


'''simple docstring'''







import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
 import torch

 from transformers import (
     SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     SqueezeBertForMaskedLM,
     SqueezeBertForMultipleChoice,
     SqueezeBertForQuestionAnswering,
     SqueezeBertForSequenceClassification,
     SqueezeBertForTokenClassification,
     SqueezeBertModel,
 )


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  def __init__(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=1_3				,							SCREAMING_SNAKE_CASE__     :							List[str]=7				,							SCREAMING_SNAKE_CASE__     :							int=True				,							SCREAMING_SNAKE_CASE__     :							Dict=True				,							SCREAMING_SNAKE_CASE__     :							int=False				,							SCREAMING_SNAKE_CASE__     :							List[Any]=True				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=9_9				,							SCREAMING_SNAKE_CASE__     :							Any=3_2				,							SCREAMING_SNAKE_CASE__     :							List[Any]=5				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=4				,							SCREAMING_SNAKE_CASE__     :							str=6_4				,							SCREAMING_SNAKE_CASE__     :							Tuple="gelu"				,							SCREAMING_SNAKE_CASE__     :							int=0.1				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=0.1				,							SCREAMING_SNAKE_CASE__     :							int=5_1_2				,							SCREAMING_SNAKE_CASE__     :							Tuple=1_6				,							SCREAMING_SNAKE_CASE__     :							List[Any]=2				,							SCREAMING_SNAKE_CASE__     :							List[Any]=0.0_2				,							SCREAMING_SNAKE_CASE__     :							List[str]=3				,							SCREAMING_SNAKE_CASE__     :							str=4				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=None				,							SCREAMING_SNAKE_CASE__     :							Any=2				,							SCREAMING_SNAKE_CASE__     :							List[str]=2				,							SCREAMING_SNAKE_CASE__     :							Any=2				,							SCREAMING_SNAKE_CASE__     :							List[str]=2				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=4				,							SCREAMING_SNAKE_CASE__     :							Any=1				,							):





       '''simple docstring'''

       __a       =   parent
       __a       =   batch_size
       __a       =   seq_length
       __a       =   is_training
       __a       =   use_input_mask
       __a       =   use_token_type_ids
       __a       =   use_labels
       __a       =   vocab_size
       __a       =   hidden_size
       __a       =   num_hidden_layers
       __a       =   num_attention_heads
       __a       =   intermediate_size
       __a       =   hidden_act
       __a       =   hidden_dropout_prob
       __a       =   attention_probs_dropout_prob
       __a       =   max_position_embeddings
       __a       =   type_vocab_size
       __a       =   type_sequence_label_size
       __a       =   initializer_range
       __a       =   num_labels
       __a       =   num_choices
       __a       =   scope
       __a       =   q_groups
       __a       =   k_groups
       __a       =   v_groups
       __a       =   post_attention_groups
       __a       =   intermediate_groups
       __a       =   output_groups

  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size						)

       __a       =   None
       if self.use_input_mask:
            __a       =   random_attention_mask([self.batch_size, self.seq_length]						)

       __a       =   None
       __a       =   None
       __a       =   None
       if self.use_labels:
            __a       =   ids_tensor([self.batch_size]				,							self.type_sequence_label_size						)
            __a       =   ids_tensor([self.batch_size, self.seq_length]				,							self.num_labels						)
            __a       =   ids_tensor([self.batch_size]				,							self.num_choices						)

       __a       =   self.get_config()

       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       return SqueezeBertConfig(
           embedding_size=self.hidden_size				,							vocab_size=self.vocab_size				,							hidden_size=self.hidden_size				,							num_hidden_layers=self.num_hidden_layers				,							num_attention_heads=self.num_attention_heads				,							intermediate_size=self.intermediate_size				,							hidden_act=self.hidden_act				,							attention_probs_dropout_prob=self.hidden_dropout_prob				,							attention_dropout=self.attention_probs_dropout_prob				,							max_position_embeddings=self.max_position_embeddings				,							initializer_range=self.initializer_range				,							q_groups=self.q_groups				,							k_groups=self.k_groups				,							v_groups=self.v_groups				,							post_attention_groups=self.post_attention_groups				,							intermediate_groups=self.intermediate_groups				,							output_groups=self.output_groups				,							)

  def __a						(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Any				,							SCREAMING_SNAKE_CASE__     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       __a       =   SqueezeBertModel(config=SCREAMING_SNAKE_CASE__						)
       model.to(SCREAMING_SNAKE_CASE__						)
       model.eval()
       __a       =   model(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)
       __a       =   model(SCREAMING_SNAKE_CASE__						)
       self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)						)

  def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							str						):





