


'''simple docstring'''





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
   import torch



class        a					:





     def __init__(    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=14    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=7    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=True    ,	SCREAMING_SNAKE_CASE_					:				Any=False    ,	SCREAMING_SNAKE_CASE_					:				int=True    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=99    ,	SCREAMING_SNAKE_CASE_					:				Any=32    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=4    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=4    ,	SCREAMING_SNAKE_CASE_					:				int=4    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=37    ,	SCREAMING_SNAKE_CASE_					:				List[Any]="gelu"    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=0.1    ,	SCREAMING_SNAKE_CASE_					:				str=0.1    ,	SCREAMING_SNAKE_CASE_					:				List[str]=512    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=0.02    ,	):
        __lowerCamelCase:				str     =	parent
        __lowerCamelCase:				Optional[Any]     =	batch_size
        __lowerCamelCase:				str     =	seq_length
        __lowerCamelCase:				int     =	is_training
        __lowerCamelCase:				Union[str, Any]     =	use_input_mask
        __lowerCamelCase:				Optional[int]     =	use_token_type_ids
        __lowerCamelCase:				List[str]     =	use_labels
        __lowerCamelCase:				Optional[int]     =	vocab_size
        __lowerCamelCase:				Tuple     =	hidden_size
        __lowerCamelCase:				Optional[int]     =	rotary_dim
        __lowerCamelCase:				List[Any]     =	num_hidden_layers
        __lowerCamelCase:				str     =	num_attention_heads
        __lowerCamelCase:				Union[str, Any]     =	intermediate_size
        __lowerCamelCase:				Union[str, Any]     =	hidden_act
        __lowerCamelCase:				List[Any]     =	hidden_dropout_prob
        __lowerCamelCase:				Any     =	attention_probs_dropout_prob
        __lowerCamelCase:				str     =	max_position_embeddings
        __lowerCamelCase:				Optional[Any]     =	initializer_range
        __lowerCamelCase:				Optional[int]     =	None
        __lowerCamelCase:				Union[str, Any]     =	vocab_size - 1
        __lowerCamelCase:				Optional[Any]     =	vocab_size - 1
        __lowerCamelCase:				int     =	vocab_size - 1





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				Optional[int]     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size							)

        __lowerCamelCase:				Union[str, Any]     =	None
        if self.use_input_mask:
           __lowerCamelCase:				List[Any]     =	random_attention_mask([self.batch_size, self.seq_length]							)

        __lowerCamelCase:				Dict     =	GPTJConfig(
            vocab_size=self.vocab_size    ,	n_embd=self.hidden_size    ,	n_layer=self.num_hidden_layers    ,	n_head=self.num_attention_heads    ,	n_positions=self.max_position_embeddings    ,	use_cache=SCREAMING_SNAKE_CASE_    ,	bos_token_id=self.bos_token_id    ,	eos_token_id=self.eos_token_id    ,	pad_token_id=self.pad_token_id    ,	rotary_dim=self.rotary_dim    ,	)

        return (config, input_ids, input_mask)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				Optional[Any]     =	self.prepare_config_and_inputs()
        __lowerCamelCase:				Dict     =	config_and_inputs
        __lowerCamelCase:				Optional[Any]     =	{"""input_ids""": input_ids, """attention_mask""": attention_mask}
        return config, inputs_dict





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[Any]							):
        __lowerCamelCase:				int     =	20
        __lowerCamelCase:				List[str]     =	model_class_name(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	model.init_cache(input_ids.shape[0]    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	jnp.ones((input_ids.shape[0], max_decoder_length)    ,	dtype="""i4"""							)

        __lowerCamelCase:				Union[str, Any]     =	jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1							)[None, :]    ,	(input_ids.shape[0], input_ids.shape[-1] - 1)							)
        __lowerCamelCase:				Optional[Any]     =	model(
            input_ids[:, :-1]    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	past_key_values=SCREAMING_SNAKE_CASE_    ,	position_ids=SCREAMING_SNAKE_CASE_    ,	)

        __lowerCamelCase:				List[Any]     =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,	dtype="""i4"""							)
        __lowerCamelCase:				Tuple     =	model(
            input_ids[:, -1:]    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	past_key_values=outputs_cache.past_key_values    ,	position_ids=SCREAMING_SNAKE_CASE_    ,	)

