






import functools
import operator

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


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

a						:			Optional[Any]     	=					{
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					Tuple							=      'unispeech'
      def __init__( self   :     Tuple						, __lowercase   :     int=32						, __lowercase   :     int=768						, __lowercase   :     str=12						, __lowercase   :     int=12						, __lowercase   :     Any=3072						, __lowercase   :     List[Any]="gelu"						, __lowercase   :     int=0.1						, __lowercase   :     Optional[Any]=0.1						, __lowercase   :     Optional[Any]=0.1						, __lowercase   :     Union[str, Any]=0.0						, __lowercase   :     List[Any]=0.0						, __lowercase   :     Any=0.1						, __lowercase   :     Optional[int]=0.1						, __lowercase   :     List[str]=0.02						, __lowercase   :     Optional[Any]=1e-5						, __lowercase   :     Tuple="group"						, __lowercase   :     Optional[int]="gelu"						, __lowercase   :     Tuple=(512, 512, 512, 512, 512, 512, 512)						, __lowercase   :     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)						, __lowercase   :     Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)						, __lowercase   :     Optional[int]=False						, __lowercase   :     List[Any]=128						, __lowercase   :     Tuple=16						, __lowercase   :     List[Any]=False						, __lowercase   :     List[str]=True						, __lowercase   :     str=0.05						, __lowercase   :     int=10						, __lowercase   :     Dict=2						, __lowercase   :     Union[str, Any]=0.0						, __lowercase   :     int=10						, __lowercase   :     List[Any]=0						, __lowercase   :     Optional[Any]=320						, __lowercase   :     Optional[int]=2						, __lowercase   :     Tuple=0.1						, __lowercase   :     str=100						, __lowercase   :     Union[str, Any]=256						, __lowercase   :     Any=256						, __lowercase   :     str=0.1						, __lowercase   :     Union[str, Any]="mean"						, __lowercase   :     Union[str, Any]=False						, __lowercase   :     Dict=False						, __lowercase   :     List[str]=256						, __lowercase   :     List[str]=80						, __lowercase   :     Optional[int]=0						, __lowercase   :     Dict=1						, __lowercase   :     str=2						, __lowercase   :     Tuple=0.5						, **__lowercase   :     Optional[Any]						, )				-> int:
        super().__init__(**__lowercase						, pad_token_id=__lowercase						, bos_token_id=__lowercase						, eos_token_id=__lowercase   )
        __UpperCAmelCase				:      Union[str, Any]	 =    hidden_size
        __UpperCAmelCase				:      str	 =    feat_extract_norm
        __UpperCAmelCase				:      Tuple	 =    feat_extract_activation
        __UpperCAmelCase				:      List[Any]	 =    list(__lowercase   )
        __UpperCAmelCase				:      Union[str, Any]	 =    list(__lowercase   )
        __UpperCAmelCase				:      Optional[Any]	 =    list(__lowercase   )
        __UpperCAmelCase				:      str	 =    conv_bias
        __UpperCAmelCase				:      str	 =    num_conv_pos_embeddings
        __UpperCAmelCase				:      Optional[int]	 =    num_conv_pos_embedding_groups
        __UpperCAmelCase				:      str	 =    len(self.conv_dim   )
        __UpperCAmelCase				:      Tuple	 =    num_hidden_layers
        __UpperCAmelCase				:      Any	 =    intermediate_size
        __UpperCAmelCase				:      Optional[int]	 =    hidden_act
        __UpperCAmelCase				:      Optional[Any]	 =    num_attention_heads
        __UpperCAmelCase				:      Dict	 =    hidden_dropout
        __UpperCAmelCase				:      Optional[Any]	 =    attention_dropout
        __UpperCAmelCase				:      Any	 =    activation_dropout
        __UpperCAmelCase				:      int	 =    feat_proj_dropout
        __UpperCAmelCase				:      Tuple	 =    final_dropout
        __UpperCAmelCase				:      Union[str, Any]	 =    layerdrop
        __UpperCAmelCase				:      int	 =    layer_norm_eps
        __UpperCAmelCase				:      Optional[int]	 =    initializer_range
        __UpperCAmelCase				:      Tuple	 =    num_ctc_classes
        __UpperCAmelCase				:      Any	 =    vocab_size
        __UpperCAmelCase				:      Tuple	 =    do_stable_layer_norm
        __UpperCAmelCase				:      List[str]	 =    use_weighted_layer_sum
        __UpperCAmelCase				:      Optional[int]	 =    classifier_proj_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)`, but is `len(config.conv_dim) ="""
              f""" {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,"""
              f""" `len(config.conv_kernel) = {len(self.conv_kernel   )}`."""   )

