'''simple docstring'''

import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


_A			:      str					      =				1e-4

if is_torch_available():
 import torch

 from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
 from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder







@require_torch
class 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	str=16      ,       SCREAMING_SNAKE_CASE__   :	Any=13      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=7      ,       SCREAMING_SNAKE_CASE__   :	str=14      ,       SCREAMING_SNAKE_CASE__   :	List[str]=10      ,       SCREAMING_SNAKE_CASE__   :	int=19      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=5      ,       SCREAMING_SNAKE_CASE__   :	List[str]=4      ,       SCREAMING_SNAKE_CASE__   :	List[str]=True      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=16      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=2      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=4      ,       SCREAMING_SNAKE_CASE__   :	Dict=4      ,       SCREAMING_SNAKE_CASE__   :	Dict="gelu"      ,       SCREAMING_SNAKE_CASE__   :	int=0.1      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=0.1      ,       SCREAMING_SNAKE_CASE__   :	int=[1, 2, 3, 4, 5]      ,       SCREAMING_SNAKE_CASE__   :	Any=25      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=5      ,       )  ->    Optional[Any]:
  __lowerCAmelCase											=						d_model
  __lowerCAmelCase											=						parent
  __lowerCAmelCase											=						batch_size
  __lowerCAmelCase											=						prediction_length
  __lowerCAmelCase											=						context_length
  __lowerCAmelCase											=						cardinality
  __lowerCAmelCase											=						num_time_features
  __lowerCAmelCase											=						lags_sequence
  __lowerCAmelCase											=						embedding_dimension
  __lowerCAmelCase											=						is_training
  __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											=						context_length
  __lowerCAmelCase											=						prediction_length + label_length
  __lowerCAmelCase											=						label_length

  __lowerCAmelCase											=						moving_average
  __lowerCAmelCase											=						autocorrelation_factor
 def a					(      self   :	int							)  ->    List[str]:
  return AutoformerConfig(
      d_model=self.d_model      ,       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      ,       prediction_length=self.prediction_length      ,       context_length=self.context_length      ,       label_length=self.label_length      ,       lags_sequence=self.lags_sequence      ,       num_time_features=self.num_time_features      ,       num_static_categorical_features=1      ,       cardinality=[self.cardinality]      ,       embedding_dimension=[self.embedding_dimension]      ,       moving_average=self.moving_average      ,       )
 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    int:
  __lowerCAmelCase											=						config.context_length + max(config.lags_sequence							)

  __lowerCAmelCase											=						ids_tensor([self.batch_size, 1]      ,       config.cardinality[0]							)
  __lowerCAmelCase											=						floats_tensor([self.batch_size, _past_length, config.num_time_features]							)
  __lowerCAmelCase											=						floats_tensor([self.batch_size, _past_length]							)
  __lowerCAmelCase											=						floats_tensor([self.batch_size, _past_length]							) > 0.5

  # decoder inputs
  __lowerCAmelCase											=						floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]							)
  __lowerCAmelCase											=						floats_tensor([self.batch_size, config.prediction_length]							)

  __lowerCAmelCase											=						{
      """past_values""": past_values,
      """static_categorical_features""": static_categorical_features,
      """past_time_features""": past_time_features,
      """past_observed_mask""": past_observed_mask,
      """future_time_features""": future_time_features,
      """future_values""": future_values,
  }
  return inputs_dict
 def a					(      self   :	Union[str, Any]							)  ->    Optional[Any]:
  __lowerCAmelCase											=						self.get_config()
  __lowerCAmelCase											=						self.prepare_autoformer_inputs_dict(SCREAMING_SNAKE_CASE__							)
  return config, inputs_dict
 def a					(      self   :	str							)  ->    str:
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.prepare_config_and_inputs()
  return config, inputs_dict





 def a					(      self   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Any							)  ->    Tuple:
  __lowerCAmelCase											=						AutoformerModel(config=SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							).eval()
  __lowerCAmelCase											=						model(**SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						outputs.encoder_last_hidden_state
  __lowerCAmelCase											=						outputs.last_hidden_state

  with tempfile.TemporaryDirectory() as tmpdirname:
   __lowerCAmelCase											=						model.get_encoder()
   encoder.save_pretrained(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						AutoformerEncoder.from_pretrained(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						model.create_network_inputs(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase							,       __lowerCAmelCase											=						model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]							)

