import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
    from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder



def A__	(      lowercase:  int,		lowercase:  Tuple,		lowercase:  List[Any],		lowercase:  Dict=None,		lowercase:  Tuple=None,		lowercase:  str=None,		lowercase:  Any=None,		lowercase:  Tuple=None,		)					->		int:
       if attention_mask is None:
              A    :  Any								=input_ids.ne(config.pad_token_id       )
       if decoder_attention_mask is None:
              A    :  Tuple								=decoder_input_ids.ne(config.pad_token_id       )
       if head_mask is None:
              A    :  Union[str, Any]								=torch.ones(config.encoder_layers,		config.encoder_attention_heads,		device=__A       )
       if decoder_head_mask is None:
              A    :  str								=torch.ones(config.decoder_layers,		config.decoder_attention_heads,		device=__A       )
       if cross_attn_head_mask is None:
              A    :  Any								=torch.ones(config.decoder_layers,		config.decoder_attention_heads,		device=__A       )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }



class 			SCREAMING_SNAKE_CASE_			:
  '''simple docstring'''


  def __init__(					self     :       Any   ,						SCREAMING_SNAKE_CASE__     :       List[str]   ,						SCREAMING_SNAKE_CASE__     :       int=13   ,						SCREAMING_SNAKE_CASE__     :       int=7   ,						SCREAMING_SNAKE_CASE__     :       Dict=True   ,						SCREAMING_SNAKE_CASE__     :       Any=False   ,						SCREAMING_SNAKE_CASE__     :       str=99   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any]=16   ,						SCREAMING_SNAKE_CASE__     :       Dict=2   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any]=4   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]=4   ,						SCREAMING_SNAKE_CASE__     :       Dict="relu"   ,						SCREAMING_SNAKE_CASE__     :       List[str]=0.1   ,						SCREAMING_SNAKE_CASE__     :       List[Any]=0.1   ,						SCREAMING_SNAKE_CASE__     :       List[Any]=0.0   ,						SCREAMING_SNAKE_CASE__     :       Union[str, Any]=0.0   ,						SCREAMING_SNAKE_CASE__     :       Any=20   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any]=2   ,						SCREAMING_SNAKE_CASE__     :       Dict=1   ,						SCREAMING_SNAKE_CASE__     :       Union[str, Any]=0   ,						)     ->					Optional[int]:
         A    :  Optional[Any]								=parent
         A    :  Optional[int]								=batch_size
         A    :  List[str]								=seq_length
         A    :  Optional[Any]								=is_training
         A    :  str								=use_labels
         A    :  List[Any]								=vocab_size
         A    :  Any								=hidden_size
         A    :  str								=num_hidden_layers
         A    :  List[Any]								=num_attention_heads
         A    :  List[str]								=intermediate_size
         A    :  str								=hidden_act
         A    :  List[Any]								=hidden_dropout_prob
         A    :  Dict								=attention_probs_dropout_prob
         A    :  Optional[int]								=encoder_layerdrop
         A    :  int								=decoder_layerdrop
         A    :  Any								=max_position_embeddings
         A    :  Optional[Any]								=eos_token_id
         A    :  List[str]								=pad_token_id
         A    :  Union[str, Any]								=bos_token_id







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Any  )     ->					List[str]:
         A    :  int								=ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size  )
         A    :  Optional[Any]								=self.eos_token_id  # Eos Token
         A    :  Tuple								=ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size  )

         # we need to clamp the input ids here to avoid having pad token in between
         # this is because for M2M100 the position_ids are prepared such that
         # all pad tokens have pos id = 2 and rest are between 2..seq_length
         # and the seq_length here is seq_length - num_pad_tokens
         # but when using past, there is no way of knowing if the past input ids had
         # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
         # position_ids being off by num_pad_tokens in past input
         A    :  Union[str, Any]								=input_ids.clamp(self.pad_token_id + 1  )
         A    :  Optional[int]								=decoder_input_ids.clamp(self.pad_token_id + 1  )

