code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from __future__ import annotations
  
  import copy
  import os
  import tempfile
  from importlib import import_module
  from math import isnan
  
  from transformers import is_tf_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow
  
  from ..test_modeling_tf_common import ids_tensor
  
  
  if is_tf_available():
  				import numpy as np
  				import tensorflow as tf
  
  				from transformers import (
  				    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
  				    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
  				    TF_MODEL_FOR_MASKED_LM_MAPPING,
  				    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
  				    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
  				    TF_MODEL_FOR_PRETRAINING_MAPPING,
  				    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
  				    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
  				    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
  				    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
  				    TFSharedEmbeddings,
  				)
  
  				if _tf_gpu_memory_limit is not None:
  								_lowerCamelCase				:	Union[str, Any]				    =       tf.config.list_physical_devices('GPU')
  								for gpu in gpus:
  												# Restrict TensorFlow to only allocate x GB of memory on the GPUs
  												try:
  																tf.config.set_logical_device_configuration(
  																    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
  																)
  																_lowerCamelCase				:	int				    =       tf.config.list_logical_devices('GPU')
  																print('Logical GPUs', logical_gpus)
  												except RuntimeError as e:
  																# Virtual devices must be set before GPUs have been initialized
  																print(e)
  
  
  
  @require_tf
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    None
  		__lowerCAmelCase 				=    ()
  		__lowerCAmelCase 				=    ()
  		__lowerCAmelCase 				=    True
  		__lowerCAmelCase 				=    True
  		__lowerCAmelCase 				=    True
  		__lowerCAmelCase 				=    False
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      _lowerCAmelCase						:      List[Any]      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      Tuple=False      ):
  			A							  =      copy.deepcopy(_lowerCAmelCase      )
  
  			if model_class in get_values(_lowerCAmelCase      ):
  				A							  =      {
  				    k: tf.tile(tf.expand_dims(_lowerCAmelCase      ,      1      )      ,      (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)      )
  				    if isinstance(_lowerCAmelCase      ,      tf.Tensor      ) and v.ndim > 0
  				    else v
  				    for k, v in inputs_dict.items()
  				}
  
  			if return_labels:
  				if model_class in get_values(_lowerCAmelCase      ):
  					A							  =      tf.ones(self.model_tester.batch_size      ,      dtype=tf.intaa      )
  				elif model_class in get_values(_lowerCAmelCase      ):
  					A							  =      tf.zeros(self.model_tester.batch_size      ,      dtype=tf.intaa      )
  					A							  =      tf.zeros(self.model_tester.batch_size      ,      dtype=tf.intaa      )
  				elif model_class in [
  				    *get_values(_lowerCAmelCase      ),
  				    *get_values(_lowerCAmelCase      ),
  				]:
  					A							  =      tf.zeros(self.model_tester.batch_size      ,      dtype=tf.intaa      )
  				elif model_class in get_values(_lowerCAmelCase      ):
  					A							  =      tf.zeros(self.model_tester.batch_size      ,      dtype=tf.intaa      )
  				elif model_class in [
  				    *get_values(_lowerCAmelCase      ),
  				    *get_values(_lowerCAmelCase      ),
  				    *get_values(_lowerCAmelCase      ),
  				    *get_values(_lowerCAmelCase      ),
  				    *get_values(_lowerCAmelCase      ),
  				]:
  					A							  =      tf.zeros(
  					    (self.model_tester.batch_size, self.model_tester.seq_length)      ,      dtype=tf.intaa      )
  			return inputs_dict
  
  
  
  
  
  
  
  		@slow
  		def        A     (self						:      Optional[Any]      ):
  			A  ,    A							  =      self.model_tester.prepare_config_and_inputs_for_common()
  			for model_class in self.all_model_classes[:2]:
  				A							  =      self._prepare_for_class(_lowerCAmelCase      ,      _lowerCAmelCase      )
  				A							  =      model_class(_lowerCAmelCase      )
  
  				@tf.function
  				def run_in_graph_mode():
  					return model(_lowerCAmelCase      )
  
  				A							  =      run_in_graph_mode()
  				self.assertIsNotNone(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		@slow
  		def        A     (self						:      List[str]      ):
  			A  ,    A							  =      self.model_tester.prepare_config_and_inputs_for_common()
  			for model_class in self.all_model_classes[:2]:
  				A							  =      self._prepare_for_class(_lowerCAmelCase      ,      _lowerCAmelCase      )
  				A							  =      model_class(_lowerCAmelCase      )
  
