



"""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()
lowerCamelCase       	=						logging.get_logger(__name__)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__=False     ,      lowerCAmelCase__=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        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__=False     ,      lowerCAmelCase__=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        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
     UpperCAmelCase_      			=							dct.pop(lowerCAmelCase_				)
     UpperCAmelCase_      			=							val
def        a__     (				):
     UpperCAmelCase_      			=							'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCAmelCase_      			=							Image.open(requests.get(lowerCAmelCase_     ,      stream=lowerCAmelCase_				).raw				)
     return im
@torch.no_grad()
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__=False				):
     UpperCAmelCase_      			=							False if 'rvlcdip' in checkpoint_url else True
     UpperCAmelCase_      			=							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:
          UpperCAmelCase_      			=							1024
          UpperCAmelCase_      			=							4096
          UpperCAmelCase_      			=							24
          UpperCAmelCase_      			=							16

     # labels
     if "rvlcdip" in checkpoint_url:
          UpperCAmelCase_      			=							16
          UpperCAmelCase_      			=							'huggingface/label-files'
          UpperCAmelCase_      			=							'rvlcdip-id2label.json'
          UpperCAmelCase_      			=							json.load(open(hf_hub_download(lowerCAmelCase_     ,      lowerCAmelCase_     ,      repo_type="dataset"				)     ,      "r"				)				)
          UpperCAmelCase_      			=							{int(lowerCAmelCase_				): 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(lowerCAmelCase_     ,      map_location="cpu"				)['model']

     UpperCAmelCase_      			=							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
     UpperCAmelCase_      			=							BeitForMaskedImageModeling(lowerCAmelCase_				) if has_lm_head else BeitForImageClassification(lowerCAmelCase_				)
     model.eval()
     model.load_state_dict(lowerCAmelCase_				)

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

     UpperCAmelCase_      			=							image_processor(images=lowerCAmelCase_     ,      return_tensors="pt"				)
     UpperCAmelCase_      			=							encoding['pixel_values']

     UpperCAmelCase_      			=							model(lowerCAmelCase_				)
     UpperCAmelCase_      			=							outputs.logits

     # verify logits
     UpperCAmelCase_      			=							[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:
               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(lowerCAmelCase_     ,      lowerCAmelCase_				)     ,      organization="nielsr"     ,      commit_message="Add image processor"     ,      use_temp_dir=lowerCAmelCase_     ,      )
          model.push_to_hub(
              repo_path_or_name=Path(lowerCAmelCase_     ,      lowerCAmelCase_				)     ,      organization="nielsr"     ,      commit_message="Add model"     ,      use_temp_dir=lowerCAmelCase_     ,      )


if __name__ == "__main__":
  lowerCamelCase       	=						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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
  import torch

  from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


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


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
             # The dog is cute and lives in the garden house

             UpperCAmelCase_      			=							torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
             UpperCAmelCase_      			=							torch.tensor(
                 [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
             #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
             #  xlmr.eval()
             #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
             with torch.no_grad():
                   UpperCAmelCase_      			=							model(_UpperCAmelCase   )["last_hidden_state"].detach()
             self.assertEqual(output.shape		,    _UpperCAmelCase   )
             # compare the actual values for a slice of last dim
             self.assertTrue(torch.allclose(output[:, :, -1]		,    _UpperCAmelCase		,    atol=1e-3   )   )







       @slow
       def 					lowercase__    (							self      :  List[Any]   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
             # The dog is cute and lives in the garden house

             UpperCAmelCase_      			=							torch.Size((1, 12, 1024)   )  # batch_size, sequence_length, embedding_vector_dim
             UpperCAmelCase_      			=							torch.tensor(
                 [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
             #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
             #  xlmr.eval()
             #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
             with torch.no_grad():
                   UpperCAmelCase_      			=							model(_UpperCAmelCase   )["last_hidden_state"].detach()
             self.assertEqual(output.shape		,    _UpperCAmelCase   )
             # compare the actual values for a slice of last dim
             self.assertTrue(torch.allclose(output[:, :, -1]		,    _UpperCAmelCase		,    atol=1e-3   )   )




