


import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
     raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
     raise Exception('''requires mxnet == 1.5.0''')

logging.set_verbosity_info()
snake_case    :   int	    =					logging.get_logger(__name__)

snake_case    :   Optional[int]	    =					'''The Nymphenburg Palace is a beautiful palace in Munich!'''
def     __lowerCamelCase						(			UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      a						:Optional[Any]										=	{
          '''attention_cell''': '''multi_head''',
          '''num_layers''': 4,
          '''units''': 1024,
          '''hidden_size''': 768,
          '''max_length''': 512,
          '''num_heads''': 8,
          '''scaled''': True,
          '''dropout''': 0.1,
          '''use_residual''': True,
          '''embed_size''': 1024,
          '''embed_dropout''': 0.1,
          '''word_embed''': None,
          '''layer_norm_eps''': 1E-5,
          '''token_type_vocab_size''': 2,
      }

      a						:List[str]										=	bort_4_8_768_1024_hparams

      # Let's construct the original Bort model here
      # Taken from official BERT implementation, see:
      # https://github.com/alexa/bort/blob/master/bort/bort.py
      a						:int										=	BERTEncoder(
          attention_cell=predefined_args['''attention_cell''']			,  num_layers=predefined_args['''num_layers''']			,  units=predefined_args['''units''']			,  hidden_size=predefined_args['''hidden_size''']			,  max_length=predefined_args['''max_length''']			,  num_heads=predefined_args['''num_heads''']			,  scaled=predefined_args['''scaled''']			,  dropout=predefined_args['''dropout''']			,  output_attention=UpperCAmelCase_			,  output_all_encodings=UpperCAmelCase_			,  use_residual=predefined_args['''use_residual''']			,  activation=predefined_args.get('''activation'''			,  '''gelu'''						)			,  layer_norm_eps=predefined_args.get('''layer_norm_eps'''			,  UpperCAmelCase_						)			,  )

      # Vocab information needs to be fetched first
      # It's the same as RoBERTa, so RobertaTokenizer can be used later
      a						:List[str]										=	'''openwebtext_ccnews_stories_books_cased'''

      # Specify download folder to Gluonnlp's vocab
      a						:Optional[int]										=	os.path.join(get_home_dir()			,  '''models'''						)
      a						:Tuple										=	_load_vocab(UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_			,  cls=UpperCAmelCase_						)

      a						:Tuple										=	nlp.model.BERTModel(
          UpperCAmelCase_			,  len(UpperCAmelCase_						)			,  units=predefined_args['''units''']			,  embed_size=predefined_args['''embed_size''']			,  embed_dropout=predefined_args['''embed_dropout''']			,  word_embed=predefined_args['''word_embed''']			,  use_pooler=UpperCAmelCase_			,  use_token_type_embed=UpperCAmelCase_			,  token_type_vocab_size=predefined_args['''token_type_vocab_size''']			,  use_classifier=UpperCAmelCase_			,  use_decoder=UpperCAmelCase_			,  )

      original_bort.load_parameters(UpperCAmelCase_			,  cast_dtype=UpperCAmelCase_			,  ignore_extra=UpperCAmelCase_						)
      a						:Union[str, Any]										=	original_bort._collect_params_with_prefix()

      # Build our config 🤗
      a						:Union[str, Any]										=	{
          '''architectures''': ['''BertForMaskedLM'''],
          '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
          '''hidden_act''': '''gelu''',
          '''hidden_dropout_prob''': predefined_args['''dropout'''],
          '''hidden_size''': predefined_args['''embed_size'''],
          '''initializer_range''': 0.02,
          '''intermediate_size''': predefined_args['''hidden_size'''],
          '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
          '''max_position_embeddings''': predefined_args['''max_length'''],
          '''model_type''': '''bort''',
          '''num_attention_heads''': predefined_args['''num_heads'''],
          '''num_hidden_layers''': predefined_args['''num_layers'''],
          '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
          '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
          '''vocab_size''': len(UpperCAmelCase_						),
      }

      a						:Dict										=	BertConfig.from_dict(UpperCAmelCase_						)
      a						:int										=	BertForMaskedLM(UpperCAmelCase_						)
      hf_bort_model.eval()

