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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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




def       __lowerCAmelCase      (						A_   : str						,	A_   : Dict=False					)    ->  int:
  __UpperCAmelCase						    =       []
  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'''deit.encoder.layer.{i}.layernorm_before.weight''')					)
    rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')					)
    rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')					)
    rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')					)
    rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')					)
    rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')					)
    rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')					)
    rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')					)
    rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')					)
    rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')					)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("cls_token", "deit.embeddings.cls_token"),
          ("dist_token", "deit.embeddings.distillation_token"),
          ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
          ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
          ("pos_embed", "deit.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 "deit" from all keys that start with "deit"
    __UpperCAmelCase						    =       [(pair[0], pair[1][4:]) if pair[1].startswith("deit"					) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ("norm.weight", "deit.layernorm.weight"),
            ("norm.bias", "deit.layernorm.bias"),
            ("head.weight", "cls_classifier.weight"),
            ("head.bias", "cls_classifier.bias"),
            ("head_dist.weight", "distillation_classifier.weight"),
            ("head_dist.bias", "distillation_classifier.bias"),
        ]					)

  return rename_keys




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




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : Optional[int]						,	A_   : List[Any]					)    ->  Optional[Any]:
  __UpperCAmelCase						    =       dct.pop(A_					)
  __UpperCAmelCase						    =       val




def       __lowerCAmelCase      (						)    ->  Optional[int]:
  __UpperCAmelCase						    =       "http://images.cocodataset.org/val2017/000000039769.jpg"
  __UpperCAmelCase						    =       Image.open(requests.get(A_						,	stream=A_					).raw					)
  return im




@torch.no_grad()
def       __lowerCAmelCase      (						A_   : Dict						,	A_   : Optional[int]					)    ->  List[str]:
  __UpperCAmelCase						    =       DeiTConfig()
  # all deit models have fine-tuned heads
  __UpperCAmelCase						    =       False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  __UpperCAmelCase						    =       10_00
  __UpperCAmelCase						    =       "huggingface/label-files"
  __UpperCAmelCase						    =       "imagenet-1k-id2label.json"
  __UpperCAmelCase						    =       json.load(open(hf_hub_download(A_						,	A_						,	repo_type="dataset"					)						,	"r"					)					)
  __UpperCAmelCase						    =       {int(A_					): v for k, v in idalabel.items()}
  __UpperCAmelCase						    =       idalabel
  __UpperCAmelCase						    =       {v: k for k, v in idalabel.items()}
  __UpperCAmelCase						    =       int(deit_name[-6:-4]					)
  __UpperCAmelCase						    =       int(deit_name[-3:]					)
  # size of the architecture
  if deit_name[9:].startswith("tiny"					):
    __UpperCAmelCase						    =       1_92
    __UpperCAmelCase						    =       7_68
    __UpperCAmelCase						    =       12
    __UpperCAmelCase						    =       3
  elif deit_name[9:].startswith("small"					):
    __UpperCAmelCase						    =       3_84
    __UpperCAmelCase						    =       15_36
    __UpperCAmelCase						    =       12
    __UpperCAmelCase						    =       6
  if deit_name[9:].startswith("base"					):
    pass
  elif deit_name[4:].startswith("large"					):
    __UpperCAmelCase						    =       10_24
    __UpperCAmelCase						    =       40_96
    __UpperCAmelCase						    =       24
    __UpperCAmelCase						    =       16

  # load original model from timm
  __UpperCAmelCase						    =       timm.create_model(A_						,	pretrained=A_					)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  __UpperCAmelCase						    =       timm_model.state_dict()
  __UpperCAmelCase						    =       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
  __UpperCAmelCase						    =       DeiTForImageClassificationWithTeacher(A_					).eval()
  model.load_state_dict(A_					)

  # Check outputs on an image, prepared by DeiTImageProcessor
  __UpperCAmelCase						    =       int(
      (2_56 / 2_24) * config.image_size					)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  __UpperCAmelCase						    =       DeiTImageProcessor(size=A_						,	crop_size=config.image_size					)
  __UpperCAmelCase						    =       image_processor(images=prepare_img()						,	return_tensors="pt"					)
  __UpperCAmelCase						    =       encoding["pixel_values"]
  __UpperCAmelCase						    =       model(A_					)

  __UpperCAmelCase						    =       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 {deit_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_							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--deit_name""",
           default="""vit_deit_base_distilled_patch16_224""",
           type=str,
           help="""Name of the DeiT 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_							=      parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)