         A    :  Any								=self.get_config()
         A    :  Tuple								=prepare_mam_aaa_inputs_dict(lowercase__   ,						lowercase__   ,						lowercase__  )
         return config, inputs_dict







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Any  )     ->					List[Any]:
         return MaMaaaConfig(
             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   ,						encoder_layerdrop=self.encoder_layerdrop   ,						decoder_layerdrop=self.decoder_layerdrop   ,						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   ,						)







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Optional[Any]  )     ->					Any:
         A	,	A    :  Tuple								=self.prepare_config_and_inputs()
         return config, inputs_dict







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Optional[Any]   ,						SCREAMING_SNAKE_CASE__     :       Dict   ,						SCREAMING_SNAKE_CASE__     :       Union[str, Any]  )     ->					Optional[int]:
         A    :  Tuple								=MaMaaaModel(config=lowercase__  ).get_decoder().to(lowercase__  ).eval()
         A    :  str								=inputs_dict['input_ids']
         A    :  str								=inputs_dict['attention_mask']
         A    :  List[Any]								=inputs_dict['head_mask']

         # first forward pass
         A    :  Union[str, Any]								=model(lowercase__   ,						attention_mask=lowercase__   ,						head_mask=lowercase__   ,						use_cache=lowercase__  )

         A	,	A    :  Union[str, Any]								=outputs.to_tuple()

         # create hypothetical multiple next token and extent to next_input_ids
         A    :  Union[str, Any]								=ids_tensor((self.batch_size, 3)   ,						config.vocab_size  )
         A    :  Optional[Any]								=ids_tensor((self.batch_size, 3)   ,						2  )

         # append to next input_ids and
         A    :  Any								=torch.cat([input_ids, next_tokens]   ,						dim=-1  )
         A    :  int								=torch.cat([attention_mask, next_attn_mask]   ,						dim=-1  )

         A    :  Dict								=model(lowercase__   ,						attention_mask=lowercase__  )['last_hidden_state']
         A    :  List[Any]								=model(lowercase__   ,						attention_mask=lowercase__   ,						past_key_values=lowercase__  )[
             'last_hidden_state'
         ]

         # select random slice
         A    :  Optional[int]								=ids_tensor((1,)   ,						output_from_past.shape[-1]  ).item()
         A    :  List[Any]								=output_from_no_past[:, -3:, random_slice_idx].detach()
         A    :  str								=output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(lowercase__   ,						lowercase__   ,						atol=1e-2  )  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       str   ,						SCREAMING_SNAKE_CASE__     :       str   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any]  )     ->					Tuple:
         A    :  Union[str, Any]								=MaMaaaModel(config=lowercase__  ).to(lowercase__  ).eval()
         A    :  int								=model(**lowercase__  )

         A    :  int								=outputs.encoder_last_hidden_state
         A    :  Union[str, Any]								=outputs.last_hidden_state

         with tempfile.TemporaryDirectory() as tmpdirname:
                A    :  str								=model.get_encoder()
                encoder.save_pretrained(lowercase__  )
                A    :  Any								=MaMaaaEncoder.from_pretrained(lowercase__  ).to(lowercase__  )

         A    :  Optional[Any]								=encoder(inputs_dict['input_ids']   ,						attention_mask=inputs_dict['attention_mask']  )[
             0
         ]

         self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3  )

         with tempfile.TemporaryDirectory() as tmpdirname:
                A    :  Tuple								=model.get_decoder()
                decoder.save_pretrained(lowercase__  )
                A    :  Optional[Any]								=MaMaaaDecoder.from_pretrained(lowercase__  ).to(lowercase__  )

         A    :  Optional[Any]								=decoder(
             input_ids=inputs_dict['decoder_input_ids']   ,						attention_mask=inputs_dict['decoder_attention_mask']   ,						encoder_hidden_states=lowercase__   ,						encoder_attention_mask=inputs_dict['attention_mask']   ,						)[0]

