



"""simple docstring"""
import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       SCREAMING_SNAKE_CASE__  (					__lowercase      ,			unittest.TestCase ):
   __lowerCAmelCase  :   int						=						GPTaTokenizer
   __lowerCAmelCase  :   int						=						GPTaTokenizerFast
   __lowerCAmelCase  :   Optional[Any]						=						True
   __lowerCAmelCase  :   Tuple						=						{'add_prefix_space': True}
   __lowerCAmelCase  :   List[str]						=						False

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCAmelCase	:					Any	=	[
        "l",
        "o",
        "w",
        "e",
        "r",
        "s",
        "t",
        "i",
        "d",
        "n",
        "\u0120",
        "\u0120l",
        "\u0120n",
        "\u0120lo",
        "\u0120low",
        "er",
        "\u0120lowest",
        "\u0120newer",
        "\u0120wider",
        "<unk>",
        "<|endoftext|>",
    ]
    UpperCAmelCase	:					Any	=	dict(zip(_SCREAMING_SNAKE_CASE      ,     range(len(_SCREAMING_SNAKE_CASE						)						)						)						)
    UpperCAmelCase	:					Optional[Any]	=	["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
    UpperCAmelCase	:					Any	=	{"unk_token": "<unk>"}

    UpperCAmelCase	:					Tuple	=	os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
    UpperCAmelCase	:					str	=	os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES["""merges_file"""]						)
    with open(self.vocab_file      ,     """w"""      ,     encoding="""utf-8"""						) as fp:
     fp.write(json.dumps(_SCREAMING_SNAKE_CASE						) + """\n"""						)
    with open(self.merges_file      ,     """w"""      ,     encoding="""utf-8"""						) as fp:
     fp.write("""\n""".join(_SCREAMING_SNAKE_CASE						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     **_SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    kwargs.update(self.special_tokens_map						)
    return GPTaTokenizer.from_pretrained(self.tmpdirname      ,     **_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     **_SCREAMING_SNAKE_CASE						)  ->      Any:





    '''simple docstring'''




    kwargs.update(self.special_tokens_map						)
    return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,     **_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	"lower newer"
    UpperCAmelCase	:					Optional[Any]	=	"lower newer"
    return input_text, output_text

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





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	GPTaTokenizer(self.vocab_file      ,     self.merges_file      ,     **self.special_tokens_map						)
    UpperCAmelCase	:					int	=	"lower newer"
    UpperCAmelCase	:					Tuple	=	["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
    UpperCAmelCase	:					Any	=	tokenizer.tokenize(_SCREAMING_SNAKE_CASE      ,     add_prefix_space=_SCREAMING_SNAKE_CASE						)
    self.assertListEqual(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[str]	=	tokens + [tokenizer.unk_token]
    UpperCAmelCase	:					Optional[int]	=	[14, 15, 10, 9, 3, 2, 15, 19]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						)      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    if not self.test_rust_tokenizer:
     return

    UpperCAmelCase	:					Optional[int]	=	self.get_tokenizer()
    UpperCAmelCase	:					str	=	self.get_rust_tokenizer(add_prefix_space=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Union[str, Any]	=	"lower newer"

    # Testing tokenization
    UpperCAmelCase	:					int	=	tokenizer.tokenize(_SCREAMING_SNAKE_CASE      ,     add_prefix_space=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Tuple	=	rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE						)
    self.assertListEqual(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

    # Testing conversion to ids without special tokens
    UpperCAmelCase	:					Optional[Any]	=	tokenizer.encode(_SCREAMING_SNAKE_CASE      ,     add_special_tokens=_SCREAMING_SNAKE_CASE      ,     add_prefix_space=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[Any]	=	rust_tokenizer.encode(_SCREAMING_SNAKE_CASE      ,     add_special_tokens=_SCREAMING_SNAKE_CASE						)
    self.assertListEqual(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

    # Testing conversion to ids with special tokens
    UpperCAmelCase	:					List[str]	=	self.get_rust_tokenizer(add_prefix_space=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	tokenizer.encode(_SCREAMING_SNAKE_CASE      ,     add_prefix_space=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Dict	=	rust_tokenizer.encode(_SCREAMING_SNAKE_CASE						)
    self.assertListEqual(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