      # Parameter mapping table (Gluonnlp to Transformers)
      # * denotes layer index
      #
      # | Gluon Parameter                                                | Transformers Parameter
      # | -------------------------------------------------------------- | ----------------------
      # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
      # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
      # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
      # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
      # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
      # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
      # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
      # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
      # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
      # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
      # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
      # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
      # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
      # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
      # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
      # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
      # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
      # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
      # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
      # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

      # Helper function to convert MXNET Arrays to PyTorch
      def to_torch(UpperCAmelCase_						:		Optional[Any]						) -> nn.Parameter:
            return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()						)						)

      # Check param shapes and map new HF param back
      def check_and_map_params(UpperCAmelCase_						:		Dict			,  UpperCAmelCase_						:		str						):
            a						:Optional[int]										=	hf_param.shape

            a						:str										=	to_torch(params[gluon_param]						)
            a						:Any										=	gluon_param.shape

            assert (
                shape_hf == shape_gluon
            ), F'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

            return gluon_param

      a						:List[Any]										=	check_and_map_params(
          hf_bort_model.bert.embeddings.word_embeddings.weight			,  '''word_embed.0.weight'''						)
      a						:Tuple										=	check_and_map_params(
          hf_bort_model.bert.embeddings.position_embeddings.weight			,  '''encoder.position_weight'''						)
      a						:int										=	check_and_map_params(
          hf_bort_model.bert.embeddings.LayerNorm.bias			,  '''encoder.layer_norm.beta'''						)
      a						:Any										=	check_and_map_params(
          hf_bort_model.bert.embeddings.LayerNorm.weight			,  '''encoder.layer_norm.gamma'''						)

      # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
      a						:int										=	torch.zeros_like(
          hf_bort_model.bert.embeddings.token_type_embeddings.weight.data						)

      for i in range(hf_bort_config.num_hidden_layers						):
            a						:BertLayer										=	hf_bort_model.bert.encoder.layer[i]

            # self attention
            a						:BertSelfAttention										=	layer.attention.self

            a						:List[str]										=	check_and_map_params(
                self_attn.key.bias.data			,  F'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''						)

            a						:Dict										=	check_and_map_params(
                self_attn.key.weight.data			,  F'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''						)
            a						:Tuple										=	check_and_map_params(
                self_attn.query.bias.data			,  F'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''						)
            a						:Any										=	check_and_map_params(
                self_attn.query.weight.data			,  F'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''						)
            a						:Dict										=	check_and_map_params(
                self_attn.value.bias.data			,  F'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''						)
            a						:int										=	check_and_map_params(
                self_attn.value.weight.data			,  F'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''						)

            # self attention output
            a						:BertSelfOutput										=	layer.attention.output

            a						:List[Any]										=	check_and_map_params(
                self_output.dense.bias			,  F'''encoder.transformer_cells.{i}.proj.bias'''						)
            a						:List[Any]										=	check_and_map_params(
                self_output.dense.weight			,  F'''encoder.transformer_cells.{i}.proj.weight'''						)
            a						:Optional[int]										=	check_and_map_params(
                self_output.LayerNorm.bias			,  F'''encoder.transformer_cells.{i}.layer_norm.beta'''						)
            a						:Optional[Any]										=	check_and_map_params(
                self_output.LayerNorm.weight			,  F'''encoder.transformer_cells.{i}.layer_norm.gamma'''						)

            # intermediate
            a						:BertIntermediate										=	layer.intermediate

            a						:str										=	check_and_map_params(
                intermediate.dense.bias			,  F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''						)
            a						:Tuple										=	check_and_map_params(
                intermediate.dense.weight			,  F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''						)

            # output
            a						:BertOutput										=	layer.output

            a						:Union[str, Any]										=	check_and_map_params(
                bert_output.dense.bias			,  F'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''						)
            a						:Optional[int]										=	check_and_map_params(
                bert_output.dense.weight			,  F'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''						)
            a						:Optional[Any]										=	check_and_map_params(
                bert_output.LayerNorm.bias			,  F'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''						)
            a						:Union[str, Any]										=	check_and_map_params(
                bert_output.LayerNorm.weight			,  F'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma'''						)

