





'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


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






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     List[Any]:
     __A						: Optional[Any]      =       SwinConfig(
         embed_dim=1_92 ,							depths=(2, 2, 18, 2) ,							num_heads=(6, 12, 24, 48) ,							window_size=12 ,							out_features=['stage2', 'stage3', 'stage4'] ,							)

     __A						: str      =       DetaConfig(
         backbone_config=__snake_case ,							num_queries=9_00 ,							encoder_ffn_dim=20_48 ,							decoder_ffn_dim=20_48 ,							num_feature_levels=5 ,							assign_first_stage=__snake_case ,							with_box_refine=__snake_case ,							two_stage=__snake_case ,							)

     # set labels
     __A						: List[str]      =       'huggingface/label-files'
     if "o365" in model_name:
          __A						: Optional[Any]      =       3_66
          __A						: Any      =       'object365-id2label.json'
     else:
          __A						: Optional[Any]      =       91
          __A						: Any      =       'coco-detection-id2label.json'

     __A						: List[str]      =       num_labels
     __A						: List[str]      =       json.load(open(cached_download(hf_hub_url(__snake_case ,							__snake_case ,							repo_type='dataset'			)			) ,							'r'			)			)
     __A						: Optional[Any]      =       {int(__snake_case			): v for k, v in idalabel.items()}
     __A						: Dict      =       idalabel
     __A						: List[str]      =       {v: k for k, v in idalabel.items()}

     return config






def        _lowerCAmelCase			(  __snake_case  : List[Any]			)     ->     str:
     __A						: Optional[Any]      =       []

     # stem
     # fmt: off
     rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight')			)
     rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias')			)
     rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight')			)
     rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias')			)
     # stages
     for i in range(len(config.backbone_config.depths			)			):
          for j in range(config.backbone_config.depths[i]			):
               rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')			)

          if i < 3:
               rename_keys.append((f'backbone.0.body.layers.{i}.downsample.reduction.weight', f'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.weight', f'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')			)
               rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.bias', f'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')			)

     rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight')			)
     rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias')			)
     rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight')			)
     rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias')			)
     rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight')			)
     rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias')			)

     # transformer encoder
     for i in range(config.encoder_layers			):
          rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight')			)
          rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias')			)

     # transformer decoder
     for i in range(config.decoder_layers			):
          rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight')			)
          rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias')			)

     # fmt: on

     return rename_keys






def        _lowerCAmelCase			(  __snake_case  : Any ,							__snake_case  : Union[str, Any] ,							__snake_case  : List[str]			)     ->     Union[str, Any]:
     __A						: Optional[Any]      =       dct.pop(__snake_case			)
     __A						: Tuple      =       val






def        _lowerCAmelCase			(  __snake_case  : int ,							__snake_case  : Union[str, Any]			)     ->     int:
     __A						: List[str]      =       [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
     for i in range(len(backbone_config.depths			)			):
          __A						: Any      =       num_features[i]
          for j in range(backbone_config.depths[i]			):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               __A						: List[Any]      =       state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'			)
               __A						: Union[str, Any]      =       state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'			)
               # next, add query, keys and values (in that order) to the state dict
               __A						: Dict      =       in_proj_weight[:dim, :]
               __A						: Dict      =       in_proj_bias[: dim]
               __A						: List[Any]      =       in_proj_weight[
                   dim : dim * 2, :
               ]
               __A						: Tuple      =       in_proj_bias[
                   dim : dim * 2
               ]
               __A						: Optional[Any]      =       in_proj_weight[
                   -dim :, :
               ]
               __A						: Tuple      =       in_proj_bias[-dim :]
               # fmt: on






def        _lowerCAmelCase			(  __snake_case  : int ,							__snake_case  : List[str]			)     ->     Optional[Any]:
     # transformer decoder self-attention layers
     __A						: Optional[Any]      =       config.d_model
     for i in range(config.decoder_layers			):
          # read in weights + bias of input projection layer of self-attention
          __A						: Union[str, Any]      =       state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'			)
          __A						: Optional[Any]      =       state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_bias'			)
          # next, add query, keys and values (in that order) to the state dict
          __A						: Tuple      =       in_proj_weight[:hidden_size, :]
          __A						: List[str]      =       in_proj_bias[:hidden_size]
          __A						: List[str]      =       in_proj_weight[
              hidden_size : hidden_size * 2, :
          ]
          __A						: str      =       in_proj_bias[hidden_size : hidden_size * 2]
          __A						: int      =       in_proj_weight[-hidden_size:, :]
          __A						: List[str]      =       in_proj_bias[-hidden_size:]






def        _lowerCAmelCase			(  )     ->     int:
     __A						: int      =       'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A						: str      =       Image.open(requests.get(__snake_case ,							stream=__snake_case			).raw			)

     return im






@torch.no_grad()
def        _lowerCAmelCase			(  __snake_case  : Union[str, Any] ,							__snake_case  : Dict ,							__snake_case  : List[str]			)     ->     Optional[int]:
     __A						: Dict      =       get_deta_config(__snake_case			)

