


from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						def __init__( self				:					Tuple	,				_lowerCamelCase				:					AutoencoderKL	,				_lowerCamelCase				:					CLIPTextModel	,				_lowerCamelCase				:					CLIPTokenizer	,				_lowerCamelCase				:					UNetaDConditionModel	,				_lowerCamelCase				:					DDIMScheduler	,				_lowerCamelCase				:					float = 0.93	,				_lowerCamelCase				:					bool = True	,				)							->					Union[str, Any]:
										__magic_name__	  = mixing_coeff
										__magic_name__	  = leapfrog_steps

										super().__init__()
										self.register_modules(
										    vae=_lowerCamelCase	,				text_encoder=_lowerCamelCase	,				tokenizer=_lowerCamelCase	,				unet=_lowerCamelCase	,				scheduler=_lowerCamelCase	,				)

										__magic_name__	  = 2 ** (len(self.vae.config.block_out_channels  ) - 1)
										__magic_name__	  = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor  )
						def 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Optional[str] = None	,				_lowerCamelCase				:					bool = False  )							->					Any:
										__magic_name__	  = self.tokenizer(
										    _lowerCamelCase	,				padding="max_length"	,				max_length=self.tokenizer.model_max_length	,				truncation=_lowerCamelCase	,				return_tensors="pt"	,				)

										__magic_name__	  = self.text_encoder(text_inputs.input_ids.to(self.device  )  ).last_hidden_state

										__magic_name__	  = prompt_embeds.to(dtype=self.text_encoder.dtype	,				device=self.device  )

										if do_classifier_free_guidance:
														__magic_name__	  = "" if negative_prompt is None else negative_prompt

														__magic_name__	  = self.tokenizer(
														    _lowerCamelCase	,				padding="max_length"	,				max_length=self.tokenizer.model_max_length	,				truncation=_lowerCamelCase	,				return_tensors="pt"	,				)

														__magic_name__	  = self.text_encoder(uncond_input.input_ids.to(self.device  )  ).last_hidden_state

														__magic_name__	  = torch.cat([negative_prompt_embeds, prompt_embeds]  )

										return prompt_embeds
						def 					__A					( self				:					Tuple	,				_lowerCamelCase				:					torch.Tensor	,				_lowerCamelCase				:					torch.Tensor  )							->					Any:
										__magic_name__	  = self.mixing_coeff * x + (1 - self.mixing_coeff) * y
										__magic_name__	  = self.mixing_coeff * y + (1 - self.mixing_coeff) * x

										return [x, y]
						def 					__A					( self				:					Tuple	,				_lowerCamelCase				:					torch.Tensor	,				_lowerCamelCase				:					torch.Tensor  )							->					str:
										__magic_name__	  = (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
										__magic_name__	  = (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

										return [x, y]
						def 					__A					( self				:					str	,				_lowerCamelCase				:					torch.Tensor  )							->					str:
										# as self.alphas_cumprod is always in cpu
										__magic_name__	  = int(_lowerCamelCase  )

										__magic_name__	  = self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

										return alpha_prod, 1 - alpha_prod
						def 					__A					( self				:					List[Any]	,				_lowerCamelCase				:					torch.Tensor	,				_lowerCamelCase				:					torch.Tensor	,				_lowerCamelCase				:					torch.Tensor	,				_lowerCamelCase				:					torch.Tensor	,				)							->					int:
										__magic_name__	  = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

										__magic_name__  ,      __magic_name__	  = self._get_alpha_and_beta(_lowerCamelCase  )
										__magic_name__  ,      __magic_name__	  = self._get_alpha_and_beta(_lowerCamelCase  )

										__magic_name__	  = (alpha_prod_t_prev / alpha_prod_t) ** 0.5
										__magic_name__	  = -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

										__magic_name__	  = (base - b_t * model_output) / a_t

										return model_input, next_model_input.to(base.dtype  )
						def 					__A					( self				:					int	,				_lowerCamelCase				:					torch.Tensor	,				_lowerCamelCase				:					torch.Tensor	,				_lowerCamelCase				:					torch.Tensor	,				_lowerCamelCase				:					torch.Tensor	,				)							->					Any:
										__magic_name__	  = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

										__magic_name__  ,      __magic_name__	  = self._get_alpha_and_beta(_lowerCamelCase  )
										__magic_name__  ,      __magic_name__	  = self._get_alpha_and_beta(_lowerCamelCase  )