        __lowerCamelCase:				List[str]     =	model(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
        self.parent.assertTrue(diff < 1E-3    ,	msg=F'Max diff is {diff}'							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        __lowerCamelCase:				str     =	20
        __lowerCamelCase:				Optional[Any]     =	model_class_name(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				str     =	jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])							)]    ,	axis=-1    ,	)

        __lowerCamelCase:				Optional[int]     =	model.init_cache(input_ids.shape[0]    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1							)[None, :]    ,	(input_ids.shape[0], input_ids.shape[-1] - 1)							)

        __lowerCamelCase:				List[Any]     =	model(
            input_ids[:, :-1]    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	past_key_values=SCREAMING_SNAKE_CASE_    ,	position_ids=SCREAMING_SNAKE_CASE_    ,	)
        __lowerCamelCase:				Any     =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,	dtype="""i4"""							)
        __lowerCamelCase:				Tuple     =	model(
            input_ids[:, -1:]    ,	past_key_values=outputs_cache.past_key_values    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	position_ids=SCREAMING_SNAKE_CASE_    ,	)

        __lowerCamelCase:				Optional[int]     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[int]     =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
        self.parent.assertTrue(diff < 1E-3    ,	msg=F'Max diff is {diff}'							)



@require_flax
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  List[str]						      =	(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
     UpperCAmelCase__ :  str						      =	(FlaxGPTJForCausalLM,) if is_flax_available() else ()





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				int     =	FlaxGPTJModelTester(self							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        for model_class_name in self.all_model_classes:
           __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        for model_class_name in self.all_model_classes:
           __lowerCamelCase:				Optional[Any]     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     @tooslow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				List[str]     =	GPTaTokenizer.from_pretrained("""gpt2"""    ,	pad_token="""<|endoftext|>"""    ,	padding_side="""left"""							)
        __lowerCamelCase:				Union[str, Any]     =	tokenizer(["""Hello this is a long string""", """Hey"""]    ,	return_tensors="""np"""    ,	padding=SCREAMING_SNAKE_CASE_    ,	truncation=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				int     =	FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""							)
        __lowerCamelCase:				Union[str, Any]     =	False
        __lowerCamelCase:				str     =	model.config.eos_token_id

        __lowerCamelCase:				Optional[Any]     =	jax.jit(model.generate							)

        __lowerCamelCase:				Union[str, Any]     =	jit_generate(
            inputs["""input_ids"""]    ,	attention_mask=inputs["""attention_mask"""]    ,	pad_token_id=tokenizer.pad_token_id							).sequences

        __lowerCamelCase:				Union[str, Any]     =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_    ,	skip_special_tokens=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[Any]     =	[
            """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
            """Hey, I'm a little late to the party. I'm going to""",
        ]

        self.assertListEqual(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     @is_pt_flax_cross_test
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				str     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
              # prepare inputs
              __lowerCamelCase:				List[str]     =	self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				Optional[int]     =	{k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

              # load corresponding PyTorch class
              __lowerCamelCase:				List[str]     =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
              __lowerCamelCase:				Optional[Any]     =	getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

              __lowerCamelCase:				Tuple     =	pt_inputs["""input_ids"""].shape
              __lowerCamelCase:				Union[str, Any]     =	np.random.randint(0    ,	seq_length - 1    ,	size=(batch_size,)							)
              for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_							):
                 __lowerCamelCase:				List[str]     =	0
                 __lowerCamelCase:				Union[str, Any]     =	1
                 __lowerCamelCase:				str     =	0
                 __lowerCamelCase:				Union[str, Any]     =	1
              __lowerCamelCase:				Tuple     =	pt_model_class(SCREAMING_SNAKE_CASE_							).eval()
              __lowerCamelCase:				Tuple     =	model_class(SCREAMING_SNAKE_CASE_    ,	dtype=jnp.floataa							)

              __lowerCamelCase:				List[Any]     =	convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,	SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				List[str]     =	fx_state

              with torch.no_grad():
                 __lowerCamelCase:				str     =	pt_model(**SCREAMING_SNAKE_CASE_							).to_tuple()