  				@tf.function(experimental_compile=_lowerCAmelCase      )
  				def run_in_graph_mode():
  					return model(_lowerCAmelCase      )
  
  				A							  =      run_in_graph_mode()
  				self.assertIsNotNone(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		@slow
  		def        A     (self						:      Tuple      ):
  			# This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
  			A  ,    A							  =      self.model_tester.prepare_config_and_inputs_for_common()
  			for model_class in self.all_model_classes[:2]:
  				A							  =      model_class(_lowerCAmelCase      )
  				if getattr(_lowerCAmelCase      ,      """hf_compute_loss"""      ,      _lowerCAmelCase      ):
  					# Test that model correctly compute the loss with kwargs
  					A							  =      self._prepare_for_class(inputs_dict.copy()      ,      _lowerCAmelCase      ,      return_labels=_lowerCAmelCase      )
  					# Is there a better way to remove these decoder inputs?
  					A							  =      {
  					    key: val
  					    for key, val in prepared_for_class.items()
  					    if key not in ("""head_mask""", """decoder_head_mask""", """cross_attn_head_mask""", """decoder_input_ids""")
  					}
  
  					A							  =      {
  					    """labels""",
  					    """label""",
  					    """label_ids""",
  					    """start_positions""",
  					    """start_position""",
  					    """end_positions""",
  					    """end_position""",
  					    """next_sentence_label""",
  					}
  					A							  =      possible_label_cols.intersection(set(_lowerCAmelCase      )      )
  					self.assertGreater(len(_lowerCAmelCase      )      ,      0      ,      msg="""No matching label names found!"""      )
  					A							  =      {key: val for key, val in prepared_for_class.items() if key in label_names}
  					A							  =      {key: val for key, val in prepared_for_class.items() if key not in label_names}
  					self.assertGreater(len(_lowerCAmelCase      )      ,      0      )
  
  					# Make sure it works with XLA!
  					model.compile(optimizer=tf.keras.optimizers.SGD(0.0      )      ,      jit_compile=_lowerCAmelCase      )
  					# Make sure the model fits without crashing regardless of where we pass the labels
  					A							  =      model.fit(
  					    _lowerCAmelCase      ,      validation_data=_lowerCAmelCase      ,      steps_per_epoch=1      ,      validation_steps=1      ,      shuffle=_lowerCAmelCase      ,      verbose=0      ,      )
  					A							  =      history.history["""loss"""][0]
  					self.assertTrue(not isnan(_lowerCAmelCase      )      )
  					A							  =      history.history["""val_loss"""][0]
  					self.assertTrue(not isnan(_lowerCAmelCase      )      )
  
  					# Now test it with separate labels, to make sure that path works in XLA too.
  					A							  =      model_class(_lowerCAmelCase      )
  					model.compile(optimizer=tf.keras.optimizers.SGD(0.0      )      ,      jit_compile=_lowerCAmelCase      )
  					A							  =      model.fit(
  					    _lowerCAmelCase      ,      _lowerCAmelCase      ,      validation_data=(inputs_minus_labels, labels)      ,      steps_per_epoch=1      ,      validation_steps=1      ,      shuffle=_lowerCAmelCase      ,      verbose=0      ,      )
  
  					A							  =      history.history["""loss"""][0]
  					self.assertTrue(not isnan(_lowerCAmelCase      )      )
  					A							  =      history.history["""val_loss"""][0]
  					self.assertTrue(not isnan(_lowerCAmelCase      )      )
  
  
  
  
  
  
  
  		@slow
  		def        A     (self						:      Union[str, Any]      ):
  			A  ,    A							  =      self.model_tester.prepare_config_and_inputs_for_common()
  			A							  =      True
  			A							  =      True
  
  			if hasattr(_lowerCAmelCase      ,      """use_cache"""      ):
  				A							  =      True
  