  __lowerCAmelCase											=						torch.cat(
      (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])      ,       dim=-1      ,       )
  __lowerCAmelCase											=						encoder(inputs_embeds=SCREAMING_SNAKE_CASE__							)[0]
  self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3							)

  __lowerCAmelCase											=						(
      torch.mean(transformer_inputs[:, : config.context_length, ...]      ,       dim=1							)
      .unsqueeze(1							)
      .repeat(1      ,       config.prediction_length      ,       1							)
  )
  __lowerCAmelCase											=						torch.zeros(
      [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]      ,       device=enc_input.device      ,       )

  __lowerCAmelCase											=						torch.cat(
      (
          torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)      ,       dim=1							),
          feature[:, config.context_length - config.label_length :, ...],
      )      ,       dim=-1      ,       )
  __lowerCAmelCase											=						torch.cat(
      (
          torch.cat((trend_input[:, -config.label_length :, ...], mean)      ,       dim=1							),
          feature[:, config.context_length - config.label_length :, ...],
      )      ,       dim=-1      ,       )

  with tempfile.TemporaryDirectory() as tmpdirname:
   __lowerCAmelCase											=						model.get_decoder()
   decoder.save_pretrained(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						AutoformerDecoder.from_pretrained(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						decoder(
      trend=SCREAMING_SNAKE_CASE__      ,       inputs_embeds=SCREAMING_SNAKE_CASE__      ,       encoder_hidden_states=SCREAMING_SNAKE_CASE__      ,       )[0]

  self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3							)







@require_torch
class 				_lowercase     (				UpperCAmelCase__  ,							UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
 _SCREAMING_SNAKE_CASE     :       int      	=					(AutoformerForPrediction,) if is_torch_available() else ()
 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					{"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
 _SCREAMING_SNAKE_CASE     :       Any      	=					False
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					False
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					False
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					False
 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					False
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					False
 def a					(      self   :	Optional[Any]							)  ->    Tuple:
  __lowerCAmelCase											=						AutoformerModelTester(self							)
  __lowerCAmelCase											=						ConfigTester(self      ,       config_class=SCREAMING_SNAKE_CASE__      ,       has_text_modality=SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Tuple							)  ->    Optional[int]:
  self.config_tester.run_common_tests()
 def a					(      self   :	Dict							)  ->    int:
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  for model_class in self.all_model_classes:
   __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)

   with tempfile.TemporaryDirectory() as tmpdirname:
    model.save_pretrained(SCREAMING_SNAKE_CASE__							)
    __lowerCAmelCase							,       __lowerCAmelCase											=						model_class.from_pretrained(SCREAMING_SNAKE_CASE__      ,       output_loading_info=SCREAMING_SNAKE_CASE__							)
   self.assertEqual(info["""missing_keys"""]      ,       []							)
 def a					(      self   :	str							)  ->    Optional[int]:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()
  self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE__							)
 @unittest.skip(reason="""Model has no tokens embeddings"""							)
 def a					(      self   :	List[str]							)  ->    Any:
  pass
 def a					(      self   :	List[Any]							)  ->    List[str]:
  __lowerCAmelCase											=						inspect.signature(getattr(SCREAMING_SNAKE_CASE__      ,       """forward"""							)							)
  # The main input is the name of the argument after `self`
  __lowerCAmelCase											=						list(model_signature.parameters.keys()							)[1]
  self.assertEqual(AutoformerModel.main_input_name      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	int							)  ->    int:
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						inspect.signature(model.forward							)
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   __lowerCAmelCase											=						[*signature.parameters.keys()]

