





'''simple docstring'''

import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a   	=    logging.get_logger(__name__)

a   	=    {
    "b0": efficientnet.EfficientNetBa,
    "b1": efficientnet.EfficientNetBa,
    "b2": efficientnet.EfficientNetBa,
    "b3": efficientnet.EfficientNetBa,
    "b4": efficientnet.EfficientNetBa,
    "b5": efficientnet.EfficientNetBa,
    "b6": efficientnet.EfficientNetBa,
    "b7": efficientnet.EfficientNetBa,
}

a   	=    {
    "b0": {
        "hidden_dim": 1280,
        "width_coef": 1.0,
        "depth_coef": 1.0,
        "image_size": 224,
        "dropout_rate": 0.2,
        "dw_padding": [],
    },
    "b1": {
        "hidden_dim": 1280,
        "width_coef": 1.0,
        "depth_coef": 1.1,
        "image_size": 240,
        "dropout_rate": 0.2,
        "dw_padding": [16],
    },
    "b2": {
        "hidden_dim": 1408,
        "width_coef": 1.1,
        "depth_coef": 1.2,
        "image_size": 260,
        "dropout_rate": 0.3,
        "dw_padding": [5, 8, 16],
    },
    "b3": {
        "hidden_dim": 1536,
        "width_coef": 1.2,
        "depth_coef": 1.4,
        "image_size": 300,
        "dropout_rate": 0.3,
        "dw_padding": [5, 18],
    },
    "b4": {
        "hidden_dim": 1792,
        "width_coef": 1.4,
        "depth_coef": 1.8,
        "image_size": 380,
        "dropout_rate": 0.4,
        "dw_padding": [6],
    },
    "b5": {
        "hidden_dim": 2048,
        "width_coef": 1.6,
        "depth_coef": 2.2,
        "image_size": 456,
        "dropout_rate": 0.4,
        "dw_padding": [13, 27],
    },
    "b6": {
        "hidden_dim": 2304,
        "width_coef": 1.8,
        "depth_coef": 2.6,
        "image_size": 528,
        "dropout_rate": 0.5,
        "dw_padding": [31],
    },
    "b7": {
        "hidden_dim": 2560,
        "width_coef": 2.0,
        "depth_coef": 3.1,
        "image_size": 600,
        "dropout_rate": 0.5,
        "dw_padding": [18],
    },
}

def     __magic_name__							(				__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							EfficientNetConfig()
   __SCREAMING_SNAKE_CASE   				=							CONFIG_MAP[model_name]["""hidden_dim"""]
   __SCREAMING_SNAKE_CASE   				=							CONFIG_MAP[model_name]["""width_coef"""]
   __SCREAMING_SNAKE_CASE   				=							CONFIG_MAP[model_name]["""depth_coef"""]
   __SCREAMING_SNAKE_CASE   				=							CONFIG_MAP[model_name]["""image_size"""]
   __SCREAMING_SNAKE_CASE   				=							CONFIG_MAP[model_name]["""dropout_rate"""]
   __SCREAMING_SNAKE_CASE   				=							CONFIG_MAP[model_name]["""dw_padding"""]

   __SCREAMING_SNAKE_CASE   				=							"""huggingface/label-files"""
   __SCREAMING_SNAKE_CASE   				=							"""imagenet-1k-id2label.json"""
   __SCREAMING_SNAKE_CASE   				=							1000
   __SCREAMING_SNAKE_CASE   				=							json.load(open(hf_hub_download(__UpperCAmelCase				,						__UpperCAmelCase				,						repo_type="""dataset"""   )				,						"""r"""   )   )
   __SCREAMING_SNAKE_CASE   				=							{int(__UpperCAmelCase   ): v for k, v in idalabel.items()}

