"""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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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






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






def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    __SCREAMING_SNAKE_CASE							:  List[str]   ):
     lowercase_			:       Union[str, Any]									=			dct.pop(UpperCAmelCase__   )
     lowercase_			:       List[Any]									=			val






def     lowercase__( ):
     lowercase_			:       Dict									=			'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase_			:       List[str]									=			Image.open(requests.get(UpperCAmelCase__  ,    stream=UpperCAmelCase__   ).raw   )
     return im






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

     # load original model from timm
     lowercase_			:       Optional[int]									=			timm.create_model(UpperCAmelCase__  ,    pretrained=UpperCAmelCase__   )
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     lowercase_			:       List[Any]									=			timm_model.state_dict()
     lowercase_			:       List[str]									=			create_rename_keys(UpperCAmelCase__  ,    UpperCAmelCase__   )
     for src, dest in rename_keys:
          rename_key(UpperCAmelCase__  ,    UpperCAmelCase__  ,    UpperCAmelCase__   )
     read_in_q_k_v(UpperCAmelCase__  ,    UpperCAmelCase__  ,    UpperCAmelCase__   )

     # load HuggingFace model
     lowercase_			:       Any									=			DeiTForImageClassificationWithTeacher(UpperCAmelCase__   ).eval()
     model.load_state_dict(UpperCAmelCase__   )

     # Check outputs on an image, prepared by DeiTImageProcessor
     lowercase_			:       Tuple									=			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
     lowercase_			:       List[Any]									=			DeiTImageProcessor(size=UpperCAmelCase__  ,    crop_size=config.image_size   )
     lowercase_			:       Any									=			image_processor(images=prepare_img()  ,    return_tensors='pt'   )
     lowercase_			:       Tuple									=			encoding['pixel_values']
     lowercase_			:       Dict									=			model(UpperCAmelCase__   )

     lowercase_			:       str									=			timm_model(UpperCAmelCase__   )
     assert timm_logits.shape == outputs.logits.shape
     assert torch.allclose(UpperCAmelCase__  ,    outputs.logits  ,    atol=1E-3   )

     Path(UpperCAmelCase__   ).mkdir(exist_ok=UpperCAmelCase__   )
     print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}'''   )
     model.save_pretrained(UpperCAmelCase__   )
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
     image_processor.save_pretrained(UpperCAmelCase__   )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE								=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."
       )

       __SCREAMING_SNAKE_CASE								=parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


"""simple docstring"""







import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


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

       from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline







@require_flax
class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        with tempfile.TemporaryDirectory() as tmpdirname:
             # pipeline has Flax weights
             lowercase_			:       Optional[int]									=			FlaxDiffusionPipeline.from_pretrained(
                 'hf-internal-testing/tiny-stable-diffusion-pipe'  ,safety_checker=__UpperCamelCase  ,cache_dir=__UpperCamelCase    )

             lowercase_			:       Dict									=			[t[-1] for t in os.walk(os.path.join(__UpperCamelCase  ,os.listdir(__UpperCamelCase    )[0]  ,'snapshots'    )    )]
             lowercase_			:       Tuple									=			[item for sublist in all_root_files for item in sublist]

             # None of the downloaded files should be a PyTorch file even if we have some here:
             # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
             assert not any(f.endswith('.bin'    ) for f in files    )







@slow
@require_flax
class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       List[Any]									=			FlaxStableDiffusionPipeline.from_pretrained(
            'hf-internal-testing/tiny-stable-diffusion-pipe'  ,safety_checker=__UpperCamelCase    )

        lowercase_			:       Dict									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       Optional[Any]									=			jax.random.PRNGKey(0    )
        lowercase_			:       Optional[int]									=			4

        lowercase_			:       int									=			jax.device_count()
        lowercase_			:       Optional[int]									=			num_samples * [prompt]
        lowercase_			:       List[Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       Dict									=			replicate(__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Dict									=			shard(__UpperCamelCase    )

        lowercase_			:       List[Any]									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 64, 64, 3)
        if jax.device_count() == 8:
             assert np.abs(np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 4.151_4745    ) < 1e-3
             assert np.abs(np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 4_9947.875    ) < 5e-1

        lowercase_			:       List[Any]									=			pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]    )    )    )
        assert len(__UpperCamelCase    ) == num_samples
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       Any									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='flax'  ,safety_checker=__UpperCamelCase    )