     # load original state dict
     if model_name == "deta-swin-large":
          __A						: Any      =       hf_hub_download(repo_id='nielsr/deta-checkpoints' ,							filename='adet_swin_ft.pth'			)
     elif model_name == "deta-swin-large-o365":
          __A						: List[str]      =       hf_hub_download(repo_id='jozhang97/deta-swin-l-o365' ,							filename='deta_swin_pt_o365.pth'			)
     else:
          raise ValueError(f'Model name {model_name} not supported'			)

     __A						: Optional[int]      =       torch.load(__snake_case ,							map_location='cpu'			)['model']

     # original state dict
     for name, param in state_dict.items():
          print(__snake_case ,							param.shape			)

     # rename keys
     __A						: List[Any]      =       create_rename_keys(__snake_case			)
     for src, dest in rename_keys:
          rename_key(__snake_case ,							__snake_case ,							__snake_case			)
     read_in_swin_q_k_v(__snake_case ,							config.backbone_config			)
     read_in_decoder_q_k_v(__snake_case ,							__snake_case			)

     # fix some prefixes
     for key in state_dict.copy().keys():
          if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
               __A						: str      =       state_dict.pop(__snake_case			)
               __A						: Optional[int]      =       val
          if "input_proj" in key:
               __A						: List[Any]      =       state_dict.pop(__snake_case			)
               __A						: List[str]      =       val
          if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
               __A						: Tuple      =       state_dict.pop(__snake_case			)
               __A						: Optional[Any]      =       val

    # finally, create HuggingFace model and load state dict
     __A						: Optional[Any]      =       DetaForObjectDetection(__snake_case			)
     model.load_state_dict(__snake_case			)
     model.eval()

     __A						: List[Any]      =       'cuda' if torch.cuda.is_available() else 'cpu'
     model.to(__snake_case			)

     # load image processor
     __A						: Any      =       DetaImageProcessor(format='coco_detection'			)

     # verify our conversion on image
     __A						: Union[str, Any]      =       prepare_img()
     __A						: List[str]      =       processor(images=__snake_case ,							return_tensors='pt'			)
     __A						: int      =       encoding['pixel_values']
     __A						: List[str]      =       model(pixel_values.to(__snake_case			)			)

     # verify logits
     print('Logits:' ,							outputs.logits[0, :3, :3]			)
     print('Boxes:' ,							outputs.pred_boxes[0, :3, :3]			)
     if model_name == "deta-swin-large":
          __A						: Dict      =       torch.tensor(
              [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]]			)
          __A						: Any      =       torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]]			)
     elif model_name == "deta-swin-large-o365":
          __A						: Optional[int]      =       torch.tensor(
              [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]]			)
          __A						: Optional[int]      =       torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]]			)
     assert torch.allclose(outputs.logits[0, :3, :3] ,							expected_logits.to(__snake_case			) ,							atol=1e-4			)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,							expected_boxes.to(__snake_case			) ,							atol=1e-4			)
     print('Everything ok!'			)

     if pytorch_dump_folder_path:
          # Save model and processor
          logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'			)
          Path(__snake_case			).mkdir(exist_ok=__snake_case			)
          model.save_pretrained(__snake_case			)
          processor.save_pretrained(__snake_case			)

     # Push to hub
     if push_to_hub:
          print('Pushing model and processor to hub...'			)
          model.push_to_hub(f'jozhang97/{model_name}'			)
          processor.push_to_hub(f'jozhang97/{model_name}'			)


if __name__ == "__main__":
  lowercase__    :      Any						=    argparse.ArgumentParser()

  parser.add_argument(
      '''--model_name''',
      type=str,
      default='''deta-swin-large''',
      choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
      help='''Name of the model you\'d like to convert.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default=None,
      type=str,
      help='''Path to the folder to output PyTorch model.''',
  )
  parser.add_argument(
      '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
  )
  lowercase__    :      Any						=    parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''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  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with tempfile.TemporaryDirectory() as tmpdirname:
               # pipeline has Flax weights
               __A						: Union[str, Any]      =       FlaxDiffusionPipeline.from_pretrained(
                   'hf-internal-testing/tiny-stable-diffusion-pipe'						,	safety_checker=_UpperCAmelCase						,	cache_dir=_UpperCAmelCase)

               __A						: Optional[Any]      =       [t[-1] for t in os.walk(os.path.join(_UpperCAmelCase						,	os.listdir(_UpperCAmelCase)[0]						,	'snapshots'))]
               __A						: int      =       [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  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A						: Optional[int]      =       FlaxStableDiffusionPipeline.from_pretrained(
              'hf-internal-testing/tiny-stable-diffusion-pipe'						,	safety_checker=_UpperCAmelCase)

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

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

          __A						: Tuple      =       jax.device_count()
          __A						: Union[str, Any]      =       num_samples * [prompt]
          __A						: Tuple      =       pipeline.prepare_inputs(_UpperCAmelCase)

