






"""simple docstring"""







import unittest

from transformers import BertGenerationConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import BertGenerationDecoder, BertGenerationEncoder







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Optional[Any]				,		__A:     List[Any]				,		__A:     Optional[int]=13				,		__A:     Tuple=7				,		__A:     Optional[Any]=True				,		__A:     Tuple=True				,		__A:     str=99				,		__A:     int=32				,		__A:     Union[str, Any]=5				,		__A:     Optional[Any]=4				,		__A:     Any=37				,		__A:     Union[str, Any]="gelu"				,		__A:     int=0.1				,		__A:     Optional[Any]=0.1				,		__A:     Optional[int]=50				,		__A:     str=0.0_2				,		__A:     Dict=True				,		__A:     Dict=None				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  seq_length
												a__          =  is_training
												a__          =  use_input_mask
												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__          =  initializer_range
												a__          =  use_labels
												a__          =  scope




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												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]    )

												if self.use_labels:
																	a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size    )

												a__          =  self.get_config()

												return config, input_ids, input_mask, token_labels




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												return BertGenerationConfig(
												    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				,		is_decoder=__A				,		initializer_range=self.initializer_range				,		)




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  self.prepare_config_and_inputs()

												a__          =  True
												a__          =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		vocab_size=2    )

												return (
												    config,
												    input_ids,
												    input_mask,
												    token_labels,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)




							def  lowercase		(   self:     str				,		__A:     List[str]				,		__A:     Optional[int]				,		__A:     Union[str, Any]				,		__A:     Optional[int]				,		**__A:     Union[str, Any]				,		):

												'''simple docstring'''

												a__          =  BertGenerationEncoder(config=__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A				,		attention_mask=__A    )
												a__          =  model(__A    )
												self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)    )




							def  lowercase		(   self:     Optional[Any]				,		__A:     Any				,		__A:     int				,		__A:     Optional[int]				,		__A:     int				,		__A:     List[Any]				,		__A:     Any				,		**__A:     List[str]				,		):

												'''simple docstring'''

												a__          =  True
												a__          =  BertGenerationEncoder(config=__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		encoder_attention_mask=__A				,		)
												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		)
												self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)    )




							def  lowercase		(   self:     Dict				,		__A:     Tuple				,		__A:     str				,		__A:     str				,		__A:     Any				,		__A:     int				,		__A:     Any				,		**__A:     Dict				,		):

												'''simple docstring'''

												a__          =  True
												a__          =  True
												a__          =  BertGenerationDecoder(config=__A    ).to(__A    ).eval()

												# first forward pass
												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		encoder_attention_mask=__A				,		use_cache=__A				,		)
												a__          =  outputs.past_key_values

												# 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)				,		vocab_size=2    )

												# append to next input_ids and
												a__          =  torch.cat([input_ids, next_tokens]				,		dim=-1    )
												a__          =  torch.cat([input_mask, next_mask]				,		dim=-1    )

												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		encoder_attention_mask=__A				,		output_hidden_states=__A				,		)['''hidden_states'''][0]
												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		encoder_attention_mask=__A				,		past_key_values=__A				,		output_hidden_states=__A				,		)['''hidden_states'''][0]

												# 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(__A				,		__A				,		atol=1e-3    )    )




							def  lowercase		(   self:     Optional[Any]				,		__A:     List[Any]				,		__A:     List[Any]				,		__A:     Union[str, Any]				,		__A:     Optional[Any]				,		*__A:     Optional[int]				,		):

												'''simple docstring'''

												a__          =  BertGenerationDecoder(__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A				,		attention_mask=__A				,		labels=__A    )
												self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.vocab_size)    )





							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

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







@require_torch
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE    =(BertGenerationDecoder,) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE    =(
							    {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
							    if is_torch_available()
							    else {}
							)




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  BertGenerationEncoderTester(self    )
												a__          =  ConfigTester(self				,		config_class=__A				,		hidden_size=37    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




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

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__       ,a__       ,a__       ,a__          =  self.model_tester.prepare_config_and_inputs()
												a__          =  '''bert'''
												self.model_tester.create_and_check_model(__A				,		__A				,		__A				,		__A    )




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(*__A    )




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(*__A    )




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

												'''simple docstring'''

												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  self.model_tester.prepare_config_and_inputs_for_decoder()

												a__          =  None

												self.model_tester.create_and_check_model_as_decoder(
												    __A				,		__A				,		__A				,		__A				,		__A				,		__A				,		)




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_for_causal_lm(*__A    )





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )
												self.assertIsNotNone(__A    )







@require_torch
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )
												a__          =  torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]    )
												with torch.no_grad():
																	a__          =  model(__A    )[0]
												a__          =  torch.Size([1, 8, 1024]    )
												self.assertEqual(output.shape				,		__A    )
												a__          =  torch.tensor(
												    [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]]    )
												self.assertTrue(torch.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )







@require_torch
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )
												a__          =  torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]    )
												with torch.no_grad():
																	a__          =  model(__A    )[0]
												a__          =  torch.Size([1, 8, 50358]    )
												self.assertEqual(output.shape				,		__A    )
												a__          =  torch.tensor(
												    [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]]    )
												self.assertTrue(torch.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )










"""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       :	int             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False ,     lowerCamelCase_=False):
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False ,     lowerCamelCase_=False):
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  dct.pop(lowerCamelCase_)
					a__          =  val





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





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					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__          =  1024
										a__          =  4096
										a__          =  24
										a__          =  16

					# labels
					if "rvlcdip" in checkpoint_url:
										a__          =  16
										a__          =  '''huggingface/label-files'''
										a__          =  '''rvlcdip-id2label.json'''
										a__          =  json.load(open(hf_hub_download(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, 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:
															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__":
	__a       :	Dict             = 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       :	List[Any]             = parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