              __lowerCamelCase:				Tuple     =	fx_model(**SCREAMING_SNAKE_CASE_							).to_tuple()
              self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	len(SCREAMING_SNAKE_CASE_							)    ,	"""Output lengths differ between Flax and PyTorch"""							)
              for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
                 self.assert_almost_equals(fx_output[:, -1]    ,	pt_output[:, -1].numpy()    ,	4E-2							)

              with tempfile.TemporaryDirectory() as tmpdirname:
                 pt_model.save_pretrained(SCREAMING_SNAKE_CASE_							)
                 __lowerCamelCase:				Dict     =	model_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,	from_pt=SCREAMING_SNAKE_CASE_							)

              __lowerCamelCase:				Optional[Any]     =	fx_model_loaded(**SCREAMING_SNAKE_CASE_							).to_tuple()
              self.assertEqual(
                  len(SCREAMING_SNAKE_CASE_							)    ,	len(SCREAMING_SNAKE_CASE_							)    ,	"""Output lengths differ between Flax and PyTorch"""							)
              for fx_output_loaded, pt_output in zip(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
                 self.assert_almost_equals(fx_output_loaded[:, -1]    ,	pt_output[:, -1].numpy()    ,	4E-2							)





     @is_pt_flax_cross_test
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				str     =	self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
              # prepare inputs
              __lowerCamelCase:				List[Any]     =	self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				List[Any]     =	{k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

              # load corresponding PyTorch class
              __lowerCamelCase:				Optional[Any]     =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
              __lowerCamelCase:				List[Any]     =	getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

              __lowerCamelCase:				Dict     =	pt_model_class(SCREAMING_SNAKE_CASE_							).eval()
              __lowerCamelCase:				Optional[Any]     =	model_class(SCREAMING_SNAKE_CASE_    ,	dtype=jnp.floataa							)

              __lowerCamelCase:				str     =	load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_    ,	fx_model.params							)
              __lowerCamelCase:				Dict     =	pt_inputs["""input_ids"""].shape
              __lowerCamelCase:				Any     =	np.random.randint(0    ,	seq_length - 1    ,	size=(batch_size,)							)
              for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_							):
                 __lowerCamelCase:				str     =	0
                 __lowerCamelCase:				List[Any]     =	1
                 __lowerCamelCase:				Dict     =	0
                 __lowerCamelCase:				Any     =	1

              # make sure weights are tied in PyTorch
              pt_model.tie_weights()

              with torch.no_grad():
                 __lowerCamelCase:				Any     =	pt_model(**SCREAMING_SNAKE_CASE_							).to_tuple()

              __lowerCamelCase:				Tuple     =	fx_model(**SCREAMING_SNAKE_CASE_							).to_tuple()
              self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	len(SCREAMING_SNAKE_CASE_							)    ,	"""Output lengths differ between Flax and PyTorch"""							)
              for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
                 self.assert_almost_equals(fx_output[:, -1]    ,	pt_output[:, -1].numpy()    ,	4E-2							)

              with tempfile.TemporaryDirectory() as tmpdirname:
                 fx_model.save_pretrained(SCREAMING_SNAKE_CASE_							)
                 __lowerCamelCase:				Tuple     =	pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,	from_flax=SCREAMING_SNAKE_CASE_							)

              with torch.no_grad():
                 __lowerCamelCase:				Any     =	pt_model_loaded(**SCREAMING_SNAKE_CASE_							).to_tuple()

              self.assertEqual(
                  len(SCREAMING_SNAKE_CASE_							)    ,	len(SCREAMING_SNAKE_CASE_							)    ,	"""Output lengths differ between Flax and PyTorch"""							)
              for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
                 self.assert_almost_equals(fx_output[:, -1]    ,	pt_output[:, -1].numpy()    ,	4E-2							)







     @tooslow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        for model_class_name in self.all_model_classes:
           __lowerCamelCase:				Optional[Any]     =	model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""							)
           __lowerCamelCase:				int     =	model(np.ones((1, 1)							)							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)






