


import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        __snake_case	(       _SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= DiTPipeline
    lowercase       			= CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
    lowercase       			= PipelineTesterMixin.required_optional_params - {
        'latents',
        'num_images_per_prompt',
        'callback',
        'callback_steps',
    }
    lowercase       			= CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
    lowercase       			= False

    def __lowercase       (       self   :  Union[str, Any]		)       ->  Optional[int]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				List[str]      =       TransformeraDModel(
          sample_size=16   ,						num_layers=2   ,						patch_size=4   ,						attention_head_dim=8   ,						num_attention_heads=2   ,						in_channels=4   ,						out_channels=8   ,						attention_bias=__a   ,						activation_fn="""gelu-approximate"""   ,						num_embeds_ada_norm=10_00   ,						norm_type="""ada_norm_zero"""   ,						norm_elementwise_affine=__a   ,						)
      lowerCAmelCase_      :				List[Any]      =       AutoencoderKL()
      lowerCAmelCase_      :				Optional[int]      =       DDIMScheduler()
      lowerCAmelCase_      :				Dict      =       {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
      return components

    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  str=0		)       ->  Dict:
      if str(__a		).startswith("""mps"""		):
        lowerCAmelCase_      :				Tuple      =       torch.manual_seed(__a		)
      else:
        lowerCAmelCase_      :				str      =       torch.Generator(device=__a		).manual_seed(__a		)
      lowerCAmelCase_      :				List[str]      =       {
          """class_labels""": [1],
          """generator""": generator,
          """num_inference_steps""": 2,
          """output_type""": """numpy""",
      }
      return inputs

    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      lowerCAmelCase_      :				int      =       """cpu"""

      lowerCAmelCase_      :				List[str]      =       self.get_dummy_components()
      lowerCAmelCase_      :				Optional[Any]      =       self.pipeline_class(**__a		)
      pipe.to(__a		)
      pipe.set_progress_bar_config(disable=__a		)

      lowerCAmelCase_      :				Any      =       self.get_dummy_inputs(__a		)
      lowerCAmelCase_      :				List[str]      =       pipe(**__a		).images
      lowerCAmelCase_      :				List[str]      =       image[0, -3:, -3:, -1]

      self.assertEqual(image.shape   ,						(1, 16, 16, 3)		)
      lowerCAmelCase_      :				Optional[Any]      =       np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]		)
      lowerCAmelCase_      :				Optional[Any]      =       np.abs(image_slice.flatten() - expected_slice		).max()
      self.assertLessEqual(__a   ,						1E-3		)

    def __lowercase       (       self   :  Union[str, Any]		)       ->  Optional[int]:
      self._test_inference_batch_single_identical(relax_max_difference=__a   ,						expected_max_diff=1E-3		)





    @unittest.skipIf(
        torch_device != """cuda""" or not is_xformers_available()   ,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,						)
    def __lowercase       (       self   :  str		)       ->  int:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)







@require_torch_gpu
@slow
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  Optional[Any]		)       ->  List[Any]:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

    def __lowercase       (       self   :  Optional[Any]		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Tuple      =       torch.manual_seed(0		)

      lowerCAmelCase_      :				str      =       DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""		)
      pipe.to("""cuda"""		)

      lowerCAmelCase_      :				List[Any]      =       ["""vase""", """umbrella""", """white shark""", """white wolf"""]
      lowerCAmelCase_      :				Tuple      =       pipe.get_label_ids(__a		)

      lowerCAmelCase_      :				Optional[int]      =       pipe(__a   ,						generator=__a   ,						num_inference_steps=40   ,						output_type="""np"""		).images

      for word, image in zip(__a   ,						__a		):
        lowerCAmelCase_      :				Union[str, Any]      =       load_numpy(
            F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'		)
        assert np.abs((expected_image - image).max()		) < 1E-2





    def __lowercase       (       self   :  Optional[int]		)       ->  Dict:
      lowerCAmelCase_      :				int      =       DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""		)
      lowerCAmelCase_      :				Tuple      =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
      pipe.to("""cuda"""		)

      lowerCAmelCase_      :				Optional[int]      =       ["""vase""", """umbrella"""]
      lowerCAmelCase_      :				List[str]      =       pipe.get_label_ids(__a		)

      lowerCAmelCase_      :				int      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pipe(__a   ,						generator=__a   ,						num_inference_steps=25   ,						output_type="""np"""		).images

      for word, image in zip(__a   ,						__a		):
        lowerCAmelCase_      :				int      =       load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            F'/dit/{word}_512.npy'		)

        assert np.abs((expected_image - image).max()		) < 1E-1







'''simple docstring'''






