


"""simple docstring"""






import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
       BioGptForCausalLM,
       BioGptForSequenceClassification,
       BioGptForTokenClassification,
       BioGptModel,
       BioGptTokenizer,
   )
   from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST


class     __lowerCamelCase  :

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=13    ,				__UpperCAmelCase=7    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=99    ,				__UpperCAmelCase=32    ,				__UpperCAmelCase=5    ,				__UpperCAmelCase=4    ,				__UpperCAmelCase=37    ,				__UpperCAmelCase="gelu"    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=512    ,				__UpperCAmelCase=16    ,				__UpperCAmelCase=2    ,				__UpperCAmelCase=0.02    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=4    ,				__UpperCAmelCase=None    ,				)       ->							int:
          _a    	=				parent
          _a    	=				batch_size
          _a    	=				seq_length
          _a    	=				is_training
          _a    	=				use_input_mask
          _a    	=				use_token_type_ids
          _a    	=				use_labels
          _a    	=				vocab_size
          _a    	=				hidden_size
          _a    	=				num_hidden_layers
          _a    	=				num_attention_heads
          _a    	=				intermediate_size
          _a    	=				hidden_act
          _a    	=				hidden_dropout_prob
          _a    	=				attention_probs_dropout_prob
          _a    	=				max_position_embeddings
          _a    	=				type_vocab_size
          _a    	=				type_sequence_label_size
          _a    	=				initializer_range
          _a    	=				num_labels
          _a    	=				num_choices
          _a    	=				scope


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size							)

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

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

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

          _a    	=				self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          return BioGptConfig(
              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    ,				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=__UpperCAmelCase    ,				initializer_range=self.initializer_range    ,				)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							int:
          _a    	=				BioGptModel(config=__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase							)
          _a    	=				model(__UpperCAmelCase							)
          self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							int:
          _a    	=				BioGptForCausalLM(config=__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase    ,				token_type_ids=__UpperCAmelCase    ,				labels=__UpperCAmelCase							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				*__UpperCAmelCase							)       ->							Any:
          _a    	=				BioGptModel(config=__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()

          # create attention mask
          _a    	=				torch.ones(input_ids.shape    ,				dtype=torch.long    ,				device=__UpperCAmelCase							)
          _a    	=				self.seq_length // 2
          _a    	=				0

          # first forward pass
          _a	,      _a    	=				model(__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase							).to_tuple()

          # create hypothetical next token and extent to next_input_ids
          _a    	=				ids_tensor((self.batch_size, 1)    ,				config.vocab_size							)

          # change a random masked slice from input_ids
          _a    	=				ids_tensor((1,)    ,				__UpperCAmelCase							).item() + 1
          _a    	=				ids_tensor((self.batch_size, 1)    ,				config.vocab_size							).squeeze(-1							)
          _a    	=				random_other_next_tokens

          # append to next input_ids and attn_mask
          _a    	=				torch.cat([input_ids, next_tokens]    ,				dim=-1							)
          _a    	=				torch.cat(
              [attn_mask, torch.ones((attn_mask.shape[0], 1)    ,				dtype=torch.long    ,				device=__UpperCAmelCase							)]    ,				dim=1    ,				)

          # get two different outputs
          _a    	=				model(__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase							)['''last_hidden_state''']
          _a    	=				model(__UpperCAmelCase    ,				past_key_values=__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase							)['''last_hidden_state''']

          # select random slice
          _a    	=				ids_tensor((1,)    ,				output_from_past.shape[-1]							).item()
          _a    	=				output_from_no_past[:, -1, random_slice_idx].detach()
          _a    	=				output_from_past[:, 0, random_slice_idx].detach()

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(__UpperCAmelCase    ,				__UpperCAmelCase    ,				atol=1e-3							)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				*__UpperCAmelCase							)       ->							Dict:
          _a    	=				BioGptModel(config=__UpperCAmelCase							).to(__UpperCAmelCase							).eval()

          _a    	=				torch.ones(input_ids.shape    ,				dtype=torch.long    ,				device=__UpperCAmelCase							)

          # first forward pass
          _a    	=				model(__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase    ,				use_cache=__UpperCAmelCase							)

          _a	,      _a    	=				outputs.to_tuple()

          # create hypothetical multiple next token and extent to next_input_ids
          _a    	=				ids_tensor((self.batch_size, 3)    ,				config.vocab_size							)
          _a    	=				ids_tensor((self.batch_size, 3)    ,				2							)