   __SCREAMING_SNAKE_CASE   				=							idalabel
   __SCREAMING_SNAKE_CASE   				=							{v: k for k, v in idalabel.items()}
   return config

def     __magic_name__							(				)    ->       List[str]:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							CONFIG_MAP[model_name]["""image_size"""]
   __SCREAMING_SNAKE_CASE   				=							EfficientNetImageProcessor(
       size={"""height""": size, """width""": size}				,						image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]				,						image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]				,						do_center_crop=__UpperCAmelCase				,						)
   return preprocessor

def     __magic_name__							(				__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[v.split("""_"""   )[0].split("""block"""   )[1] for v in original_param_names if v.startswith("""block"""   )]
   __SCREAMING_SNAKE_CASE   				=							sorted(set(__UpperCAmelCase   )   )
   __SCREAMING_SNAKE_CASE   				=							len(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							{b: str(__UpperCAmelCase   ) for b, i in zip(__UpperCAmelCase				,						range(__UpperCAmelCase   )   )}

   __SCREAMING_SNAKE_CASE   				=							[]
   rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""")   )
   rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""")   )
   rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""")   )
   rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""")   )
   rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""")   )

   for b in block_names:
      __SCREAMING_SNAKE_CASE   				=							block_name_mapping[b]
      rename_keys.append((f"""block{b}_expand_conv/kernel:0""", f"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")   )
      rename_keys.append((f"""block{b}_expand_bn/gamma:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")   )
      rename_keys.append((f"""block{b}_expand_bn/beta:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")   )
      rename_keys.append(
          (f"""block{b}_expand_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")   )
      rename_keys.append(
          (f"""block{b}_expand_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")   )
      rename_keys.append(
          (f"""block{b}_dwconv/depthwise_kernel:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")   )
      rename_keys.append((f"""block{b}_bn/gamma:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")   )
      rename_keys.append((f"""block{b}_bn/beta:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")   )
      rename_keys.append(
          (f"""block{b}_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")   )
      rename_keys.append(
          (f"""block{b}_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")   )

      rename_keys.append((f"""block{b}_se_reduce/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")   )
      rename_keys.append((f"""block{b}_se_reduce/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")   )
      rename_keys.append((f"""block{b}_se_expand/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")   )
      rename_keys.append((f"""block{b}_se_expand/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")   )
      rename_keys.append(
          (f"""block{b}_project_conv/kernel:0""", f"""encoder.blocks.{hf_b}.projection.project_conv.weight""")   )
      rename_keys.append((f"""block{b}_project_bn/gamma:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.weight""")   )
      rename_keys.append((f"""block{b}_project_bn/beta:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.bias""")   )
      rename_keys.append(
          (f"""block{b}_project_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")   )
      rename_keys.append(
          (f"""block{b}_project_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")   )

   rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""")   )
   rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""")   )
   rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""")   )
   rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""")   )
   rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""")   )

   __SCREAMING_SNAKE_CASE   				=							{}
   for item in rename_keys:
      if item[0] in original_param_names:
         __SCREAMING_SNAKE_CASE   				=							"""efficientnet.""" + item[1]

   __SCREAMING_SNAKE_CASE   				=							"""classifier.weight"""
   __SCREAMING_SNAKE_CASE   				=							"""classifier.bias"""
   return key_mapping

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   for key, value in tf_params.items():
      if "normalization" in key:
         continue

      __SCREAMING_SNAKE_CASE   				=							key_mapping[key]
      if "_conv" in key and "kernel" in key:
         __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(__UpperCAmelCase   ).permute(3				,						2				,						0				,						1   )
      elif "depthwise_kernel" in key:
         __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(__UpperCAmelCase   ).permute(2				,						3				,						0				,						1   )
      elif "kernel" in key:
         __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(np.transpose(__UpperCAmelCase   )   )
      else:
         __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(__UpperCAmelCase   )

      # Replace HF parameters with original TF model parameters
      assert hf_params[hf_key].shape == new_hf_value.shape
      hf_params[hf_key].copy_(__UpperCAmelCase   )

@torch.no_grad()
def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							model_classes[model_name](
       include_top=__UpperCAmelCase				,						weights="""imagenet"""				,						input_tensor=__UpperCAmelCase				,						input_shape=__UpperCAmelCase				,						pooling=__UpperCAmelCase				,						classes=1000				,						classifier_activation="""softmax"""				,						)

