





"""simple docstring"""





import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      str ):


        '''simple docstring'''




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

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						List[Any]       =						FlaxStableDiffusionPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2'''						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)

        __UpperCAmelCase    :						Dict       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						Any       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						replicate(__lowercase )
        __UpperCAmelCase    :						Optional[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						str       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						Dict       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						Tuple       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        __UpperCAmelCase    :						Any       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        __UpperCAmelCase    :						Union[str, Any]       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						str       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						List[Any]       =						jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2







      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						'''stabilityai/stable-diffusion-2'''
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						FlaxDPMSolverMultistepScheduler.from_pretrained(__lowercase						,				subfolder='''scheduler''' )
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						FlaxStableDiffusionPipeline.from_pretrained(
            __lowercase						,				scheduler=__lowercase						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)
        __UpperCAmelCase    :						Dict       =						scheduler_params

        __UpperCAmelCase    :						int       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						List[Any]       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						replicate(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						List[Any]       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						List[Any]       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

        __UpperCAmelCase    :						List[str]       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						Any       =						jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2






"""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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						SwinvaConfig()
  __UpperCAmelCase    :						Any       =						swinva_name.split('''_'''  )

  __UpperCAmelCase    :						Optional[Any]       =						name_split[1]
  if "to" in name_split[3]:
    __UpperCAmelCase    :						List[str]       =						int(name_split[3][-3:]  )
  else:
    __UpperCAmelCase    :						Union[str, Any]       =						int(name_split[3]  )
  if "to" in name_split[2]:
    __UpperCAmelCase    :						List[str]       =						int(name_split[2][-2:]  )
  else:
    __UpperCAmelCase    :						str       =						int(name_split[2][6:]  )

  if model_size == "tiny":
    __UpperCAmelCase    :						Union[str, Any]       =						96
    __UpperCAmelCase    :						str       =						(2, 2, 6, 2)
    __UpperCAmelCase    :						Union[str, Any]       =						(3, 6, 12, 24)
  elif model_size == "small":
    __UpperCAmelCase    :						List[Any]       =						96
    __UpperCAmelCase    :						Union[str, Any]       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						str       =						(3, 6, 12, 24)
  elif model_size == "base":
    __UpperCAmelCase    :						Any       =						1_28
    __UpperCAmelCase    :						int       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						Dict       =						(4, 8, 16, 32)
  else:
    __UpperCAmelCase    :						Union[str, Any]       =						1_92
    __UpperCAmelCase    :						Union[str, Any]       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						str       =						(6, 12, 24, 48)

  if "to" in swinva_name:
    __UpperCAmelCase    :						Optional[int]       =						(12, 12, 12, 6)

  if ("22k" in swinva_name) and ("to" not in swinva_name):
    __UpperCAmelCase    :						int       =						2_18_41
    __UpperCAmelCase    :						Optional[Any]       =						'''huggingface/label-files'''
    __UpperCAmelCase    :						Optional[Any]       =						'''imagenet-22k-id2label.json'''
    __UpperCAmelCase    :						Any       =						json.load(open(hf_hub_download(UpperCAmelCase_  ,			UpperCAmelCase_  ,			repo_type='''dataset'''  )  ,			'''r'''  )  )
    __UpperCAmelCase    :						List[Any]       =						{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
    __UpperCAmelCase    :						Optional[Any]       =						idalabel
    __UpperCAmelCase    :						Optional[int]       =						{v: k for k, v in idalabel.items()}

  else:
    __UpperCAmelCase    :						List[Any]       =						10_00
    __UpperCAmelCase    :						str       =						'''huggingface/label-files'''
    __UpperCAmelCase    :						Tuple       =						'''imagenet-1k-id2label.json'''
    __UpperCAmelCase    :						Optional[int]       =						json.load(open(hf_hub_download(UpperCAmelCase_  ,			UpperCAmelCase_  ,			repo_type='''dataset'''  )  ,			'''r'''  )  )
    __UpperCAmelCase    :						str       =						{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
    __UpperCAmelCase    :						Any       =						idalabel
    __UpperCAmelCase    :						List[Any]       =						{v: k for k, v in idalabel.items()}

  __UpperCAmelCase    :						Optional[int]       =						img_size
  __UpperCAmelCase    :						int       =						num_classes
  __UpperCAmelCase    :						Tuple       =						embed_dim
  __UpperCAmelCase    :						str       =						depths
  __UpperCAmelCase    :						Optional[Any]       =						num_heads
  __UpperCAmelCase    :						Optional[int]       =						window_size

  return config


def       lowerCamelCase_       (   UpperCAmelCase_  )						->str:





  """simple docstring"""


  if "patch_embed.proj" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''patch_embed.proj'''  ,			'''embeddings.patch_embeddings.projection'''  )
  if "patch_embed.norm" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''patch_embed.norm'''  ,			'''embeddings.norm'''  )
  if "layers" in name:
    __UpperCAmelCase    :						Optional[int]       =						'''encoder.''' + name
  if "attn.proj" in name:
    __UpperCAmelCase    :						Optional[Any]       =						name.replace('''attn.proj'''  ,			'''attention.output.dense'''  )
  if "attn" in name:
    __UpperCAmelCase    :						List[Any]       =						name.replace('''attn'''  ,			'''attention.self'''  )
  if "norm1" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''norm1'''  ,			'''layernorm_before'''  )
  if "norm2" in name:
    __UpperCAmelCase    :						Dict       =						name.replace('''norm2'''  ,			'''layernorm_after'''  )
  if "mlp.fc1" in name:
    __UpperCAmelCase    :						str       =						name.replace('''mlp.fc1'''  ,			'''intermediate.dense'''  )
  if "mlp.fc2" in name:
    __UpperCAmelCase    :						int       =						name.replace('''mlp.fc2'''  ,			'''output.dense'''  )
  if "q_bias" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''q_bias'''  ,			'''query.bias'''  )
  if "k_bias" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''k_bias'''  ,			'''key.bias'''  )
  if "v_bias" in name:
    __UpperCAmelCase    :						str       =						name.replace('''v_bias'''  ,			'''value.bias'''  )
  if "cpb_mlp" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''cpb_mlp'''  ,			'''continuous_position_bias_mlp'''  )
  if name == "norm.weight":
    __UpperCAmelCase    :						int       =						'''layernorm.weight'''
  if name == "norm.bias":
    __UpperCAmelCase    :						Optional[Any]       =						'''layernorm.bias'''

  if "head" in name:
    __UpperCAmelCase    :						List[Any]       =						name.replace('''head'''  ,			'''classifier'''  )
  else:
    __UpperCAmelCase    :						List[Any]       =						'''swinv2.''' + name

  return name


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->int:





  """simple docstring"""


  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase    :						Any       =						orig_state_dict.pop(UpperCAmelCase_  )

    if "mask" in key:
      continue
    elif "qkv" in key:
      __UpperCAmelCase    :						int       =						key.split('''.'''  )
      __UpperCAmelCase    :						int       =						int(key_split[1]  )
      __UpperCAmelCase    :						str       =						int(key_split[3]  )
      __UpperCAmelCase    :						List[str]       =						model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        __UpperCAmelCase    :						Optional[Any]       =						val[:dim, :]
        __UpperCAmelCase    :						Union[str, Any]       =						val[dim : dim * 2, :]
        __UpperCAmelCase    :						List[Any]       =						val[-dim:, :]
      else:
        __UpperCAmelCase    :						Optional[int]       =						val[:dim]
        __UpperCAmelCase    :						Any       =						val[
            dim : dim * 2
        ]
        __UpperCAmelCase    :						List[str]       =						val[-dim:]
    else:
      __UpperCAmelCase    :						Tuple       =						val

  return orig_state_dict


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						timm.create_model(UpperCAmelCase_  ,			pretrained=UpperCAmelCase_  )
  timm_model.eval()

  __UpperCAmelCase    :						Any       =						get_swinva_config(UpperCAmelCase_  )
  __UpperCAmelCase    :						List[str]       =						SwinvaForImageClassification(UpperCAmelCase_  )
  model.eval()

  __UpperCAmelCase    :						int       =						convert_state_dict(timm_model.state_dict()  ,			UpperCAmelCase_  )
  model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Optional[Any]       =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''

  __UpperCAmelCase    :						Optional[Any]       =						AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''  ,			'''-'''  )  )  )
  __UpperCAmelCase    :						List[Any]       =						Image.open(requests.get(UpperCAmelCase_  ,			stream=UpperCAmelCase_  ).raw  )
  __UpperCAmelCase    :						Union[str, Any]       =						image_processor(images=UpperCAmelCase_  ,			return_tensors='''pt'''  )

  __UpperCAmelCase    :						List[Any]       =						timm_model(inputs['''pixel_values''']  )
  __UpperCAmelCase    :						Tuple       =						model(**UpperCAmelCase_  ).logits

  assert torch.allclose(UpperCAmelCase_  ,			UpperCAmelCase_  ,			atol=1e-3  )

  print(f'''Saving model {swinva_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_  )

  model.push_to_hub(
      repo_path_or_name=Path(UpperCAmelCase_  ,			UpperCAmelCase_  )  ,			organization='''nandwalritik'''  ,			commit_message='''Add model'''  ,			)


if __name__ == "__main__":
  lowercase__				:List[Any]				   =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--swinv2_name',
      default='swinv2_tiny_patch4_window8_256',
      type=str,
      help='Name of the Swinv2 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.'
  )

  lowercase__				:List[Any]				   =       parser.parse_args()
  convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)