'''simple docstring'''





import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			CLIPTokenizer
  _SCREAMING_SNAKE_CASE    				=			CLIPTokenizerFast
  _SCREAMING_SNAKE_CASE    				=			True
  _SCREAMING_SNAKE_CASE    				=			{}
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      int       ):
   super().setUp()

   # fmt: off
   __a       =  ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
   # fmt: on
   __a       =  dict(zip(__SCREAMING_SNAKE_CASE     ,							range(len(__SCREAMING_SNAKE_CASE       )       )       )       )
   __a       =  ["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
   __a       =  {"unk_token": "<unk>"}

   __a       =  os.path.join(self.tmpdirname     ,							VOCAB_FILES_NAMES["vocab_file"]       )
   __a       =  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 		_UpperCAmelCase   (		self :      int     ,							**__SCREAMING_SNAKE_CASE :      Dict       ):
   kwargs.update(self.special_tokens_map       )
   return CLIPTokenizer.from_pretrained(self.tmpdirname     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int     ,							**__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
   kwargs.update(self.special_tokens_map       )
   return CLIPTokenizerFast.from_pretrained(self.tmpdirname     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
   __a       =  "lower newer"
   __a       =  "lower newer"
   return input_text, output_text

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  CLIPTokenizer(self.vocab_file     ,							self.merges_file     ,							**self.special_tokens_map       )
   __a       =  "lower newer"
   __a       =  ["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
   __a       =  tokenizer.tokenize(__SCREAMING_SNAKE_CASE       )
   self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  tokens + [tokenizer.unk_token]
   __a       =  [10, 2, 16, 9, 3, 2, 16, 20]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )

  @require_ftfy
  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
     __a       =  self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
     __a       =  self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

     __a       =  "A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
     __a       =  tokenizer_s.tokenize(__SCREAMING_SNAKE_CASE       )
     __a       =  tokenizer_r.tokenize(__SCREAMING_SNAKE_CASE       )

     self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

     # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
     # with Tilde) encoded in 2 different ways
     __a       =  "xa\u0303y" + " " + "x\xe3y"
     __a       =  tokenizer_s.tokenize(__SCREAMING_SNAKE_CASE       )
     __a       =  tokenizer_r.tokenize(__SCREAMING_SNAKE_CASE       )

     self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

     # Test that the tokenization is identical on unicode of space type
     __a       =  [
         "\u0009",  # (horizontal tab, '\t')
         "\u000B",  # (vertical tab)
         "\u000C",  # (form feed)
         "\u0020",  # (space, ' ')
         "\u200E",  # (left-to-right mark):w
         "\u200F",  # (right-to-left mark)
     ]
     for unicode_seq in spaces_unicodes:
      __a       =  tokenizer_s.tokenize(__SCREAMING_SNAKE_CASE       )
      __a       =  tokenizer_r.tokenize(__SCREAMING_SNAKE_CASE       )

      self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

     # Test that the tokenization is identical on unicode of line break type
     __a       =  [
         "\u000A",  # (line feed, '\n')
         "\r\n",  # (carriage return and line feed, '\r\n')
         "\u000D",  # (carriage return, '\r')
         "\r",  # (carriage return, '\r')
         "\u000D",  # (carriage return, '\r')
         "\u2028",  # (line separator)
         "\u2029",  # (paragraph separator)
         # "\u0085", # (next line)
     ]

     # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
     # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
     # space (and thus into an empty list).

     for unicode_seq in line_break_unicodes:
      __a       =  tokenizer_s.tokenize(__SCREAMING_SNAKE_CASE       )
      __a       =  tokenizer_r.tokenize(__SCREAMING_SNAKE_CASE       )

      self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
     __a       =  "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
     __a       =  f"""{text_of_1_token} {text_of_1_token}"""

     __a       =  self.rust_tokenizer_class.from_pretrained(
         __SCREAMING_SNAKE_CASE     ,							use_fast=__SCREAMING_SNAKE_CASE     ,							)
     __a       =  tokenizer_r(__SCREAMING_SNAKE_CASE     ,							return_offsets_mapping=__SCREAMING_SNAKE_CASE     ,							add_special_tokens=__SCREAMING_SNAKE_CASE       )
     self.assertEqual(encoding.offset_mapping[0]     ,							(0, len(__SCREAMING_SNAKE_CASE       ))       )
     self.assertEqual(
         encoding.offset_mapping[1]     ,							(len(__SCREAMING_SNAKE_CASE       ) + 1, len(__SCREAMING_SNAKE_CASE       ) + 1 + len(__SCREAMING_SNAKE_CASE       ))     ,							)