        lowercase_			:       Any									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       List[str]									=			jax.random.PRNGKey(0    )
        lowercase_			:       Tuple									=			50

        lowercase_			:       Optional[Any]									=			jax.device_count()
        lowercase_			:       List[Any]									=			num_samples * [prompt]
        lowercase_			:       Union[str, Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       List[str]									=			replicate(__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			shard(__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 0.0565_2401)    ) < 1e-3
             assert np.abs((np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 238_3808.2)    ) < 5e-1
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       Tuple									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa  ,safety_checker=__UpperCamelCase    )

        lowercase_			:       List[Any]									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       Dict									=			jax.random.PRNGKey(0    )
        lowercase_			:       Optional[int]									=			50

        lowercase_			:       Tuple									=			jax.device_count()
        lowercase_			:       Dict									=			num_samples * [prompt]
        lowercase_			:       Any									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       int									=			replicate(__UpperCamelCase    )
        lowercase_			:       int									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			shard(__UpperCamelCase    )

        lowercase_			:       List[str]									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 0.0400_3906)    ) < 1e-3
             assert np.abs((np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 237_3516.75)    ) < 5e-1
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       str									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa    )

        lowercase_			:       Optional[int]									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       Optional[Any]									=			jax.random.PRNGKey(0    )
        lowercase_			:       Union[str, Any]									=			50

        lowercase_			:       Optional[Any]									=			jax.device_count()
        lowercase_			:       Tuple									=			num_samples * [prompt]
        lowercase_			:       Union[str, Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       List[Any]									=			replicate(__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			shard(__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 0.0400_3906)    ) < 1e-3
             assert np.abs((np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 237_3516.75)    ) < 5e-1
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_			:       Tuple									=			FlaxDDIMScheduler(
            beta_start=0.0_0085  ,beta_end=0.012  ,beta_schedule='scaled_linear'  ,set_alpha_to_one=__UpperCamelCase  ,steps_offset=1  ,)

        lowercase_							,						lowercase_			:       str									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa  ,scheduler=__UpperCamelCase  ,safety_checker=__UpperCamelCase  ,)
        lowercase_			:       Optional[int]									=			scheduler.create_state()

        lowercase_			:       List[Any]									=			scheduler_state

        lowercase_			:       List[Any]									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       str									=			jax.random.PRNGKey(0    )
        lowercase_			:       str									=			50

        lowercase_			:       List[Any]									=			jax.device_count()
        lowercase_			:       List[str]									=			num_samples * [prompt]
        lowercase_			:       Union[str, Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       List[Any]									=			replicate(__UpperCamelCase    )
        lowercase_			:       int									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       str									=			shard(__UpperCamelCase    )

        lowercase_			:       str									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 0.0_4504_3945)    ) < 1e-3
             assert np.abs((np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 234_7693.5)    ) < 5e-1







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


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       Union[str, Any]									=			jax.device_count()
        lowercase_			:       List[str]									=			num_samples * [prompt]
        lowercase_			:       int									=			jax.random.split(jax.random.PRNGKey(0    )  ,__UpperCamelCase    )

        lowercase_							,						lowercase_			:       Dict									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa  ,safety_checker=__UpperCamelCase  ,)

        lowercase_			:       str									=			replicate(__UpperCamelCase    )
        lowercase_			:       int									=			pipeline.prepare_inputs(__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			shard(__UpperCamelCase    )
        lowercase_			:       Tuple									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images
        assert images.shape == (num_samples, 1, 512, 512, 3)
        lowercase_			:       Union[str, Any]									=			images[2, 0, 256, 10:17, 1]

        # With memory efficient attention
        lowercase_							,						lowercase_			:       List[Any]									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa  ,safety_checker=__UpperCamelCase  ,use_memory_efficient_attention=__UpperCamelCase  ,)

        lowercase_			:       List[Any]									=			replicate(__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )
        lowercase_			:       List[Any]									=			shard(__UpperCamelCase    )
        lowercase_			:       Any									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images
        assert images_eff.shape == (num_samples, 1, 512, 512, 3)
        lowercase_			:       Optional[int]									=			images[2, 0, 256, 10:17, 1]

        # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
        # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
        assert abs(slice_eff - slice    ).max() < 1e-2

