


import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


lowerCamelCase__							:						int							     =   datasets.utils.logging.get_logger(__name__)
@dataclass
class 		__magic_name__						(datasets.BuilderConfig				):





     '''simple docstring'''



     __lowercase				:							Optional[datasets.Features]	   =       None
     __lowercase				:							str	   =       "utf-8"
     __lowercase				:							Optional[str]	   =       None
     __lowercase				:							Optional[str]	   =       None
     __lowercase				:							bool	   =       True  # deprecated
     __lowercase				:							Optional[int]	   =       None  # deprecated
     __lowercase				:							int	   =       10 << 20  # 10MB
     __lowercase				:							Optional[bool]	   =       None




class 		__magic_name__						(datasets.ArrowBasedBuilder				):





     '''simple docstring'''



     __lowercase				:							int	   =       JsonConfig

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       if self.config.block_size is not None:
         logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''   )
         snake_case__						= self.config.block_size
       if self.config.use_threads is not True:
         logger.warning(
             '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''   )
       if self.config.newlines_in_values is not None:
         raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''   )
       return datasets.DatasetInfo(features=self.config.features   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:int   ):
       if not self.config.data_files:
         raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""   )
       snake_case__						= dl_manager.download_and_extract(self.config.data_files   )
       if isinstance(_a     ,		(str, list, tuple)   ):
         snake_case__						= data_files
         if isinstance(_a     ,		_a   ):
           snake_case__						= [files]
         snake_case__						= [dl_manager.iter_files(_a   ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,		gen_kwargs={'''files''': files}   )]
       snake_case__						= []
       for split_name, files in data_files.items():
         if isinstance(_a     ,		_a   ):
           snake_case__						= [files]
         snake_case__						= [dl_manager.iter_files(_a   ) for file in files]
         splits.append(datasets.SplitGenerator(name=_a     ,		gen_kwargs={'''files''': files}   )   )
       return splits

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:pa.Table   ):
       if self.config.features is not None:
         # adding missing columns
         for column_name in set(self.config.features   ) - set(pa_table.column_names   ):
           snake_case__						= self.config.features.arrow_schema.field(_a   ).type
           snake_case__						= pa_table.append_column(_a     ,		pa.array([None] * len(_a   )     ,		type=_a   )   )
         # more expensive cast to support nested structures with keys in a different order
         # allows str <-> int/float or str to Audio for example
         snake_case__						= table_cast(_a     ,		self.config.features.arrow_schema   )
       return pa_table







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:List[Any]   ):
       for file_idx, file in enumerate(itertools.chain.from_iterable(_a   )   ):
         # If the file is one json object and if we need to look at the list of items in one specific field
         if self.config.field is not None:
           with open(_a     ,		encoding=self.config.encoding     ,		errors=self.config.encoding_errors   ) as f:
             snake_case__						= json.load(_a   )

           # We keep only the field we are interested in
           snake_case__						= dataset[self.config.field]

           # We accept two format: a list of dicts or a dict of lists
           if isinstance(_a     ,		(list, tuple)   ):
             snake_case__						= set().union(*[row.keys() for row in dataset]   )
             snake_case__						= {col: [row.get(_a   ) for row in dataset] for col in keys}
           else:
             snake_case__						= dataset
           snake_case__						= pa.Table.from_pydict(_a   )
           yield file_idx, self._cast_table(_a   )