          # shard inputs and rng
          __A						: str      =       replicate(_UpperCAmelCase)
          __A						: Tuple      =       jax.random.split(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Union[str, Any]      =       shard(_UpperCAmelCase)

          __A						: Union[str, 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() - 4.1514745) < 1e-3
               assert np.abs(np.abs(_UpperCAmelCase						,	dtype=np.floataa).sum() - 49947.875) < 5e-1

          __A						: List[str]      =       pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
          assert len(_UpperCAmelCase) == num_samples


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A						: str      =       FlaxStableDiffusionPipeline.from_pretrained(
              'CompVis/stable-diffusion-v1-4'						,	revision='flax'						,	safety_checker=_UpperCAmelCase)

          __A						: 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'
          )

          __A						: Tuple      =       jax.random.PRNGKey(0)
          __A						: Any      =       50

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

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

          __A						: Tuple      =       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() - 0.05652401)) < 1e-3
               assert np.abs((np.abs(_UpperCAmelCase						,	dtype=np.floataa).sum() - 2383808.2)) < 5e-1


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A						: List[str]      =       FlaxStableDiffusionPipeline.from_pretrained(
              'CompVis/stable-diffusion-v1-4'						,	revision='bf16'						,	dtype=jnp.bfloataa						,	safety_checker=_UpperCAmelCase)

          __A						: 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'
          )

          __A						: str      =       jax.random.PRNGKey(0)
          __A						: Any      =       50

          __A						: Optional[int]      =       jax.device_count()
          __A						: int      =       num_samples * [prompt]
          __A						: Optional[int]      =       pipeline.prepare_inputs(_UpperCAmelCase)

          # shard inputs and rng
          __A						: Optional[int]      =       replicate(_UpperCAmelCase)
          __A						: List[str]      =       jax.random.split(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Dict      =       shard(_UpperCAmelCase)

          __A						: 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() - 0.04003906)) < 1e-3
               assert np.abs((np.abs(_UpperCAmelCase						,	dtype=np.floataa).sum() - 2373516.75)) < 5e-1


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A						: Optional[Any]      =       FlaxStableDiffusionPipeline.from_pretrained(
              'CompVis/stable-diffusion-v1-4'						,	revision='bf16'						,	dtype=jnp.bfloataa)

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

          __A						: Any      =       jax.random.PRNGKey(0)
          __A						: List[str]      =       50

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

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

          __A						: 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() - 0.04003906)) < 1e-3
               assert np.abs((np.abs(_UpperCAmelCase						,	dtype=np.floataa).sum() - 2373516.75)) < 5e-1


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       FlaxDDIMScheduler(
              beta_start=0.00085						,	beta_end=0.012						,	beta_schedule='scaled_linear'						,	set_alpha_to_one=_UpperCAmelCase						,	steps_offset=1						,	)

          __A ,__A						: Any      =       FlaxStableDiffusionPipeline.from_pretrained(
              'CompVis/stable-diffusion-v1-4'						,	revision='bf16'						,	dtype=jnp.bfloataa						,	scheduler=_UpperCAmelCase						,	safety_checker=_UpperCAmelCase						,	)
          __A						: Optional[Any]      =       scheduler.create_state()

          __A						: Any      =       scheduler_state

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

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

          __A						: Optional[Any]      =       jax.device_count()
          __A						: Any      =       num_samples * [prompt]
          __A						: Optional[Any]      =       pipeline.prepare_inputs(_UpperCAmelCase)

          # shard inputs and rng
          __A						: int      =       replicate(_UpperCAmelCase)
          __A						: Any      =       jax.random.split(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Tuple      =       shard(_UpperCAmelCase)

          __A						: Union[str, 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() - 0.045043945)) < 1e-3
               assert np.abs((np.abs(_UpperCAmelCase						,	dtype=np.floataa).sum() - 2347693.5)) < 5e-1


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						: int      =       jax.device_count()
          __A						: List[Any]      =       num_samples * [prompt]
          __A						: List[Any]      =       jax.random.split(jax.random.PRNGKey(0)						,	_UpperCAmelCase)

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

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

          # With memory efficient attention
          __A ,__A						: str      =       FlaxStableDiffusionPipeline.from_pretrained(
              'CompVis/stable-diffusion-v1-4'						,	revision='bf16'						,	dtype=jnp.bfloataa						,	safety_checker=_UpperCAmelCase						,	use_memory_efficient_attention=_UpperCAmelCase						,	)

          __A						: Any      =       replicate(_UpperCAmelCase)
          __A						: List[Any]      =       pipeline.prepare_inputs(_UpperCAmelCase)
          __A						: Optional[Any]      =       shard(_UpperCAmelCase)
          __A						: List[Any]      =       pipeline(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	jit=_UpperCAmelCase).images
          assert images_eff.shape == (num_samples, 1, 512, 512, 3)
          __A						: List[Any]      =       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