  			A							  =      getattr(self.model_tester      ,      """encoder_seq_length"""      ,      self.model_tester.seq_length      )
  			A							  =      getattr(self.model_tester      ,      """key_length"""      ,      _lowerCAmelCase      )
  
  			for model_class in self.all_model_classes[:2]:
  				A							  =      self._prepare_for_class(_lowerCAmelCase      ,      _lowerCAmelCase      )
  				A							  =      model_class(_lowerCAmelCase      )
  				model.build()
  				A							  =      len(model(_lowerCAmelCase      )      )
  
  				for key in list(class_inputs_dict.keys()      ):
  					# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
  					if key not in model.input_signature:
  						del class_inputs_dict[key]
  					# Check it's a tensor, in case the inputs dict has some bools in it too
  					elif isinstance(class_inputs_dict[key]      ,      tf.Tensor      ) and class_inputs_dict[key].dtype.is_integer:
  						A							  =      tf.cast(class_inputs_dict[key]      ,      tf.intaa      )
  
  				if set(class_inputs_dict.keys()      ) != set(model.input_signature.keys()      ):
  					continue  # Some models have inputs that the preparation functions don't create, we skip those
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_pretrained(_lowerCAmelCase      ,      saved_model=_lowerCAmelCase      )
  					A							  =      os.path.join(_lowerCAmelCase      ,      """saved_model"""      ,      """1"""      )
  					A							  =      tf.keras.models.load_model(_lowerCAmelCase      )
  					A							  =      model(_lowerCAmelCase      )
  
  					if self.is_encoder_decoder:
  						A							  =      outputs["""encoder_hidden_states"""]
  						A							  =      outputs["""encoder_attentions"""]
  					else:
  						A							  =      outputs["""hidden_states"""]
  						A							  =      outputs["""attentions"""]
  
  					self.assertEqual(len(_lowerCAmelCase      )      ,      _lowerCAmelCase      )
  
  					A							  =      getattr(
  					    self.model_tester      ,      """expected_num_hidden_layers"""      ,      self.model_tester.num_hidden_layers + 1      )
  
  					self.assertEqual(len(_lowerCAmelCase      )      ,      _lowerCAmelCase      )
  					self.assertListEqual(
  					    list(output_hidden_states[0].shape[-2:]      )      ,      [self.model_tester.seq_length, self.model_tester.hidden_size]      ,      )
  
  					self.assertEqual(len(_lowerCAmelCase      )      ,      self.model_tester.num_hidden_layers      )
  					self.assertListEqual(
  					    list(output_attentions[0].shape[-3:]      )      ,      [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]      ,      )
  
  
  
  
  
  
  
  		@slow
  		def        A     (self						:      Dict      ):
  			tf.keras.mixed_precision.set_global_policy("""mixed_float16"""      )
  
  			# try/finally block to ensure subsequent tests run in float32
  			try:
  				A  ,    A							  =      self.model_tester.prepare_config_and_inputs_for_common()
  				for model_class in self.all_model_classes[:2]:
  					A							  =      self._prepare_for_class(_lowerCAmelCase      ,      _lowerCAmelCase      )
  					A							  =      model_class(_lowerCAmelCase      )
  					A							  =      model(_lowerCAmelCase      )
  
  					self.assertIsNotNone(_lowerCAmelCase      )
  			finally:
  				tf.keras.mixed_precision.set_global_policy("""float32"""      )
  
  
  
  
  
  
  
  		@slow
  		def        A     (self						:      int      ):
  			A  ,    A							  =      self.model_tester.prepare_config_and_inputs_for_common()
  			# head_mask and decoder_head_mask has different shapes than other input args
  			if "head_mask" in inputs_dict:
  				del inputs_dict["head_mask"]
  			if "decoder_head_mask" in inputs_dict:
  				del inputs_dict["decoder_head_mask"]
  			if "cross_attn_head_mask" in inputs_dict:
  				del inputs_dict["cross_attn_head_mask"]
  			A							  =      {
  			    module_member
  			    for model_class in self.all_model_classes
  			    for module in (import_module(model_class.__module__      ),)
  			    for module_member_name in dir(_lowerCAmelCase      )
  			    if module_member_name.endswith("""MainLayer"""      )
  			    for module_member in (getattr(_lowerCAmelCase      ,      _lowerCAmelCase      ),)
  			    if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      )
  			    and tf.keras.layers.Layer in module_member.__bases__
  			    and getattr(_lowerCAmelCase      ,      """_keras_serializable"""      ,      _lowerCAmelCase      )
  			}
  
  			for main_layer_class in tf_main_layer_classes:
  				# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
  				if "T5" in main_layer_class.__name__:
  					# Take the same values than in TFT5ModelTester for this shared layer
  					A							  =      TFSharedEmbeddings(self.model_tester.vocab_size      ,      self.model_tester.hidden_size      ,      name="""shared"""      )
  					A							  =      False
  					A							  =      main_layer_class(_lowerCAmelCase      ,      embed_tokens=_lowerCAmelCase      )
  				else:
  					A							  =      main_layer_class(_lowerCAmelCase      )
  