       '''simple docstring'''

       __a       =   SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE__						)
       model.to(SCREAMING_SNAKE_CASE__						)
       model.eval()
       __a       =   model(SCREAMING_SNAKE_CASE__				,							attention_mask=SCREAMING_SNAKE_CASE__				,							labels=SCREAMING_SNAKE_CASE__						)
       self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.vocab_size)						)

  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       __a       =   SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE__						)
       model.to(SCREAMING_SNAKE_CASE__						)
       model.eval()
       __a       =   model(
           SCREAMING_SNAKE_CASE__				,							attention_mask=SCREAMING_SNAKE_CASE__				,							start_positions=SCREAMING_SNAKE_CASE__				,							end_positions=SCREAMING_SNAKE_CASE__						)
       self.parent.assertEqual(result.start_logits.shape				,							(self.batch_size, self.seq_length)						)
       self.parent.assertEqual(result.end_logits.shape				,							(self.batch_size, self.seq_length)						)

  def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''simple docstring'''

       __a       =   self.num_labels
       __a       =   SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE__						)
       model.to(SCREAMING_SNAKE_CASE__						)
       model.eval()
       __a       =   model(SCREAMING_SNAKE_CASE__				,							attention_mask=SCREAMING_SNAKE_CASE__				,							labels=SCREAMING_SNAKE_CASE__						)
       self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)						)

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Any				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   self.num_labels
       __a       =   SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE__						)
       model.to(SCREAMING_SNAKE_CASE__						)
       model.eval()

       __a       =   model(SCREAMING_SNAKE_CASE__				,							attention_mask=SCREAMING_SNAKE_CASE__				,							labels=SCREAMING_SNAKE_CASE__						)
       self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.num_labels)						)

  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Any				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

       __a       =   self.num_choices
       __a       =   SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE__						)
       model.to(SCREAMING_SNAKE_CASE__						)
       model.eval()
       __a       =   input_ids.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
       __a       =   input_mask.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
       __a       =   model(
           SCREAMING_SNAKE_CASE__				,							attention_mask=SCREAMING_SNAKE_CASE__				,							labels=SCREAMING_SNAKE_CASE__				,							)
       self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_choices)						)


  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   self.prepare_config_and_inputs()
       ((__a) ,						(__a) ,						(__a) ,						(__a) ,						(__a) ,						(__a))       =   config_and_inputs
       __a       =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
       return config, inputs_dict


@require_torch
class  lowerCAmelCase_						(       snake_case__		,    snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :Any												=(
      (
          SqueezeBertModel,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
      )
      if is_torch_available()
      else None
  )
  a_       :str												=(
      {
          """feature-extraction""": SqueezeBertModel,
          """fill-mask""": SqueezeBertForMaskedLM,
          """question-answering""": SqueezeBertForQuestionAnswering,
          """text-classification""": SqueezeBertForSequenceClassification,
          """token-classification""": SqueezeBertForTokenClassification,
          """zero-shot""": SqueezeBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  a_       :List[str]												=False
  a_       :List[str]												=True
  a_       :List[str]												=False

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   SqueezeBertModelTester(self						)
       __a       =   ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE__				,							dim=3_7						)

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       self.config_tester.run_common_tests()

  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE__						)


  @slow
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __a       =   SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE__						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE__						)


@require_sentencepiece
@require_tokenizers
@require_torch
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli"""						)

       __a       =   torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]]						)
       __a       =   model(SCREAMING_SNAKE_CASE__						)[0]
       __a       =   torch.Size((1, 3)						)
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       __a       =   torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]]						)
       self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							atol=1E-4						)						)










'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class  lowerCAmelCase_						(       snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :Optional[int]												=ConsistencyModelPipeline
  a_       :List[Any]												=UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  a_       :Optional[int]												=UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