      # Save space and energy 🎄
      hf_bort_model.half()

      # Compare output of both models
      a						:List[Any]										=	RobertaTokenizer.from_pretrained('''roberta-base'''						)

      a						:Any										=	tokenizer.encode_plus(UpperCAmelCase_						)['''input_ids''']

      # Get gluon output
      a						:Tuple										=	mx.nd.array([input_ids]						)
      a						:int										=	original_bort(inputs=UpperCAmelCase_			,  token_types=[]						)

      # Get Transformer output (save and reload model again)
      hf_bort_model.save_pretrained(UpperCAmelCase_						)
      a						:List[str]										=	BertModel.from_pretrained(UpperCAmelCase_						)
      hf_bort_model.eval()

      a						:int										=	tokenizer.encode_plus(UpperCAmelCase_			,  return_tensors='''pt'''						)
      a						:Any										=	hf_bort_model(**UpperCAmelCase_						)[0]

      a						:Optional[int]										=	output_gluon[0].asnumpy()
      a						:Union[str, Any]										=	output_hf[0].detach().numpy()

      a						:int										=	np.max(np.abs(hf_layer - gluon_layer						)						).item()
      a						:Optional[int]										=	np.allclose(UpperCAmelCase_			,  UpperCAmelCase_			,  atol=1E-3						)

      if success:
            print('''✔️ Both model do output the same tensors'''						)
      else:
            print('''❌ Both model do **NOT** output the same tensors'''						)
            print('''Absolute difference is:'''			,  UpperCAmelCase_						)


if __name__ == "__main__":
     snake_case    :   int	    =					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     snake_case    :   int	    =					parser.parse_args()
     convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 		_snake_case  (				_snake_case						,   _snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	CycleDiffusionPipeline
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     'negative_prompt',
     'height',
     'width',
     'negative_prompt_embeds',
 }
 SCREAMING_SNAKE_CASE__				=	PipelineTesterMixin.required_optional_params - {'latents'}
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}							)
 SCREAMING_SNAKE_CASE__				=	IMAGE_TO_IMAGE_IMAGE_PARAMS
 SCREAMING_SNAKE_CASE__				=	IMAGE_TO_IMAGE_IMAGE_PARAMS




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:Optional[Any]										=	UNetaDConditionModel(
           block_out_channels=(32, 64) ,  layers_per_block=2 ,  sample_size=32 ,  in_channels=4 ,  out_channels=4 ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,  cross_attention_dim=32 ,  )
       a						:List[str]										=	DDIMScheduler(
           beta_start=0.0_0085 ,  beta_end=0.012 ,  beta_schedule='''scaled_linear''' ,  num_train_timesteps=1000 ,  clip_sample=_lowerCamelCase ,  set_alpha_to_one=_lowerCamelCase ,  )
       torch.manual_seed(0   )
       a						:List[str]										=	AutoencoderKL(
           block_out_channels=[32, 64] ,  in_channels=3 ,  out_channels=3 ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,  latent_channels=4 ,  )
       torch.manual_seed(0   )
       a						:Dict										=	CLIPTextConfig(
           bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=32 ,  intermediate_size=37 ,  layer_norm_eps=1e-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=1000 ,  )
       a						:str										=	CLIPTextModel(_lowerCamelCase   )
       a						:List[str]										=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       a						:Union[str, Any]										=	{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=0   ):
       a						:Tuple										=	floats_tensor((1, 3, 32, 32) ,  rng=random.Random(_lowerCamelCase   )   ).to(_lowerCamelCase   )
       a						:Tuple										=	image / 2 + 0.5
       if str(_lowerCamelCase   ).startswith('''mps'''   ):
             a						:List[str]										=	torch.manual_seed(_lowerCamelCase   )
       else:
             a						:Any										=	torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
       a						:int										=	{
           '''prompt''': '''An astronaut riding an elephant''',
           '''source_prompt''': '''An astronaut riding a horse''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''eta''': 0.1,
           '''strength''': 0.8,
           '''guidance_scale''': 3,
           '''source_guidance_scale''': 1,
           '''output_type''': '''numpy''',
       }
       return inputs




