




'''simple docstring'''







import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_PRETRAINING_MAPPING,
       MobileBertForMaskedLM,
       MobileBertForMultipleChoice,
       MobileBertForNextSentencePrediction,
       MobileBertForPreTraining,
       MobileBertForQuestionAnswering,
       MobileBertForSequenceClassification,
       MobileBertForTokenClassification,
       MobileBertModel,
   )


class     _a						:
       def __init__(			self		:			List[str]     ,       lowercase		:			Dict     ,       lowercase		:			List[Any]=13     ,       lowercase		:			Optional[Any]=7     ,       lowercase		:			Any=True     ,       lowercase		:			str=True     ,       lowercase		:			List[Any]=True     ,       lowercase		:			str=True     ,       lowercase		:			List[str]=99     ,       lowercase		:			int=64     ,       lowercase		:			List[Any]=32     ,       lowercase		:			str=5     ,       lowercase		:			Optional[int]=4     ,       lowercase		:			int=37     ,       lowercase		:			str="gelu"     ,       lowercase		:			Any=0.1     ,       lowercase		:			Optional[Any]=0.1     ,       lowercase		:			Optional[int]=512     ,       lowercase		:			Union[str, Any]=16     ,       lowercase		:			List[str]=2     ,       lowercase		:			Tuple=0.02     ,       lowercase		:			List[Any]=3     ,       lowercase		:			int=4     ,       lowercase		:			Optional[Any]=None     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	seq_length
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_input_mask
           UpperCAmelCase     =	use_token_type_ids
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	embedding_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	type_vocab_size
           UpperCAmelCase     =	type_sequence_label_size
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	num_labels
           UpperCAmelCase     =	num_choices
           UpperCAmelCase     =	scope
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size       )

           UpperCAmelCase     =	None
           if self.use_input_mask:
               UpperCAmelCase     =	random_attention_mask([self.batch_size, self.seq_length]       )

           UpperCAmelCase     =	None
           if self.use_token_type_ids:
               UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.type_vocab_size       )

           UpperCAmelCase     =	None
           UpperCAmelCase     =	None
           UpperCAmelCase     =	None
           if self.use_labels:
               UpperCAmelCase     =	ids_tensor([self.batch_size]     ,       self.type_sequence_label_size       )
               UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.num_labels       )
               UpperCAmelCase     =	ids_tensor([self.batch_size]     ,       self.num_choices       )

           UpperCAmelCase     =	self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           return MobileBertConfig(
               vocab_size=self.vocab_size     ,       hidden_size=self.hidden_size     ,       num_hidden_layers=self.num_hidden_layers     ,       num_attention_heads=self.num_attention_heads     ,       intermediate_size=self.intermediate_size     ,       embedding_size=self.embedding_size     ,       hidden_act=self.hidden_act     ,       hidden_dropout_prob=self.hidden_dropout_prob     ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,       max_position_embeddings=self.max_position_embeddings     ,       type_vocab_size=self.type_vocab_size     ,       is_decoder=lowercase     ,       initializer_range=self.initializer_range     ,       )
       def 					A   (			self		:			Tuple     ,       lowercase		:			Optional[Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[int]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			str     ,       lowercase		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertModel(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase       )
           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase       )
           UpperCAmelCase     =	model(lowercase       )

