





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_           =      logging.get_logger(__name__)






def lowerCamelCase__   (							snake_case_     :  str	,				snake_case_     :  Dict=False	)				->    Dict:
   __snake_case								=       []
   for i in range(config.num_hidden_layers	):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")	)
      rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")	)
      rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")	)
      rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")	)
      rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")	)
      rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")	)
      rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")	)
      rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")	)
      rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")	)
      rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")	)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ('''cls_token''', '''vit.embeddings.cls_token'''),
           ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
           ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
           ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
       ]	)

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ('''norm.weight''', '''layernorm.weight'''),
              ('''norm.bias''', '''layernorm.bias'''),
              ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
              ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
          ]	)

      # if just the base model, we should remove "vit" from all keys that start with "vit"
      __snake_case								=       [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''	) else pair for pair in rename_keys]
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ('''norm.weight''', '''vit.layernorm.weight'''),
              ('''norm.bias''', '''vit.layernorm.bias'''),
              ('''head.weight''', '''classifier.weight'''),
              ('''head.bias''', '''classifier.bias'''),
          ]	)

   return rename_keys






def lowerCamelCase__   (							snake_case_     :  Union[str, Any]	,				snake_case_     :  List[str]	,				snake_case_     :  Union[str, Any]=False	)				->    Optional[Any]:
   for i in range(config.num_hidden_layers	):
      if base_model:
         __snake_case								=       ''''''
      else:
         __snake_case								=       '''vit.'''
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      __snake_case								=       state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""	)
      __snake_case								=       state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""	)
      # next, add query, keys and values (in that order) to the state dict
      __snake_case								=       in_proj_weight[
          : config.hidden_size, :
      ]
      __snake_case								=       in_proj_bias[: config.hidden_size]
      __snake_case								=       in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      __snake_case								=       in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      __snake_case								=       in_proj_weight[
          -config.hidden_size :, :
      ]
      __snake_case								=       in_proj_bias[-config.hidden_size :]






def lowerCamelCase__   (							snake_case_     :  Optional[int]	)				->    Optional[int]:
   __snake_case								=       ['''head.weight''', '''head.bias''']
   for k in ignore_keys:
      state_dict.pop(snake_case_	,				snake_case_	)






def lowerCamelCase__   (							snake_case_     :  Any	,				snake_case_     :  Dict	,				snake_case_     :  List[str]	)				->    Any:
   __snake_case								=       dct.pop(snake_case_	)
   __snake_case								=       val






def lowerCamelCase__   (							)				->    Tuple:
   __snake_case								=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   __snake_case								=       Image.open(requests.get(snake_case_	,				stream=snake_case_	).raw	)
   return im






@torch.no_grad()
def lowerCamelCase__   (							snake_case_     :  Any	,				snake_case_     :  Tuple	)				->    Any:
   __snake_case								=       ViTConfig()
   __snake_case								=       False
   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   if vit_name[-5:] == "in21k":
      __snake_case								=       True
      __snake_case								=       int(vit_name[-12:-10]	)
      __snake_case								=       int(vit_name[-9:-6]	)
   else:
      __snake_case								=       1000
      __snake_case								=       '''huggingface/label-files'''
      __snake_case								=       '''imagenet-1k-id2label.json'''
      __snake_case								=       json.load(open(hf_hub_download(snake_case_	,				snake_case_	,				repo_type='''dataset'''	)	,				'''r'''	)	)
      __snake_case								=       {int(snake_case_	): v for k, v in idalabel.items()}
      __snake_case								=       idalabel
      __snake_case								=       {v: k for k, v in idalabel.items()}
      __snake_case								=       int(vit_name[-6:-4]	)
      __snake_case								=       int(vit_name[-3:]	)
   # size of the architecture
   if "deit" in vit_name:
      if vit_name[9:].startswith('''tiny'''	):
         __snake_case								=       192
         __snake_case								=       768
         __snake_case								=       12
         __snake_case								=       3
      elif vit_name[9:].startswith('''small'''	):
         __snake_case								=       384
         __snake_case								=       1536
         __snake_case								=       12
         __snake_case								=       6
      else:
         pass
   else:
      if vit_name[4:].startswith('''small'''	):
         __snake_case								=       768
         __snake_case								=       2304
         __snake_case								=       8
         __snake_case								=       8
      elif vit_name[4:].startswith('''base'''	):
         pass
      elif vit_name[4:].startswith('''large'''	):
         __snake_case								=       1024
         __snake_case								=       4096
         __snake_case								=       24
         __snake_case								=       16
      elif vit_name[4:].startswith('''huge'''	):
         __snake_case								=       1280
         __snake_case								=       5120
         __snake_case								=       32
         __snake_case								=       16

    # load original model from timm
   __snake_case								=       timm.create_model(snake_case_	,				pretrained=snake_case_	)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   __snake_case								=       timm_model.state_dict()
   if base_model:
      remove_classification_head_(snake_case_	)
   __snake_case								=       create_rename_keys(snake_case_	,				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_	,				snake_case_	)

   # load HuggingFace model
   if vit_name[-5:] == "in21k":
      __snake_case								=       ViTModel(snake_case_	).eval()
   else:
      __snake_case								=       ViTForImageClassification(snake_case_	).eval()
   model.load_state_dict(snake_case_	)

   # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
   if "deit" in vit_name:
      __snake_case								=       DeiTImageProcessor(size=config.image_size	)
   else:
      __snake_case								=       ViTImageProcessor(size=config.image_size	)
   __snake_case								=       image_processor(images=prepare_img()	,				return_tensors='''pt'''	)
   __snake_case								=       encoding['''pixel_values''']
   __snake_case								=       model(snake_case_	)

   if base_model:
      __snake_case								=       timm_model.forward_features(snake_case_	)
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(snake_case_	,				outputs.pooler_output	,				atol=1e-3	)
   else:
      __snake_case								=       timm_model(snake_case_	)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(snake_case_	,				outputs.logits	,				atol=1e-3	)

   Path(snake_case_	).mkdir(exist_ok=snake_case_	)
   print(f"""Saving model {vit_name} 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 __name__ == "__main__":
    snake_case_           =      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--vit_name',
        default='vit_base_patch16_224',
        type=str,
        help='Name of the ViT timm model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )

    snake_case_           =      parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				int				):





         """simple docstring"""



         super().tearDown()
         gc.collect()


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-canny'''    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case	,     __snake_case								=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,      controlnet=a__    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case								=       controlnet_params

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

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

         __snake_case								=       jax.random.PRNGKey(0				)
         __snake_case								=       jax.random.split(a__    ,      jax.device_count()				)

         __snake_case								=       replicate(a__				)
         __snake_case								=       shard(a__				)
         __snake_case								=       shard(a__				)

         __snake_case								=       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)

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

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-openpose'''    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case	,     __snake_case								=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,      controlnet=a__    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case								=       controlnet_params

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

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

         __snake_case								=       jax.random.PRNGKey(0				)
         __snake_case								=       jax.random.split(a__    ,      jax.device_count()				)

         __snake_case								=       replicate(a__				)
         __snake_case								=       shard(a__				)
         __snake_case								=       shard(a__				)

         __snake_case								=       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)

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

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2

