"""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()
__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""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_([^_]*)_([^_]*)$'				,			UpperCamelCase__ )
							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__		   =							1_001
							A__		   =							'imagenet-1k-id2label.json'
							A__		   =							'huggingface/label-files'
							A__		   =							json.load(open(hf_hub_download(UpperCamelCase__				,			UpperCamelCase__				,			repo_type='dataset' )				,			'r' ) )
							A__		   =							{int(UpperCamelCase__ ) + 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    (		):






							"""simple docstring"""





							A__		   =							'http://images.cocodataset.org/val2017/000000039769.jpg'
							A__		   =							Image.open(requests.get(UpperCamelCase__				,			stream=UpperCamelCase__ ).raw )
							return im






@torch.no_grad()
def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__=False ):






							"""simple docstring"""





							A__		   =							get_mobilenet_va_config(UpperCamelCase__ )

							# Load 🤗 model
							A__		   =							MobileNetVaForImageClassification(UpperCamelCase__ ).eval()

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

							# 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(**UpperCamelCase__ )
							A__		   =							outputs.logits

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

							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]				,			UpperCamelCase__				,			atol=1E-4 )

							Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
							print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
							model.save_pretrained(UpperCamelCase__ )
							print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
							image_processor.save_pretrained(UpperCamelCase__ )

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


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






"""simple docstring"""




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							 =     logging.get_logger(__name__)
@add_end_docstrings(__A       )
class 			UpperCamelCase__(	__A       ):





						def __init__(				self						,*__UpperCAmelCase						,**__UpperCAmelCase   )    -> Tuple:
													super().__init__(*__UpperCAmelCase						,**__UpperCAmelCase   )
													requires_backends(self						,'decord'   )
													self.check_model_type(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,__UpperCAmelCase=None   )    -> int:
													A__		   =							{}
													if frame_sampling_rate is not None:
																				A__		   =							frame_sampling_rate
													if num_frames is not None:
																				A__		   =							num_frames

													A__		   =							{}
													if top_k is not None:
																				A__		   =							top_k
													return preprocess_params, {}, postprocess_params





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





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=None						,__UpperCAmelCase=1   )    -> Union[str, Any]:
													if num_frames is None:
																				A__		   =							self.model.config.num_frames

													if video.startswith('http://'   ) or video.startswith('https://'   ):
																				A__		   =							BytesIO(requests.get(__UpperCAmelCase   ).content   )

													A__		   =							VideoReader(__UpperCAmelCase   )
													videoreader.seek(0   )

													A__		   =							0
													A__		   =							num_frames * frame_sampling_rate - 1
													A__		   =							np.linspace(__UpperCAmelCase						,__UpperCAmelCase						,num=__UpperCAmelCase						,dtype=np.intaa   )

													A__		   =							videoreader.get_batch(__UpperCAmelCase   ).asnumpy()
													A__		   =							list(__UpperCAmelCase   )

													A__		   =							self.image_processor(__UpperCAmelCase						,return_tensors=self.framework   )
													return model_inputs





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Dict:
													A__		   =							self.model(**__UpperCAmelCase   )
													return model_outputs





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=5   )    -> Union[str, Any]:
													if top_k > self.model.config.num_labels:
																				A__		   =							self.model.config.num_labels

													if self.framework == "pt":
																				A__		   =							model_outputs.logits.softmax(-1   )[0]
																				A__   ,     A__		   =							probs.topk(__UpperCAmelCase   )
													else:
																				raise ValueError(f'''Unsupported framework: {self.framework}'''   )

													A__		   =							scores.tolist()
													A__		   =							ids.tolist()
													return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__UpperCAmelCase						,__UpperCAmelCase   )]





