





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()
SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)



def        __lowercase       (				_SCREAMING_SNAKE_CASE     ):


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						MobileNetVaConfig(layer_norm_eps=0.001     )

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

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

      # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
      # the usual 1000. The first class (index 0) is "background".
      SCREAMING_SNAKE_CASE		=						10_01
      SCREAMING_SNAKE_CASE		=						"""imagenet-1k-id2label.json"""
      SCREAMING_SNAKE_CASE		=						"""huggingface/label-files"""
      SCREAMING_SNAKE_CASE		=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						repo_type="""dataset"""     )			,						"""r"""     )     )
      SCREAMING_SNAKE_CASE		=						{int(_SCREAMING_SNAKE_CASE     ) + 1: v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE		=						"""background"""
      SCREAMING_SNAKE_CASE		=						idalabel
      SCREAMING_SNAKE_CASE		=						{v: k for k, v in idalabel.items()}

      return config



def        __lowercase       (				):


      '''simple docstring'''


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



@torch.no_grad()
def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=False     ):


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						get_mobilenet_va_config(_SCREAMING_SNAKE_CASE     )

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

      assert logits.shape == (1, 10_01)

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						       =	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."""
      )

      SCREAMING_SNAKE_CASE_						       =	parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )









import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


SCREAMING_SNAKE_CASE_						       =	logging.getLogger(__name__)

class 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   str	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Tuple	)					->		Optional[Any]:




           '''simple docstring'''


           if not self.initialized:
                 SCREAMING_SNAKE_CASE		=						RagRetriever(
                     lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,index=lowerCamelCase__						,init_retrieval=lowerCamelCase__						,)
                 SCREAMING_SNAKE_CASE		=						True






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Any:




           '''simple docstring'''


           self.retriever.index.init_index()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[str]	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.retriever._main_retrieve(lowerCamelCase__						,lowerCamelCase__	)
           return doc_ids, retrieved_doc_embeds

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

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




           '''simple docstring'''


           if index is not None and index.is_initialized() and len(lowerCamelCase__	) > 0:
                 raise ValueError(
                     """When using Ray for distributed fine-tuning, """
                     """you'll need to provide the paths instead, """
                     """as the dataset and the index are loaded """
                     """separately. More info in examples/rag/use_own_knowledge_dataset.py """	)
           super().__init__(
               lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,index=lowerCamelCase__						,init_retrieval=lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						retrieval_workers
           if len(self.retrieval_workers	) > 0:
                 ray.get(
                     [
                         worker.create_rag_retriever.remote(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)
                         for worker in self.retrieval_workers
                     ]	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Union[str, Any]:




           '''simple docstring'''


           logger.info("""initializing retrieval"""	)

           if len(self.retrieval_workers	) > 0:
                 ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]	)
           else:
                 # Non-distributed training. Load index into this same process.
                 self.index.init_index()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   int	)					->		Dict:




           '''simple docstring'''


           if len(self.retrieval_workers	) > 0:
                 # Select a random retrieval actor.
                 SCREAMING_SNAKE_CASE		=						self.retrieval_workers[random.randint(0						,len(self.retrieval_workers	) - 1	)]
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						ray.get(random_worker.retrieve.remote(lowerCamelCase__						,lowerCamelCase__	)	)
           else:
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self._main_retrieve(lowerCamelCase__						,lowerCamelCase__	)
           return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCamelCase__	)






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Optional[Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Union[str, Any]=None						,**lowerCamelCase__						:   Optional[Any]	)					->		Any:




           '''simple docstring'''


           return super(lowerCamelCase__						,cls	).get_tokenizers(lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__	)






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Any						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[Any]=None						,**lowerCamelCase__						:   Any	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						kwargs.pop("""config"""						,lowerCamelCase__	) or RagConfig.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						RagTokenizer.from_pretrained(lowerCamelCase__						,config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rag_tokenizer.question_encoder
           SCREAMING_SNAKE_CASE		=						rag_tokenizer.generator
           if indexed_dataset is not None:
                 SCREAMING_SNAKE_CASE		=						"""custom"""
                 SCREAMING_SNAKE_CASE		=						CustomHFIndex(config.retrieval_vector_size						,lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE		=						cls._build_index(lowerCamelCase__	)
           return cls(
               lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,retrieval_workers=lowerCamelCase__						,index=lowerCamelCase__						,)