          # append to next input_ids and
          _a    	=				torch.cat([input_ids, next_tokens]    ,				dim=-1							)
          _a    	=				torch.cat([attention_mask, next_attn_mask]    ,				dim=-1							)

          _a    	=				model(__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase							)['''last_hidden_state''']
          _a    	=				model(__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase    ,				past_key_values=__UpperCAmelCase							)[
              '''last_hidden_state'''
          ]

          # select random slice
          _a    	=				ids_tensor((1,)    ,				output_from_past.shape[-1]							).item()
          _a    	=				output_from_no_past[:, -3:, random_slice_idx].detach()
          _a    	=				output_from_past[:, :, random_slice_idx].detach()

          self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(__UpperCAmelCase    ,				__UpperCAmelCase    ,				atol=1e-3							)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				*__UpperCAmelCase    ,				__UpperCAmelCase=False							)       ->							List[Any]:
          _a    	=				BioGptForCausalLM(__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          if gradient_checkpointing:
               model.gradient_checkpointing_enable()

          _a    	=				model(__UpperCAmelCase    ,				labels=__UpperCAmelCase							)
          self.parent.assertEqual(result.loss.shape    ,				()							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)							)
          result.loss.backward()


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				*__UpperCAmelCase							)       ->							str:
          _a    	=				BioGptModel(__UpperCAmelCase							)
          _a    	=				model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers							)
          for key in model.state_dict().keys():
               if "c_proj" in key and "weight" in key:
                    self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]							) - model_std							)    ,				0.001							)
                    self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]							) - 0.0							)    ,				0.01							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				*__UpperCAmelCase							)       ->							Any:
          _a    	=				self.num_labels
          _a    	=				BioGptForTokenClassification(__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase    ,				token_type_ids=__UpperCAmelCase							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.num_labels)							)





     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				self.prepare_config_and_inputs()
          (
              (
              _a
          )	,      (
              _a
          )	,      (
              _a
          )	,      (
              _a
          )	,      (
              _a
          )	,      (
              _a
          )	,      (
              _a
          )	,      
          )    	=				config_and_inputs
          _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict







@require_torch
class     __lowerCamelCase  (					a__  ,					a__  ,					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Any    =		(
         (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
         if is_torch_available()
         else ()
     )
     A_       :	Optional[Any]    =		(BioGptForCausalLM,) if is_torch_available() else ()
     A_       :	int    =		(
         {
             'feature-extraction': BioGptModel,
             'text-classification': BioGptForSequenceClassification,
             'text-generation': BioGptForCausalLM,
             'token-classification': BioGptForTokenClassification,
             'zero-shot': BioGptForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     A_       :	List[str]    =		False


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				BioGptModelTester(self							)
          _a    	=				ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				hidden_size=37							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          self.config_tester.run_common_tests()


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               _a    	=				type
               self.model_tester.create_and_check_model(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_forward_and_backwards(*__UpperCAmelCase    ,				gradient_checkpointing=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_biogpt_weight_initialization(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_biogpt_for_token_classification(*__UpperCAmelCase							)


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''							)
          model.to(__UpperCAmelCase							)
          _a    	=				BioGptTokenizer.from_pretrained('''microsoft/biogpt'''							)

          _a    	=				'''left'''

          # Define PAD Token = EOS Token = 50256
          _a    	=				tokenizer.eos_token
          _a    	=				model.config.eos_token_id

          # use different length sentences to test batching
          _a    	=				[
              '''Hello, my dog is a little''',
              '''Today, I''',
          ]

          _a    	=				tokenizer(__UpperCAmelCase    ,				return_tensors='''pt'''    ,				padding=__UpperCAmelCase							)
          _a    	=				inputs['''input_ids'''].to(__UpperCAmelCase							)

          _a    	=				model.generate(
              input_ids=__UpperCAmelCase    ,				attention_mask=inputs['''attention_mask'''].to(__UpperCAmelCase							)    ,				)

          _a    	=				tokenizer(sentences[0]    ,				return_tensors='''pt'''							).input_ids.to(__UpperCAmelCase							)
          _a    	=				model.generate(input_ids=__UpperCAmelCase							)

          _a    	=				inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
          _a    	=				tokenizer(sentences[1]    ,				return_tensors='''pt'''							).input_ids.to(__UpperCAmelCase							)
          _a    	=				model.generate(input_ids=__UpperCAmelCase    ,				max_length=model.config.max_length - num_paddings							)

