






import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase : List[str]						   =       get_tests_dir('''fixtures/test_sentencepiece.model''')

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

					'''simple docstring'''

					_A						:			Union[str, Any]									=      ReformerTokenizer
					_A						:			int									=      ReformerTokenizerFast
					_A						:			Any									=      True
					_A						:			Tuple									=      False
					_A						:			Union[str, Any]									=      True



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


												"""simple docstring"""

												super().setUp()

												__lowercase      :							str				=     ReformerTokenizer(__a		,     keep_accents=__a							)
												tokenizer.save_pretrained(self.tmpdirname							)



					def       lowerCAmelCase				(  self				:	int							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     """<s>"""
												__lowercase      :							Any				=     1

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



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


												"""simple docstring"""

												__lowercase      :							str				=     list(self.get_tokenizer().get_vocab().keys()							)

												self.assertEqual(vocab_keys[0]		,     """<unk>"""							)
												self.assertEqual(vocab_keys[1]		,     """<s>"""							)
												self.assertEqual(vocab_keys[-1]		,     """j"""							)
												self.assertEqual(len(__a							)		,     1000							)



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


												"""simple docstring"""

												self.assertEqual(self.get_tokenizer().vocab_size		,     1000							)



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


												"""simple docstring"""

												if not self.test_rust_tokenizer:
																			return

												__lowercase      :							Any				=     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(__a							)
												__lowercase      :							Any				=     rust_tokenizer.tokenize(__a							)
												self.assertListEqual(__a		,     __a							)

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

												__lowercase      :							str				=     self.get_rust_tokenizer()
												__lowercase      :							Optional[int]				=     tokenizer.encode(__a							)
												__lowercase      :							Optional[int]				=     rust_tokenizer.encode(__a							)
												self.assertListEqual(__a		,     __a							)



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	Union[str, Any]=15							)			->  int:


												"""simple docstring"""

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

																										# Simple input
																										__lowercase      :							Optional[Any]				=     """This is a simple input"""
																										__lowercase      :							Optional[int]				=     ["""This is a simple input 1""", """This is a simple input 2"""]
																										__lowercase      :							List[Any]				=     ("""This is a simple input""", """This is a pair""")
																										__lowercase      :							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(__a		,     tokenizer_r.encode		,     __a		,     max_length=__a		,     padding="""max_length"""							)

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

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

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

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

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



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


												"""simple docstring"""

												pass



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


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     ReformerTokenizer(__a		,     keep_accents=__a							)

												__lowercase      :							Optional[Any]				=     tokenizer.tokenize("""This is a test"""							)
												self.assertListEqual(__a		,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(__a							)		,     [285, 46, 10, 170, 382]		,     )

												__lowercase      :							Optional[Any]				=     tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
												self.assertListEqual(
												    __a		,     [
												        SPIECE_UNDERLINE + """I""",
												        SPIECE_UNDERLINE + """was""",
												        SPIECE_UNDERLINE + """b""",
												        """or""",
												        """n""",
												        SPIECE_UNDERLINE + """in""",
												        SPIECE_UNDERLINE + """""",
												        """9""",
												        """2""",
												        """0""",
												        """0""",
												        """0""",
												        """,""",
												        SPIECE_UNDERLINE + """and""",
												        SPIECE_UNDERLINE + """this""",
												        SPIECE_UNDERLINE + """is""",
												        SPIECE_UNDERLINE + """f""",
												        """al""",
												        """s""",
												        """é""",
												        """.""",
												    ]		,     )
												__lowercase      :							Any				=     tokenizer.convert_tokens_to_ids(__a							)
												self.assertListEqual(
												    __a		,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]		,     )

