




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	:	Optional[Any]				=   logging.get_logger(__name__)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
   snake_case				:    int           =	MobileNetVaConfig(layer_norm_eps=0.001					)

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

   snake_case				:    List[Any]           =	re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""						,lowercase					)
   if matches:
      snake_case				:    Union[str, Any]           =	float(matches[1]					)
      snake_case				:    Dict           =	int(matches[2]					)

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

   return config




def 						SCREAMING_SNAKE_CASE__ (   )  ->       Union[str, Any]:
   snake_case				:    int           =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case				:    Optional[Any]           =	Image.open(requests.get(lowercase						,stream=lowercase					).raw					)
   return im




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase=False					)  ->       int:
   snake_case				:    List[str]           =	get_mobilenet_va_config(lowercase					)

   # Load 🤗 model
   snake_case				:    str           =	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
   snake_case				:    Any           =	MobileNetVaImageProcessor(
       crop_size={"""width""": config.image_size, """height""": config.image_size}						,size={"""shortest_edge""": config.image_size + 32}						,)
   snake_case				:    Tuple           =	image_processor(images=prepare_img()						,return_tensors="""pt"""					)
   snake_case				:    str           =	model(**lowercase					)
   snake_case				:    str           =	outputs.logits

   assert logits.shape == (1, 1001)

   if model_name == "mobilenet_v1_1.0_224":
      snake_case				:    Any           =	torch.tensor([-4.1739, -1.1233, 3.1205]					)
   elif model_name == "mobilenet_v1_0.75_192":
      snake_case				:    Optional[int]           =	torch.tensor([-3.9440, -2.3141, -0.3333]					)
   else:
      snake_case				:    Any           =	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..."""					)
      snake_case				:    List[Any]           =	"""google/""" + model_name
      image_processor.push_to_hub(lowercase					)
      model.push_to_hub(lowercase					)


if __name__ == "__main__":
       lowerCamelCase	:	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.'
       )

       lowerCamelCase	:	Optional[Any]				=   parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )











from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
       import numpy as np
       from decord import VideoReader


if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

lowerCamelCase	:	int				=   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__							)
class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      def __init__(				self    ,				*A    ,				**A     )		->    Optional[int]:
         super().__init__(*A    ,				**A     )
         requires_backends(self    ,				"""decord"""     )
         self.check_model_type(A     )

      def      UpperCAmelCase				(				self    ,				A=None    ,				A=None    ,				A=None     )		->    int:
         snake_case				:    Any           =	{}
         if frame_sampling_rate is not None:
            snake_case				:    int           =	frame_sampling_rate
         if num_frames is not None:
            snake_case				:    Union[str, Any]           =	num_frames

         snake_case				:    str           =	{}
         if top_k is not None:
            snake_case				:    Optional[int]           =	top_k
         return preprocess_params, {}, postprocess_params

      def __call__(				self    ,				A    ,				**A     )		->    Dict:
         return super().__call__(A    ,				**A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A=None    ,				A=1     )		->    Tuple:
         if num_frames is None:
            snake_case				:    Tuple           =	self.model.config.num_frames

         if video.startswith("""http://"""     ) or video.startswith("""https://"""     ):
            snake_case				:    Optional[int]           =	BytesIO(requests.get(A     ).content     )

         snake_case				:    Optional[Any]           =	VideoReader(A     )
         videoreader.seek(0     )

         snake_case				:    Optional[Any]           =	0
         snake_case				:    Optional[Any]           =	num_frames * frame_sampling_rate - 1
         snake_case				:    Any           =	np.linspace(A    ,				A    ,				num=A    ,				dtype=np.intaa     )

         snake_case				:    int           =	videoreader.get_batch(A     ).asnumpy()
         snake_case				:    List[Any]           =	list(A     )

         snake_case				:    Optional[Any]           =	self.image_processor(A    ,				return_tensors=self.framework     )
         return model_inputs

      def      UpperCAmelCase				(				self    ,				A     )		->    List[str]:
         snake_case				:    Dict           =	self.model(**A     )
         return model_outputs

      def      UpperCAmelCase				(				self    ,				A    ,				A=5     )		->    int:
         if top_k > self.model.config.num_labels:
            snake_case				:    str           =	self.model.config.num_labels

         if self.framework == "pt":
            snake_case				:    List[Any]           =	model_outputs.logits.softmax(-1     )[0]
            snake_case     , snake_case				:    Tuple           =	probs.topk(A     )
         else:
            raise ValueError(f"""Unsupported framework: {self.framework}"""     )

         snake_case				:    List[Any]           =	scores.tolist()
         snake_case				:    str           =	ids.tolist()
         return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(A    ,				A     )]