    # Testing the unknown token
    UpperCAmelCase	:					List[str]	=	tokens + [rust_tokenizer.unk_token]
    UpperCAmelCase	:					List[str]	=	[14, 15, 10, 9, 3, 2, 15, 19]
    self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						)      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     *_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    pass

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=15						)  ->      str:





    '''simple docstring'''




    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
      UpperCAmelCase	:					Tuple	=	self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

      # Simple input
      UpperCAmelCase	:					Tuple	=	"This is a simple input"
      UpperCAmelCase	:					str	=	["This is a simple input 1", "This is a simple input 2"]
      UpperCAmelCase	:					Any	=	("This is a simple input", "This is a pair")
      UpperCAmelCase	:					Union[str, Any]	=	[
          ("This is a simple input 1", "This is a simple input 2"),
          ("This is a simple pair 1", "This is a simple pair 2"),
      ]

      # Simple input tests
      self.assertRaises(_SCREAMING_SNAKE_CASE      ,     tokenizer_r.encode      ,     _SCREAMING_SNAKE_CASE      ,     max_length=_SCREAMING_SNAKE_CASE      ,     padding="""max_length"""						)

      # Simple input
      self.assertRaises(_SCREAMING_SNAKE_CASE      ,     tokenizer_r.encode_plus      ,     _SCREAMING_SNAKE_CASE      ,     max_length=_SCREAMING_SNAKE_CASE      ,     padding="""max_length"""						)

      # Simple input
      self.assertRaises(
          _SCREAMING_SNAKE_CASE      ,     tokenizer_r.batch_encode_plus      ,     _SCREAMING_SNAKE_CASE      ,     max_length=_SCREAMING_SNAKE_CASE      ,     padding="""max_length"""      ,     )

      # Pair input
      self.assertRaises(_SCREAMING_SNAKE_CASE      ,     tokenizer_r.encode      ,     _SCREAMING_SNAKE_CASE      ,     max_length=_SCREAMING_SNAKE_CASE      ,     padding="""max_length"""						)

      # Pair input
      self.assertRaises(_SCREAMING_SNAKE_CASE      ,     tokenizer_r.encode_plus      ,     _SCREAMING_SNAKE_CASE      ,     max_length=_SCREAMING_SNAKE_CASE      ,     padding="""max_length"""						)

      # Pair input
      self.assertRaises(
          _SCREAMING_SNAKE_CASE      ,     tokenizer_r.batch_encode_plus      ,     _SCREAMING_SNAKE_CASE      ,     max_length=_SCREAMING_SNAKE_CASE      ,     padding="""max_length"""      ,     )

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	GPTaTokenizer.from_pretrained(self.tmpdirname      ,     pad_token="""<pad>"""						)

    # Simple input
    UpperCAmelCase	:					Dict	=	"This is a simple input"
    UpperCAmelCase	:					Optional[int]	=	["This is a simple input looooooooong", "This is a simple input"]
    UpperCAmelCase	:					Union[str, Any]	=	("This is a simple input", "This is a pair")
    UpperCAmelCase	:					int	=	[
        ("This is a simple input loooooong", "This is a simple input"),
        ("This is a simple pair loooooong", "This is a simple pair"),
    ]

    UpperCAmelCase	:					Optional[Any]	=	tokenizer.pad_token_id

    UpperCAmelCase	:					Optional[Any]	=	tokenizer(_SCREAMING_SNAKE_CASE      ,     padding="""max_length"""      ,     max_length=30      ,     return_tensors="""np"""						)
    UpperCAmelCase	:					List[str]	=	tokenizer(_SCREAMING_SNAKE_CASE      ,     padding=_SCREAMING_SNAKE_CASE      ,     truncate=_SCREAMING_SNAKE_CASE      ,     return_tensors="""np"""						)
    UpperCAmelCase	:					Dict	=	tokenizer(*_SCREAMING_SNAKE_CASE      ,     padding="""max_length"""      ,     max_length=60      ,     return_tensors="""np"""						)
    UpperCAmelCase	:					Optional[int]	=	tokenizer(_SCREAMING_SNAKE_CASE      ,     padding=_SCREAMING_SNAKE_CASE      ,     truncate=_SCREAMING_SNAKE_CASE      ,     return_tensors="""np"""						)