   __lowerCAmelCase											=						[
       """past_values""",
       """past_time_features""",
       """past_observed_mask""",
       """static_categorical_features""",
       """static_real_features""",
       """future_values""",
       """future_time_features""",
   ]

   if model.__class__.__name__ in ["AutoformerForPrediction"]:
    expected_arg_names.append("""future_observed_mask"""							)

   expected_arg_names.extend(
       [
           """decoder_attention_mask""",
           """head_mask""",
           """decoder_head_mask""",
           """cross_attn_head_mask""",
           """encoder_outputs""",
           """past_key_values""",
           """output_hidden_states""",
           """output_attentions""",
           """use_cache""",
           """return_dict""",
       ]							)

   self.assertListEqual(arg_names[: len(SCREAMING_SNAKE_CASE__							)]      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Optional[Any]							)  ->    Tuple:
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()
  __lowerCAmelCase											=						True

  __lowerCAmelCase											=						getattr(self.model_tester      ,       """seq_length"""      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						getattr(self.model_tester      ,       """decoder_seq_length"""      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						getattr(self.model_tester      ,       """encoder_seq_length"""      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						getattr(self.model_tester      ,       """d_model"""      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						getattr(self.model_tester      ,       """num_attention_heads"""      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						d_model // num_attention_heads

  for model_class in self.all_model_classes:
   __lowerCAmelCase											=						True
   __lowerCAmelCase											=						False
   __lowerCAmelCase											=						True
   __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)
   model.to(SCREAMING_SNAKE_CASE__							)
   model.eval()
   with torch.no_grad():
    __lowerCAmelCase											=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)							)
   __lowerCAmelCase											=						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       self.model_tester.num_hidden_layers							)

   # check that output_attentions also work using config
   del inputs_dict["output_attentions"]
   __lowerCAmelCase											=						True
   __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)
   model.to(SCREAMING_SNAKE_CASE__							)
   model.eval()
   with torch.no_grad():
    __lowerCAmelCase											=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)							)
   __lowerCAmelCase											=						outputs.encoder_attentions
   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       self.model_tester.num_hidden_layers							)

   self.assertListEqual(
       list(attentions[0].shape[-3:]							)      ,       [self.model_tester.num_attention_heads, encoder_seq_length, dim]      ,       )
   __lowerCAmelCase											=						len(SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						7

   if "last_hidden_state" in outputs:
    correct_outlen += 1

   if "trend" in outputs:
    correct_outlen += 1

   if "past_key_values" in outputs:
    correct_outlen += 1  # past_key_values have been returned

   if "loss" in outputs:
    correct_outlen += 1

   if "params" in outputs:
    correct_outlen += 1

   self.assertEqual(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   # decoder attentions
   __lowerCAmelCase											=						outputs.decoder_attentions
   self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       (list, tuple)							)
   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       self.model_tester.num_hidden_layers							)
   self.assertListEqual(
       list(decoder_attentions[0].shape[-3:]							)      ,       [self.model_tester.num_attention_heads, decoder_seq_length, dim]      ,       )

   # cross attentions
   __lowerCAmelCase											=						outputs.cross_attentions
   self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       (list, tuple)							)
   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       self.model_tester.num_hidden_layers							)
   self.assertListEqual(
       list(cross_attentions[0].shape[-3:]							)      ,       [self.model_tester.num_attention_heads, decoder_seq_length, dim]      ,       )

  # Check attention is always last and order is fine
  __lowerCAmelCase											=						True
  __lowerCAmelCase											=						True
  __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  with torch.no_grad():
   __lowerCAmelCase											=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)							)

  self.assertEqual(out_len + 2      ,       len(SCREAMING_SNAKE_CASE__							)							)

  __lowerCAmelCase											=						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

  self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       self.model_tester.num_hidden_layers							)
  self.assertListEqual(
      list(self_attentions[0].shape[-3:]							)      ,       [self.model_tester.num_attention_heads, encoder_seq_length, dim]      ,       )





 @is_flaky()
 def a					(      self   :	str							)  ->    Optional[Any]:
  super().test_retain_grad_hidden_states_attentions()







def      UpperCamelCase_						(					snake_case_       :       List[str]="train-batch.pt"      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""						,		filename=snake_case_						,		repo_type="""dataset"""      )
 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location=snake_case_      )
 return batch







