



'''simple docstring'''



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	lowerCAmelCase	:		List[Any]				=   '''platform'''

	import jax
	import jax.numpy as jnp

	from transformers import BlenderbotTokenizer
	from transformers.models.blenderbot.modeling_flax_blenderbot import (
	    FlaxBlenderbotForConditionalGeneration,
	    FlaxBlenderbotModel,
	    shift_tokens_right,
	)



def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						int=None				,		lowerCamelCase   :						List[str]=None				,		lowerCamelCase   :						List[Any]=None				,		lowerCamelCase   :						int=None				,		lowerCamelCase   :						Optional[Any]=None				,		lowerCamelCase   :						Any=None				,		):






	'''simple docstring'''




	if attention_mask is None:
		__lowerCAmelCase												=  np.where(input_ids != config.pad_token_id				,		1				,		0					)
	if decoder_attention_mask is None:
		__lowerCAmelCase												=  np.where(decoder_input_ids != config.pad_token_id				,		1				,		0					)
	if head_mask is None:
		__lowerCAmelCase												=  np.ones((config.encoder_layers, config.encoder_attention_heads)					)
	if decoder_head_mask is None:
		__lowerCAmelCase												=  np.ones((config.decoder_layers, config.decoder_attention_heads)					)
	if cross_attn_head_mask is None:
		__lowerCAmelCase												=  np.ones((config.decoder_layers, config.decoder_attention_heads)					)
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": attention_mask,
	}






class 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase=13			,				UpperCamelCase=7			,				UpperCamelCase=True			,				UpperCamelCase=False			,				UpperCamelCase=99			,				UpperCamelCase=16			,				UpperCamelCase=2			,				UpperCamelCase=4			,				UpperCamelCase=4			,				UpperCamelCase="gelu"			,				UpperCamelCase=0.1			,				UpperCamelCase=0.1			,				UpperCamelCase=32			,				UpperCamelCase=2			,				UpperCamelCase=1			,				UpperCamelCase=0			,				UpperCamelCase=0.02			,				)     ->			Optional[Any]:
						__lowerCAmelCase												=  parent
						__lowerCAmelCase												=  batch_size
						__lowerCAmelCase												=  seq_length
						__lowerCAmelCase												=  is_training
						__lowerCAmelCase												=  use_labels
						__lowerCAmelCase												=  vocab_size
						__lowerCAmelCase												=  hidden_size
						__lowerCAmelCase												=  num_hidden_layers
						__lowerCAmelCase												=  num_attention_heads
						__lowerCAmelCase												=  intermediate_size
						__lowerCAmelCase												=  hidden_act
						__lowerCAmelCase												=  hidden_dropout_prob
						__lowerCAmelCase												=  attention_probs_dropout_prob
						__lowerCAmelCase												=  max_position_embeddings
						__lowerCAmelCase												=  eos_token_id
						__lowerCAmelCase												=  pad_token_id
						__lowerCAmelCase												=  bos_token_id
						__lowerCAmelCase												=  initializer_range



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,				self.vocab_size     )			,				3			,				self.vocab_size     )
						__lowerCAmelCase												=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,				dtype=np.intaa     ))			,				-1     )

						__lowerCAmelCase												=  shift_tokens_right(UpperCamelCase			,				1			,				2     )

						__lowerCAmelCase												=  BlenderbotConfig(
						    vocab_size=self.vocab_size			,				d_model=self.hidden_size			,				encoder_layers=self.num_hidden_layers			,				decoder_layers=self.num_hidden_layers			,				encoder_attention_heads=self.num_attention_heads			,				decoder_attention_heads=self.num_attention_heads			,				encoder_ffn_dim=self.intermediate_size			,				decoder_ffn_dim=self.intermediate_size			,				dropout=self.hidden_dropout_prob			,				attention_dropout=self.attention_probs_dropout_prob			,				max_position_embeddings=self.max_position_embeddings			,				eos_token_id=self.eos_token_id			,				bos_token_id=self.bos_token_id			,				pad_token_id=self.pad_token_id			,				initializer_range=self.initializer_range			,				use_cache=UpperCamelCase			,				)
						__lowerCAmelCase												=  prepare_blenderbot_inputs_dict(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						return config, inputs_dict



