



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__			= get_tests_dir("""fixtures/test_sentencepiece.model""")
lowerCamelCase__			= get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

lowerCamelCase__			= """pt""" if is_torch_available() else """tf"""






@require_sentencepiece
@require_tokenizers
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Optional[Any]    =CamembertTokenizer
							__lowerCamelCase	:	Tuple    =CamembertTokenizerFast
							__lowerCamelCase	:	List[str]    =True
							__lowerCamelCase	:	int    =True





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											super().setUp()

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





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		"""<pad>"""
											__a       =		1

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





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		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(__lowercase	)      ,				1004	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											self.assertEqual(self.get_tokenizer().vocab_size      ,				1005	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		CamembertTokenizer(__lowercase	)
											tokenizer.save_pretrained(self.tmpdirname	)
											__a       =		CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

											__a       =		"""I was born in 92000, and this is falsé."""

											__a       =		tokenizer.encode(__lowercase	)
											__a       =		rust_tokenizer.encode(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											__a       =		tokenizer.encode(__lowercase      ,				add_special_tokens=__lowercase	)
											__a       =		rust_tokenizer.encode(__lowercase      ,				add_special_tokens=__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											# <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)
											__a       =		tokenizer.convert_ids_to_tokens(__lowercase	)
											__a       =		rust_tokenizer.tokenize(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											if not self.test_rust_tokenizer:
															return

											__a       =		self.get_tokenizer()
											__a       =		self.get_rust_tokenizer()

											__a       =		"""I was born in 92000, and this is falsé."""

											__a       =		tokenizer.tokenize(__lowercase	)
											__a       =		rust_tokenizer.tokenize(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											__a       =		tokenizer.encode(__lowercase      ,				add_special_tokens=__lowercase	)
											__a       =		rust_tokenizer.encode(__lowercase      ,				add_special_tokens=__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											__a       =		self.get_rust_tokenizer()
											__a       =		tokenizer.encode(__lowercase	)
											__a       =		rust_tokenizer.encode(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)







							@slow
							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		{"""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.
											__a       =		[
											    """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=__lowercase      ,				model_name="""camembert-base"""      ,				revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""      ,				sequences=__lowercase      ,				)









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__			= logging.get_logger(__name__)

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			str=False				,     _SCREAMING_SNAKE_CASE       :			Tuple=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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[Any]				,     _SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]=False				,     _SCREAMING_SNAKE_CASE       :			Any=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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			Optional[int]    ):
				"""simple docstring"""







				__a       =		dct.pop(_SCREAMING_SNAKE_CASE    )
				__a       =		val

def   lowerCAmelCase__						(							):
				"""simple docstring"""







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

@torch.no_grad()
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			List[Any]				,     _SCREAMING_SNAKE_CASE       :			str=False    ):
				"""simple docstring"""







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

				# 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(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     repo_type="""dataset"""    )				,     """r"""    )    )
								__a       =		{int(_SCREAMING_SNAKE_CASE    ): 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(_SCREAMING_SNAKE_CASE				,     map_location="""cpu"""    )["""model"""]

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

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

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

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

				__a       =		model(_SCREAMING_SNAKE_CASE    )
				__a       =		outputs.logits

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

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

				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(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )				,     organization="""nielsr"""				,     commit_message="""Add image processor"""				,     use_temp_dir=_SCREAMING_SNAKE_CASE				,     )
								model.push_to_hub(
								    repo_path_or_name=Path(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )				,     organization="""nielsr"""				,     commit_message="""Add model"""				,     use_temp_dir=_SCREAMING_SNAKE_CASE				,     )


if __name__ == "__main__":
						lowerCamelCase__			= 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__			= parser.parse_args()
						convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
