






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      :       int    =						logging.get_logger(__name__)





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case=False       ,			__snake_case=False							):
     __lowerCAmelCase             =       "backbone." if is_semantic else ""

     __lowerCAmelCase             =       []
     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    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case=False       ,			__snake_case=False							):
     for i in range(config.num_hidden_layers							):
          __lowerCAmelCase             =       "backbone." if is_semantic else ""
          # queries, keys and values
          __lowerCAmelCase             =       state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""							)
          __lowerCAmelCase             =       state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""							)
          __lowerCAmelCase             =       state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""							)

          __lowerCAmelCase             =       in_proj_weight[
              : config.hidden_size, :
          ]
          __lowerCAmelCase             =       q_bias
          __lowerCAmelCase             =       in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __lowerCAmelCase             =       in_proj_weight[
              -config.hidden_size :, :
          ]
          __lowerCAmelCase             =       v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          __lowerCAmelCase             =       state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""							)
          __lowerCAmelCase             =       state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""							)

          __lowerCAmelCase             =       gamma_a
          __lowerCAmelCase             =       gamma_a





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       dct.pop(__snake_case							)
     __lowerCAmelCase             =       val





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       "http://images.cocodataset.org/val2017/000000039769.jpg"
     __lowerCAmelCase             =       Image.open(requests.get(__snake_case       ,			stream=__snake_case							).raw							)
     return im





@torch.no_grad()
def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case=False							):
     __lowerCAmelCase             =       False if "rvlcdip" in checkpoint_url else True
     __lowerCAmelCase             =       BeitConfig(use_absolute_position_embeddings=__snake_case       ,			use_mask_token=__snake_case							)

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          __lowerCAmelCase             =       1024
          __lowerCAmelCase             =       4096
          __lowerCAmelCase             =       24
          __lowerCAmelCase             =       16

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

     # load state_dict of original model, remove and rename some keys
     __lowerCAmelCase             =       torch.hub.load_state_dict_from_url(__snake_case       ,			map_location="cpu"							)["model"]

     __lowerCAmelCase             =       create_rename_keys(__snake_case       ,			has_lm_head=__snake_case							)
     for src, dest in rename_keys:
          rename_key(__snake_case       ,			__snake_case       ,			__snake_case							)
     read_in_q_k_v(__snake_case       ,			__snake_case       ,			has_lm_head=__snake_case							)

     # load HuggingFace model
     __lowerCAmelCase             =       BeitForMaskedImageModeling(__snake_case							) if has_lm_head else BeitForImageClassification(__snake_case							)
     model.eval()
     model.load_state_dict(__snake_case							)

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

     __lowerCAmelCase             =       image_processor(images=__snake_case       ,			return_tensors="pt"							)
     __lowerCAmelCase             =       encoding["pixel_values"]

     __lowerCAmelCase             =       model(__snake_case							)
     __lowerCAmelCase             =       outputs.logits

     # verify logits
     __lowerCAmelCase             =       [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
     assert logits.shape == torch.Size(__snake_case							), "Shape of logits not as expected"

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

     if push_to_hub:
          if has_lm_head:
               __lowerCAmelCase             =       "dit-base" if "base" in checkpoint_url else "dit-large"
          else:
               __lowerCAmelCase             =       "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(__snake_case       ,			__snake_case							)       ,			organization="nielsr"       ,			commit_message="Add image processor"       ,			use_temp_dir=__snake_case       ,			)
          model.push_to_hub(
              repo_path_or_name=Path(__snake_case       ,			__snake_case							)       ,			organization="nielsr"       ,			commit_message="Add model"       ,			use_temp_dir=__snake_case       ,			)


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














import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				(PNDMScheduler,)
      snake_case_	=				(("""num_inference_steps""", 50),)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						int:
           __lowerCAmelCase             =       {
               "num_train_timesteps": 1_0_0_0,
               "beta_start": 0.0_0_0_1,
               "beta_end": 0.0_2,
               "beta_schedule": "linear",
           }

           config.update(**__UpperCamelCase						)
           return config


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=0       ,    **__UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       dict(self.forward_default_kwargs						)
           __lowerCAmelCase             =       kwargs.pop("num_inference_steps"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       self.dummy_sample
           __lowerCAmelCase             =       0.1 * sample
           __lowerCAmelCase             =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

           for scheduler_class in self.scheduler_classes:
                __lowerCAmelCase             =       self.get_scheduler_config(**__UpperCamelCase						)
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)
                scheduler.set_timesteps(__UpperCamelCase						)
                # copy over dummy past residuals
                __lowerCAmelCase             =       dummy_past_residuals[:]

                with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(__UpperCamelCase						)
                     __lowerCAmelCase             =       scheduler_class.from_pretrained(__UpperCamelCase						)
                     new_scheduler.set_timesteps(__UpperCamelCase						)
                     # copy over dummy past residuals
                     __lowerCAmelCase             =       dummy_past_residuals[:]

                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       new_scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

                assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"

                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       new_scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

                assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"


      def 			__UpperCAmelCase		(						self						)->						Dict:
           pass


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=0       ,    **__UpperCamelCase						)->						Dict:
           __lowerCAmelCase             =       dict(self.forward_default_kwargs						)
           __lowerCAmelCase             =       kwargs.pop("num_inference_steps"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       self.dummy_sample
           __lowerCAmelCase             =       0.1 * sample
           __lowerCAmelCase             =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

           for scheduler_class in self.scheduler_classes:
                __lowerCAmelCase             =       self.get_scheduler_config()
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)
                scheduler.set_timesteps(__UpperCamelCase						)

