






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__       		=  logging.get_logger(__name__)






def 	UpperCamelCase	(							snake_case__  : List[str]						,snake_case__  : str=False						,snake_case__  : List[str]=False	):




     '''simple docstring'''






     __snake_case      :Optional[int] 							=    'backbone.' if is_semantic else ''

     __snake_case      :str 							=    []
     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 	UpperCamelCase	(							snake_case__  : Tuple						,snake_case__  : List[str]						,snake_case__  : List[str]=False						,snake_case__  : Optional[int]=False	):




     '''simple docstring'''






     for i in range(config.num_hidden_layers	):
          __snake_case      :str 							=    'backbone.' if is_semantic else ''
          # queries, keys and values
          __snake_case      :Optional[Any] 							=    state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''	)
          __snake_case      :List[str] 							=    state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''	)
          __snake_case      :List[Any] 							=    state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''	)

          __snake_case      :str 							=    in_proj_weight[
              : config.hidden_size, :
          ]
          __snake_case      :Tuple 							=    q_bias
          __snake_case      :Dict 							=    in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __snake_case      :Union[str, Any] 							=    in_proj_weight[
              -config.hidden_size :, :
          ]
          __snake_case      :Any 							=    v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          __snake_case      :str 							=    state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''	)
          __snake_case      :Optional[int] 							=    state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''	)

          __snake_case      :Dict 							=    gamma_a
          __snake_case      :int 							=    gamma_a






def 	UpperCamelCase	(							snake_case__  : Optional[int]						,snake_case__  : str						,snake_case__  : List[Any]	):




     '''simple docstring'''






     __snake_case      :Dict 							=    dct.pop(__a	)
     __snake_case      :Any 							=    val






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :List[Any] 							=    'http://images.cocodataset.org/val2017/000000039769.jpg'
     __snake_case      :Dict 							=    Image.open(requests.get(__a						,stream=__a	).raw	)
     return im






@torch.no_grad()
def 	UpperCamelCase	(							snake_case__  : Any						,snake_case__  : List[Any]						,snake_case__  : int=False	):




     '''simple docstring'''






     __snake_case      :int 							=    False if 'rvlcdip' in checkpoint_url else True
     __snake_case      :Any 							=    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:
          __snake_case      :str 							=    1024
          __snake_case      :Optional[int] 							=    4096
          __snake_case      :List[str] 							=    24
          __snake_case      :str 							=    16

     # labels
     if "rvlcdip" in checkpoint_url:
          __snake_case      :Optional[Any] 							=    16
          __snake_case      :List[Any] 							=    'huggingface/label-files'
          __snake_case      :Optional[int] 							=    'rvlcdip-id2label.json'
          __snake_case      :Any 							=    json.load(open(hf_hub_download(__a						,__a						,repo_type="""dataset"""	)						,"""r"""	)	)
          __snake_case      :Any 							=    {int(__a	): v for k, v in idalabel.items()}
          __snake_case      :Optional[Any] 							=    idalabel
          __snake_case      :Optional[Any] 							=    {v: k for k, v in idalabel.items()}

     # load state_dict of original model, remove and rename some keys
     __snake_case      :Union[str, Any] 							=    torch.hub.load_state_dict_from_url(__a						,map_location="""cpu"""	)['model']

     __snake_case      :Optional[int] 							=    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
     __snake_case      :str 							=    BeitForMaskedImageModeling(__a	) if has_lm_head else BeitForImageClassification(__a	)
     model.eval()
     model.load_state_dict(__a	)

     # Check outputs on an image
     __snake_case      :Dict 							=    BeitImageProcessor(
         size=config.image_size						,resample=PILImageResampling.BILINEAR						,do_center_crop=__a	)
     __snake_case      :Union[str, Any] 							=    prepare_img()

     __snake_case      :Optional[Any] 							=    image_processor(images=__a						,return_tensors="""pt"""	)
     __snake_case      :List[str] 							=    encoding['pixel_values']