    # s
    # test single string max_length padding
    self.assertEqual(out_s["""input_ids"""].shape[-1]      ,     30						)
    self.assertTrue(pad_token_id in out_s["""input_ids"""]						)
    self.assertTrue(0 in out_s["""attention_mask"""]						)

    # s2
    # test automatic padding
    self.assertEqual(out_sa["""input_ids"""].shape[-1]      ,     33						)
    # long slice doesn't have padding
    self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]						)
    self.assertFalse(0 in out_sa["""attention_mask"""][0]						)
    # short slice does have padding
    self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]						)
    self.assertTrue(0 in out_sa["""attention_mask"""][1]						)

    # p
    # test single pair max_length padding
    self.assertEqual(out_p["""input_ids"""].shape[-1]      ,     60						)
    self.assertTrue(pad_token_id in out_p["""input_ids"""]						)
    self.assertTrue(0 in out_p["""attention_mask"""]						)

    # p2
    # test automatic padding pair
    self.assertEqual(out_pa["""input_ids"""].shape[-1]      ,     52						)
    # long slice pair doesn't have padding
    self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]						)
    self.assertFalse(0 in out_pa["""attention_mask"""][0]						)
    # short slice pair does have padding
    self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]						)
    self.assertTrue(0 in out_pa["""attention_mask"""][1]						)

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





    '''simple docstring'''




    UpperCAmelCase	:					int	=	"$$$"
    UpperCAmelCase	:					Any	=	GPTaTokenizer.from_pretrained(self.tmpdirname      ,     bos_token=_SCREAMING_SNAKE_CASE      ,     add_bos_token=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[Any]	=	"This is a simple input"
    UpperCAmelCase	:					Union[str, Any]	=	["This is a simple input 1", "This is a simple input 2"]

    UpperCAmelCase	:					Optional[int]	=	tokenizer.bos_token_id

    UpperCAmelCase	:					Tuple	=	tokenizer(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[str]	=	tokenizer(_SCREAMING_SNAKE_CASE						)

    self.assertEqual(out_s.input_ids[0]      ,     _SCREAMING_SNAKE_CASE						)
    self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids						)						)

    UpperCAmelCase	:					int	=	tokenizer.decode(out_s.input_ids						)
    UpperCAmelCase	:					Optional[int]	=	tokenizer.batch_decode(out_sa.input_ids						)

    self.assertEqual(decode_s.split()[0]      ,     _SCREAMING_SNAKE_CASE						)
    self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa						)						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    pass




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	[self.get_tokenizer(do_lower_case=_SCREAMING_SNAKE_CASE      ,     add_bos_token=_SCREAMING_SNAKE_CASE						)]
    for tokenizer in tokenizers:
     with self.subTest(F"{tokenizer.__class__.__name__}"						):
      UpperCAmelCase	:					Optional[Any]	=	"Encode this."
      UpperCAmelCase	:					Union[str, Any]	=	"This one too please."
      UpperCAmelCase	:					Optional[int]	=	tokenizer.encode(_SCREAMING_SNAKE_CASE      ,     add_special_tokens=_SCREAMING_SNAKE_CASE						)
      encoded_sequence += tokenizer.encode(_SCREAMING_SNAKE_CASE      ,     add_special_tokens=_SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					Any	=	tokenizer.encode_plus(
          _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     add_special_tokens=_SCREAMING_SNAKE_CASE      ,     return_special_tokens_mask=_SCREAMING_SNAKE_CASE      ,     )
      UpperCAmelCase	:					Optional[int]	=	encoded_sequence_dict["input_ids"]
      UpperCAmelCase	:					Optional[Any]	=	encoded_sequence_dict["special_tokens_mask"]
      self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,     len(_SCREAMING_SNAKE_CASE						)						)

      UpperCAmelCase	:					Optional[Any]	=	[
          (x if not special_tokens_mask[i] else None) for i, x in enumerate(_SCREAMING_SNAKE_CASE						)
      ]
      UpperCAmelCase	:					int	=	[x for x in filtered_sequence if x is not None]
      self.assertEqual(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)



@require_tokenizers
class       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,     from_slow=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Dict	=	"A photo of a cat"

    UpperCAmelCase	:					int	=	tokenizer.encode(
        _SCREAMING_SNAKE_CASE      ,     )
    self.assertEqual(_SCREAMING_SNAKE_CASE      ,     [2, 250, 1345, 9, 10, 4758]						)
    tokenizer.save_pretrained("""test_opt"""						)