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        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Tuple						      =	CycleDiffusionPipeline
     UpperCAmelCase__ :  Optional[Any]						      =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         "negative_prompt",
         "height",
         "width",
         "negative_prompt_embeds",
     }
     UpperCAmelCase__ :  Union[str, Any]						      =	PipelineTesterMixin.required_optional_params - {"latents"}
     UpperCAmelCase__ :  Optional[int]						      =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}   )
     UpperCAmelCase__ :  Optional[int]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase__ :  Optional[Any]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Tuple     =	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    ,	)
        __lowerCamelCase:				Dict     =	DDIMScheduler(
            beta_start=0.0_0085    ,	beta_end=0.012    ,	beta_schedule="""scaled_linear"""    ,	num_train_timesteps=1000    ,	clip_sample=SCREAMING_SNAKE_CASE_    ,	set_alpha_to_one=SCREAMING_SNAKE_CASE_    ,	)
        torch.manual_seed(0							)
        __lowerCamelCase:				Tuple     =	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							)
        __lowerCamelCase:				str     =	CLIPTextConfig(
            bos_token_id=0    ,	eos_token_id=2    ,	hidden_size=32    ,	intermediate_size=37    ,	layer_norm_eps=1E-0_5    ,	num_attention_heads=4    ,	num_hidden_layers=5    ,	pad_token_id=1    ,	vocab_size=1000    ,	)
        __lowerCamelCase:				int     =	CLIPTextModel(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

        __lowerCamelCase:				Optional[int]     =	{
            """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					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				str=0							):
        __lowerCamelCase:				Optional[Any]     =	floats_tensor((1, 3, 32, 32)    ,	rng=random.Random(SCREAMING_SNAKE_CASE_							)							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	image / 2 + 0.5
        if str(SCREAMING_SNAKE_CASE_							).startswith("""mps"""							):
           __lowerCamelCase:				int     =	torch.manual_seed(SCREAMING_SNAKE_CASE_							)
        else:
           __lowerCamelCase:				Tuple     =	torch.Generator(device=SCREAMING_SNAKE_CASE_							).manual_seed(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	{
            """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					:				Any							):
        __lowerCamelCase:				Tuple     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

        __lowerCamelCase:				Union[str, Any]     =	self.get_dummy_components()
        __lowerCamelCase:				Union[str, Any]     =	CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	output.images

        __lowerCamelCase:				int     =	images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __lowerCamelCase:				Dict     =	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					:				List[Any]							):
        __lowerCamelCase:				Optional[int]     =	self.get_dummy_components()
        for name, module in components.items():
           if hasattr(SCREAMING_SNAKE_CASE_    ,	"""half"""							):
              __lowerCamelCase:				Tuple     =	module.half()
        __lowerCamelCase:				Union[str, Any]     =	CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	output.images

        __lowerCamelCase:				Optional[int]     =	images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __lowerCamelCase:				List[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					:				int							):
        return super().test_save_load_local()





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





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





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







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



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





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





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

        __lowerCamelCase:				Dict     =	"""CompVis/stable-diffusion-v1-4"""
        __lowerCamelCase:				Optional[int]     =	DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    ,	subfolder="""scheduler"""							)
        __lowerCamelCase:				List[Any]     =	CycleDiffusionPipeline.from_pretrained(
            SCREAMING_SNAKE_CASE_    ,	scheduler=SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	torch_dtype=torch.floataa    ,	revision="""fp16"""							)

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

        __lowerCamelCase:				List[Any]     =	"""A black colored car"""
        __lowerCamelCase:				List[Any]     =	"""A blue colored car"""

        __lowerCamelCase:				List[Any]     =	torch.manual_seed(0							)
        __lowerCamelCase:				Any     =	pipe(
            prompt=SCREAMING_SNAKE_CASE_    ,	source_prompt=SCREAMING_SNAKE_CASE_    ,	image=SCREAMING_SNAKE_CASE_    ,	num_inference_steps=100    ,	eta=0.1    ,	strength=0.85    ,	guidance_scale=3    ,	source_guidance_scale=1    ,	generator=SCREAMING_SNAKE_CASE_    ,	output_type="""np"""    ,	)
        __lowerCamelCase:				Dict     =	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					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""							)
        __lowerCamelCase:				Tuple     =	load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""							)
        __lowerCamelCase:				List[str]     =	init_image.resize((512, 512)							)

        __lowerCamelCase:				List[Any]     =	"""CompVis/stable-diffusion-v1-4"""
        __lowerCamelCase:				Optional[Any]     =	DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    ,	subfolder="""scheduler"""							)
        __lowerCamelCase:				Dict     =	CycleDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_    ,	scheduler=SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_							)

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

        __lowerCamelCase:				Optional[int]     =	"""A black colored car"""
        __lowerCamelCase:				int     =	"""A blue colored car"""

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

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