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()
__A :  List[str]    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       Tuple=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       []
  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"
    lowerCAmelCase_      :				List[str]      =       [(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     UpperCamelCase_      (   A__ :       Any							,   A__ :       Any							,   A__ :       Tuple=False ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       str ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(A__							,   A__ )







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       Optional[Any]							,   A__ :       Dict ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       dct.pop(A__ )
  lowerCAmelCase_      :				Tuple      =       val







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowerCAmelCase_      :				Optional[int]      =       Image.open(requests.get(A__							,   stream=A__ ).raw )
  return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       List[Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       ViTConfig()
  lowerCAmelCase_      :				Any      =       False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_      :				int      =       True
    lowerCAmelCase_      :				Tuple      =       int(vit_name[-12:-10] )
    lowerCAmelCase_      :				Optional[int]      =       int(vit_name[-9:-6] )
  else:
    lowerCAmelCase_      :				Optional[int]      =       10_00
    lowerCAmelCase_      :				Tuple      =       """huggingface/label-files"""
    lowerCAmelCase_      :				Any      =       """imagenet-1k-id2label.json"""
    lowerCAmelCase_      :				Dict      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
    lowerCAmelCase_      :				Union[str, Any]      =       {int(A__ ): v for k, v in idalabel.items()}
    lowerCAmelCase_      :				Union[str, Any]      =       idalabel
    lowerCAmelCase_      :				Union[str, Any]      =       {v: k for k, v in idalabel.items()}
    lowerCAmelCase_      :				Optional[int]      =       int(vit_name[-6:-4] )
    lowerCAmelCase_      :				Dict      =       int(vit_name[-3:] )
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith("""tiny""" ):
      lowerCAmelCase_      :				int      =       1_92
      lowerCAmelCase_      :				List[str]      =       7_68
      lowerCAmelCase_      :				List[str]      =       12
      lowerCAmelCase_      :				int      =       3
    elif vit_name[9:].startswith("""small""" ):
      lowerCAmelCase_      :				Optional[Any]      =       3_84
      lowerCAmelCase_      :				Optional[int]      =       15_36
      lowerCAmelCase_      :				Dict      =       12
      lowerCAmelCase_      :				str      =       6
    else:
      pass
  else:
    if vit_name[4:].startswith("""small""" ):
      lowerCAmelCase_      :				Tuple      =       7_68
      lowerCAmelCase_      :				Any      =       23_04
      lowerCAmelCase_      :				List[str]      =       8
      lowerCAmelCase_      :				List[str]      =       8
    elif vit_name[4:].startswith("""base""" ):
      pass
    elif vit_name[4:].startswith("""large""" ):
      lowerCAmelCase_      :				Dict      =       10_24
      lowerCAmelCase_      :				List[Any]      =       40_96
      lowerCAmelCase_      :				Any      =       24
      lowerCAmelCase_      :				List[str]      =       16
    elif vit_name[4:].startswith("""huge""" ):
      lowerCAmelCase_      :				Optional[int]      =       12_80
      lowerCAmelCase_      :				Dict      =       51_20
      lowerCAmelCase_      :				Union[str, Any]      =       32
      lowerCAmelCase_      :				Optional[int]      =       16

    # load original model from timm
  lowerCAmelCase_      :				Union[str, Any]      =       timm.create_model(A__							,   pretrained=A__ )
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  lowerCAmelCase_      :				int      =       timm_model.state_dict()
  if base_model:
    remove_classification_head_(A__ )
  lowerCAmelCase_      :				str      =       create_rename_keys(A__							,   A__ )
  for src, dest in rename_keys:
    rename_key(A__							,   A__							,   A__ )
  read_in_q_k_v(A__							,   A__							,   A__ )

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_      :				int      =       ViTModel(A__ ).eval()
  else:
    lowerCAmelCase_      :				Optional[int]      =       ViTForImageClassification(A__ ).eval()
  model.load_state_dict(A__ )

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

  if base_model:
    lowerCAmelCase_      :				Union[str, Any]      =       timm_model.forward_features(A__ )
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(A__							,   outputs.pooler_output							,   atol=1E-3 )
  else:
    lowerCAmelCase_      :				Union[str, Any]      =       timm_model(A__ )
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(A__							,   outputs.logits							,   atol=1E-3 )

  Path(A__ ).mkdir(exist_ok=A__ )
  print(f'Saving model {vit_name} to {pytorch_dump_folder_path}' )
  model.save_pretrained(A__ )
  print(f'Saving image processor to {pytorch_dump_folder_path}' )
  image_processor.save_pretrained(A__ )


if __name__ == "__main__":
  __A :  Optional[int]    							=				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."
  )

  __A :  Union[str, Any]    							=				parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