    UpperCAmelCase	:					Union[str, Any]	=	AutoTokenizer.from_pretrained("""./test_opt"""						)
    UpperCAmelCase	:					List[str]	=	tokenizer.encode(
        _SCREAMING_SNAKE_CASE      ,     )
    self.assertEqual(_SCREAMING_SNAKE_CASE      ,     [2, 250, 1345, 9, 10, 4758]						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,     use_slow=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[Any]	=	"A photo of a cat"

    UpperCAmelCase	:					Optional[int]	=	tokenizer.encode(
        _SCREAMING_SNAKE_CASE      ,     )
    # Same as above
    self.assertEqual(_SCREAMING_SNAKE_CASE      ,     [2, 250, 1345, 9, 10, 4758]						)




   @unittest.skip("""This test is failing because of a bug in the fast tokenizer"""						)
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,     from_slow=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Union[str, Any]	=	"bos"
    UpperCAmelCase	:					Dict	=	tokenizer.get_vocab()["bos"]

    UpperCAmelCase	:					Dict	=	"A photo of a cat"
    UpperCAmelCase	:					Optional[int]	=	tokenizer.encode(
        _SCREAMING_SNAKE_CASE      ,     )
    # We changed the bos token
    self.assertEqual(_SCREAMING_SNAKE_CASE      ,     [31957, 250, 1345, 9, 10, 4758]						)
    tokenizer.save_pretrained("""./tok"""						)
    UpperCAmelCase	:					Optional[Any]	=	AutoTokenizer.from_pretrained("""./tok"""						)
    self.assertTrue(tokenizer.is_fast						)
    UpperCAmelCase	:					Optional[int]	=	tokenizer.encode(
        _SCREAMING_SNAKE_CASE      ,     )
    self.assertEqual(_SCREAMING_SNAKE_CASE      ,     [31957, 250, 1345, 9, 10, 4758]						)


import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase  :					Dict													=       logging.get_logger(__name__)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any]						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			Optional[int]=False)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[int]			   =					"backbone." if is_semantic else ""

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

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        (F'{prefix}cls_token', "beit.embeddings.cls_token"),
							        (F'{prefix}patch_embed.proj.weight', "beit.embeddings.patch_embeddings.projection.weight"),
							        (F'{prefix}patch_embed.proj.bias', "beit.embeddings.patch_embeddings.projection.bias"),
							        (F'{prefix}pos_embed', "beit.embeddings.position_embeddings"),
							    ])

							if has_lm_head:
														# mask token + layernorm
														rename_keys.extend(
														    [
														        ("mask_token", "beit.embeddings.mask_token"),
														        ("norm.weight", "layernorm.weight"),
														        ("norm.bias", "layernorm.bias"),
														    ])
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
														        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
														        ("head.weight", "classifier.weight"),
														        ("head.bias", "classifier.bias"),
														    ])

							return rename_keys




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			Optional[int]=False)					->    List[str]:






							'''simple docstring'''





							for i in range(config.num_hidden_layers):
														__UpperCamelCase	:   List[Any]			   =					"backbone." if is_semantic else ""
														# queries, keys and values
														__UpperCamelCase	:   Tuple			   =					state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight')
														__UpperCamelCase	:   Optional[Any]			   =					state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias')
														__UpperCamelCase	:   int			   =					state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias')

														__UpperCamelCase	:   Optional[int]			   =					in_proj_weight[
														    : config.hidden_size, :
														]
														__UpperCamelCase	:   Any			   =					q_bias
														__UpperCamelCase	:   Dict			   =					in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__UpperCamelCase	:   List[Any]			   =					in_proj_weight[
														    -config.hidden_size :, :
														]
														__UpperCamelCase	:   Tuple			   =					v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														__UpperCamelCase	:   Union[str, Any]			   =					state_dict.pop(F'{prefix}blocks.{i}.gamma_1')
														__UpperCamelCase	:   Optional[int]			   =					state_dict.pop(F'{prefix}blocks.{i}.gamma_2')