												__lowercase      :							Optional[Any]				=     tokenizer.convert_ids_to_tokens(__a							)
												self.assertListEqual(
												    __a		,     [
												        SPIECE_UNDERLINE + """I""",
												        SPIECE_UNDERLINE + """was""",
												        SPIECE_UNDERLINE + """b""",
												        """or""",
												        """n""",
												        SPIECE_UNDERLINE + """in""",
												        SPIECE_UNDERLINE + """""",
												        """<unk>""",
												        """2""",
												        """0""",
												        """0""",
												        """0""",
												        """,""",
												        SPIECE_UNDERLINE + """and""",
												        SPIECE_UNDERLINE + """this""",
												        SPIECE_UNDERLINE + """is""",
												        SPIECE_UNDERLINE + """f""",
												        """al""",
												        """s""",
												        """<unk>""",
												        """.""",
												    ]		,     )



					@cached_property
					def       lowerCAmelCase				(  self				:	List[Any]							)			->  Tuple:


												"""simple docstring"""

												return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""							)



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


												"""simple docstring"""

												__lowercase      :							str				=     """Hello World!"""
												__lowercase      :							Optional[int]				=     [126, 32, 262, 152, 38, 72, 287]

												self.assertListEqual(__a		,     self.big_tokenizer.encode(__a							)							)



					@slow
					def       lowerCAmelCase				(  self				:	Tuple							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     (
												    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
												    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
												)
												__lowercase      :							int				=     [
												    108,
												    265,
												    24,
												    111,
												    4,
												    258,
												    156,
												    35,
												    28,
												    275,
												    3,
												    259,
												    297,
												    260,
												    84,
												    4,
												    35,
												    110,
												    44,
												    8,
												    259,
												    91,
												    268,
												    21,
												    11,
												    209,
												    274,
												    109,
												    266,
												    277,
												    117,
												    86,
												    93,
												    315,
												    258,
												    278,
												    258,
												    277,
												    258,
												    0,
												    258,
												    288,
												    258,
												    319,
												    258,
												    0,
												    258,
												    0,
												    258,
												    0,
												    258,
												    0,
												    258,
												    287,
												    258,
												    315,
												    258,
												    289,
												    258,
												    278,
												    99,
												    269,
												    266,
												    262,
												    8,
												    259,
												    241,
												    4,
												    217,
												    230,
												    268,
												    266,
												    55,
												    168,
												    106,
												    75,
												    193,
												    266,
												    223,
												    27,
												    49,
												    26,
												    282,
												    25,
												    264,
												    299,
												    19,
												    26,
												    0,
												    258,
												    277,
												    117,
												    86,
												    93,
												    176,
												    183,
												    270,
												    11,
												    262,
												    42,
												    61,
												    265,
												]

												self.assertListEqual(__a		,     self.big_tokenizer.encode(__a							)							)



					@require_torch
					@slow
					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Dict:


												"""simple docstring"""

												import torch

												from transformers import ReformerConfig, ReformerModel

												# Build sequence
												__lowercase      :							Optional[Any]				=     list(self.big_tokenizer.get_vocab().keys()							)[:10]
												__lowercase      :							int				=     """ """.join(__a							)
												__lowercase      :							int				=     self.big_tokenizer.encode_plus(__a		,     return_tensors="""pt"""							)
												__lowercase      :							List[Any]				=     self.big_tokenizer.batch_encode_plus([sequence, sequence]		,     return_tensors="""pt"""							)

												__lowercase      :							List[str]				=     ReformerConfig()
												# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
												__lowercase      :							str				=     encoded_sequence["""input_ids"""].shape
												__lowercase      :							Optional[Any]				=     ReformerModel(__a							)

												# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
												assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

												with torch.no_grad():
																			model(**__a							)
																			model(**__a							)



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


												"""simple docstring"""

												__lowercase      :							int				=     {"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
												# fmt: on

												# This tokenizer does not know some characters like ")".
												# That is the reason why we use very simple texts here.
												# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
												__lowercase      :							List[str]				=     [
												    """This is a very simple sentence.""",
												    """The quick brown fox jumps over the lazy dog.""",
												]

												self.tokenizer_integration_test_util(
												    expected_encoding=__a		,     model_name="""google/reformer-crime-and-punishment"""		,     revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""		,     padding=__a		,     sequences=__a		,     )







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)