										__magic_name__	  = (alpha_prod_t_prev / alpha_prod_t) ** 0.5
										__magic_name__	  = -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
										__magic_name__	  = a_t * base + b_t * model_output

										return model_input, next_model_input.to(base.dtype  )
						@torch.no_grad()
						def 					__A					( self				:					Tuple	,				_lowerCamelCase				:					torch.Tensor  )							->					int:
										__magic_name__	  = 1 / self.vae.config.scaling_factor * latents
										__magic_name__	  = self.vae.decode(_lowerCamelCase  ).sample
										__magic_name__	  = (image / 2 + 0.5).clamp(0	,				1  )
										return image
						@torch.no_grad()
						def 					__A					( self				:					List[Any]	,				_lowerCamelCase				:					Image.Image	,				_lowerCamelCase				:					torch.Tensor	,				_lowerCamelCase				:					torch.Tensor	,				_lowerCamelCase				:					float	,				_lowerCamelCase				:					Optional[torch.Generator] = None	,				)							->					Optional[Any]:
										__magic_name__	  = guidance_scale > 1.0

										__magic_name__	  = image.to(device=self.device	,				dtype=text_embeds.dtype  )
										__magic_name__	  = self.vae.encode(_lowerCamelCase  ).latent_dist.sample(_lowerCamelCase  )

										__magic_name__	  = self.vae.config.scaling_factor * latent

										__magic_name__	  = [latent.clone(), latent.clone()]

										for i, t in tqdm(enumerate(_lowerCamelCase  )	,				total=len(_lowerCamelCase  )  ):
														__magic_name__	  = self.noise_mixing_layer(x=coupled_latents[0]	,				y=coupled_latents[1]  )

														# j - model_input index, k - base index
														for j in range(2  ):
																		__magic_name__	  = j ^ 1

																		if self.leapfrog_steps:
																						if i % 2 == 0:
																										__magic_name__  ,      __magic_name__	  = j, k

																		__magic_name__	  = coupled_latents[j]
																		__magic_name__	  = coupled_latents[k]

																		__magic_name__	  = torch.cat([model_input] * 2  ) if do_classifier_free_guidance else model_input

																		__magic_name__	  = self.unet(_lowerCamelCase	,				_lowerCamelCase	,				encoder_hidden_states=_lowerCamelCase  ).sample

																		if do_classifier_free_guidance:
																						__magic_name__  ,      __magic_name__	  = noise_pred.chunk(2  )
																						__magic_name__	  = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																		__magic_name__  ,      __magic_name__	  = self.noise_step(
																		    base=_lowerCamelCase	,				model_input=_lowerCamelCase	,				model_output=_lowerCamelCase	,				timestep=_lowerCamelCase	,				)

																		__magic_name__	  = model_input

										return coupled_latents

						@torch.no_grad()
						def __call__( self				:					Union[str, Any]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Image.Image	,				_lowerCamelCase				:					float = 3.0	,				_lowerCamelCase				:					int = 50	,				_lowerCamelCase				:					float = 0.8	,				_lowerCamelCase				:					Optional[str] = None	,				_lowerCamelCase				:					Optional[torch.Generator] = None	,				_lowerCamelCase				:					Optional[str] = "pil"	,				)							->					Optional[int]:
										__magic_name__	  = guidance_scale > 1.0

										__magic_name__	  = self.image_processor.preprocess(_lowerCamelCase  )

										__magic_name__	  = self._encode_prompt(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )
										__magic_name__	  = self._encode_prompt(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )

										self.scheduler.set_timesteps(_lowerCamelCase	,				self.device  )

										__magic_name__	  = num_inference_steps - int(num_inference_steps * strength  )
										__magic_name__	  = self.scheduler.timesteps[t_limit:]
										__magic_name__	  = fwd_timesteps.flip(0  )

										__magic_name__	  = self.prepare_latents(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )

										for i, t in tqdm(enumerate(_lowerCamelCase  )	,				total=len(_lowerCamelCase  )  ):
														# j - model_input index, k - base index
														for k in range(2  ):
																		__magic_name__	  = k ^ 1

																		if self.leapfrog_steps:
																						if i % 2 == 1:
																										__magic_name__  ,      __magic_name__	  = j, k

																		__magic_name__	  = coupled_latents[j]
																		__magic_name__	  = coupled_latents[k]

																		__magic_name__	  = torch.cat([model_input] * 2  ) if do_classifier_free_guidance else model_input

																		__magic_name__	  = self.unet(_lowerCamelCase	,				_lowerCamelCase	,				encoder_hidden_states=_lowerCamelCase  ).sample

																		if do_classifier_free_guidance:
																						__magic_name__  ,      __magic_name__	  = noise_pred.chunk(2  )
																						__magic_name__	  = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																		__magic_name__  ,      __magic_name__	  = self.denoise_step(
																		    base=_lowerCamelCase	,				model_input=_lowerCamelCase	,				model_output=_lowerCamelCase	,				timestep=_lowerCamelCase	,				)