   __SCREAMING_SNAKE_CASE   				=							original_model.trainable_variables
   __SCREAMING_SNAKE_CASE   				=							original_model.non_trainable_variables
   __SCREAMING_SNAKE_CASE   				=							{param.name: param.numpy() for param in tf_params}
   for param in tf_non_train_params:
      __SCREAMING_SNAKE_CASE   				=							param.numpy()
   __SCREAMING_SNAKE_CASE   				=							list(tf_params.keys()   )

   # Load HuggingFace model
   __SCREAMING_SNAKE_CASE   				=							get_efficientnet_config(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							EfficientNetForImageClassification(__UpperCAmelCase   ).eval()
   __SCREAMING_SNAKE_CASE   				=							hf_model.state_dict()

   # Create src-to-dst parameter name mapping dictionary
   print("""Converting parameters..."""   )
   __SCREAMING_SNAKE_CASE   				=							rename_keys(__UpperCAmelCase   )
   replace_params(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )

   # Initialize preprocessor and preprocess input image
   __SCREAMING_SNAKE_CASE   				=							convert_image_processor(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							preprocessor(images=prepare_img()				,						return_tensors="""pt"""   )

   # HF model inference
   hf_model.eval()
   with torch.no_grad():
      __SCREAMING_SNAKE_CASE   				=							hf_model(**__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							outputs.logits.detach().numpy()

   # Original model inference
   __SCREAMING_SNAKE_CASE   				=							False
   __SCREAMING_SNAKE_CASE   				=							CONFIG_MAP[model_name]["""image_size"""]
   __SCREAMING_SNAKE_CASE   				=							prepare_img().resize((image_size, image_size)				,						resample=PIL.Image.NEAREST   )
   __SCREAMING_SNAKE_CASE   				=							image.img_to_array(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							np.expand_dims(__UpperCAmelCase				,						axis=0   )
   __SCREAMING_SNAKE_CASE   				=							original_model.predict(__UpperCAmelCase   )

   # Check whether original and HF model outputs match  -> np.allclose
   assert np.allclose(__UpperCAmelCase				,						__UpperCAmelCase				,						atol=1e-3   ), "The predicted logits are not the same."
   print("""Model outputs match!"""   )

   if save_model:
      # Create folder to save model
      if not os.path.isdir(__UpperCAmelCase   ):
         os.mkdir(__UpperCAmelCase   )
      # Save converted model and image processor
      hf_model.save_pretrained(__UpperCAmelCase   )
      preprocessor.save_pretrained(__UpperCAmelCase   )

   if push_to_hub:
      # Push model and image processor to hub
      print(f"""Pushing converted {model_name} to the hub..."""   )
      __SCREAMING_SNAKE_CASE   				=							f"""efficientnet-{model_name}"""
      preprocessor.push_to_hub(__UpperCAmelCase   )
      hf_model.push_to_hub(__UpperCAmelCase   )


if __name__ == "__main__":
    a   	=    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="b0",
        type=str,
        help="Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default="hf_model",
        type=str,
        help="Path to the output PyTorch model directory.",
    )
    parser.add_argument("--save_model", action="store_true", help="Save model to local")
    parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

    a   	=    parser.parse_args()
    convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)












'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


a   	=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
a   	=    "cuda" if torch.cuda.is_available() else "cpu"

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=100				,						__UpperCAmelCase=" "   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							text.split(__UpperCAmelCase   )
   return [character.join(text[i : i + n]   ).strip() for i in range(0				,						len(__UpperCAmelCase   )				,						__UpperCAmelCase   )]

def     __magic_name__							(				__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							[], []
   for title, text in zip(documents["""title"""]				,						documents["""text"""]   ):
      if text is not None:
         for passage in split_text(__UpperCAmelCase   ):
            titles.append(title if title is not None else """"""   )
            texts.append(__UpperCAmelCase   )
   return {"title": titles, "text": texts}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							ctx_tokenizer(
       documents["""title"""]				,						documents["""text"""]				,						truncation=__UpperCAmelCase				,						padding="""longest"""				,						return_tensors="""pt"""   )["""input_ids"""]
   __SCREAMING_SNAKE_CASE   				=							ctx_encoder(input_ids.to(device=__UpperCAmelCase   )				,						return_dict=__UpperCAmelCase   ).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						)    ->       Any:

   '''simple docstring'''







   logger.info("""Step 1 - Create the dataset"""   )
   ######################################

   # The dataset needed for RAG must have three columns:
   # - title (string): title of the document
   # - text (string): text of a passage of the document
   # - embeddings (array of dimension d): DPR representation of the passage
   # Let's say you have documents in tab-separated csv files with columns "title" and "text"
   assert os.path.isfile(rag_example_args.csv_path   ), "Please provide a valid path to a csv file"

   # You can load a Dataset object this way
   __SCREAMING_SNAKE_CASE   				=							load_dataset(
       """csv"""				,						data_files=[rag_example_args.csv_path]				,						split="""train"""				,						delimiter="""\t"""				,						column_names=["""title""", """text"""]   )

   # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

   # Then split the documents into passages of 100 words
   __SCREAMING_SNAKE_CASE   				=							dataset.map(__UpperCAmelCase				,						batched=__UpperCAmelCase				,						num_proc=processing_args.num_proc   )

   # And compute the embeddings
   __SCREAMING_SNAKE_CASE   				=							DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
   __SCREAMING_SNAKE_CASE   				=							Features(
       {"""text""": Value("""string"""   ), """title""": Value("""string"""   ), """embeddings""": Sequence(Value("""float32"""   )   )}   )  # optional, save as float32 instead of float64 to save space
   __SCREAMING_SNAKE_CASE   				=							dataset.map(
       partial(__UpperCAmelCase				,						ctx_encoder=__UpperCAmelCase				,						ctx_tokenizer=__UpperCAmelCase   )				,						batched=__UpperCAmelCase				,						batch_size=processing_args.batch_size				,						features=__UpperCAmelCase				,						)

   # And finally save your dataset
   __SCREAMING_SNAKE_CASE   				=							os.path.join(rag_example_args.output_dir				,						"""my_knowledge_dataset"""   )
   dataset.save_to_disk(__UpperCAmelCase   )
   # from datasets import load_from_disk
   # dataset = load_from_disk(passages_path)  # to reload the dataset

   ######################################
   logger.info("""Step 2 - Index the dataset"""   )
   ######################################

   # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
   __SCREAMING_SNAKE_CASE   				=							faiss.IndexHNSWFlat(index_hnsw_args.d				,						index_hnsw_args.m				,						faiss.METRIC_INNER_PRODUCT   )
   dataset.add_faiss_index("""embeddings"""				,						custom_index=__UpperCAmelCase   )

   # And save the index
   __SCREAMING_SNAKE_CASE   				=							os.path.join(rag_example_args.output_dir				,						"""my_knowledge_dataset_hnsw_index.faiss"""   )
   dataset.get_index("""embeddings"""   ).save(__UpperCAmelCase   )
   # dataset.load_faiss_index("embeddings", index_path)  # to reload the index







@dataclass
class __a			:
   __UpperCamelCase :			str						   =						field(
       default=str(Path(_snake_case      ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'      ), metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=_snake_case, metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}, )
   __UpperCamelCase :			str						   =						field(
       default='facebook/rag-sequence-nq', metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}, )
   __UpperCamelCase :			str						   =						field(
       default='facebook/dpr-ctx_encoder-multiset-base', metadata={
           'help': (
               'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
               ' \'facebook/dpr-ctx_encoder-multiset-base\''
           )
       }, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=str(Path(_snake_case      ).parent / 'test_run' / 'dummy-kb'      ), metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}, )







@dataclass
class __a			:
   __UpperCamelCase :			Optional[int]						   =						field(
       default=_snake_case, metadata={
           'help': 'The number of processes to use to split the documents into passages. Default is single process.'
       }, )
   __UpperCamelCase :			int						   =						field(
       default=16, metadata={
           'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
       }, )







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						field(
       default=768, metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}, )
   __UpperCamelCase :			int						   =						field(
       default=128, metadata={
           'help': (
               'The number of bi-directional links created for every new element during the HNSW index construction.'
           )
       }, )


if __name__ == "__main__":
    logging.basicConfig(level=logging.WARNING)
    logger.setLevel(logging.INFO)

    a   	=    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    a	, a	, a   	=    parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        a   	=    rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)