         # If the file has one json object per line
         else:
           with open(_a     ,		'''rb'''   ) as f:
             snake_case__						= 0
             # Use block_size equal to the chunk size divided by 32 to leverage multithreading
             # Set a default minimum value of 16kB if the chunk size is really small
             snake_case__						= max(self.config.chunksize // 32     ,		16 << 10   )
             snake_case__						= (
                 self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
             )
             while True:
               snake_case__						= f.read(self.config.chunksize   )
               if not batch:
                 break
               # Finish current line
               try:
                 batch += f.readline()
               except (AttributeError, io.UnsupportedOperation):
                 batch += readline(_a   )
               # PyArrow only accepts utf-8 encoded bytes
               if self.config.encoding != "utf-8":
                 snake_case__						= batch.decode(self.config.encoding     ,		errors=_a   ).encode('''utf-8'''   )
               try:
                 while True:
                   try:
                     snake_case__						= paj.read_json(
                         io.BytesIO(_a   )     ,		read_options=paj.ReadOptions(block_size=_a   )   )
                     break
                   except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                     if (
                         isinstance(_a     ,		pa.ArrowInvalid   )
                         and "straddling" not in str(_a   )
                         or block_size > len(_a   )
                     ):
                       raise
                     else:
                       # Increase the block size in case it was too small.
                       # The block size will be reset for the next file.
                       logger.debug(
                           F"""Batch of {len(_a   )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""   )
                       block_size *= 2
               except pa.ArrowInvalid as e:
                 try:
                   with open(
                       _a     ,		encoding=self.config.encoding     ,		errors=self.config.encoding_errors   ) as f:
                     snake_case__						= json.load(_a   )
                 except json.JSONDecodeError:
                   logger.error(F"""Failed to read file '{file}' with error {type(_a   )}: {e}"""   )
                   raise e
                 # If possible, parse the file as a list of json objects and exit the loop
                 if isinstance(_a     ,		_a   ):  # list is the only sequence type supported in JSON
                   try:
                     snake_case__						= set().union(*[row.keys() for row in dataset]   )
                     snake_case__						= {col: [row.get(_a   ) for row in dataset] for col in keys}
                     snake_case__						= pa.Table.from_pydict(_a   )
                   except (pa.ArrowInvalid, AttributeError) as e:
                     logger.error(F"""Failed to read file '{file}' with error {type(_a   )}: {e}"""   )
                     raise ValueError(F"""Not able to read records in the JSON file at {file}."""   ) from None
                   yield file_idx, self._cast_table(_a   )
                   break
                 else:
                   logger.error(F"""Failed to read file '{file}' with error {type(_a   )}: {e}"""   )
                   raise ValueError(
                       F"""Not able to read records in the JSON file at {file}. """
                       F"""You should probably indicate the field of the JSON file containing your records. """
                       F"""This JSON file contain the following fields: {str(list(dataset.keys()   )   )}. """
                       F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """   ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
               yield (file_idx, batch_idx), self._cast_table(_a   )
               batch_idx += 1





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()
lowerCamelCase__							:						int							     =   logging.get_logger(__name__)







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







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[Any]:
  snake_case__						= ['''head.weight''', '''head.bias''']
  for k in ignore_keys:
    state_dict.pop(__lowerCAmelCase							,   __lowerCAmelCase				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				Union[str, Any]:
  snake_case__						= dct.pop(__lowerCAmelCase				)
  snake_case__						= val







def SCREAMING_SNAKE_CASE	(			)    ->				str:
  snake_case__						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  snake_case__						= Image.open(requests.get(__lowerCAmelCase							,   stream=__lowerCAmelCase				).raw				)
  return im







@torch.no_grad()
def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				Dict:
  snake_case__						= ViTConfig()
  snake_case__						= False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    snake_case__						= True
    snake_case__						= int(vit_name[-12:-10]				)
    snake_case__						= int(vit_name[-9:-6]				)
  else:
    snake_case__						= 1000
    snake_case__						= '''huggingface/label-files'''
    snake_case__						= '''imagenet-1k-id2label.json'''
    snake_case__						= json.load(open(hf_hub_download(__lowerCAmelCase							,   __lowerCAmelCase							,   repo_type='''dataset'''				)							,   '''r'''				)				)
    snake_case__						= {int(__lowerCAmelCase				): v for k, v in idalabel.items()}
    snake_case__						= idalabel
    snake_case__						= {v: k for k, v in idalabel.items()}
    snake_case__						= int(vit_name[-6:-4]				)
    snake_case__						= int(vit_name[-3:]				)
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith('''tiny'''				):
      snake_case__						= 192
      snake_case__						= 768
      snake_case__						= 12
      snake_case__						= 3
    elif vit_name[9:].startswith('''small'''				):
      snake_case__						= 384
      snake_case__						= 1536
      snake_case__						= 12
      snake_case__						= 6
    else:
      pass
  else:
    if vit_name[4:].startswith('''small'''				):
      snake_case__						= 768
      snake_case__						= 2304
      snake_case__						= 8
      snake_case__						= 8
    elif vit_name[4:].startswith('''base'''				):
      pass
    elif vit_name[4:].startswith('''large'''				):
      snake_case__						= 1024
      snake_case__						= 4096
      snake_case__						= 24
      snake_case__						= 16
    elif vit_name[4:].startswith('''huge'''				):
      snake_case__						= 1280
      snake_case__						= 5120
      snake_case__						= 32
      snake_case__						= 16

    # load original model from timm
  snake_case__						= timm.create_model(__lowerCAmelCase							,   pretrained=__lowerCAmelCase				)
  timm_model.eval()

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

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

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

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

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


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[Any]							     =   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."""
   )

   lowerCamelCase__							:						str							     =   parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