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase							,       __lowerCAmelCase												=  self.prepare_config_and_inputs()
						return config, inputs_dict



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			List[Any]:
						__lowerCAmelCase												=  20
						__lowerCAmelCase												=  model_class_name(UpperCamelCase     )

						__lowerCAmelCase												=  model.encode(inputs_dict["input_ids"]     )

						__lowerCAmelCase							,       __lowerCAmelCase												=  (
						    inputs_dict["decoder_input_ids"],
						    inputs_dict["decoder_attention_mask"],
						)

						__lowerCAmelCase												=  model.init_cache(decoder_input_ids.shape[0]			,				UpperCamelCase			,				UpperCamelCase     )
						__lowerCAmelCase												=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,				dtype="i4"     )

						__lowerCAmelCase												=  jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]			,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,				)
						__lowerCAmelCase												=  model.decode(
						    decoder_input_ids[:, :-1]			,				UpperCamelCase			,				decoder_attention_mask=UpperCamelCase			,				past_key_values=UpperCamelCase			,				decoder_position_ids=UpperCamelCase			,				)

						__lowerCAmelCase												=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,				dtype="i4"     )
						__lowerCAmelCase												=  model.decode(
						    decoder_input_ids[:, -1:]			,				UpperCamelCase			,				decoder_attention_mask=UpperCamelCase			,				past_key_values=outputs_cache.past_key_values			,				decoder_position_ids=UpperCamelCase			,				)

						__lowerCAmelCase												=  model.decode(UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  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        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			List[str]:
						__lowerCAmelCase												=  20
						__lowerCAmelCase												=  model_class_name(UpperCamelCase     )

						__lowerCAmelCase												=  model.encode(inputs_dict["input_ids"]     )

						__lowerCAmelCase							,       __lowerCAmelCase												=  (
						    inputs_dict["decoder_input_ids"],
						    inputs_dict["decoder_attention_mask"],
						)

						__lowerCAmelCase												=  jnp.concatenate(
						    [
						        decoder_attention_mask,
						        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
						    ]			,				axis=-1			,				)

						__lowerCAmelCase												=  model.init_cache(decoder_input_ids.shape[0]			,				UpperCamelCase			,				UpperCamelCase     )
						__lowerCAmelCase												=  jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]			,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,				)

						__lowerCAmelCase												=  model.decode(
						    decoder_input_ids[:, :-1]			,				UpperCamelCase			,				decoder_attention_mask=UpperCamelCase			,				past_key_values=UpperCamelCase			,				decoder_position_ids=UpperCamelCase			,				)
						__lowerCAmelCase												=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,				dtype="i4"     )
						__lowerCAmelCase												=  model.decode(
						    decoder_input_ids[:, -1:]			,				UpperCamelCase			,				past_key_values=outputs_cache.past_key_values			,				decoder_attention_mask=UpperCamelCase			,				decoder_position_ids=UpperCamelCase			,				)

						__lowerCAmelCase												=  model.decode(UpperCamelCase			,				UpperCamelCase			,				decoder_attention_mask=UpperCamelCase     )

						__lowerCAmelCase												=  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 					UpperCAmelCase__	(		unittest.TestCase ):
					a      :     Optional[Any]        =							9_9



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  np.array(
						    [
						        [71, 82, 18, 33, 46, 91, 2],
						        [68, 34, 26, 58, 30, 82, 2],
						        [5, 97, 17, 39, 94, 40, 2],
						        [76, 83, 94, 25, 70, 78, 2],
						        [87, 59, 41, 35, 48, 66, 2],
						        [55, 13, 16, 58, 5, 2, 1],  # note padding
						        [64, 27, 31, 51, 12, 75, 2],
						        [52, 64, 86, 17, 83, 39, 2],
						        [48, 61, 9, 24, 71, 82, 2],
						        [26, 1, 60, 48, 22, 13, 2],
						        [21, 5, 62, 28, 14, 76, 2],
						        [45, 98, 37, 86, 59, 48, 2],
						        [70, 70, 50, 9, 28, 0, 2],
						    ]			,				dtype=np.intaa			,				)