           self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)       )
           self.parent.assertEqual(result.pooler_output.shape     ,       (self.batch_size, self.hidden_size)       )
       def 					A   (			self		:			Dict     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			List[str]     ,       lowercase		:			List[str]     ,       lowercase		:			Dict     ,       lowercase		:			List[str]     ,       lowercase		:			Optional[int]     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertForMaskedLM(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)       )
       def 					A   (			self		:			Dict     ,       lowercase		:			Any     ,       lowercase		:			Dict     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertForNextSentencePrediction(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(
               lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase     ,       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, 2)       )
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			Tuple     ,       lowercase		:			Optional[Any]     ,       lowercase		:			int     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertForPreTraining(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(
               lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase     ,       next_sentence_label=lowercase     ,       )
           self.parent.assertEqual(result.prediction_logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)       )
           self.parent.assertEqual(result.seq_relationship_logits.shape     ,       (self.batch_size, 2)       )
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			Any     ,       lowercase		:			Dict     ,       lowercase		:			str     ,       lowercase		:			Optional[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			int     ,       lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertForQuestionAnswering(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(
               lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       start_positions=lowercase     ,       end_positions=lowercase     ,       )
           self.parent.assertEqual(result.start_logits.shape     ,       (self.batch_size, self.seq_length)       )
           self.parent.assertEqual(result.end_logits.shape     ,       (self.batch_size, self.seq_length)       )
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			Tuple     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			List[str]     ,       lowercase		:			str     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_labels
           UpperCAmelCase     =	MobileBertForSequenceClassification(lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)       )
       def 					A   (			self		:			List[Any]     ,       lowercase		:			int     ,       lowercase		:			List[Any]     ,       lowercase		:			Tuple     ,       lowercase		:			Optional[int]     ,       lowercase		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_labels
           UpperCAmelCase     =	MobileBertForTokenClassification(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.num_labels)       )
       def 					A   (			self		:			int     ,       lowercase		:			Optional[int]     ,       lowercase		:			Tuple     ,       lowercase		:			List[Any]     ,       lowercase		:			Tuple     ,       lowercase		:			str     ,       lowercase		:			Tuple     ,       lowercase		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_choices
           UpperCAmelCase     =	MobileBertForMultipleChoice(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	input_ids.unsqueeze(1       ).expand(-1     ,       self.num_choices     ,       -1       ).contiguous()
           UpperCAmelCase     =	token_type_ids.unsqueeze(1       ).expand(-1     ,       self.num_choices     ,       -1       ).contiguous()
           UpperCAmelCase     =	input_mask.unsqueeze(1       ).expand(-1     ,       self.num_choices     ,       -1       ).contiguous()
           UpperCAmelCase     =	model(
               lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase     ,       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_choices)       )







       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           (
               (
               UpperCAmelCase  
           ) ,  (
               UpperCAmelCase  
           ) ,  (
               UpperCAmelCase  
           ) ,  (
               UpperCAmelCase  
           ) ,  (
               UpperCAmelCase  
           ) ,  (
               UpperCAmelCase  
           ) ,  (
               UpperCAmelCase  
           ) ,    
           )     =	config_and_inputs
           UpperCAmelCase     =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict






@require_torch
class     _a						(			__a							,							__a							,							unittest.TestCase				):
       __a       :  Dict            =      (
           (
               MobileBertModel,
               MobileBertForMaskedLM,
               MobileBertForMultipleChoice,
               MobileBertForNextSentencePrediction,
               MobileBertForPreTraining,
               MobileBertForQuestionAnswering,
               MobileBertForSequenceClassification,
               MobileBertForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       __a       :  List[str]            =      (
           {
               """feature-extraction""": MobileBertModel,
               """fill-mask""": MobileBertForMaskedLM,
               """question-answering""": MobileBertForQuestionAnswering,
               """text-classification""": MobileBertForSequenceClassification,
               """token-classification""": MobileBertForTokenClassification,
               """zero-shot""": MobileBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __a       :  Any            =      True
       def 					A   (			self		:			List[Any]     ,       lowercase		:			int     ,       lowercase		:			Any     ,       lowercase		:			Optional[Any]=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	super()._prepare_for_class(lowercase     ,       lowercase     ,       return_labels=lowercase       )

           if return_labels:
               if model_class in get_values(lowercase       ):
                   UpperCAmelCase     =	torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.seq_length)     ,       dtype=torch.long     ,       device=lowercase       )
                   UpperCAmelCase     =	torch.zeros(
                       self.model_tester.batch_size     ,       dtype=torch.long     ,       device=lowercase       )
           return inputs_dict
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertModelTester(self       )
           UpperCAmelCase     =	ConfigTester(self     ,       config_class=lowercase     ,       hidden_size=37       )
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_model(*lowercase       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowercase       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowercase       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowercase       )
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_pretraining(*lowercase       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_question_answering(*lowercase       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowercase       )







       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_token_classification(*lowercase       )






def 		snake_case_							(_a			:							List[Any]  ):
    return torch.tensor(
        _a					,     dtype=torch.long					,     device=_a					,     )


