'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:	int    =logging.get_logger(__name__)





def       lowerCAmelCase_							(   snake_case_			:					Any   ,       snake_case_			:					int=False     )       -> Tuple:


     '''simple docstring'''




     UpperCAmelCase_			       =  []
     for i in range(config.num_hidden_layers     ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")     )
          rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")     )
          rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")     )
          rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")     )
          rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")     )
          rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")     )
          rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")     )
          rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")     )
          rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")     )
          rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")     )

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ("cls_token", "vit.embeddings.cls_token"),
             ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
             ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
             ("pos_embed", "vit.embeddings.position_embeddings"),
         ]     )

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ("norm.weight", "layernorm.weight"),
                  ("norm.bias", "layernorm.bias"),
                  ("pre_logits.fc.weight", "pooler.dense.weight"),
                  ("pre_logits.fc.bias", "pooler.dense.bias"),
              ]     )

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          UpperCAmelCase_			       =  [(pair[0], pair[1][4:]) if pair[1].startswith("vit"     ) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("norm.weight", "vit.layernorm.weight"),
                  ("norm.bias", "vit.layernorm.bias"),
                  ("head.weight", "classifier.weight"),
                  ("head.bias", "classifier.bias"),
              ]     )

     return rename_keys





def       lowerCAmelCase_							(   snake_case_			:					Optional[int]   ,       snake_case_			:					List[Any]   ,       snake_case_			:					Union[str, Any]=False     )       -> Union[str, Any]:


     '''simple docstring'''




     for i in range(config.num_hidden_layers     ):
          if base_model:
               UpperCAmelCase_			       =  ""
          else:
               UpperCAmelCase_			       =  "vit."
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          UpperCAmelCase_			       =  state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""     )
          UpperCAmelCase_			       =  state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""     )
          # next, add query, keys and values (in that order) to the state dict
          UpperCAmelCase_			       =  in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCAmelCase_			       =  in_proj_bias[: config.hidden_size]
          UpperCAmelCase_			       =  in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCAmelCase_			       =  in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          UpperCAmelCase_			       =  in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCAmelCase_			       =  in_proj_bias[-config.hidden_size :]





def       lowerCAmelCase_							(   snake_case_			:					int     )       -> Union[str, Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  ["head.weight", "head.bias"]
     for k in ignore_keys:
          state_dict.pop(snake_case_   ,       snake_case_     )





def       lowerCAmelCase_							(   snake_case_			:					Any   ,       snake_case_			:					Any   ,       snake_case_			:					Any     )       -> Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_			       =  dct.pop(snake_case_     )
     UpperCAmelCase_			       =  val





def       lowerCAmelCase_							(   )       -> Dict:


     '''simple docstring'''




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





@torch.no_grad()
def       lowerCAmelCase_							(   snake_case_			:					int   ,       snake_case_			:					Union[str, Any]     )       -> Dict:


     '''simple docstring'''




     UpperCAmelCase_			       =  ViTConfig()
     UpperCAmelCase_			       =  False
     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     if vit_name[-5:] == "in21k":
          UpperCAmelCase_			       =  True
          UpperCAmelCase_			       =  int(vit_name[-12:-10]     )
          UpperCAmelCase_			       =  int(vit_name[-9:-6]     )
     else:
          UpperCAmelCase_			       =  10_00
          UpperCAmelCase_			       =  "huggingface/label-files"
          UpperCAmelCase_			       =  "imagenet-1k-id2label.json"
          UpperCAmelCase_			       =  json.load(open(hf_hub_download(snake_case_   ,       snake_case_   ,       repo_type="dataset"     )   ,       "r"     )     )
          UpperCAmelCase_			       =  {int(snake_case_     ): v for k, v in idalabel.items()}
          UpperCAmelCase_			       =  idalabel
          UpperCAmelCase_			       =  {v: k for k, v in idalabel.items()}
          UpperCAmelCase_			       =  int(vit_name[-6:-4]     )
          UpperCAmelCase_			       =  int(vit_name[-3:]     )
     # size of the architecture
     if "deit" in vit_name:
          if vit_name[9:].startswith("tiny"     ):
               UpperCAmelCase_			       =  1_92
               UpperCAmelCase_			       =  7_68
               UpperCAmelCase_			       =  12
               UpperCAmelCase_			       =  3
          elif vit_name[9:].startswith("small"     ):
               UpperCAmelCase_			       =  3_84
               UpperCAmelCase_			       =  15_36
               UpperCAmelCase_			       =  12
               UpperCAmelCase_			       =  6
          else:
               pass
     else:
          if vit_name[4:].startswith("small"     ):
               UpperCAmelCase_			       =  7_68
               UpperCAmelCase_			       =  23_04
               UpperCAmelCase_			       =  8
               UpperCAmelCase_			       =  8
          elif vit_name[4:].startswith("base"     ):
               pass
          elif vit_name[4:].startswith("large"     ):
               UpperCAmelCase_			       =  10_24
               UpperCAmelCase_			       =  40_96
               UpperCAmelCase_			       =  24
               UpperCAmelCase_			       =  16
          elif vit_name[4:].startswith("huge"     ):
               UpperCAmelCase_			       =  12_80
               UpperCAmelCase_			       =  51_20
               UpperCAmelCase_			       =  32
               UpperCAmelCase_			       =  16