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







@require_torch
class 			SCREAMING_SNAKE_CASE_			(       lowerCAmelCase_ ,    lowerCAmelCase_ ,    lowerCAmelCase_ ,    unittest.TestCase      ):
  '''simple docstring'''


  lowercase		:  Union[str, Any]							  =					(
      (
          MaMaaaModel,
          MaMaaaForConditionalGeneration,
      )
      if is_torch_available()
      else ()
  )
  lowercase		:  List[str]							  =					(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
  lowercase		:  Any							  =					(
      {
          "conversational": MaMaaaForConditionalGeneration,
          "feature-extraction": MaMaaaModel,
          "summarization": MaMaaaForConditionalGeneration,
          "text2text-generation": MaMaaaForConditionalGeneration,
          "translation": MaMaaaForConditionalGeneration,
      }
      if is_torch_available()
      else {}
  )
  lowercase		:  List[Any]							  =					True
  lowercase		:  Any							  =					True
  lowercase		:  Optional[Any]							  =					False
  lowercase		:  Optional[Any]							  =					False







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Tuple   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any]   ,						SCREAMING_SNAKE_CASE__     :       Union[str, Any]   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any]   ,						SCREAMING_SNAKE_CASE__     :       Dict   ,						SCREAMING_SNAKE_CASE__     :       List[str]  )     ->					Optional[int]:



         if pipeline_test_casse_name == "TranslationPipelineTests":
                # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
                # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
                return True

         return False







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Optional[int]  )     ->					Optional[Any]:
         A    :  List[Any]								=MaMaaaModelTester(self  )
         A    :  Dict								=ConfigTester(self   ,						config_class=lowercase__  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       List[Any]  )     ->					Optional[Any]:
         self.config_tester.run_common_tests()







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Optional[Any]  )     ->					Union[str, Any]:
         A	,	A    :  str								=self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
                A    :  Any								=model_class(lowercase__  )

                with tempfile.TemporaryDirectory() as tmpdirname:
                       model.save_pretrained(lowercase__  )
                       A	,	A    :  List[str]								=model_class.from_pretrained(lowercase__   ,						output_loading_info=lowercase__  )
                self.assertEqual(info['missing_keys']   ,						[]  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       str  )     ->					Union[str, Any]:
         A    :  Tuple								=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase__  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       str  )     ->					Optional[int]:
         A    :  Union[str, Any]								=self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_encoder_decoder_model_standalone(*lowercase__  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Any  )     ->					str:
         A	,	A    :  Optional[Any]								=self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                A    :  Tuple								=model_class(lowercase__  )
                model.to(lowercase__  )
                model.eval()

                A    :  int								=copy.deepcopy(self._prepare_for_class(lowercase__   ,						lowercase__  )  )

                if not self.is_encoder_decoder:
                       A    :  Union[str, Any]								=inputs['input_ids']
                       del inputs["input_ids"]
                else:
                       A    :  Dict								=inputs['input_ids']
                       A    :  str								=inputs.get('decoder_input_ids'   ,						lowercase__  )
                       del inputs["input_ids"]
                       inputs.pop('decoder_input_ids'   ,						lowercase__  )

                A    :  Any								=model.get_input_embeddings()
                if not self.is_encoder_decoder:
                       A    :  List[str]								=wte(lowercase__  )
                else:
                       A    :  int								=wte(lowercase__  )
                       A    :  Optional[Any]								=wte(lowercase__  )

                with torch.no_grad():
                       model(**lowercase__  )[0]







  def 		SCREAMING_SNAKE_CASE_		(					self     :       str  )     ->					Optional[Any]:
         A	,	A    :  Union[str, Any]								=self.model_tester.prepare_config_and_inputs()
         A    :  str								=input_dict['input_ids']
         A    :  int								=input_ids.ne(1  ).to(lowercase__  )
         A    :  List[str]								=MaMaaaForConditionalGeneration(lowercase__  ).eval().to(lowercase__  )
         if torch_device == "cuda":
                model.half()
         model.generate(lowercase__   ,						attention_mask=lowercase__  )
         model.generate(num_beams=4   ,						do_sample=lowercase__   ,						early_stopping=lowercase__   ,						num_return_sequences=3  )







def A__	(      lowercase:  int       )					->		List[Any]:
       return torch.tensor(__A,		dtype=torch.long,		device=__A       )