A									=1E-3







@require_torch
@require_sentencepiece
@require_tokenizers
class     _a						(			unittest.TestCase				):
       @slow
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertModel.from_pretrained('''google/mobilebert-uncased'''       ).to(lowercase       )
           UpperCAmelCase     =	_long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]       )
           with torch.no_grad():
               UpperCAmelCase     =	model(lowercase       )[0]
           UpperCAmelCase     =	torch.Size((1, 9, 512)       )
           self.assertEqual(output.shape     ,       lowercase       )
           UpperCAmelCase     =	torch.tensor(
               [
                   [
                       [-2.4_736_526E07, 8.2_691_656E04, 1.6_521_838E05],
                       [-5.7_541_704E-01, 3.9_056_022E00, 4.4_011_507E00],
                       [2.6_047_359E00, 1.5_677_652E00, -1.7_324_188E-01],
                   ]
               ]     ,       device=lowercase     ,       )

           # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
           # ~1 difference, it's therefore not a good idea to measure using addition.
           # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
           # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
           UpperCAmelCase     =	torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE       )
           UpperCAmelCase     =	torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE       )

           self.assertTrue(lower_bound and upper_bound       )







'''simple docstring'''







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()
A									=logging.get_logger(__name__)





def 		snake_case_							(_a			:							Tuple					,     _a			:							Any=False					,     _a			:							str=False  ):
    UpperCAmelCase     =	'''backbone.''' if is_semantic else ''''''

    UpperCAmelCase     =	[]
    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_							(_a			:							str					,     _a			:							List[Any]					,     _a			:							List[Any]=False					,     _a			:							Union[str, Any]=False  ):
    for i in range(config.num_hidden_layers  ):
        UpperCAmelCase     =	'''backbone.''' if is_semantic else ''''''
        # queries, keys and values
        UpperCAmelCase     =	state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"  )
        UpperCAmelCase     =	state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"  )
        UpperCAmelCase     =	state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"  )

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

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

        UpperCAmelCase     =	gamma_a
        UpperCAmelCase     =	gamma_a





def 		snake_case_							(_a			:							Union[str, Any]					,     _a			:							str					,     _a			:							Optional[int]  ):
    UpperCAmelCase     =	dct.pop(_a  )
    UpperCAmelCase     =	val





def 		snake_case_							():
    UpperCAmelCase     =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase     =	Image.open(requests.get(_a					,     stream=_a  ).raw  )
    return im





@torch.no_grad()
def 		snake_case_							(_a			:							Optional[Any]					,     _a			:							Optional[Any]					,     _a			:							Optional[Any]=False  ):
    UpperCAmelCase     =	False if '''rvlcdip''' in checkpoint_url else True
    UpperCAmelCase     =	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:
        UpperCAmelCase     =	1_0_2_4
        UpperCAmelCase     =	4_0_9_6
        UpperCAmelCase     =	2_4
        UpperCAmelCase     =	1_6

    # labels
    if "rvlcdip" in checkpoint_url:
        UpperCAmelCase     =	1_6
        UpperCAmelCase     =	'''huggingface/label-files'''
        UpperCAmelCase     =	'''rvlcdip-id2label.json'''
        UpperCAmelCase     =	json.load(open(hf_hub_download(_a					,     _a					,     repo_type='''dataset'''  )					,     '''r'''  )  )
        UpperCAmelCase     =	{int(_a  ): v for k, v in idalabel.items()}
        UpperCAmelCase     =	idalabel
        UpperCAmelCase     =	{v: k for k, v in idalabel.items()}

    # load state_dict of original model, remove and rename some keys
    UpperCAmelCase     =	torch.hub.load_state_dict_from_url(_a					,     map_location='''cpu'''  )['''model''']

    UpperCAmelCase     =	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
    UpperCAmelCase     =	BeitForMaskedImageModeling(_a  ) if has_lm_head else BeitForImageClassification(_a  )
    model.eval()
    model.load_state_dict(_a  )

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

    UpperCAmelCase     =	image_processor(images=_a					,     return_tensors='''pt'''  )
    UpperCAmelCase     =	encoding['''pixel_values''']

    UpperCAmelCase     =	model(_a  )
    UpperCAmelCase     =	outputs.logits

    # verify logits
    UpperCAmelCase     =	[1, 1_6] if '''rvlcdip''' in checkpoint_url else [1, 1_9_6, 8_1_9_2]
    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:
            UpperCAmelCase     =	'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
        else:
            UpperCAmelCase     =	'''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__":
   A									=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',
   )
   A									=parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




