"""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__    							= logging.get_logger(__name__)




def        lowerCAmelCase_			(					__A							)					->    Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=  MobileNetVaConfig(layer_norm_eps=0.001							)

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

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

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

      return config




def        lowerCAmelCase_			(					)					->    int:







      '''simple docstring'''


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




@torch.no_grad()
def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=False							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  get_mobilenet_va_config(__a							)

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

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

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

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

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

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

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

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


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




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			List[Any]              =      'facebook/bart-large-mnli'
   __UpperCAmelCase			:			Optional[Any]              =      (
       'This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '
       'should be the text to classify, and `labels`, which should be the list of labels to use for classification. '
       'It returns the most likely label in the list of provided `labels` for the input text.'
   )
   __UpperCAmelCase			:			Optional[int]              =      'text_classifier'
   __UpperCAmelCase			:			int              =      AutoTokenizer
   __UpperCAmelCase			:			Dict              =      AutoModelForSequenceClassification

   __UpperCAmelCase			:			int              =      ['text', ['text']]
   __UpperCAmelCase			:			Optional[int]              =      ['text']


   def       lowercase_				(self					:						List[Any]		)						->    List[str]:
         """simple docstring"""



         super().setup()
         UpperCAmelCase__								=  self.model.config
         UpperCAmelCase__								=  -1
         for idx, label in config.idalabel.items():
               if label.lower().startswith("entail"		):
                     UpperCAmelCase__								=  int(__UpperCAmelCase		)
         if self.entailment_id == -1:
               raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."		)


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						int		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  labels
         return self.pre_processor(
             [text] * len(__UpperCAmelCase		)	,  [f"""This example is {label}""" for label in labels]	,  return_tensors="pt"	,  padding="max_length"	,  )





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Tuple		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  outputs.logits
         UpperCAmelCase__								=  torch.argmax(logits[:, 2]		).item()
         return self._labels[label_id]