_lowercase				:   Any   				=1E-4



@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 			SCREAMING_SNAKE_CASE_			(       unittest.TestCase      ):
  '''simple docstring'''


  @cached_property
  def 		SCREAMING_SNAKE_CASE_		(					self     :       Optional[Any]  )     ->					Optional[Any]:
         return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       int  )     ->					Optional[Any]:
         A    :  Dict								=MaMaaaModel.from_pretrained('facebook/m2m100_418M'  ).to(lowercase__  )
         A    :  int								=_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]  )
         A    :  Dict								=_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]  )
         A    :  Tuple								=prepare_mam_aaa_inputs_dict(model.config   ,						lowercase__   ,						lowercase__  )
         with torch.no_grad():
                A    :  int								=model(**lowercase__  )[0]
         A    :  Union[str, Any]								=torch.Size((1, 11, 10_24)  )
         self.assertEqual(output.shape   ,						lowercase__  )
         # change to expected output here
         A    :  List[str]								=torch.tensor(
             [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]   ,						device=lowercase__  )
         self.assertTrue(torch.allclose(output[:, :3, :3]   ,						lowercase__   ,						atol=lowercase__  )  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Any  )     ->					Union[str, Any]:
         A    :  List[str]								=MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'  ).to(lowercase__  )

         # change to intended input
         A    :  Union[str, Any]								=_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]  )
         A    :  Optional[int]								=_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]  )
         A    :  List[str]								=prepare_mam_aaa_inputs_dict(model.config   ,						lowercase__   ,						lowercase__  )
         with torch.no_grad():
                A    :  Dict								=model(**lowercase__  )[0]
         A    :  List[str]								=torch.Size((1, 11, model.config.vocab_size)  )
         self.assertEqual(output.shape   ,						lowercase__  )
         # change to expected output here
         A    :  Union[str, Any]								=torch.tensor(
             [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]   ,						device=lowercase__  )
         self.assertTrue(torch.allclose(output[:, :3, :3]   ,						lowercase__   ,						atol=lowercase__  )  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       str  )     ->					List[str]:
         A    :  str								=MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'  ).to(lowercase__  )
         A    :  Tuple								=MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'   ,						src_lang='fr'   ,						tgt_lang='en'  )

         A    :  Any								=[
             'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
             'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
             'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
             ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
             ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
         ]

         # The below article tests that we don't add any hypotheses outside of the top n_beams
         A    :  List[Any]								=tokenizer(lowercase__   ,						padding=lowercase__   ,						return_tensors='pt'  )

         A    :  int								=model.generate(
             input_ids=dct['input_ids'].to(lowercase__  )   ,						attention_mask=dct['attention_mask'].to(lowercase__  )   ,						num_beams=5   ,						forced_bos_token_id=tokenizer.get_lang_id('en'  )   ,						)

         A    :  List[str]								=[
             'The NSA case highlights the total absence of intelligence debate',
             'I think there are two levels of response from the French government.',
             'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
             ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
             ' communications in France.',
         ]

         A    :  List[str]								=tokenizer.batch_decode(
             hypotheses_batch.tolist()   ,						clean_up_tokenization_spaces=lowercase__   ,						skip_special_tokens=lowercase__  )
         assert generated == expected_en









import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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        lowerCAmelCase__  (					__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    KandinskyVaaPipeline
       lowercase_						=    [
           """image_embeds""",
           """negative_image_embeds""",
       ]
       lowercase_						=    ["""image_embeds""", """negative_image_embeds"""]
       lowercase_						=    [
           """generator""",
           """height""",
           """width""",
           """latents""",
           """guidance_scale""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       lowercase_						=    False

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 3_2

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 3_2

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.time_input_dim

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.time_input_dim * 4

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 1_0_0

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             torch.manual_seed(0     )