						__lowerCAmelCase												=  input_ids.shape[0]
						__lowerCAmelCase												=  BlenderbotConfig(
						    vocab_size=self.vocab_size			,				d_model=24			,				encoder_layers=2			,				decoder_layers=2			,				encoder_attention_heads=2			,				decoder_attention_heads=2			,				encoder_ffn_dim=32			,				decoder_ffn_dim=32			,				max_position_embeddings=48			,				eos_token_id=2			,				pad_token_id=1			,				bos_token_id=0			,				)
						return config, input_ids, batch_size



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  self._get_config_and_data()
						__lowerCAmelCase												=  FlaxBlenderbotForConditionalGeneration(UpperCamelCase     )
						__lowerCAmelCase												=  lm_model(input_ids=UpperCamelCase     )
						__lowerCAmelCase												=  (batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs["logits"].shape			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			int:
						__lowerCAmelCase												=  BlenderbotConfig(
						    vocab_size=self.vocab_size			,				d_model=14			,				encoder_layers=2			,				decoder_layers=2			,				encoder_attention_heads=2			,				decoder_attention_heads=2			,				encoder_ffn_dim=8			,				decoder_ffn_dim=8			,				max_position_embeddings=48			,				)
						__lowerCAmelCase												=  FlaxBlenderbotForConditionalGeneration(UpperCamelCase     )
						__lowerCAmelCase												=  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,				dtype=np.intaa     )
						__lowerCAmelCase												=  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,				dtype=np.intaa     )
						__lowerCAmelCase												=  lm_model(input_ids=UpperCamelCase			,				decoder_input_ids=UpperCamelCase     )
						__lowerCAmelCase												=  (*summary.shape, config.vocab_size)
						self.assertEqual(outputs["logits"].shape			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,				dtype=np.intaa     )
						__lowerCAmelCase												=  shift_tokens_right(UpperCamelCase			,				1			,				2     )
						__lowerCAmelCase												=  np.equal(UpperCamelCase			,				1     ).astype(np.floataa     ).sum()
						__lowerCAmelCase												=  np.equal(UpperCamelCase			,				1     ).astype(np.floataa     ).sum()
						self.assertEqual(shifted.shape			,				input_ids.shape     )
						self.assertEqual(UpperCamelCase			,				n_pad_before - 1     )
						self.assertTrue(np.equal(shifted[:, 0]			,				2     ).all()     )






@require_flax
class 					UpperCAmelCase__	(		UpperCamelCase__							,       unittest.TestCase							,       UpperCamelCase__ ):
					a      :     Tuple        =							True
					a      :     Tuple        =							(
					    (
					        FlaxBlenderbotModel,
					        FlaxBlenderbotForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					a      :     Optional[Any]        =							(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  FlaxBlenderbotModelTester(self     )



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__     ):
								__lowerCAmelCase												=  self._prepare_for_class(UpperCamelCase			,				UpperCamelCase     )
								__lowerCAmelCase												=  model_class(UpperCamelCase     )

								@jax.jit
								def encode_jitted(UpperCamelCase			,				UpperCamelCase=None			,				**UpperCamelCase     ):
									return model.encode(input_ids=UpperCamelCase			,				attention_mask=UpperCamelCase     )

								with self.subTest("JIT Enabled"     ):
									__lowerCAmelCase												=  encode_jitted(**UpperCamelCase     ).to_tuple()

								with self.subTest("JIT Disabled"     ):
									with jax.disable_jit():
										__lowerCAmelCase												=  encode_jitted(**UpperCamelCase     ).to_tuple()

								self.assertEqual(len(UpperCamelCase     )			,				len(UpperCamelCase     )     )
								for jitted_output, output in zip(UpperCamelCase			,				UpperCamelCase     ):
									self.assertEqual(jitted_output.shape			,				output.shape     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase							,       __lowerCAmelCase												=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__     ):
								__lowerCAmelCase												=  model_class(UpperCamelCase     )
								__lowerCAmelCase												=  model.encode(inputs_dict["input_ids"]			,				inputs_dict["attention_mask"]     )

								__lowerCAmelCase												=  {
								    "decoder_input_ids": inputs_dict["decoder_input_ids"],
								    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
								    "encoder_outputs": encoder_outputs,
								}

