






import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase : str						   =       get_tests_dir('''fixtures/test_sentencepiece.model''')
lowerCamelCase : int						   =       get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

lowerCamelCase : List[Any]						   =       '''pt''' if is_torch_available() else '''tf'''

@require_sentencepiece
@require_tokenizers
class 		lowerCAmelCase     ( __a      ,       unittest.TestCase			):

					'''simple docstring'''

					_A						:			str									=      CamembertTokenizer
					_A						:			Optional[int]									=      CamembertTokenizerFast
					_A						:			Dict									=      True
					_A						:			int									=      True



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Any:


												"""simple docstring"""

												super().setUp()

												# We have a SentencePiece fixture for testing
												__lowercase      :							Dict				=     CamembertTokenizer(lowerCAmelCase_							)
												tokenizer.save_pretrained(self.tmpdirname							)



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							int				=     """<pad>"""
												__lowercase      :							Dict				=     1

												self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_							)		,     lowerCAmelCase_							)
												self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_							)		,     lowerCAmelCase_							)



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


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     list(self.get_tokenizer().get_vocab().keys()							)

												self.assertEqual(vocab_keys[0]		,     """<s>NOTUSED"""							)
												self.assertEqual(vocab_keys[1]		,     """<pad>"""							)
												self.assertEqual(vocab_keys[-1]		,     """<mask>"""							)
												self.assertEqual(len(lowerCAmelCase_							)		,     1004							)



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


												"""simple docstring"""

												self.assertEqual(self.get_tokenizer().vocab_size		,     1005							)



					def       lowerCAmelCase				(  self				:	str							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Any				=     CamembertTokenizer(lowerCAmelCase_							)
												tokenizer.save_pretrained(self.tmpdirname							)
												__lowercase      :							str				=     CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

												__lowercase      :							List[Any]				=     """I was born in 92000, and this is falsé."""

												__lowercase      :							List[str]				=     tokenizer.encode(lowerCAmelCase_							)
												__lowercase      :							Dict				=     rust_tokenizer.encode(lowerCAmelCase_							)
												self.assertListEqual(lowerCAmelCase_		,     lowerCAmelCase_							)

												__lowercase      :							List[str]				=     tokenizer.encode(lowerCAmelCase_		,     add_special_tokens=lowerCAmelCase_							)
												__lowercase      :							List[Any]				=     rust_tokenizer.encode(lowerCAmelCase_		,     add_special_tokens=lowerCAmelCase_							)
												self.assertListEqual(lowerCAmelCase_		,     lowerCAmelCase_							)

												# <unk> tokens are not the same for `rust` than for `slow`.
												# Because spm gives back raw token instead of `unk` in EncodeAsPieces
												# tokens = tokenizer.tokenize(sequence)
												__lowercase      :							Dict				=     tokenizer.convert_ids_to_tokens(lowerCAmelCase_							)
												__lowercase      :							Any				=     rust_tokenizer.tokenize(lowerCAmelCase_							)
												self.assertListEqual(lowerCAmelCase_		,     lowerCAmelCase_							)



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  str:


												"""simple docstring"""

												if not self.test_rust_tokenizer:
																			return

												__lowercase      :							Dict				=     self.get_tokenizer()
												__lowercase      :							Union[str, Any]				=     self.get_rust_tokenizer()

												__lowercase      :							Dict				=     """I was born in 92000, and this is falsé."""

												__lowercase      :							Any				=     tokenizer.tokenize(lowerCAmelCase_							)
												__lowercase      :							int				=     rust_tokenizer.tokenize(lowerCAmelCase_							)
												self.assertListEqual(lowerCAmelCase_		,     lowerCAmelCase_							)

												__lowercase      :							int				=     tokenizer.encode(lowerCAmelCase_		,     add_special_tokens=lowerCAmelCase_							)
												__lowercase      :							Optional[Any]				=     rust_tokenizer.encode(lowerCAmelCase_		,     add_special_tokens=lowerCAmelCase_							)
												self.assertListEqual(lowerCAmelCase_		,     lowerCAmelCase_							)