     __snake_case      :List[Any] 							=    model(__a	)
     __snake_case      :Dict 							=    outputs.logits

     # verify logits
     __snake_case      :Optional[Any] 							=    [1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8192]
     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:
               __snake_case      :Tuple 							=    'dit-base' if 'base' in checkpoint_url else 'dit-large'
          else:
               __snake_case      :Union[str, Any] 							=    '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__":
     lowerCamelCase__       		=  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__       		=  parser.parse_args()
     convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""







import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list







@require_torchaudio
@require_sentencepiece
class 					A_(unittest.TestCase      ):







			"""simple docstring"""



			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    'laion/clap-htsat-unfused'
						_lowerCamelCase					:					Optional[Any]	       =    tempfile.mkdtemp()
			def    _lowerCAmelCase		(	self				,   **A							):
						return RobertaTokenizer.from_pretrained(self.checkpoint				,   **A							)
			def    _lowerCAmelCase		(	self				,   **A							):
						return ClapFeatureExtractor.from_pretrained(self.checkpoint				,   **A							)
			def    _lowerCAmelCase		(	self							):
						shutil.rmtree(self.tmpdirname							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[Any]	       =    self.get_tokenizer()
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_feature_extractor()

						_lowerCamelCase					:					List[str]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						processor.save_pretrained(self.tmpdirname							)
						_lowerCamelCase					:					str	       =    ClapProcessor.from_pretrained(self.tmpdirname							)

						self.assertEqual(processor.tokenizer.get_vocab()				,   tokenizer.get_vocab()							)
						self.assertIsInstance(processor.tokenizer				,   A							)

						self.assertEqual(processor.feature_extractor.to_json_string()				,   feature_extractor.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    ClapProcessor(tokenizer=self.get_tokenizer()				,   feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						_lowerCamelCase					:					Tuple	       =    self.get_tokenizer(bos_token='(BOS)'				,   eos_token='(EOS)'							)
						_lowerCamelCase					:					str	       =    self.get_feature_extractor(do_normalize=A				,   padding_value=1.0							)

						_lowerCamelCase					:					List[Any]	       =    ClapProcessor.from_pretrained(
						    self.tmpdirname				,   bos_token='(BOS)'				,   eos_token='(EOS)'				,   do_normalize=A				,   padding_value=1.0							)

						self.assertEqual(processor.tokenizer.get_vocab()				,   tokenizer_add_kwargs.get_vocab()							)
						self.assertIsInstance(processor.tokenizer				,   A							)

						self.assertEqual(processor.feature_extractor.to_json_string()				,   feature_extractor_add_kwargs.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[int]	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Optional[int]	       =    self.get_tokenizer()

						_lowerCamelCase					:					Any	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						_lowerCamelCase					:					int	       =    floats_list((3, 1000)							)

						_lowerCamelCase					:					str	       =    feature_extractor(A				,   return_tensors='np'							)
						_lowerCamelCase					:					List[str]	       =    processor(audios=A				,   return_tensors='np'							)

						for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()				,   input_processor[key].sum()				,   delta=1E-2							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    self.get_feature_extractor()
						_lowerCamelCase					:					str	       =    self.get_tokenizer()

						_lowerCamelCase					:					List[Any]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						_lowerCamelCase					:					List[str]	       =    'This is a test string'

						_lowerCamelCase					:					Any	       =    processor(text=A							)

						_lowerCamelCase					:					Optional[int]	       =    tokenizer(A							)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]				,   encoded_processor[key]							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_tokenizer()

						_lowerCamelCase					:					Optional[int]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						_lowerCamelCase					:					Dict	       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						_lowerCamelCase					:					int	       =    processor.batch_decode(A							)
						_lowerCamelCase					:					int	       =    tokenizer.batch_decode(A							)

						self.assertListEqual(A				,   A							)

			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Optional[Any]	       =    self.get_tokenizer()

						_lowerCamelCase					:					int	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						self.assertListEqual(
						    processor.model_input_names[2:]				,   feature_extractor.model_input_names				,   msg='`processor` and `feature_extractor` model input names do not match'				,   )