														__UpperCamelCase	:   Any			   =					gamma_a
														__UpperCamelCase	:   List[Any]			   =					gamma_a




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Optional[Any])					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					dct.pop(_lowerCamelCase)
							__UpperCamelCase	:   str			   =					val




def 	_SCREAMING_SNAKE_CASE					(		)					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
							__UpperCamelCase	:   Dict			   =					Image.open(requests.get(_lowerCamelCase						,  stream=_lowerCamelCase).raw)
							return im




@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Dict=False)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					False if "rvlcdip" in checkpoint_url else True
							__UpperCamelCase	:   int			   =					BeitConfig(use_absolute_position_embeddings=_lowerCamelCase						,  use_mask_token=_lowerCamelCase)

							# size of the architecture
							if "large" in checkpoint_url or "dit-l" in checkpoint_url:
														__UpperCamelCase	:   Optional[int]			   =					1_024
														__UpperCamelCase	:   Union[str, Any]			   =					4_096
														__UpperCamelCase	:   Tuple			   =					24
														__UpperCamelCase	:   int			   =					16

							# labels
							if "rvlcdip" in checkpoint_url:
														__UpperCamelCase	:   List[str]			   =					16
														__UpperCamelCase	:   str			   =					"huggingface/label-files"
														__UpperCamelCase	:   str			   =					"rvlcdip-id2label.json"
														__UpperCamelCase	:   Tuple			   =					json.load(open(hf_hub_download(_lowerCamelCase						,  _lowerCamelCase						,  repo_type="dataset")						,  "r"))
														__UpperCamelCase	:   Dict			   =					{int(_lowerCamelCase): v for k, v in idalabel.items()}
														__UpperCamelCase	:   Optional[Any]			   =					idalabel
														__UpperCamelCase	:   List[Any]			   =					{v: k for k, v in idalabel.items()}

							# load state_dict of original model, remove and rename some keys
							__UpperCamelCase	:   str			   =					torch.hub.load_state_dict_from_url(_lowerCamelCase						,  map_location="cpu")["model"]

							__UpperCamelCase	:   Optional[Any]			   =					create_rename_keys(_lowerCamelCase						,  has_lm_head=_lowerCamelCase)
							for src, dest in rename_keys:
														rename_key(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase)
							read_in_q_k_v(_lowerCamelCase						,  _lowerCamelCase						,  has_lm_head=_lowerCamelCase)

							# load HuggingFace model
							__UpperCamelCase	:   List[Any]			   =					BeitForMaskedImageModeling(_lowerCamelCase) if has_lm_head else BeitForImageClassification(_lowerCamelCase)
							model.eval()
							model.load_state_dict(_lowerCamelCase)

							# Check outputs on an image
							__UpperCamelCase	:   Dict			   =					BeitImageProcessor(
							    size=config.image_size						,  resample=PILImageResampling.BILINEAR						,  do_center_crop=_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					prepare_img()

							__UpperCamelCase	:   Union[str, Any]			   =					image_processor(images=_lowerCamelCase						,  return_tensors="pt")
							__UpperCamelCase	:   str			   =					encoding["pixel_values"]

							__UpperCamelCase	:   str			   =					model(_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					outputs.logits

							# verify logits
							__UpperCamelCase	:   Tuple			   =					[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
							assert logits.shape == torch.Size(_lowerCamelCase), "Shape of logits not as expected"

							Path(_lowerCamelCase).mkdir(exist_ok=_lowerCamelCase)
							print(F'Saving model 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 push_to_hub:
														if has_lm_head:
																					__UpperCamelCase	:   List[str]			   =					"dit-base" if "base" in checkpoint_url else "dit-large"
														else:
																					__UpperCamelCase	:   Optional[Any]			   =					"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
														image_processor.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase						,  _lowerCamelCase)						,  organization="nielsr"						,  commit_message="Add image processor"						,  use_temp_dir=_lowerCamelCase						,  )
														model.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase						,  _lowerCamelCase)						,  organization="nielsr"						,  commit_message="Add model"						,  use_temp_dir=_lowerCamelCase						,  )


if __name__ == "__main__":
		lowercase  :					Optional[Any]													=       argparse.ArgumentParser()

		parser.add_argument(
		    '--checkpoint_url',
		    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
		    type=str,
		    help='URL to the original PyTorch checkpoint (.pth file).',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
		)
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		)
		lowercase  :					Tuple													=       parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)