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 			UpperCAmelCase__    (		snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	int    		= DiTPipeline
  lowerCAmelCase__		:	Any    		= CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  lowerCAmelCase__		:	Optional[Any]    		= PipelineTesterMixin.required_optional_params - {
      'latents',
      'num_images_per_prompt',
      'callback',
      'callback_steps',
  }
  lowerCAmelCase__		:	List[Any]    		= CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
  lowerCAmelCase__		:	Optional[int]    		= False






  def 	_UpperCAmelCase				(				self:			str   ) -> List[Any]:




    '''simple docstring'''




    torch.manual_seed(0   )
    __UpperCAmelCase						    =       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=__lowerCAmelCase				,				activation_fn="gelu-approximate"				,				num_embeds_ada_norm=1_000				,				norm_type="ada_norm_zero"				,				norm_elementwise_affine=__lowerCAmelCase				,				)
    __UpperCAmelCase						    =       AutoencoderKL()
    __UpperCAmelCase						    =       DDIMScheduler()
    __UpperCAmelCase						    =       {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
    return components






  def 	_UpperCAmelCase				(				self:			str				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Optional[int]=0   ) -> int:




    '''simple docstring'''




    if str(__lowerCAmelCase   ).startswith("mps"   ):
      __UpperCAmelCase						    =       torch.manual_seed(__lowerCAmelCase   )
    else:
      __UpperCAmelCase						    =       torch.Generator(device=__lowerCAmelCase   ).manual_seed(__lowerCAmelCase   )
    __UpperCAmelCase						    =       {
        "class_labels": [1],
        "generator": generator,
        "num_inference_steps": 2,
        "output_type": "numpy",
    }
    return inputs






  def 	_UpperCAmelCase				(				self:			Tuple   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       "cpu"

    __UpperCAmelCase						    =       self.get_dummy_components()
    __UpperCAmelCase						    =       self.pipeline_class(**__lowerCAmelCase   )
    pipe.to(__lowerCAmelCase   )
    pipe.set_progress_bar_config(disable=__lowerCAmelCase   )

    __UpperCAmelCase						    =       self.get_dummy_inputs(__lowerCAmelCase   )
    __UpperCAmelCase						    =       pipe(**__lowerCAmelCase   ).images
    __UpperCAmelCase						    =       image[0, -3:, -3:, -1]

    self.assertEqual(image.shape				,				(1, 16, 16, 3)   )
    __UpperCAmelCase						    =       np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]   )
    __UpperCAmelCase						    =       np.abs(image_slice.flatten() - expected_slice   ).max()
    self.assertLessEqual(__lowerCAmelCase				,				1E-3   )






  def 	_UpperCAmelCase				(				self:			int   ) -> str:




    '''simple docstring'''




    self._test_inference_batch_single_identical(relax_max_difference=__lowerCAmelCase				,				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 	_UpperCAmelCase				(				self:			Optional[int]   ) -> str:




    '''simple docstring'''




    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )






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



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			Any   ) -> Optional[Any]:




    '''simple docstring'''




    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()






  def 	_UpperCAmelCase				(				self:			Dict   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       torch.manual_seed(0   )

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

    __UpperCAmelCase						    =       ["vase", "umbrella", "white shark", "white wolf"]
    __UpperCAmelCase						    =       pipe.get_label_ids(__lowerCAmelCase   )

    __UpperCAmelCase						    =       pipe(__lowerCAmelCase				,				generator=__lowerCAmelCase				,				num_inference_steps=40				,				output_type="np"   ).images

    for word, image in zip(__lowerCAmelCase				,				__lowerCAmelCase   ):
      __UpperCAmelCase						    =       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 	_UpperCAmelCase				(				self:			Any   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"   )
    __UpperCAmelCase						    =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
    pipe.to("cuda"   )

    __UpperCAmelCase						    =       ["vase", "umbrella"]
    __UpperCAmelCase						    =       pipe.get_label_ids(__lowerCAmelCase   )

    __UpperCAmelCase						    =       torch.manual_seed(0   )
    __UpperCAmelCase						    =       pipe(__lowerCAmelCase				,				generator=__lowerCAmelCase				,				num_inference_steps=25				,				output_type="np"   ).images

    for word, image in zip(__lowerCAmelCase				,				__lowerCAmelCase   ):
      __UpperCAmelCase						    =       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



