




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			:	str							 =     logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_=False				,       lowercase_=False		)     -> List[Any]:



       """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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_=False				,       lowercase_=False		)     -> str:



       """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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Optional[Any]:



       """simple docstring"""

       A__   				=					dct.pop(lowercase_		)
       A__   				=					val







def SCREAMING_SNAKE_CASE       ( )     -> Union[str, Any]:



       """simple docstring"""

       A__   				=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A__   				=					Image.open(requests.get(lowercase_				,       stream=lowercase_		).raw		)
       return im







@torch.no_grad()
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_=False		)     -> int:



       """simple docstring"""

       A__   				=					False if '''rvlcdip''' in checkpoint_url else True
       A__   				=					BeitConfig(use_absolute_position_embeddings=lowercase_				,       use_mask_token=lowercase_		)

       # size of the architecture
       if "large" in checkpoint_url or "dit-l" in checkpoint_url:
              A__   				=					1_024
              A__   				=					4_096
              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(lowercase_				,       lowercase_				,       repo_type='''dataset'''		)				,       '''r'''		)		)
              A__   				=					{int(lowercase_		): 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(lowercase_				,       map_location='''cpu'''		)['''model''']

       A__   				=					create_rename_keys(lowercase_				,       has_lm_head=lowercase_		)
       for src, dest in rename_keys:
              rename_key(lowercase_				,       lowercase_				,       lowercase_		)
       read_in_q_k_v(lowercase_				,       lowercase_				,       has_lm_head=lowercase_		)

       # load HuggingFace model
       A__   				=					BeitForMaskedImageModeling(lowercase_		) if has_lm_head else BeitForImageClassification(lowercase_		)
       model.eval()
       model.load_state_dict(lowercase_		)

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

       A__   				=					image_processor(images=lowercase_				,       return_tensors='''pt'''		)
       A__   				=					encoding['''pixel_values''']

       A__   				=					model(lowercase_		)
       A__   				=					outputs.logits

       # verify logits
       A__   				=					[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8_192]
       assert logits.shape == torch.Size(lowercase_		), "Shape of logits not as expected"

       Path(lowercase_		).mkdir(exist_ok=lowercase_		)
       print(f"""Saving model to {pytorch_dump_folder_path}"""		)
       model.save_pretrained(lowercase_		)
       print(f"""Saving image processor to {pytorch_dump_folder_path}"""		)
       image_processor.save_pretrained(lowercase_		)

       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(lowercase_				,       lowercase_		)				,       organization='''nielsr'''				,       commit_message='''Add image processor'''				,       use_temp_dir=lowercase_				,       )
              model.push_to_hub(
                  repo_path_or_name=Path(lowercase_				,       lowercase_		)				,       organization='''nielsr'''				,       commit_message='''Add model'''				,       use_temp_dir=lowercase_				,       )


if __name__ == "__main__":
  _lowerCamelCase			:	List[Any]							 =     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			:	str							 =     parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   IFInpaintingPipeline
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    UpperCAmelCase__													=   PipelineTesterMixin.required_optional_params - {'''latents'''}


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Dict:





           '''simple docstring'''

           return self._get_dummy_components()


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			int=0)		->int:





           '''simple docstring'''

           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__   				=					torch.manual_seed(UpperCAmelCase__)
           else:
                  A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)

           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)

           A__   				=					{
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''image''': image,
               '''mask_image''': mask_image,
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''output_type''': '''numpy''',
           }

           return inputs


    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()      ,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''      ,				)
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->List[Any]:





           '''simple docstring'''

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->int:





           '''simple docstring'''

           self._test_save_load_optional_components()


    @unittest.skipIf(torch_device != '''cuda'''      ,				reason='''float16 requires CUDA''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[str]:





           '''simple docstring'''

           super().test_save_load_floataa(expected_max_diff=1e-1)


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->List[Any]:





           '''simple docstring'''

           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Any:





           '''simple docstring'''

           self._test_save_load_local()





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Union[str, Any]:





           '''simple docstring'''

           self._test_inference_batch_single_identical(
               expected_max_diff=1e-2      ,				)