  				A							  =      {
  				    name: tf.keras.Input(tensor.shape[1:]      ,      dtype=tensor.dtype      ) for name, tensor in inputs_dict.items()
  				}
  
  				if hasattr(self.model_tester      ,      """num_labels"""      ):
  					A							  =      self.model_tester.num_labels
  				else:
  					A							  =      2
  
  				A							  =      tf.data.Dataset.from_tensor_slices(
  				    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)      ))      ).batch(1      )
  
  				A							  =      main_layer(_lowerCAmelCase      )[0]
  				A							  =      tf.keras.layers.Dense(_lowerCAmelCase      ,      activation="""softmax"""      ,      name="""outputs"""      )(_lowerCAmelCase      )
  				A							  =      tf.keras.models.Model(inputs=_lowerCAmelCase      ,      outputs=[outputs]      )
  
  				model.compile(loss="""binary_crossentropy"""      ,      optimizer="""adam"""      ,      metrics=["""binary_accuracy"""]      )
  				model.fit(_lowerCAmelCase      ,      epochs=1      )
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					A							  =      os.path.join(_lowerCAmelCase      ,      """keras_model.h5"""      )
  					model.save(_lowerCAmelCase      )
  					if "T5" in main_layer_class.__name__:
  						A							  =      tf.keras.models.load_model(
  						    _lowerCAmelCase      ,      custom_objects={
  						        main_layer_class.__name__: main_layer_class,
  						        """TFSharedEmbeddings""": TFSharedEmbeddings,
  						    }      ,      )
  					else:
  						A							  =      tf.keras.models.load_model(
  						    _lowerCAmelCase      ,      custom_objects={main_layer_class.__name__: main_layer_class}      )
  					assert isinstance(_lowerCAmelCase      ,      tf.keras.Model      )
  					model(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		@slow
  		def        A     (self						:      int      ):
  			A  ,    A							  =      self.model_tester.prepare_config_and_inputs_for_common()
  
  			for model_class in self.all_model_classes[:2]:
  				A							  =      model_class(_lowerCAmelCase      )
  
  				A							  =      copy.deepcopy(_lowerCAmelCase      )
  
  				if not self.is_encoder_decoder:
  					A							  =      inputs["""input_ids"""]
  					del inputs["input_ids"]
  				else:
  					A							  =      inputs["""input_ids"""]
  					A							  =      inputs.get("""decoder_input_ids"""      ,      _lowerCAmelCase      )
  					del inputs["input_ids"]
  					inputs.pop("""decoder_input_ids"""      ,      _lowerCAmelCase      )
  
  				if not self.is_encoder_decoder:
  					A							  =      model.get_input_embeddings()(_lowerCAmelCase      )
  				else:
  					A							  =      model.get_input_embeddings()(_lowerCAmelCase      )
  					A							  =      model.get_input_embeddings()(_lowerCAmelCase      )
  
  				A							  =      self._prepare_for_class(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				@tf.function
  				def run_in_graph_mode():
  					return model(_lowerCAmelCase      )
  
  				A							  =      run_in_graph_mode()
  				self.assertIsNotNone(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Tuple      ):
  			# special tokens cannot be bad tokens
  			A							  =      []
  			if model.config.bos_token_id is not None:
  				special_tokens.append(model.config.bos_token_id      )
  			if model.config.pad_token_id is not None:
  				special_tokens.append(model.config.pad_token_id      )
  			if model.config.eos_token_id is not None:
  				special_tokens.append(model.config.eos_token_id      )
  
  			# create random bad tokens that are not special tokens
  			A							  =      []
  			while len(_lowerCAmelCase      ) < num_bad_tokens:
  				A							  =      tf.squeeze(ids_tensor((1, 1)      ,      self.model_tester.vocab_size      )      ,      0      ).numpy()[0]
  				if token not in special_tokens:
  					bad_tokens.append(_lowerCAmelCase      )
  			return bad_tokens
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ,      _lowerCAmelCase						:      Optional[int]      ):
  			for token_id in output_ids[0].numpy().tolist():
  				self.assertGreaterEqual(_lowerCAmelCase      ,      0      )
  				self.assertLess(_lowerCAmelCase      ,      self.model_tester.vocab_size      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      List[Any]      ):
  			# for all bad word tokens
  			for bad_word_ids in bad_words_ids:
  				# for all slices in batch
  				for generated_ids_slice in generated_ids:
  					# for all word idx
  					for i in range(len(_lowerCAmelCase      )      ,      len(_lowerCAmelCase      )      ):
  						# if tokens match
  						if generated_ids_slice[i - len(_lowerCAmelCase      ) : i] == bad_word_ids:
  							return True
  			return False
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import DiffusionPipeline
  from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor
  
