






import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

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

					'''simple docstring'''

					_A						:			str									=      LongformerTokenizer
					_A						:			int									=      True
					_A						:			Optional[int]									=      LongformerTokenizerFast
					_A						:			int									=      True



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


												"""simple docstring"""

												super().setUp()

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

												__lowercase      :							Dict				=     os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""vocab_file"""]							)
												__lowercase      :							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(__a							) + """\n"""							)
												with open(self.merges_file		,     """w"""		,     encoding="""utf-8"""							) as fp:
																			fp.write("""\n""".join(__a							)							)



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


												"""simple docstring"""

												kwargs.update(self.special_tokens_map							)
												return self.tokenizer_class.from_pretrained(self.tmpdirname		,     **__a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     **__a				:	Tuple							)			->  str:


												"""simple docstring"""

												kwargs.update(self.special_tokens_map							)
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,     **__a							)



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


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     """lower newer"""
												__lowercase      :							int				=     """lower newer"""
												return input_text, output_text



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


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.tokenizer_class(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map							)
												__lowercase      :							Dict				=     """lower newer"""
												__lowercase      :							Optional[Any]				=     ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
												__lowercase      :							str				=     tokenizer.tokenize(__a							)  # , add_prefix_space=True)
												self.assertListEqual(__a		,     __a							)

												__lowercase      :							int				=     tokens + [tokenizer.unk_token]
												__lowercase      :							str				=     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__a							)		,     __a							)



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


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     self.get_tokenizer()

												self.assertListEqual(tokenizer.encode("""Hello world!"""		,     add_special_tokens=__a							)		,     [0, 31414, 232, 328, 2]							)
												self.assertListEqual(
												    tokenizer.encode("""Hello world! cécé herlolip 418"""		,     add_special_tokens=__a							)		,     [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]		,     )



					@slow
					def       lowerCAmelCase				(  self				:	Tuple							)			->  str:


												"""simple docstring"""

												__lowercase      :							Any				=     self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""							)

												__lowercase      :							Optional[Any]				=     tokenizer.encode("""sequence builders"""		,     add_special_tokens=__a							)
												__lowercase      :							List[str]				=     tokenizer.encode("""multi-sequence build"""		,     add_special_tokens=__a							)

												__lowercase      :							Optional[Any]				=     tokenizer.encode(
												    """sequence builders"""		,     add_special_tokens=__a		,     add_prefix_space=__a							)
												__lowercase      :							Union[str, Any]				=     tokenizer.encode(
												    """sequence builders"""		,     """multi-sequence build"""		,     add_special_tokens=__a		,     add_prefix_space=__a							)

												__lowercase      :							List[Any]				=     tokenizer.build_inputs_with_special_tokens(__a							)
												__lowercase      :							Any				=     tokenizer.build_inputs_with_special_tokens(__a		,     __a							)

												assert encoded_sentence == encoded_text_from_decode
												assert encoded_pair == encoded_pair_from_decode



					def       lowerCAmelCase				(  self				:	Dict							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     self.get_tokenizer()

												__lowercase      :							Tuple				=     """Encode this sequence."""
												__lowercase      :							Optional[Any]				=     tokenizer.byte_encoder[""" """.encode("""utf-8"""							)[0]]

												# Testing encoder arguments
												__lowercase      :							Dict				=     tokenizer.encode(__a		,     add_special_tokens=__a		,     add_prefix_space=__a							)
												__lowercase      :							Tuple				=     tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
												self.assertNotEqual(__a		,     __a							)

												__lowercase      :							List[str]				=     tokenizer.encode(__a		,     add_special_tokens=__a		,     add_prefix_space=__a							)
												__lowercase      :							Any				=     tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
												self.assertEqual(__a		,     __a							)

												tokenizer.add_special_tokens({"""bos_token""": """<s>"""}							)
												__lowercase      :							str				=     tokenizer.encode(__a		,     add_special_tokens=__a							)
												__lowercase      :							Dict				=     tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
												self.assertNotEqual(__a		,     __a							)

												# Testing spaces after special tokens
												__lowercase      :							List[Any]				=     """<mask>"""
												tokenizer.add_special_tokens(
												    {"""mask_token""": AddedToken(__a		,     lstrip=__a		,     rstrip=__a							)}							)  # mask token has a left space
												__lowercase      :							Dict				=     tokenizer.convert_tokens_to_ids(__a							)

												__lowercase      :							List[str]				=     """Encode <mask> sequence"""
												__lowercase      :							List[str]				=     """Encode <mask>sequence"""

