


'''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     :  Dict											=    logging.get_logger(__name__)






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[Any]       =							MobileNetVaConfig(layer_norm_eps=0.001       )

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

       _lowerCAmelCase     :				Union[str, Any]       =							re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$' ,    _A       )
       if matches:
              _lowerCAmelCase     :				int       =							float(matches[1]       )
              _lowerCAmelCase     :				int       =							int(matches[2]       )

       # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
       # the usual 1000. The first class (index 0) is "background".
       _lowerCAmelCase     :				List[str]       =							1_0_0_1
       _lowerCAmelCase     :				Union[str, Any]       =							'imagenet-1k-id2label.json'
       _lowerCAmelCase     :				Optional[int]       =							'huggingface/label-files'
       _lowerCAmelCase     :				Tuple       =							json.load(open(hf_hub_download(_A ,    _A ,    repo_type='dataset'       ) ,    'r'       )       )
       _lowerCAmelCase     :				Dict       =							{int(_A       ) + 1: v for k, v in idalabel.items()}
       _lowerCAmelCase     :				str       =							'background'
       _lowerCAmelCase     :				Optional[Any]       =							idalabel
       _lowerCAmelCase     :				Optional[Any]       =							{v: k for k, v in idalabel.items()}

       return config






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							'http://images.cocodataset.org/val2017/000000039769.jpg'
       _lowerCAmelCase     :				int       =							Image.open(requests.get(_A ,    stream=_A       ).raw       )
       return im






@torch.no_grad()
def       lowercase					(_A ,    _A ,    _A ,    _A=False       ):







       """simple docstring"""







       _lowerCAmelCase     :				str       =							get_mobilenet_va_config(_A       )

       # Load 🤗 model
       _lowerCAmelCase     :				Tuple       =							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
       _lowerCAmelCase     :				List[str]       =							MobileNetVaImageProcessor(
           crop_size={'width': config.image_size, 'height': config.image_size} ,    size={'shortest_edge': config.image_size + 3_2} ,    )
       _lowerCAmelCase     :				List[str]       =							image_processor(images=prepare_img() ,    return_tensors='pt'       )
       _lowerCAmelCase     :				Dict       =							model(**_A       )
       _lowerCAmelCase     :				Optional[int]       =							outputs.logits

       assert logits.shape == (1, 1_0_0_1)

       if model_name == "mobilenet_v1_1.0_224":
              _lowerCAmelCase     :				Optional[int]       =							torch.tensor([-4.1_739, -1.1_233, 3.1_205]       )
       elif model_name == "mobilenet_v1_0.75_192":
              _lowerCAmelCase     :				List[str]       =							torch.tensor([-3.9_440, -2.3_141, -0.3_333]       )
       else:
              _lowerCAmelCase     :				str       =							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...'       )
              _lowerCAmelCase     :				int       =							'google/' + model_name
              image_processor.push_to_hub(_A       )
              model.push_to_hub(_A       )


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





'''simple docstring'''







import argparse
from collections import defaultdict

import yaml


lowerCAmelCase     :  Dict											=    """docs/source/en/_toctree.yml"""






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[int]       =							defaultdict(_A       )
       _lowerCAmelCase     :				Tuple       =							[]
       _lowerCAmelCase     :				Dict       =							[]
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'local': doc['local'], 'title': doc['title']}       )
              else:
                     new_doc_list.append(_A       )

       _lowerCAmelCase     :				str       =							new_doc_list
       _lowerCAmelCase     :				List[Any]       =							[key for key, value in counts.items() if value > 1]

       _lowerCAmelCase     :				List[str]       =							[]
       for duplicate_key in duplicates:
              _lowerCAmelCase     :				Any       =							list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}       )
              if len(_A       ) > 1:
                     raise ValueError(
                         f'{duplicate_key} is present several times in the documentation table of content at '
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'       )
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}       )

       # Add none duplicate-keys
       new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]       )
       _lowerCAmelCase     :				Any       =							sorted(_A ,    key=lambda _A       : s["title"].lower()       )

       # "overview" gets special treatment and is always first
       if len(_A       ) > 1:
              raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'       )

       overview_doc.extend(_A       )

       # Sort
       return overview_doc






def       lowercase					(_A=False       ):







       """simple docstring"""







       with open(_A ,    encoding='utf-8'       ) as f:
              _lowerCAmelCase     :				Union[str, Any]       =							yaml.safe_load(f.read()       )

       # Get to the API doc
       _lowerCAmelCase     :				Tuple       =							0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       _lowerCAmelCase     :				str       =							content[api_idx]['sections']

       # Then to the model doc
       _lowerCAmelCase     :				Dict       =							0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       _lowerCAmelCase     :				Optional[int]       =							api_doc[scheduler_idx]['sections']
       _lowerCAmelCase     :				Any       =							clean_doc_toc(_A       )

       _lowerCAmelCase     :				int       =							False
       if new_scheduler_doc != scheduler_doc:
              _lowerCAmelCase     :				str       =							True
              if overwrite:
                     _lowerCAmelCase     :				int       =							new_scheduler_doc

       if diff:
              if overwrite:
                     _lowerCAmelCase     :				Dict       =							api_doc
                     with open(_A ,    'w' ,    encoding='utf-8'       ) as f:
                            f.write(yaml.dump(_A ,    allow_unicode=_A       )       )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'       )






def       lowercase					(_A=False       ):







       """simple docstring"""







       with open(_A ,    encoding='utf-8'       ) as f:
              _lowerCAmelCase     :				Optional[Any]       =							yaml.safe_load(f.read()       )

       # Get to the API doc
       _lowerCAmelCase     :				Optional[Any]       =							0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       _lowerCAmelCase     :				Union[str, Any]       =							content[api_idx]['sections']

       # Then to the model doc
       _lowerCAmelCase     :				Optional[int]       =							0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       _lowerCAmelCase     :				List[Any]       =							False
       _lowerCAmelCase     :				List[Any]       =							api_doc[pipeline_idx]['sections']
       _lowerCAmelCase     :				Union[str, Any]       =							[]

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     _lowerCAmelCase     :				Tuple       =							pipeline_doc['section']
                     _lowerCAmelCase     :				Optional[int]       =							clean_doc_toc(_A       )
                     if overwrite:
                            _lowerCAmelCase     :				str       =							new_sub_pipeline_doc
              new_pipeline_docs.append(_A       )

       # sort overall pipeline doc
       _lowerCAmelCase     :				str       =							clean_doc_toc(_A       )

       if new_pipeline_docs != pipeline_docs:
              _lowerCAmelCase     :				Any       =							True
              if overwrite:
                     _lowerCAmelCase     :				Optional[int]       =							new_pipeline_docs

       if diff:
              if overwrite:
                     _lowerCAmelCase     :				str       =							api_doc
                     with open(_A ,    'w' ,    encoding='utf-8'       ) as f:
                            f.write(yaml.dump(_A ,    allow_unicode=_A       )       )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'       )


if __name__ == "__main__":
       lowerCAmelCase     :  Tuple											=    argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       lowerCAmelCase     :  List[Any]											=    parser.parse_args()

       check_scheduler_doc(args.fix_and_overwrite)
       check_pipeline_doc(args.fix_and_overwrite)