								@jax.jit
								def decode_jitted(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ):
									return model.decode(
									    decoder_input_ids=UpperCamelCase			,				decoder_attention_mask=UpperCamelCase			,				encoder_outputs=UpperCamelCase			,				)

								with self.subTest("JIT Enabled"     ):
									__lowerCAmelCase												=  decode_jitted(**UpperCamelCase     ).to_tuple()

								with self.subTest("JIT Disabled"     ):
									with jax.disable_jit():
										__lowerCAmelCase												=  decode_jitted(**UpperCamelCase     ).to_tuple()

								self.assertEqual(len(UpperCamelCase     )			,				len(UpperCamelCase     )     )
								for jitted_output, output in zip(UpperCamelCase			,				UpperCamelCase     ):
									self.assertEqual(jitted_output.shape			,				output.shape     )



					@slow
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						for model_class_name in self.all_model_classes:
							__lowerCAmelCase												=  model_class_name.from_pretrained("facebook/blenderbot-400M-distill"     )
							# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
							__lowerCAmelCase												=  np.ones((1, 1)     ) * model.config.eos_token_id
							__lowerCAmelCase												=  model(UpperCamelCase     )
							self.assertIsNotNone(UpperCamelCase     )



					@unittest.skipUnless(jax_device != "cpu"			,				"3B test too slow on CPU."     )
					@slow
					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
						__lowerCAmelCase												=  {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

						__lowerCAmelCase												=  FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"			,				from_pt=UpperCamelCase     )
						__lowerCAmelCase												=  BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"     )

						__lowerCAmelCase												=  ["Sam"]
						__lowerCAmelCase												=  tokenizer(UpperCamelCase			,				return_tensors="jax"     )

						__lowerCAmelCase												=  model.generate(**UpperCamelCase			,				**UpperCamelCase     )
						__lowerCAmelCase												=  "Sam is a great name. It means \"sun\" in Gaelic."

						__lowerCAmelCase												=  tokenizer.batch_decode(UpperCamelCase			,				**UpperCamelCase     )
						assert generated_txt[0].strip() == tgt_text




'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 					UpperCAmelCase__	(		UpperCamelCase__							,       unittest.TestCase ):
					a      :     List[Any]        =							KandinskyImgaImgPipeline
					a      :     Union[str, Any]        =							["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
					a      :     List[Any]        =							[
					    """prompt""",
					    """negative_prompt""",
					    """image_embeds""",
					    """negative_image_embeds""",
					    """image""",
					]
					a      :     Any        =							[
					    """generator""",
					    """height""",
					    """width""",
					    """strength""",
					    """guidance_scale""",
					    """negative_prompt""",
					    """num_inference_steps""",
					    """return_dict""",
					    """guidance_scale""",
					    """num_images_per_prompt""",
					    """output_type""",
					    """return_dict""",
					]
					a      :     Union[str, Any]        =							False



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return 32



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						return 32



					@property
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						return self.time_input_dim



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return self.time_input_dim * 4



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return 100



					@property
					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"     )
						return tokenizer



					@property
					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						torch.manual_seed(0     )
						__lowerCAmelCase												=  MCLIPConfig(
						    numDims=self.cross_attention_dim			,				transformerDimensions=self.text_embedder_hidden_size			,				hidden_size=self.text_embedder_hidden_size			,				intermediate_size=37			,				num_attention_heads=4			,				num_hidden_layers=5			,				vocab_size=1005			,				)

						__lowerCAmelCase												=  MultilingualCLIP(UpperCamelCase     )
						__lowerCAmelCase												=  text_encoder.eval()

						return text_encoder



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						torch.manual_seed(0     )

						__lowerCAmelCase												=  {
						    "in_channels": 4,
						    # Out channels is double in channels because predicts mean and variance
						    "out_channels": 8,
						    "addition_embed_type": "text_image",
						    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
						    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
						    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
						    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    "layers_per_block": 1,
						    "encoder_hid_dim": self.text_embedder_hidden_size,
						    "encoder_hid_dim_type": "text_image_proj",
						    "cross_attention_dim": self.cross_attention_dim,
						    "attention_head_dim": 4,
						    "resnet_time_scale_shift": "scale_shift",
						    "class_embed_type": None,
						}

						__lowerCAmelCase												=  UNetaDConditionModel(**UpperCamelCase     )
						return model



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}



					@property
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						torch.manual_seed(0     )
						__lowerCAmelCase												=  VQModel(**self.dummy_movq_kwargs     )
						return model