    # load original model from timm
     UpperCAmelCase_			       =  timm.create_model(snake_case_   ,       pretrained=snake_case_     )
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     UpperCAmelCase_			       =  timm_model.state_dict()
     if base_model:
          remove_classification_head_(snake_case_     )
     UpperCAmelCase_			       =  create_rename_keys(snake_case_   ,       snake_case_     )
     for src, dest in rename_keys:
          rename_key(snake_case_   ,       snake_case_   ,       snake_case_     )
     read_in_q_k_v(snake_case_   ,       snake_case_   ,       snake_case_     )

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          UpperCAmelCase_			       =  ViTModel(snake_case_     ).eval()
     else:
          UpperCAmelCase_			       =  ViTForImageClassification(snake_case_     ).eval()
     model.load_state_dict(snake_case_     )

     # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
     if "deit" in vit_name:
          UpperCAmelCase_			       =  DeiTImageProcessor(size=config.image_size     )
     else:
          UpperCAmelCase_			       =  ViTImageProcessor(size=config.image_size     )
     UpperCAmelCase_			       =  image_processor(images=prepare_img()   ,       return_tensors="pt"     )
     UpperCAmelCase_			       =  encoding["pixel_values"]
     UpperCAmelCase_			       =  model(snake_case_     )

     if base_model:
          UpperCAmelCase_			       =  timm_model.forward_features(snake_case_     )
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(snake_case_   ,       outputs.pooler_output   ,       atol=1E-3     )
     else:
          UpperCAmelCase_			       =  timm_model(snake_case_     )
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(snake_case_   ,       outputs.logits   ,       atol=1E-3     )

     Path(snake_case_     ).mkdir(exist_ok=snake_case_     )
     print(f"""Saving model {vit_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 __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:	Dict    =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--vit_name',
        default='vit_base_patch16_224',
        type=str,
        help='Name of the ViT timm model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )

    SCREAMING_SNAKE_CASE_:	List[str]    =parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








'''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


__SCREAMING_SNAKE_CASE					:				Optional[int]									=		logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE					:				Dict									=		'''cuda''' if torch.cuda.is_available() else '''cpu'''







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=1_0_0						,  UpperCamelCase_=" " ):
							A_			    =		text.split(UpperCamelCase_ )
							return [character.join(text[i : i + n] ).strip() for i in range(0						,  len(UpperCamelCase_ )						,  UpperCamelCase_ )]







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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  ):
							######################################
							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
							A_			    =		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
							A_			    =		dataset.map(UpperCamelCase_						,  batched=UpperCamelCase_						,  num_proc=processing_args.num_proc )

							# And compute the embeddings
							A_			    =		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=UpperCamelCase_ )
							A_			    =		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
							A_			    =		Features(
							    {"text": Value("string" ), "title": Value("string" ), "embeddings": Sequence(Value("float32" ) )} )  # optional, save as float32 instead of float64 to save space
							A_			    =		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
							A_			    =		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
							A_			    =		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
							A_			    =		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 					__lowerCAmelCase   :

						"""simple docstring"""






						_UpperCAmelCase		:       str   						=field(
						    default=str(Path(lowercase						).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=lowercase				,			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(lowercase						).parent / "test_run" / "dummy-kb"						)				,			metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}				,			)






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






						_UpperCAmelCase		:       Optional[int]   						=field(
						    default=lowercase				,			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 					__lowerCAmelCase   :

						"""simple docstring"""






						_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)

							__SCREAMING_SNAKE_CASE					:				List[str]									=		HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE					:				Dict									=		parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														__SCREAMING_SNAKE_CASE					:				List[str]									=		rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)