												__lowercase      :							Dict				=     self.get_rust_tokenizer()
												__lowercase      :							Union[str, Any]				=     tokenizer.encode(lowerCAmelCase_							)
												__lowercase      :							str				=     rust_tokenizer.encode(lowerCAmelCase_							)
												self.assertListEqual(lowerCAmelCase_		,     lowerCAmelCase_							)



					@slow
					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Dict				=     {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
												# fmt: on

												# camembert is a french model. So we also use french texts.
												__lowercase      :							str				=     [
												    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
												    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
												    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
												    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
												    """telles que la traduction et la synthèse de texte.""",
												]

												self.tokenizer_integration_test_util(
												    expected_encoding=lowerCAmelCase_		,     model_name="""camembert-base"""		,     revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""		,     sequences=lowerCAmelCase_		,     )







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()
lowerCamelCase : List[Any]						   =       logging.get_logger(__name__)
def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						str=False       ,       lowerCAmelCase_       :						Any=False      ):
							__lowercase      :							Any				=     """backbone.""" if is_semantic else """"""

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

														__lowercase      :							List[str]				=     in_proj_weight[
														    : config.hidden_size, :
														]
														__lowercase      :							Union[str, Any]				=     q_bias
														__lowercase      :							Any				=     in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__lowercase      :							Union[str, Any]				=     in_proj_weight[
														    -config.hidden_size :, :
														]
														__lowercase      :							str				=     v_bias

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

														__lowercase      :							List[str]				=     gamma_a
														__lowercase      :							Optional[int]				=     gamma_a
def       snake_case_		(     lowerCAmelCase_       :						List[str]       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						int      ):
							__lowercase      :							Tuple				=     dct.pop(lowerCAmelCase_      )
							__lowercase      :							Tuple				=     val
def       snake_case_		(     ):
							__lowercase      :							Optional[int]				=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
							__lowercase      :							Any				=     Image.open(requests.get(lowerCAmelCase_       ,       stream=lowerCAmelCase_      ).raw      )
							return im
@torch.no_grad()
def       snake_case_		(     lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Optional[int]=False      ):
							__lowercase      :							Dict				=     False if """rvlcdip""" in checkpoint_url else True
							__lowercase      :							Tuple				=     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:
														__lowercase      :							Union[str, Any]				=     1024
														__lowercase      :							Optional[int]				=     4096
														__lowercase      :							List[Any]				=     24
														__lowercase      :							Dict				=     16

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

							# load state_dict of original model, remove and rename some keys
							__lowercase      :							Optional[int]				=     torch.hub.load_state_dict_from_url(lowerCAmelCase_       ,       map_location="""cpu"""      )["""model"""]

							__lowercase      :							Union[str, 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
							__lowercase      :							Dict				=     BeitForMaskedImageModeling(lowerCAmelCase_      ) if has_lm_head else BeitForImageClassification(lowerCAmelCase_      )
							model.eval()
							model.load_state_dict(lowerCAmelCase_      )

							# Check outputs on an image
							__lowercase      :							List[str]				=     BeitImageProcessor(
							    size=config.image_size       ,       resample=PILImageResampling.BILINEAR       ,       do_center_crop=lowerCAmelCase_      )
							__lowercase      :							List[str]				=     prepare_img()

							__lowercase      :							Optional[Any]				=     image_processor(images=lowerCAmelCase_       ,       return_tensors="""pt"""      )
							__lowercase      :							Optional[int]				=     encoding["""pixel_values"""]

							__lowercase      :							str				=     model(lowerCAmelCase_      )
							__lowercase      :							Tuple				=     outputs.logits

							# verify logits
							__lowercase      :							str				=     [1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
							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:
																					__lowercase      :							Optional[Any]				=     """dit-base""" if """base""" in checkpoint_url else """dit-large"""
														else:
																					__lowercase      :							Tuple				=     """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__":
	lowerCamelCase : List[str]						   =       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''',
	)
	lowerCamelCase : List[str]						   =       parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)