  # Override required_optional_params to remove num_images_per_prompt
  a_       :Optional[Any]												=frozenset(
      [
          """num_inference_steps""",
          """generator""",
          """latents""",
          """output_type""",
          """return_dict""",
          """callback""",
          """callback_steps""",
      ]      )

  @property
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   UNetaDModel.from_pretrained(
           """diffusers/consistency-models-test"""				,							subfolder="""test_unet"""				,							)
       return unet

  @property
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   UNetaDModel.from_pretrained(
           """diffusers/consistency-models-test"""				,							subfolder="""test_unet_class_cond"""				,							)
       return unet

  def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							int=False						):





       '''simple docstring'''

       if class_cond:
            __a       =   self.dummy_cond_unet
       else:
            __a       =   self.dummy_uncond_unet

       # Default to CM multistep sampler
       __a       =   CMStochasticIterativeScheduler(
           num_train_timesteps=4_0				,							sigma_min=0.0_0_2				,							sigma_max=8_0.0				,							)

       __a       =   {
           """unet""": unet,
           """scheduler""": scheduler,
       }

       return components

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							int=0						):





       '''simple docstring'''

       if str(SCREAMING_SNAKE_CASE__						).startswith("""mps"""						):
            __a       =   torch.manual_seed(SCREAMING_SNAKE_CASE__						)
       else:
            __a       =   torch.Generator(device=SCREAMING_SNAKE_CASE__						).manual_seed(SCREAMING_SNAKE_CASE__						)

       __a       =   {
           """batch_size""": 1,
           """num_inference_steps""": None,
           """timesteps""": [2_2, 0],
           """generator""": generator,
           """output_type""": """np""",
       }

       return inputs

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
       __a       =   self.get_dummy_components()
       __a       =   ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   pipe.to(SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 3_2, 3_2, 3)

       __a       =   image[0, -3:, -3:, -1]
       __a       =   np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3

  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
       __a       =   self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__						)
       __a       =   ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   pipe.to(SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   0
       __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 3_2, 3_2, 3)

       __a       =   image[0, -3:, -3:, -1]
       __a       =   np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
       __a       =   self.get_dummy_components()
       __a       =   ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   pipe.to(SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   1
       __a       =   None
       __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 3_2, 3_2, 3)

       __a       =   image[0, -3:, -3:, -1]
       __a       =   np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3


  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
       __a       =   self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__						)
       __a       =   ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   pipe.to(SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   1
       __a       =   None
       __a       =   0
       __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 3_2, 3_2, 3)

       __a       =   image[0, -3:, -3:, -1]
       __a       =   np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3


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





  """simple docstring"""



  def __a						(					self     :							int						):





       '''simple docstring'''

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

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Dict=0				,							SCREAMING_SNAKE_CASE__     :							str=False				,							SCREAMING_SNAKE_CASE__     :							int="cpu"				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=torch.floataa				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=(1, 3, 6_4, 6_4)						):





       '''simple docstring'''

       __a       =   torch.manual_seed(SCREAMING_SNAKE_CASE__						)

       __a       =   {
           """num_inference_steps""": None,
           """timesteps""": [2_2, 0],
           """class_labels""": 0,
           """generator""": generator,
           """output_type""": """np""",
       }

       if get_fixed_latents:
            __a       =   self.get_fixed_latents(seed=SCREAMING_SNAKE_CASE__				,							device=SCREAMING_SNAKE_CASE__				,							dtype=SCREAMING_SNAKE_CASE__				,							shape=SCREAMING_SNAKE_CASE__						)
            __a       =   latents

       return inputs

  def __a						(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							str=0				,							SCREAMING_SNAKE_CASE__     :							List[str]="cpu"				,							SCREAMING_SNAKE_CASE__     :							List[str]=torch.floataa				,							SCREAMING_SNAKE_CASE__     :							List[str]=(1, 3, 6_4, 6_4)						):





       '''simple docstring'''

       if type(SCREAMING_SNAKE_CASE__						) == str:
            __a       =   torch.device(SCREAMING_SNAKE_CASE__						)
       __a       =   torch.Generator(device=SCREAMING_SNAKE_CASE__						).manual_seed(SCREAMING_SNAKE_CASE__						)
       __a       =   randn_tensor(SCREAMING_SNAKE_CASE__				,							generator=SCREAMING_SNAKE_CASE__				,							device=SCREAMING_SNAKE_CASE__				,							dtype=SCREAMING_SNAKE_CASE__						)
       return latents

