


import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard






@slow
@require_flax
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       Optional[int]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase							,      __UpperCamelCase    							=FlaxControlNetModel.from_pretrained(
             'lllyasviel/sd-controlnet-canny'					,   from_pt=A_					,   dtype=jnp.bfloataa    )
         __UpperCamelCase							,      __UpperCamelCase    							=FlaxStableDiffusionControlNetPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'					,   controlnet=A_					,   from_pt=A_					,   dtype=jnp.bfloataa    )
         __UpperCamelCase    							=controlnet_params

         __UpperCamelCase    							='bird'
         __UpperCamelCase    							=jax.device_count()
         __UpperCamelCase    							=pipe.prepare_text_inputs([prompts] * num_samples    )

         __UpperCamelCase    							=load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'    )
         __UpperCamelCase    							=pipe.prepare_image_inputs([canny_image] * num_samples    )

         __UpperCamelCase    							=jax.random.PRNGKey(0    )
         __UpperCamelCase    							=jax.random.split(A_					,   jax.device_count()    )

         __UpperCamelCase    							=replicate(A_    )
         __UpperCamelCase    							=shard(A_    )
         __UpperCamelCase    							=shard(A_    )

         __UpperCamelCase    							=pipe(
             prompt_ids=A_					,   image=A_					,   params=A_					,   prng_seed=A_					,   num_inference_steps=50					,   jit=A_					,   ).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         __UpperCamelCase    							=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
         __UpperCamelCase    							=images[0, 253:256, 253:256, -1]

         __UpperCamelCase    							=jnp.asarray(jax.device_get(image_slice.flatten()    )    )
         __UpperCamelCase    							=jnp.array(
             [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]    )
         print(f'output_slice: {output_slice}'    )
         assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase							,      __UpperCamelCase    							=FlaxControlNetModel.from_pretrained(
             'lllyasviel/sd-controlnet-openpose'					,   from_pt=A_					,   dtype=jnp.bfloataa    )
         __UpperCamelCase							,      __UpperCamelCase    							=FlaxStableDiffusionControlNetPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'					,   controlnet=A_					,   from_pt=A_					,   dtype=jnp.bfloataa    )
         __UpperCamelCase    							=controlnet_params

         __UpperCamelCase    							='Chef in the kitchen'
         __UpperCamelCase    							=jax.device_count()
         __UpperCamelCase    							=pipe.prepare_text_inputs([prompts] * num_samples    )

         __UpperCamelCase    							=load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'    )
         __UpperCamelCase    							=pipe.prepare_image_inputs([pose_image] * num_samples    )

         __UpperCamelCase    							=jax.random.PRNGKey(0    )
         __UpperCamelCase    							=jax.random.split(A_					,   jax.device_count()    )

         __UpperCamelCase    							=replicate(A_    )
         __UpperCamelCase    							=shard(A_    )
         __UpperCamelCase    							=shard(A_    )

         __UpperCamelCase    							=pipe(
             prompt_ids=A_					,   image=A_					,   params=A_					,   prng_seed=A_					,   num_inference_steps=50					,   jit=A_					,   ).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         __UpperCamelCase    							=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
         __UpperCamelCase    							=images[0, 253:256, 253:256, -1]

         __UpperCamelCase    							=jnp.asarray(jax.device_get(image_slice.flatten()    )    )
         __UpperCamelCase    							=jnp.array(
             [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]    )
         print(f'output_slice: {output_slice}'    )
         assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2









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()
_snake_case             =     logging.get_logger(__name__)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				"backbone." if is_semantic else ""

  lowerCamelCase :					int    =				[]
  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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

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

    lowerCamelCase :					str    =				in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCamelCase :					Any    =				q_bias
    lowerCamelCase :					Any    =				in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCamelCase :					Optional[int]    =				in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCamelCase :					int    =				v_bias

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

    lowerCamelCase :					int    =				gamma_a
    lowerCamelCase :					Optional[Any]    =				gamma_a






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				dct.pop(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					List[Any]    =				val






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase :					Optional[Any]    =				Image.open(requests.get(SCREAMING_SNAKE_CASE_      ,						stream=SCREAMING_SNAKE_CASE_	).raw	)
  return im






@torch.no_grad()
def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				False if "rvlcdip" in checkpoint_url else True
  lowerCamelCase :					str    =				BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE_      ,						use_mask_token=SCREAMING_SNAKE_CASE_	)

  # size of the architecture
  if "large" in checkpoint_url or "dit-l" in checkpoint_url:
    lowerCamelCase :					Union[str, Any]    =				1024
    lowerCamelCase :					Any    =				4096
    lowerCamelCase :					str    =				24
    lowerCamelCase :					List[Any]    =				16

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

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

  lowerCamelCase :					Tuple    =				create_rename_keys(SCREAMING_SNAKE_CASE_      ,						has_lm_head=SCREAMING_SNAKE_CASE_	)
  for src, dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
  read_in_q_k_v(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						has_lm_head=SCREAMING_SNAKE_CASE_	)

  # load HuggingFace model
  lowerCamelCase :					List[Any]    =				BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE_	) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE_	)
  model.eval()
  model.load_state_dict(SCREAMING_SNAKE_CASE_	)

  # Check outputs on an image
  lowerCamelCase :					str    =				BeitImageProcessor(
      size=config.image_size      ,						resample=PILImageResampling.BILINEAR      ,						do_center_crop=SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				prepare_img()

  lowerCamelCase :					Optional[int]    =				image_processor(images=SCREAMING_SNAKE_CASE_      ,						return_tensors="pt"	)
  lowerCamelCase :					Optional[Any]    =				encoding["pixel_values"]

  lowerCamelCase :					Optional[Any]    =				model(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Dict    =				outputs.logits

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

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

  if push_to_hub:
    if has_lm_head:
      lowerCamelCase :					Optional[Any]    =				"dit-base" if "base" in checkpoint_url else "dit-large"
    else:
      lowerCamelCase :					Dict    =				"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(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)      ,						organization="nielsr"      ,						commit_message="Add image processor"      ,						use_temp_dir=SCREAMING_SNAKE_CASE_      ,						)
    model.push_to_hub(
        repo_path_or_name=Path(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)      ,						organization="nielsr"      ,						commit_message="Add model"      ,						use_temp_dir=SCREAMING_SNAKE_CASE_      ,						)


if __name__ == "__main__":
   _snake_case             =     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''',
   )
   _snake_case             =     parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