  
  
  class 							__UpperCAmelCase  (       unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      bool = False      ):
  			A							  =      10
  
  			if only_cross_attention:
  				A							  =      12
  			else:
  				# when only cross attention is not set, the cross attention dim must be the same as the query dim
  				A							  =      query_dim
  
  			return {
  			    "query_dim": query_dim,
  			    "cross_attention_dim": cross_attention_dim,
  			    "heads": 2,
  			    "dim_head": 4,
  			    "added_kv_proj_dim": 6,
  			    "norm_num_groups": 1,
  			    "only_cross_attention": only_cross_attention,
  			    "processor": AttnAddedKVProcessor(),
  			}
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Any      ):
  			A							  =      2
  
  			A							  =      torch.rand(_lowerCAmelCase      ,      _lowerCAmelCase      ,      3      ,      2      )
  			A							  =      torch.rand(_lowerCAmelCase      ,      4      ,      _lowerCAmelCase      )
  			A							  =      None
  
  			return {
  			    "hidden_states": hidden_states,
  			    "encoder_hidden_states": encoder_hidden_states,
  			    "attention_mask": attention_mask,
  			}
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ):
  			# self and cross attention
  
  			torch.manual_seed(0      )
  
  			A							  =      self.get_constructor_arguments(only_cross_attention=_lowerCAmelCase      )
  			A							  =      Attention(**_lowerCAmelCase      )
  
  			self.assertTrue(attn.to_k is not None      )
  			self.assertTrue(attn.to_v is not None      )
  
  			A							  =      self.get_forward_arguments(
  			    query_dim=constructor_args["""query_dim"""]      ,      added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )
  
  			A							  =      attn(**_lowerCAmelCase      )
  
  			# only self attention
  
  			torch.manual_seed(0      )
  
  			A							  =      self.get_constructor_arguments(only_cross_attention=_lowerCAmelCase      )
  			A							  =      Attention(**_lowerCAmelCase      )
  
  			self.assertTrue(attn.to_k is None      )
  			self.assertTrue(attn.to_v is None      )
  
  			A							  =      self.get_forward_arguments(
  			    query_dim=constructor_args["""query_dim"""]      ,      added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )
  
  			A							  =      attn(**_lowerCAmelCase      )
  
  			self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()      )
  
  
  
  
  
  
  class 							__UpperCAmelCase  (       unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ):
  			A							  =      DiffusionPipeline.from_pretrained("""hf-internal-testing/tiny-stable-diffusion-pipe"""      ,      safety_checker=_lowerCAmelCase      )
  
  			A							  =      pipe(
  			    """foo"""      ,      num_inference_steps=2      ,      generator=torch.Generator("""cpu"""      ).manual_seed(0      )      ,      output_type="""np"""      ,      ).images
  
  			# the initial conversion succeeds
  			A							  =      DiffusionPipeline.from_pretrained(
  			    """hf-internal-testing/tiny-stable-diffusion-pipe"""      ,      device_map="""sequential"""      ,      safety_checker=_lowerCAmelCase      )
  
  			A							  =      pipe(
  			    """foo"""      ,      num_inference_steps=2      ,      generator=torch.Generator("""cpu"""      ).manual_seed(0      )      ,      output_type="""np"""      ,      ).images
  
  			with tempfile.TemporaryDirectory() as tmpdir:
  				# save the converted model
  				pipe.save_pretrained(_lowerCAmelCase      )
  
  				# can also load the converted weights
  				A							  =      DiffusionPipeline.from_pretrained(_lowerCAmelCase      ,      device_map="""sequential"""      ,      safety_checker=_lowerCAmelCase      )
  
  			A							  =      pipe(
  			    """foo"""      ,      num_inference_steps=2      ,      generator=torch.Generator("""cpu"""      ).manual_seed(0      )      ,      output_type="""np"""      ,      ).images
  
  			self.assertTrue(np.allclose(_lowerCAmelCase      ,      _lowerCAmelCase      ,      atol=1e-5      )      )
  			self.assertTrue(np.allclose(_lowerCAmelCase      ,      _lowerCAmelCase      ,      atol=1e-5      )      )
  
  