@require_torch
@slow
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def a					(      self   :	Union[str, Any]							)  ->    List[Any]:
  __lowerCAmelCase											=						AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						prepare_batch()

  with torch.no_grad():
   __lowerCAmelCase											=						model(
       past_values=batch["""past_values"""]      ,       past_time_features=batch["""past_time_features"""]      ,       past_observed_mask=batch["""past_observed_mask"""]      ,       static_categorical_features=batch["""static_categorical_features"""]      ,       future_values=batch["""future_values"""]      ,       future_time_features=batch["""future_time_features"""]      ,       )[0]

  __lowerCAmelCase											=						torch.Size(
      (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)							)
  self.assertEqual(output.shape      ,       SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.tensor(
      [[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]]      ,       device=SCREAMING_SNAKE_CASE__							)
  self.assertTrue(torch.allclose(output[0, :3, :3]      ,       SCREAMING_SNAKE_CASE__      ,       atol=SCREAMING_SNAKE_CASE__							)							)
 def a					(      self   :	str							)  ->    Tuple:
  __lowerCAmelCase											=						AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						prepare_batch("""val-batch.pt"""							)
  with torch.no_grad():
   __lowerCAmelCase											=						model(
       past_values=batch["""past_values"""]      ,       past_time_features=batch["""past_time_features"""]      ,       past_observed_mask=batch["""past_observed_mask"""]      ,       static_categorical_features=batch["""static_categorical_features"""]      ,       ).encoder_last_hidden_state
  __lowerCAmelCase											=						torch.Size((64, model.config.context_length, model.config.d_model)							)
  self.assertEqual(output.shape      ,       SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.tensor(
      [[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]]      ,       device=SCREAMING_SNAKE_CASE__							)
  self.assertTrue(torch.allclose(output[0, :3, :3]      ,       SCREAMING_SNAKE_CASE__      ,       atol=SCREAMING_SNAKE_CASE__							)							)





 def a					(      self   :	List[str]							)  ->    Tuple:
  __lowerCAmelCase											=						AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						prepare_batch("""val-batch.pt"""							)
  with torch.no_grad():
   __lowerCAmelCase											=						model.generate(
       static_categorical_features=batch["""static_categorical_features"""]      ,       past_time_features=batch["""past_time_features"""]      ,       past_values=batch["""past_values"""]      ,       future_time_features=batch["""future_time_features"""]      ,       past_observed_mask=batch["""past_observed_mask"""]      ,       )
  __lowerCAmelCase											=						torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)							)
  self.assertEqual(outputs.sequences.shape      ,       SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6]      ,       device=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						outputs.sequences.mean(dim=1							)
  self.assertTrue(torch.allclose(mean_prediction[0, -3:]      ,       SCREAMING_SNAKE_CASE__      ,       rtol=1e-1							)							)







'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					KandinskyVaaControlnetImgaImgPipeline
 _SCREAMING_SNAKE_CASE     :       Dict      	=					["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
 _SCREAMING_SNAKE_CASE     :       Dict      	=					[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					False
 @property
 def a					(      self   :	int							)  ->    Optional[Any]:
  return 32
 @property
 def a					(      self   :	Union[str, Any]							)  ->    Dict:
  return 32
 @property
 def a					(      self   :	str							)  ->    Union[str, Any]:
  return self.time_input_dim
 @property
 def a					(      self   :	Tuple							)  ->    Optional[Any]:
  return self.time_input_dim * 4
 @property
 def a					(      self   :	Union[str, Any]							)  ->    List[Any]:
  return 1_00
 @property
 def a					(      self   :	Optional[int]							)  ->    Any:
  torch.manual_seed(0							)

  __lowerCAmelCase											=						{
      """in_channels""": 8,
      # Out channels is double in channels because predicts mean and variance
      """out_channels""": 8,
      """addition_embed_type""": """image_hint""",
      """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""": """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(**SCREAMING_SNAKE_CASE__							)
  return model
 @property
 def a					(      self   :	Tuple							)  ->    Optional[Any]:
  return {
      "block_out_channels": [32, 32, 64, 64],
      "down_block_types": [
          "DownEncoderBlock2D",
          "DownEncoderBlock2D",
          "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
      "vq_embed_dim": 4,
  }
 @property
 def a					(      self   :	Optional[Any]							)  ->    Union[str, Any]:
  torch.manual_seed(0							)
  __lowerCAmelCase											=						VQModel(**self.dummy_movq_kwargs							)
  return model
 def a					(      self   :	Any							)  ->    Dict:
  __lowerCAmelCase											=						self.dummy_unet
  __lowerCAmelCase											=						self.dummy_movq

