    A__      : int=False	,    A__      : List[str]=256	,    A__      : Union[str, Any]=0	,    A__      : int=1	,    A__      : Optional[Any]=2	,    **A__      : List[Any]	,    ):


            """simple docstring"""







            super().__init__(**A__	,    pad_token_id=A__	,    bos_token_id=A__	,    eos_token_id=A__					)
            __lowerCamelCase						:       List[str]				   =				hidden_size
            __lowerCamelCase						:       Tuple				   =				feat_extract_norm
            __lowerCamelCase						:       Tuple				   =				feat_extract_activation
            __lowerCamelCase						:       List[Any]				   =				list(A__					)
            __lowerCamelCase						:       int				   =				list(A__					)
            __lowerCamelCase						:       Optional[Any]				   =				list(A__					)
            __lowerCamelCase						:       Tuple				   =				conv_bias
            __lowerCamelCase						:       List[str]				   =				num_conv_pos_embeddings
            __lowerCamelCase						:       Tuple				   =				num_conv_pos_embedding_groups
            __lowerCamelCase						:       int				   =				len(self.conv_dim					)
            __lowerCamelCase						:       Optional[int]				   =				num_hidden_layers
            __lowerCamelCase						:       Dict				   =				intermediate_size
            __lowerCamelCase						:       Optional[int]				   =				squeeze_factor
            __lowerCamelCase						:       Union[str, Any]				   =				max_position_embeddings
            __lowerCamelCase						:       int				   =				position_buckets
            __lowerCamelCase						:       Tuple				   =				share_att_key
            __lowerCamelCase						:       Any				   =				relative_attention
            __lowerCamelCase						:       Any				   =				norm_rel_ebd
            __lowerCamelCase						:       Dict				   =				list(A__					)
            __lowerCamelCase						:       Optional[Any]				   =				hidden_act
            __lowerCamelCase						:       List[str]				   =				num_attention_heads
            __lowerCamelCase						:       List[str]				   =				hidden_dropout
            __lowerCamelCase						:       Union[str, Any]				   =				attention_dropout
            __lowerCamelCase						:       Tuple				   =				activation_dropout
            __lowerCamelCase						:       Union[str, Any]				   =				feat_proj_dropout
            __lowerCamelCase						:       Union[str, Any]				   =				final_dropout
            __lowerCamelCase						:       List[str]				   =				layer_norm_eps
            __lowerCamelCase						:       Tuple				   =				feature_layer_norm_eps
            __lowerCamelCase						:       Union[str, Any]				   =				initializer_range
            __lowerCamelCase						:       List[str]				   =				vocab_size

            if (
                (len(self.conv_stride					) != self.num_feat_extract_layers)
                or (len(self.conv_kernel					) != self.num_feat_extract_layers)
                or (len(self.conv_dim					) != self.num_feat_extract_layers)
            ):
                 raise ValueError(
                     """Configuration for convolutional layers is incorrect."""
                     """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
                     f"but is `len(config.conv_dim) = {len(self.conv_dim					)}`, `len(config.conv_stride)"
                     f"= {len(self.conv_stride					)}`, `len(config.conv_kernel) = {len(self.conv_kernel					)}`."					)

            # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
            __lowerCamelCase						:       Any				   =				apply_spec_augment
            __lowerCamelCase						:       str				   =				mask_time_prob
            __lowerCamelCase						:       Tuple				   =				mask_time_length
            __lowerCamelCase						:       Optional[int]				   =				mask_time_min_masks
            __lowerCamelCase						:       Dict				   =				mask_feature_prob
            __lowerCamelCase						:       Optional[Any]				   =				mask_feature_length
            __lowerCamelCase						:       str				   =				mask_feature_min_masks

            # ctc loss
            __lowerCamelCase						:       Any				   =				ctc_loss_reduction
            __lowerCamelCase						:       str				   =				ctc_zero_infinity

            # sequence classification
            __lowerCamelCase						:       Dict				   =				use_weighted_layer_sum
            __lowerCamelCase						:       List[Any]				   =				classifier_proj_size




       @property
       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            return functools.reduce(operator.mul	,    self.conv_stride	,    1					)