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,							subfolder="""diffusers_cd_imagenet64_l2"""						)
       __a       =   CMStochasticIterativeScheduler(
           num_train_timesteps=4_0				,							sigma_min=0.0_0_2				,							sigma_max=8_0.0				,							)
       __a       =   ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__				,							scheduler=SCREAMING_SNAKE_CASE__						)
       pipe.to(torch_device=SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_inputs()
       __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 6_4, 6_4, 3)

       __a       =   image[0, -3:, -3:, -1]

       __a       =   np.array([0.0_8_8_8, 0.0_8_8_1, 0.0_6_6_6, 0.0_4_7_9, 0.0_2_9_2, 0.0_1_9_5, 0.0_2_0_1, 0.0_1_6_3, 0.0_2_5_4]						)

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

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,							subfolder="""diffusers_cd_imagenet64_l2"""						)
       __a       =   CMStochasticIterativeScheduler(
           num_train_timesteps=4_0				,							sigma_min=0.0_0_2				,							sigma_max=8_0.0				,							)
       __a       =   ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__				,							scheduler=SCREAMING_SNAKE_CASE__						)
       pipe.to(torch_device=SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_inputs()
       __a       =   1
       __a       =   None
       __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 6_4, 6_4, 3)

       __a       =   image[0, -3:, -3:, -1]

       __a       =   np.array([0.0_3_4_0, 0.0_1_5_2, 0.0_0_6_3, 0.0_2_6_7, 0.0_2_2_1, 0.0_1_0_7, 0.0_4_1_6, 0.0_1_8_6, 0.0_2_1_7]						)

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

  @require_torch_a
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,							subfolder="""diffusers_cd_imagenet64_l2"""						)
       __a       =   CMStochasticIterativeScheduler(
           num_train_timesteps=4_0				,							sigma_min=0.0_0_2				,							sigma_max=8_0.0				,							)
       __a       =   ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__				,							scheduler=SCREAMING_SNAKE_CASE__						)
       pipe.to(torch_device=SCREAMING_SNAKE_CASE__				,							torch_dtype=torch.floataa						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__				,							device=SCREAMING_SNAKE_CASE__						)
       # Ensure usage of flash attention in torch 2.0
       with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__				,							enable_math=SCREAMING_SNAKE_CASE__				,							enable_mem_efficient=SCREAMING_SNAKE_CASE__						):
            __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 6_4, 6_4, 3)

       __a       =   image[0, -3:, -3:, -1]

       __a       =   np.array([0.1_8_7_5, 0.1_4_2_8, 0.1_2_8_9, 0.2_1_5_1, 0.2_0_9_2, 0.1_4_7_7, 0.1_8_7_7, 0.1_6_4_1, 0.1_3_5_3]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3


  @require_torch_a
  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,							subfolder="""diffusers_cd_imagenet64_l2"""						)
       __a       =   CMStochasticIterativeScheduler(
           num_train_timesteps=4_0				,							sigma_min=0.0_0_2				,							sigma_max=8_0.0				,							)
       __a       =   ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__				,							scheduler=SCREAMING_SNAKE_CASE__						)
       pipe.to(torch_device=SCREAMING_SNAKE_CASE__				,							torch_dtype=torch.floataa						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__				,							device=SCREAMING_SNAKE_CASE__						)
       __a       =   1
       __a       =   None
       # Ensure usage of flash attention in torch 2.0
       with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__				,							enable_math=SCREAMING_SNAKE_CASE__				,							enable_mem_efficient=SCREAMING_SNAKE_CASE__						):
            __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 6_4, 6_4, 3)

       __a       =   image[0, -3:, -3:, -1]

       __a       =   np.array([0.1_6_6_3, 0.1_9_4_8, 0.2_2_7_5, 0.1_6_8_0, 0.1_2_0_4, 0.1_2_4_5, 0.1_8_5_8, 0.1_3_3_8, 0.2_0_9_5]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3