          _a    	=				tokenizer.batch_decode(__UpperCAmelCase    ,				skip_special_tokens=__UpperCAmelCase							)
          _a    	=				tokenizer.decode(output_non_padded[0]    ,				skip_special_tokens=__UpperCAmelCase							)
          _a    	=				tokenizer.decode(output_padded[0]    ,				skip_special_tokens=__UpperCAmelCase							)

          _a    	=				[
              '''Hello, my dog is a little bit bigger than a little bit.''',
              '''Today, I have a good idea of how to use the information''',
          ]
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				[non_padded_sentence, padded_sentence]							)


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _a    	=				BioGptModel.from_pretrained(__UpperCAmelCase							)
               self.assertIsNotNone(__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()
          _a    	=				3
          _a    	=				input_dict['''input_ids''']
          _a    	=				input_ids.ne(1							).to(__UpperCAmelCase							)
          _a    	=				ids_tensor([self.model_tester.batch_size]    ,				self.model_tester.type_sequence_label_size							)
          _a    	=				BioGptForSequenceClassification(__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase    ,				labels=__UpperCAmelCase							)
          self.assertEqual(result.logits.shape    ,				(self.model_tester.batch_size, self.model_tester.num_labels)							)





     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()
          _a    	=				3
          _a    	=				'''multi_label_classification'''
          _a    	=				input_dict['''input_ids''']
          _a    	=				input_ids.ne(1							).to(__UpperCAmelCase							)
          _a    	=				ids_tensor(
              [self.model_tester.batch_size, config.num_labels]    ,				self.model_tester.type_sequence_label_size							).to(torch.float							)
          _a    	=				BioGptForSequenceClassification(__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase    ,				labels=__UpperCAmelCase							)
          self.assertEqual(result.logits.shape    ,				(self.model_tester.batch_size, self.model_tester.num_labels)							)







@require_torch
class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''							)
          _a    	=				torch.tensor([[2, 4805, 9, 656, 21]]							)
          _a    	=				model(__UpperCAmelCase							)[0]

          _a    	=				42384

          _a    	=				torch.Size((1, 5, vocab_size)							)
          self.assertEqual(output.shape    ,				__UpperCAmelCase							)

          _a    	=				torch.tensor(
              [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]							)

          self.assertTrue(torch.allclose(output[:, :3, :3]    ,				__UpperCAmelCase    ,				atol=1e-4							)							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				BioGptTokenizer.from_pretrained('''microsoft/biogpt'''							)
          _a    	=				BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''							)
          model.to(__UpperCAmelCase							)

          torch.manual_seed(0							)
          _a    	=				tokenizer('''COVID-19 is'''    ,				return_tensors='''pt'''							).to(__UpperCAmelCase							)
          _a    	=				model.generate(
              **__UpperCAmelCase    ,				min_length=100    ,				max_length=1024    ,				num_beams=5    ,				early_stopping=__UpperCAmelCase    ,				)
          _a    	=				tokenizer.decode(output_ids[0]    ,				skip_special_tokens=__UpperCAmelCase							)

          _a    	=				(
              '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
              ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
              ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
              ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
              ''' more than 800,000 deaths.'''
          )
          self.assertEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)



"""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()
__snake_case		=       logging.get_logger(__name__)
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					Optional[int]=False,     _lowerCAmelCase     :					Any=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       A_   (   _lowerCAmelCase     :					int,     _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					List[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       A_   (   _lowerCAmelCase     :					Tuple,     _lowerCAmelCase     :					Any,     _lowerCAmelCase     :					List[Any]       ):






     """simple docstring"""

     _a    	=				dct.pop(_lowerCAmelCase       )
     _a    	=				val
def       A_   (   ):






     """simple docstring"""

     _a    	=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a    	=				Image.open(requests.get(_lowerCAmelCase,     stream=_lowerCAmelCase       ).raw       )
     return im
@torch.no_grad()
def       A_   (   _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Optional[int]=False       ):






     """simple docstring"""

     _a    	=				False if '''rvlcdip''' in checkpoint_url else True
     _a    	=				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:
          _a    	=				10_24
          _a    	=				40_96
          _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(_lowerCAmelCase,     _lowerCAmelCase,     repo_type='''dataset'''       ),     '''r'''       )       )
          _a    	=				{int(_lowerCAmelCase       ): 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(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']

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

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

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

     _a    	=				model(_lowerCAmelCase       )
     _a    	=				outputs.logits

     # verify logits
     _a    	=				[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 1_96, 81_92]
     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:
               _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(_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__":
   __snake_case		=       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''',
   )
   __snake_case		=       parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)