import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  ['image_processor', 'tokenizer']
  lowercase_								=  'FlavaImageProcessor'
  lowercase_								=  ('BertTokenizer', 'BertTokenizerFast')




  def __init__(   self  :						int  ,			a_  :						Optional[int]=None  ,			a_  :						List[Any]=None  ,			**a_  :						str					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		None
       if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.'  ,			a_  ,			)
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		kwargs.pop('feature_extractor'					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
            raise ValueError('You need to specify an `image_processor`.'					)
       if tokenizer is None:
            raise ValueError('You need to specify a `tokenizer`.'					)

       super().__init__(a_  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					Any      =		self.image_processor




  def __call__(   self  :						List[Any]  ,			a_  :						Optional[ImageInput] = None  ,			a_  :						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None  ,			a_  :						bool = True  ,			a_  :						Union[bool, str, PaddingStrategy] = False  ,			a_  :						Union[bool, str, TruncationStrategy] = False  ,			a_  :						Optional[int] = None  ,			a_  :						int = 0  ,			a_  :						Optional[int] = None  ,			a_  :						Optional[bool] = None  ,			a_  :						Optional[bool] = None  ,			a_  :						Optional[bool] = None  ,			a_  :						Optional[bool] = None  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			a_  :						bool = True  ,			a_  :						Optional[Union[str, TensorType]] = None  ,			**a_  :						Optional[int]  ,			)->							int:




       """simple docstring"""

       if text is None and images is None:
            raise ValueError('You have to specify either text or images. Both cannot be none.'					)

       if text is not None:
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.tokenizer(
                text=a_  ,			add_special_tokens=a_  ,			padding=a_  ,			truncation=a_  ,			max_length=a_  ,			stride=a_  ,			pad_to_multiple_of=a_  ,			return_token_type_ids=a_  ,			return_attention_mask=a_  ,			return_overflowing_tokens=a_  ,			return_special_tokens_mask=a_  ,			return_offsets_mapping=a_  ,			return_length=a_  ,			verbose=a_  ,			return_tensors=a_  ,			**a_  ,			)
       if images is not None:
            SCREAMING_SNAKE_CASE__					:					str      =		self.image_processor(
                a_  ,			return_image_mask=a_  ,			return_codebook_pixels=a_  ,			return_tensors=a_  ,			**a_  ,			)

       if text is not None and images is not None:
            encoding.update(a_					)
            return encoding
       elif text is not None:
            return encoding
       else:
            return BatchEncoding(data=dict(**a_					)  ,			tensor_type=a_					)




  def 						__lowercase(   self  :						Optional[int]  ,			*a_  :						int  ,			**a_  :						int					)->							Tuple:




       """simple docstring"""
       return self.tokenizer.batch_decode(*a_  ,			**a_					)




  def 						__lowercase(   self  :						List[Any]  ,			*a_  :						List[str]  ,			**a_  :						Tuple					)->							Dict:




       """simple docstring"""
       return self.tokenizer.decode(*a_  ,			**a_					)




  @property
  def 						__lowercase(   self  :						Optional[int]					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)




  @property
  def 						__lowercase(   self  :						str					)->							Optional[Any]:




       """simple docstring"""
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,			a_  ,			)
       return self.image_processor_class




  @property
  def 						__lowercase(   self  :						Dict					)->							Union[str, Any]:




       """simple docstring"""
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,			a_  ,			)
       return self.image_processor











'''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
 )