                # copy over dummy past residuals (must be after setting timesteps)
                __lowerCAmelCase             =       dummy_past_residuals[:]

                with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(__UpperCamelCase						)
                     __lowerCAmelCase             =       scheduler_class.from_pretrained(__UpperCamelCase						)
                     # copy over dummy past residuals
                     new_scheduler.set_timesteps(__UpperCamelCase						)

                     # copy over dummy past residual (must be after setting timesteps)
                     __lowerCAmelCase             =       dummy_past_residuals[:]

                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       new_scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

                assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"

                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       new_scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

                assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config(**__UpperCamelCase						)
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

           __lowerCAmelCase             =       1_0
           __lowerCAmelCase             =       self.dummy_model()
           __lowerCAmelCase             =       self.dummy_sample_deter
           scheduler.set_timesteps(__UpperCamelCase						)

           for i, t in enumerate(scheduler.prk_timesteps						):
                __lowerCAmelCase             =       model(__UpperCamelCase       ,    __UpperCamelCase						)
                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps						):
                __lowerCAmelCase             =       model(__UpperCamelCase       ,    __UpperCamelCase						)
                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						).prev_sample

           return sample


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       dict(self.forward_default_kwargs						)

           __lowerCAmelCase             =       kwargs.pop("num_inference_steps"       ,    __UpperCamelCase						)

           for scheduler_class in self.scheduler_classes:
                __lowerCAmelCase             =       self.get_scheduler_config()
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

                __lowerCAmelCase             =       self.dummy_sample
                __lowerCAmelCase             =       0.1 * sample

                if num_inference_steps is not None and hasattr(__UpperCamelCase       ,    "set_timesteps"						):
                     scheduler.set_timesteps(__UpperCamelCase						)
                elif num_inference_steps is not None and not hasattr(__UpperCamelCase       ,    "set_timesteps"						):
                     __lowerCAmelCase             =       num_inference_steps

                # copy over dummy past residuals (must be done after set_timesteps)
                __lowerCAmelCase             =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
                __lowerCAmelCase             =       dummy_past_residuals[:]

                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    0       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    1       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

                self.assertEqual(output_a.shape       ,    sample.shape						)
                self.assertEqual(output_a.shape       ,    output_a.shape						)

                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    0       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    1       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

                self.assertEqual(output_a.shape       ,    sample.shape						)
                self.assertEqual(output_a.shape       ,    output_a.shape						)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           for timesteps in [1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Any:
           for steps_offset in [0, 1]:
                self.check_over_configs(steps_offset=__UpperCamelCase						)

           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config(steps_offset=1						)
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)
           scheduler.set_timesteps(1_0						)
           assert torch.equal(
               scheduler.timesteps       ,    torch.LongTensor(
                   [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]						)       ,    )


      def 			__UpperCAmelCase		(						self						)->						int:
           for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]       ,    [0.0_0_2, 0.0_2]						):
                self.check_over_configs(beta_start=__UpperCamelCase       ,    beta_end=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           for schedule in ["linear", "squaredcos_cap_v2"]:
                self.check_over_configs(beta_schedule=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           for t in [1, 5, 1_0]:
                self.check_over_forward(time_step=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           for t, num_inference_steps in zip([1, 5, 1_0]       ,    [1_0, 5_0, 1_0_0]						):
                self.check_over_forward(num_inference_steps=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						int:
           # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
           __lowerCAmelCase             =       2_7

           for scheduler_class in self.scheduler_classes:
                __lowerCAmelCase             =       self.dummy_sample
                __lowerCAmelCase             =       0.1 * sample

                __lowerCAmelCase             =       self.get_scheduler_config()
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

                scheduler.set_timesteps(__UpperCamelCase						)

                # before power of 3 fix, would error on first step, so we only need to do two
                for i, t in enumerate(scheduler.prk_timesteps[:2]						):
                     __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						).prev_sample


      def 			__UpperCAmelCase		(						self						)->						Dict:
           with self.assertRaises(__UpperCamelCase						):
                __lowerCAmelCase             =       self.scheduler_classes[0]
                __lowerCAmelCase             =       self.get_scheduler_config()
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

                scheduler.step_plms(self.dummy_sample       ,    1       ,    self.dummy_sample						).prev_sample


      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.full_loop()
           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 1_9_8.1_3_1_8						) < 1e-2
           assert abs(result_mean.item() - 0.2_5_8_0						) < 1e-3


      def 			__UpperCAmelCase		(						self						)->						str:
           __lowerCAmelCase             =       self.full_loop(prediction_type="v_prediction"						)
           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 6_7.3_9_8_6						) < 1e-2
           assert abs(result_mean.item() - 0.0_8_7_8						) < 1e-3


      def 			__UpperCAmelCase		(						self						)->						Any:
           # We specify different beta, so that the first alpha is 0.99
           __lowerCAmelCase             =       self.full_loop(set_alpha_to_one=__UpperCamelCase       ,    beta_start=0.0_1						)
           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 2_3_0.0_3_9_9						) < 1e-2
           assert abs(result_mean.item() - 0.2_9_9_5						) < 1e-3


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           # We specify different beta, so that the first alpha is 0.99
           __lowerCAmelCase             =       self.full_loop(set_alpha_to_one=__UpperCamelCase       ,    beta_start=0.0_1						)
           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 1_8_6.9_4_8_2						) < 1e-2
           assert abs(result_mean.item() - 0.2_4_3_4						) < 1e-3