     __a       =  f""" {text}"""

     __a       =  self.rust_tokenizer_class.from_pretrained(
         __SCREAMING_SNAKE_CASE     ,							use_fast=__SCREAMING_SNAKE_CASE     ,							)
     __a       =  tokenizer_r(__SCREAMING_SNAKE_CASE     ,							return_offsets_mapping=__SCREAMING_SNAKE_CASE     ,							add_special_tokens=__SCREAMING_SNAKE_CASE       )
     self.assertEqual(encoding.offset_mapping[0]     ,							(1, 1 + len(__SCREAMING_SNAKE_CASE       ))       )
     self.assertEqual(
         encoding.offset_mapping[1]     ,							(1 + len(__SCREAMING_SNAKE_CASE       ) + 1, 1 + len(__SCREAMING_SNAKE_CASE       ) + 1 + len(__SCREAMING_SNAKE_CASE       ))     ,							)

  def 		_UpperCAmelCase   (		self :      int       ):
   # Test related to the breaking change introduced in transformers v4.17.0
   # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
   with self.assertRaises(__SCREAMING_SNAKE_CASE       ) as context:
    self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer"       )

   self.assertTrue(
       context.exception.args[0].startswith(
           "The `backend_tokenizer` provided does not match the expected format."       )       )

  @require_ftfy
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   super().test_tokenization_python_rust_equals()

  def 		_UpperCAmelCase   (		self :      str       ):
   # CLIP always lower cases letters
   pass



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()
SCREAMING_SNAKE_CASE :	str							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 __a       =  "backbone." if is_semantic else ""

 __a       =  []
 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   __A		(			_A							,				_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 for i in range(config.num_hidden_layers					):
  __a       =  "backbone." if is_semantic else ""
  # queries, keys and values
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""					)

  __a       =  in_proj_weight[
      : config.hidden_size, :
  ]
  __a       =  q_bias
  __a       =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __a       =  in_proj_weight[
      -config.hidden_size :, :
  ]
  __a       =  v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""					)

  __a       =  gamma_a
  __a       =  gamma_a



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  dct.pop(_A					)
 __a       =  val



def   __A		(			):



 """simple docstring"""


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



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A=False					):



 """simple docstring"""


 __a       =  False if "rvlcdip" in checkpoint_url else True
 __a       =  BeitConfig(use_absolute_position_embeddings=_A							,				use_mask_token=_A					)

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  __a       =  1024
  __a       =  4096
  __a       =  24
  __a       =  16

 # labels
 if "rvlcdip" in checkpoint_url:
  __a       =  16
  __a       =  "huggingface/label-files"
  __a       =  "rvlcdip-id2label.json"
  __a       =  json.load(open(hf_hub_download(_A							,				_A							,				repo_type="dataset"					)							,				"r"					)					)
  __a       =  {int(_A					): v for k, v in idalabel.items()}
  __a       =  idalabel
  __a       =  {v: k for k, v in idalabel.items()}

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

 __a       =  create_rename_keys(_A							,				has_lm_head=_A					)
 for src, dest in rename_keys:
  rename_key(_A							,				_A							,				_A					)
 read_in_q_k_v(_A							,				_A							,				has_lm_head=_A					)

 # load HuggingFace model
 __a       =  BeitForMaskedImageModeling(_A					) if has_lm_head else BeitForImageClassification(_A					)
 model.eval()
 model.load_state_dict(_A					)

 # Check outputs on an image
 __a       =  BeitImageProcessor(
     size=config.image_size							,				resample=PILImageResampling.BILINEAR							,				do_center_crop=_A					)
 __a       =  prepare_img()

 __a       =  image_processor(images=_A							,				return_tensors="pt"					)
 __a       =  encoding["pixel_values"]

 __a       =  model(_A					)
 __a       =  outputs.logits

 # verify logits
 __a       =  [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
 assert logits.shape == torch.Size(_A					), "Shape of logits not as expected"

 Path(_A					).mkdir(exist_ok=_A					)
 print(f"""Saving model to {pytorch_dump_folder_path}"""					)
 model.save_pretrained(_A					)
 print(f"""Saving image processor to {pytorch_dump_folder_path}"""					)
 image_processor.save_pretrained(_A					)

 if push_to_hub:
  if has_lm_head:
   __a       =  "dit-base" if "base" in checkpoint_url else "dit-large"
  else:
   __a       =  "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(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add image processor"							,				use_temp_dir=_A							,				)
  model.push_to_hub(
      repo_path_or_name=Path(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add model"							,				use_temp_dir=_A							,				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	int							     =		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""",
  )
  SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