  __lowerCAmelCase											=						{
      """num_train_timesteps""": 10_00,
      """beta_schedule""": """linear""",
      """beta_start""": 0.0_0_0_8_5,
      """beta_end""": 0.0_1_2,
      """clip_sample""": False,
      """set_alpha_to_one""": False,
      """steps_offset""": 0,
      """prediction_type""": """epsilon""",
      """thresholding""": False,
  }

  __lowerCAmelCase											=						DDIMScheduler(**SCREAMING_SNAKE_CASE__							)

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

  return components
 def a					(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	List[str]=0							)  ->    Dict:
  __lowerCAmelCase											=						floats_tensor((1, self.text_embedder_hidden_size)      ,       rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						floats_tensor((1, self.text_embedder_hidden_size)      ,       rng=random.Random(seed + 1							)							).to(
      SCREAMING_SNAKE_CASE__							)
  # create init_image
  __lowerCAmelCase											=						floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						image.cpu().permute(0      ,       2      ,       3      ,       1							)[0]
  __lowerCAmelCase											=						Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__							)							).convert("""RGB"""							).resize((2_56, 2_56)							)
  # create hint
  __lowerCAmelCase											=						floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)

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





 def a					(      self   :	List[Any]							)  ->    int:
  __lowerCAmelCase											=						"""cpu"""

  __lowerCAmelCase											=						self.get_dummy_components()

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

  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

  __lowerCAmelCase											=						pipe(
      **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)      ,       return_dict=SCREAMING_SNAKE_CASE__      ,       )[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(
      [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]							)
  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 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






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





 def a					(      self   :	int							)  ->    Optional[Any]:
  __lowerCAmelCase											=						load_numpy(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
      """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""							)

  __lowerCAmelCase											=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
  __lowerCAmelCase											=						init_image.resize((5_12, 5_12)							)

  __lowerCAmelCase											=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
      """/kandinskyv22/hint_image_cat.png"""							)
  __lowerCAmelCase											=						torch.from_numpy(np.array(SCREAMING_SNAKE_CASE__							)							).float() / 2_5_5.0
  __lowerCAmelCase											=						hint.permute(2      ,       0      ,       1							).unsqueeze(0							)

  __lowerCAmelCase											=						"""A robot, 4k photo"""

  __lowerCAmelCase											=						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
      """kandinsky-community/kandinsky-2-2-prior"""      ,       torch_dtype=torch.floataa							)
  pipe_prior.to(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
      """kandinsky-community/kandinsky-2-2-controlnet-depth"""      ,       torch_dtype=torch.floataa							)
  __lowerCAmelCase											=						pipeline.to(SCREAMING_SNAKE_CASE__							)

  pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.Generator(device="""cpu"""							).manual_seed(0							)

  __lowerCAmelCase							,       __lowerCAmelCase											=						pipe_prior(
      SCREAMING_SNAKE_CASE__      ,       image=SCREAMING_SNAKE_CASE__      ,       strength=0.8_5      ,       generator=SCREAMING_SNAKE_CASE__      ,       negative_prompt=""""""      ,       ).to_tuple()

  __lowerCAmelCase											=						pipeline(
      image=SCREAMING_SNAKE_CASE__      ,       image_embeds=SCREAMING_SNAKE_CASE__      ,       negative_image_embeds=SCREAMING_SNAKE_CASE__      ,       hint=SCREAMING_SNAKE_CASE__      ,       generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=1_00      ,       height=5_12      ,       width=5_12      ,       strength=0.5      ,       output_type="""np"""      ,       )

  __lowerCAmelCase											=						output.images[0]

  assert image.shape == (5_12, 5_12, 3)

  assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)