												__lowercase      :							Union[str, Any]				=     tokenizer.encode(__a							)
												__lowercase      :							Dict				=     encoded.index(__a							)
												__lowercase      :							List[Any]				=     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
												self.assertEqual(__a		,     __a							)

												__lowercase      :							int				=     tokenizer.encode(__a							)
												__lowercase      :							Union[str, Any]				=     encoded.index(__a							)
												__lowercase      :							List[Any]				=     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
												self.assertNotEqual(__a		,     __a							)



					def       lowerCAmelCase				(  self				:	int							)			->  str:


												"""simple docstring"""

												pass



					def       lowerCAmelCase				(  self				:	int							)			->  Dict:


												"""simple docstring"""

												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
																										__lowercase      :							List[str]				=     self.rust_tokenizer_class.from_pretrained(__a		,     **__a							)
																										__lowercase      :							List[Any]				=     self.tokenizer_class.from_pretrained(__a		,     **__a							)
																										__lowercase      :							Optional[Any]				=     """A, <mask> AllenNLP sentence."""
																										__lowercase      :							Union[str, Any]				=     tokenizer_r.encode_plus(__a		,     add_special_tokens=__a		,     return_token_type_ids=__a							)
																										__lowercase      :							Optional[Any]				=     tokenizer_p.encode_plus(__a		,     add_special_tokens=__a		,     return_token_type_ids=__a							)

																										# token_type_ids should put 0 everywhere
																										self.assertEqual(sum(tokens_r["""token_type_ids"""]							)		,     sum(tokens_p["""token_type_ids"""]							)							)

																										# attention_mask should put 1 everywhere, so sum over length should be 1
																										self.assertEqual(
																										    sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)		,     sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)		,     )

																										__lowercase      :							Dict				=     tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
																										__lowercase      :							str				=     tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)

																										# Rust correctly handles the space before the mask while python doesnt
																										self.assertSequenceEqual(tokens_p["""input_ids"""]		,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)
																										self.assertSequenceEqual(tokens_r["""input_ids"""]		,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)

																										self.assertSequenceEqual(
																										    __a		,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
																										self.assertSequenceEqual(
																										    __a		,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)



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


												"""simple docstring"""

												for trim_offsets, add_prefix_space in itertools.product([True, False]		,     repeat=2							):
																			__lowercase      :							Dict				=     self.rust_tokenizer_class.from_pretrained(
																			    self.tmpdirname		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)

																			__lowercase      :							Optional[int]				=     json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
																			__lowercase      :							Any				=     json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

																			self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]		,     __a							)

																			self.assertEqual(post_processor_state["""add_prefix_space"""]		,     __a							)
																			self.assertEqual(post_processor_state["""trim_offsets"""]		,     __a							)



					def       lowerCAmelCase				(  self				:	int							)			->  Tuple:


												"""simple docstring"""

												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
																										__lowercase      :							List[str]				=     """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																										__lowercase      :							int				=     F"{text_of_1_token} {text_of_1_token}"

																										__lowercase      :							List[str]				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							Any				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (0, len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (len(__a							) + 1, len(__a							) + 1 + len(__a							))		,     )

																										__lowercase      :							str				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							Tuple				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (0, len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (len(__a							) + 1, len(__a							) + 1 + len(__a							))		,     )

																										__lowercase      :							Optional[int]				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							str				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (0, len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (len(__a							), len(__a							) + 1 + len(__a							))		,     )

																										__lowercase      :							str				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							int				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (0, len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (len(__a							), len(__a							) + 1 + len(__a							))		,     )

																										__lowercase      :							Any				=     F" {text}"

																										# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																										#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																										# )
																										# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																										# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																										# self.assertEqual(
																										#     encoding.offset_mapping[1],
																										#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																										# )

																										__lowercase      :							Optional[Any]				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							str				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (1, 1 + len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (1 + len(__a							) + 1, 1 + len(__a							) + 1 + len(__a							))		,     )

																										__lowercase      :							int				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							Dict				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (0, 1 + len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (1 + len(__a							), 1 + len(__a							) + 1 + len(__a							))		,     )

																										__lowercase      :							int				=     self.rust_tokenizer_class.from_pretrained(
																										    __a		,     use_fast=__a		,     add_prefix_space=__a		,     trim_offsets=__a							)
																										__lowercase      :							Tuple				=     tokenizer_r(__a		,     return_offsets_mapping=__a		,     add_special_tokens=__a							)
																										self.assertEqual(encoding.offset_mapping[0]		,     (0, 1 + len(__a							))							)
																										self.assertEqual(
																										    encoding.offset_mapping[1]		,     (1 + len(__a							), 1 + len(__a							) + 1 + len(__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)