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

        # parameters for pretraining with codevector quantized representations
        __UpperCAmelCase				:      Optional[Any]	 =    num_codevectors_per_group
        __UpperCAmelCase				:      List[Any]	 =    num_codevector_groups
        __UpperCAmelCase				:      Any	 =    contrastive_logits_temperature
        __UpperCAmelCase				:      List[Any]	 =    feat_quantizer_dropout
        __UpperCAmelCase				:      List[str]	 =    num_negatives
        __UpperCAmelCase				:      Dict	 =    codevector_dim
        __UpperCAmelCase				:      Tuple	 =    proj_codevector_dim
        __UpperCAmelCase				:      int	 =    diversity_loss_weight

        # ctc loss
        __UpperCAmelCase				:      Tuple	 =    ctc_loss_reduction
        __UpperCAmelCase				:      Tuple	 =    ctc_zero_infinity

        # pretraining loss
        __UpperCAmelCase				:      List[Any]	 =    replace_prob




      @property
      def       UpperCAmelCase ( self   :     List[Any]   )				-> Union[str, Any]:
        return functools.reduce(operator.mul						, self.conv_stride						, 1   )










'''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()
__UpperCamelCase   :							Tuple         =   logging.get_logger(__name__)



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[Any]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     MobileNetVaConfig(layer_norm_eps=0.0_0_1     )

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

   __a					  =     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$',       SCREAMING_SNAKE_CASE__     )
   if matches:
      __a					  =     float(matches[1]     )
      __a					  =     int(matches[2]     )

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

   return config



def  __UpperCAmelCase						(					)	->      Union[str, Any]:

   """simple docstring"""
   __a					  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   __a					  =     Image.open(requests.get(SCREAMING_SNAKE_CASE__,       stream=SCREAMING_SNAKE_CASE__     ).raw     )
   return im



@torch.no_grad()
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Union[str, Any],       SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						str=False     )	->      int:

   """simple docstring"""
   __a					  =     get_mobilenet_va_config(SCREAMING_SNAKE_CASE__     )

   # Load 🤗 model
   __a					  =     MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE__     ).eval()

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

   # Check outputs on an image, prepared by MobileNetV1ImageProcessor
   __a					  =     MobileNetVaImageProcessor(
       crop_size={'width': config.image_size, 'height': config.image_size},       size={'shortest_edge': config.image_size + 32},       )
   __a					  =     image_processor(images=prepare_img(),       return_tensors='pt'     )
   __a					  =     model(**SCREAMING_SNAKE_CASE__     )
   __a					  =     outputs.logits

   assert logits.shape == (1, 1001)

   if model_name == "mobilenet_v1_1.0_224":
      __a					  =     torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]     )
   elif model_name == "mobilenet_v1_0.75_192":
      __a					  =     torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]     )
   else:
      __a					  =     None

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

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

   if push_to_hub:
      print('Pushing to the hub...'     )
      __a					  =     'google/' + model_name
      image_processor.push_to_hub(SCREAMING_SNAKE_CASE__     )
      model.push_to_hub(SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 __UpperCamelCase   :							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   :							Any         =   parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )