




"""simple docstring"""







import logging
import random

import ray

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


__A :	Any       =   logging.getLogger(__name__)




class    lowerCAmelCase__							:


				"""simple docstring"""





				def __init__(  self       :						str    ):
						__lowercase					:		int        =					False



				def    snake_case     (  self       :						Any     , lowercase__       :						Union[str, Any]     , lowercase__       :						int     , lowercase__       :						Dict     , lowercase__       :						int    ):




						if not self.initialized:
								__lowercase					:		Any        =					RagRetriever(
								    __lowerCamelCase     , question_encoder_tokenizer=__lowerCamelCase     , generator_tokenizer=__lowerCamelCase     , index=__lowerCamelCase     , init_retrieval=__lowerCamelCase     , )
								__lowercase					:		Optional[int]        =					True



				def    snake_case     (  self       :						Tuple    ):
						self.retriever.index.init_index()



				def    snake_case     (  self       :						Union[str, Any]     , lowercase__       :						Any     , lowercase__       :						Optional[Any]    ):
						__lowercase    ,__lowercase					:		str        =					self.retriever._main_retrieve(__lowerCamelCase     , __lowerCamelCase    )
						return doc_ids, retrieved_doc_embeds





class    lowerCAmelCase__							(      UpperCAmelCase_     ):


				"""simple docstring"""





				def __init__(  self       :						Any     , lowercase__       :						str     , lowercase__       :						List[Any]     , lowercase__       :						List[Any]     , lowercase__       :						Optional[Any]     , lowercase__       :						Optional[int]=None    ):




						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     , )
						__lowercase					:		Union[str, Any]        =					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    snake_case     (  self       :						str    ):
						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    snake_case     (  self       :						List[str]     , lowercase__       :						Tuple     , lowercase__       :						Any    ):




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



				@classmethod
				def    snake_case     (  cls       :						Optional[Any]     , lowercase__       :						int     , lowercase__       :						Any=None     , **lowercase__       :						str    ):
						return super(__lowerCamelCase     , cls    ).get_tokenizers(__lowerCamelCase     , __lowerCamelCase     , **__lowerCamelCase    )



				@classmethod
				def    snake_case     (  cls       :						List[str]     , lowercase__       :						str     , lowercase__       :						str     , lowercase__       :						Optional[int]=None     , **lowercase__       :						str    ):
						__lowercase					:		Tuple        =					kwargs.pop("config"     , __lowerCamelCase    ) or RagConfig.from_pretrained(__lowerCamelCase     , **__lowerCamelCase    )
						__lowercase					:		List[str]        =					RagTokenizer.from_pretrained(__lowerCamelCase     , config=__lowerCamelCase    )
						__lowercase					:		int        =					rag_tokenizer.question_encoder
						__lowercase					:		int        =					rag_tokenizer.generator
						if indexed_dataset is not None:
								__lowercase					:		Any        =					"custom"
								__lowercase					:		int        =					CustomHFIndex(config.retrieval_vector_size     , __lowerCamelCase    )
						else:
								__lowercase					:		str        =					cls._build_index(__lowerCamelCase    )
						return cls(
						    __lowerCamelCase     , question_encoder_tokenizer=__lowerCamelCase     , generator_tokenizer=__lowerCamelCase     , retrieval_workers=__lowerCamelCase     , index=__lowerCamelCase     , )







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_   :      str			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     MobileNetVaConfig(layer_norm_eps=0.001							)

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

		__lowercase											=     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,   snake_case							)
		if matches:
				__lowercase											=     float(matches[1]							)
				__lowercase											=     int(matches[2]							)

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

		return config



def  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     get_mobilenet_va_config(snake_case							)

		# Load 🤗 model
		__lowercase											=     MobileNetVaForImageClassification(snake_case							).eval()

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

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

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

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

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

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

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, 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.'''
		)

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