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.dummy_text_encoder
						__lowerCAmelCase												=  self.dummy_tokenizer
						__lowerCAmelCase												=  self.dummy_unet
						__lowerCAmelCase												=  self.dummy_movq

						__lowerCAmelCase												=  {
						    "num_train_timesteps": 1000,
						    "beta_schedule": "linear",
						    "beta_start": 0.0_00_85,
						    "beta_end": 0.0_12,
						    "clip_sample": False,
						    "set_alpha_to_one": False,
						    "steps_offset": 0,
						    "prediction_type": "epsilon",
						    "thresholding": False,
						}

						__lowerCAmelCase												=  DDIMScheduler(**UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "unet": unet,
						    "scheduler": scheduler,
						    "movq": movq,
						}

						return components



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=0     )     ->			Optional[Any]:
						__lowerCAmelCase												=  floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )
						__lowerCAmelCase												=  floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1     )     ).to(UpperCamelCase     )
						# create init_image
						__lowerCAmelCase												=  floats_tensor((1, 3, 64, 64)			,				rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )
						__lowerCAmelCase												=  image.cpu().permute(0			,				2			,				3			,				1     )[0]
						__lowerCAmelCase												=  Image.fromarray(np.uinta(UpperCamelCase     )     ).convert("RGB"     ).resize((256, 256)     )

						if str(UpperCamelCase     ).startswith("mps"     ):
							__lowerCAmelCase												=  torch.manual_seed(UpperCamelCase     )
						else:
							__lowerCAmelCase												=  torch.Generator(device=UpperCamelCase     ).manual_seed(UpperCamelCase     )
						__lowerCAmelCase												=  {
						    "prompt": "horse",
						    "image": init_image,
						    "image_embeds": image_embeds,
						    "negative_image_embeds": negative_image_embeds,
						    "generator": generator,
						    "height": 64,
						    "width": 64,
						    "num_inference_steps": 10,
						    "guidance_scale": 7.0,
						    "strength": 0.2,
						    "output_type": "np",
						}
						return inputs



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  "cpu"

						__lowerCAmelCase												=  self.get_dummy_components()

						__lowerCAmelCase												=  self.pipeline_class(**UpperCamelCase     )
						__lowerCAmelCase												=  pipe.to(UpperCamelCase     )

						pipe.set_progress_bar_config(disable=UpperCamelCase     )

						__lowerCAmelCase												=  pipe(**self.get_dummy_inputs(UpperCamelCase     )     )
						__lowerCAmelCase												=  output.images

						__lowerCAmelCase												=  pipe(
						    **self.get_dummy_inputs(UpperCamelCase     )			,				return_dict=UpperCamelCase			,				)[0]

						__lowerCAmelCase												=  image[0, -3:, -3:, -1]
						__lowerCAmelCase												=  image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						__lowerCAmelCase												=  np.array(
						    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]     )
						assert (
						    np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
						), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2
						), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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



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



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinsky/kandinsky_img2img_frog.npy"     )

						__lowerCAmelCase												=  load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"     )
						__lowerCAmelCase												=  "A red cartoon frog, 4k"

						__lowerCAmelCase												=  KandinskyPriorPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1-prior"			,				torch_dtype=torch.floataa     )
						pipe_prior.to(UpperCamelCase     )

						__lowerCAmelCase												=  KandinskyImgaImgPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1"			,				torch_dtype=torch.floataa     )
						__lowerCAmelCase												=  pipeline.to(UpperCamelCase     )

						pipeline.set_progress_bar_config(disable=UpperCamelCase     )

						__lowerCAmelCase												=  torch.Generator(device="cpu"     ).manual_seed(0     )
						__lowerCAmelCase							,       __lowerCAmelCase												=  pipe_prior(
						    UpperCamelCase			,				generator=UpperCamelCase			,				num_inference_steps=5			,				negative_prompt=""			,				).to_tuple()

						__lowerCAmelCase												=  pipeline(
						    UpperCamelCase			,				image=UpperCamelCase			,				image_embeds=UpperCamelCase			,				negative_image_embeds=UpperCamelCase			,				generator=UpperCamelCase			,				num_inference_steps=100			,				height=768			,				width=768			,				strength=0.2			,				output_type="np"			,				)

						__lowerCAmelCase												=  output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(UpperCamelCase			,				UpperCamelCase     )