             __A    ={
                 '''in_channels''': 4,
                 # Out channels is double in channels because predicts mean and variance
                 '''out_channels''': 8,
                 '''addition_embed_type''': '''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''': '''image_proj''',
                 '''cross_attention_dim''': self.cross_attention_dim,
                 '''attention_head_dim''': 4,
                 '''resnet_time_scale_shift''': '''scale_shift''',
                 '''class_embed_type''': None,
             }

             __A    =UNetaDConditionModel(**lowercase__     )
             return model

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return {
                 "block_out_channels": [3_2, 6_4],
                 "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": 1_2,
                 "out_channels": 3,
                 "up_block_types": [
                     "AttnUpDecoderBlock2D",
                     "UpDecoderBlock2D",
                 ],
                 "vq_embed_dim": 4,
             }

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             torch.manual_seed(0     )
             __A    =VQModel(**self.dummy_movq_kwargs     )
             return model

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.dummy_unet
             __A    =self.dummy_movq

             __A    =DDIMScheduler(
                 num_train_timesteps=1_0_0_0 ,   beta_schedule='''linear''' ,   beta_start=0.0_0085 ,   beta_end=0.012 ,   clip_sample=lowercase__ ,   set_alpha_to_one=lowercase__ ,   steps_offset=1 ,   prediction_type='''epsilon''' ,   thresholding=lowercase__ ,   )

             __A    ={
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''movq''': movq,
             }
             return components

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__=0     ):







             '''simple docstring'''



             __A    =floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(lowercase__     )     ).to(lowercase__     )
             __A    =floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(seed + 1     )     ).to(
                 lowercase__     )
             if str(lowercase__     ).startswith('''mps'''     ):
                   __A    =torch.manual_seed(lowercase__     )
             else:
                   __A    =torch.Generator(device=lowercase__     ).manual_seed(lowercase__     )
             __A    ={
                 '''image_embeds''': image_embeds,
                 '''negative_image_embeds''': negative_image_embeds,
                 '''generator''': generator,
                 '''height''': 6_4,
                 '''width''': 6_4,
                 '''guidance_scale''': 4.0,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''np''',
             }
             return inputs


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''cpu'''

             __A    =self.get_dummy_components()

             __A    =self.pipeline_class(**lowercase__     )
             __A    =pipe.to(lowercase__     )

             pipe.set_progress_bar_config(disable=lowercase__     )

             __A    =pipe(**self.get_dummy_inputs(lowercase__     )     )
             __A    =output.images

             __A    =pipe(
                 **self.get_dummy_inputs(lowercase__     ) ,   return_dict=lowercase__ ,   )[0]

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

             assert image.shape == (1, 6_4, 6_4, 3)

             __A    =np.array(
                 [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]     )

             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        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =load_numpy(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''     )

             __A    =KandinskyVaaPriorPipeline.from_pretrained(
                 '''kandinsky-community/kandinsky-2-2-prior''' ,   torch_dtype=torch.floataa     )
             pipe_prior.to(lowercase__     )

             __A    =KandinskyVaaPipeline.from_pretrained(
                 '''kandinsky-community/kandinsky-2-2-decoder''' ,   torch_dtype=torch.floataa     )
             __A    =pipeline.to(lowercase__     )
             pipeline.set_progress_bar_config(disable=lowercase__     )

             __A    ='''red cat, 4k photo'''

             __A    =torch.Generator(device='''cuda'''     ).manual_seed(0     )
             __A    ,		__A    =pipe_prior(
                 lowercase__ ,   generator=lowercase__ ,   num_inference_steps=5 ,   negative_prompt='''''' ,   ).to_tuple()

             __A    =torch.Generator(device='''cuda'''     ).manual_seed(0     )
             __A    =pipeline(
                 image_embeds=lowercase__ ,   negative_image_embeds=lowercase__ ,   generator=lowercase__ ,   num_inference_steps=1_0_0 ,   output_type='''np''' ,   )

             __A    =output.images[0]

             assert image.shape == (5_1_2, 5_1_2, 3)

             assert_mean_pixel_difference(lowercase__ ,   lowercase__     )