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       a						:Optional[Any]										=	self.get_dummy_components()
       a						:Dict										=	CycleDiffusionPipeline(**_lowerCamelCase   )
       a						:Optional[Any]										=	pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:List[str]										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Any										=	pipe(**_lowerCamelCase   )
       a						:List[Any]										=	output.images

       a						:str										=	images[0, -3:, -3:, -1]

       assert images.shape == (1, 32, 32, 3)
       a						:List[Any]										=	np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2




 @unittest.skipIf(torch_device != '''cuda''' ,  '''This test requires a GPU'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	self.get_dummy_components()
       for name, module in components.items():
             if hasattr(_lowerCamelCase ,  '''half'''   ):
                   a						:Union[str, Any]										=	module.half()
       a						:List[Any]										=	CycleDiffusionPipeline(**_lowerCamelCase   )
       a						:Dict										=	pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Tuple										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Optional[int]										=	pipe(**_lowerCamelCase   )
       a						:Optional[Any]										=	output.images

       a						:List[Any]										=	images[0, -3:, -3:, -1]

       assert images.shape == (1, 32, 32, 3)
       a						:str										=	np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_save_load_local()




 @unittest.skip('''non-deterministic pipeline'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_inference_batch_single_identical()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_dict_tuple_outputs_equivalent()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_save_load_optional_components()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_attention_slicing_forward_pass()







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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/cycle-diffusion/black_colored_car.png'''   )
       a						:Optional[int]										=	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''   )
       a						:Optional[Any]										=	init_image.resize((512, 512)   )

       a						:List[str]										=	'''CompVis/stable-diffusion-v1-4'''
       a						:List[str]										=	DDIMScheduler.from_pretrained(_lowerCamelCase ,  subfolder='''scheduler'''   )
       a						:Tuple										=	CycleDiffusionPipeline.from_pretrained(
           _lowerCamelCase ,  scheduler=_lowerCamelCase ,  safety_checker=_lowerCamelCase ,  torch_dtype=torch.floataa ,  revision='''fp16'''   )

       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )
       pipe.enable_attention_slicing()

       a						:Optional[Any]										=	'''A black colored car'''
       a						:Any										=	'''A blue colored car'''

       a						:str										=	torch.manual_seed(0   )
       a						:List[Any]										=	pipe(
           prompt=_lowerCamelCase ,  source_prompt=_lowerCamelCase ,  image=_lowerCamelCase ,  num_inference_steps=100 ,  eta=0.1 ,  strength=0.85 ,  guidance_scale=3 ,  source_guidance_scale=1 ,  generator=_lowerCamelCase ,  output_type='''np''' ,  )
       a						:int										=	output.images

       # the values aren't exactly equal, but the images look the same visually
       assert np.abs(image - expected_image   ).max() < 5e-1




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/cycle-diffusion/black_colored_car.png'''   )
       a						:Optional[int]										=	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''   )
       a						:List[str]										=	init_image.resize((512, 512)   )

       a						:List[str]										=	'''CompVis/stable-diffusion-v1-4'''
       a						:Any										=	DDIMScheduler.from_pretrained(_lowerCamelCase ,  subfolder='''scheduler'''   )
       a						:int										=	CycleDiffusionPipeline.from_pretrained(_lowerCamelCase ,  scheduler=_lowerCamelCase ,  safety_checker=_lowerCamelCase   )

       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )
       pipe.enable_attention_slicing()

       a						:Optional[int]										=	'''A black colored car'''
       a						:Any										=	'''A blue colored car'''

       a						:Optional[int]										=	torch.manual_seed(0   )
       a						:Union[str, Any]										=	pipe(
           prompt=_lowerCamelCase ,  source_prompt=_lowerCamelCase ,  image=_lowerCamelCase ,  num_inference_steps=100 ,  eta=0.1 ,  strength=0.85 ,  guidance_scale=3 ,  source_guidance_scale=1 ,  generator=_lowerCamelCase ,  output_type='''np''' ,  )
       a						:Optional[int]										=	output.images

       assert np.abs(image - expected_image   ).max() < 2e-2