																		__magic_name__	  = model_input

														__magic_name__	  = self.denoise_mixing_layer(x=coupled_latents[0]	,				y=coupled_latents[1]  )

										# either one is fine
										__magic_name__	  = coupled_latents[0]

										if output_type not in ["latent", "pt", "np", "pil"]:
														__magic_name__	  = (
														    f'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
														    "`pil`, `np`, `pt`, `latent`"
														)
														deprecate("Unsupported output_type"	,				"1.0.0"	,				_lowerCamelCase	,				standard_warn=_lowerCamelCase  )
														__magic_name__	  = "np"

										if output_type == "latent":
														__magic_name__	  = final_latent
										else:
														__magic_name__	  = self.decode_latents(_lowerCamelCase  )
														__magic_name__	  = self.image_processor.postprocess(_lowerCamelCase	,				output_type=_lowerCamelCase  )

										return image







'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaForMaskedLM,
						    TFDebertaForQuestionAnswering,
						    TFDebertaForSequenceClassification,
						    TFDebertaForTokenClassification,
						    TFDebertaModel,
						)






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Dict	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Dict=13	,				_lowerCamelCase				:					Union[str, Any]=7	,				_lowerCamelCase				:					Union[str, Any]=True	,				_lowerCamelCase				:					Optional[Any]=True	,				_lowerCamelCase				:					Tuple=True	,				_lowerCamelCase				:					Any=True	,				_lowerCamelCase				:					int=99	,				_lowerCamelCase				:					Any=32	,				_lowerCamelCase				:					Any=2	,				_lowerCamelCase				:					str=4	,				_lowerCamelCase				:					str=37	,				_lowerCamelCase				:					Optional[int]="gelu"	,				_lowerCamelCase				:					Optional[Any]=0.1	,				_lowerCamelCase				:					Dict=0.1	,				_lowerCamelCase				:					int=5_12	,				_lowerCamelCase				:					Union[str, Any]=16	,				_lowerCamelCase				:					int=2	,				_lowerCamelCase				:					List[Any]=0.02	,				_lowerCamelCase				:					str=3	,				_lowerCamelCase				:					Tuple=4	,				_lowerCamelCase				:					int=None	,				)							->					Optional[int]:
										__magic_name__	  = parent
										__magic_name__	  = 13
										__magic_name__	  = 7
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = 99
										__magic_name__	  = 32
										__magic_name__	  = 2
										__magic_name__	  = 4
										__magic_name__	  = 37
										__magic_name__	  = "gelu"
										__magic_name__	  = 0.1
										__magic_name__	  = 0.1
										__magic_name__	  = 5_12
										__magic_name__	  = 16
										__magic_name__	  = False
										__magic_name__	  = -1
										__magic_name__	  = True
										__magic_name__	  = 2
										__magic_name__	  = 0.02
										__magic_name__	  = 3
										__magic_name__	  = 4
										__magic_name__	  = None
						def 					__A					( self				:					Any  )							->					Union[str, Any]:
										__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size  )

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

										__magic_name__	  = None
										if self.use_token_type_ids:
														__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.type_vocab_size  )

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

										__magic_name__	  = DebertaConfig(
										    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	,				hidden_dropout_prob=self.hidden_dropout_prob	,				attention_probs_dropout_prob=self.attention_probs_dropout_prob	,				max_position_embeddings=self.max_position_embeddings	,				type_vocab_size=self.type_vocab_size	,				relative_attention=self.relative_attention	,				max_relative_positions=self.max_relative_positions	,				position_biased_input=self.position_biased_input	,				initializer_range=self.initializer_range	,				return_dict=_lowerCamelCase	,				)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Union[str, Any]  )							->					Optional[int]:
										__magic_name__	  = TFDebertaModel(config=_lowerCamelCase  )
										__magic_name__	  = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

										__magic_name__	  = [input_ids, input_mask]
										__magic_name__	  = model(_lowerCamelCase  )

										__magic_name__	  = model(_lowerCamelCase  )

										self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)  )
						def 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Tuple  )							->					Dict:
										__magic_name__	  = TFDebertaForMaskedLM(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Tuple  )							->					str:
										__magic_name__	  = self.num_labels
										__magic_name__	  = TFDebertaForSequenceClassification(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					List[str]  )							->					Tuple:
										__magic_name__	  = self.num_labels
										__magic_name__	  = TFDebertaForTokenClassification(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.num_labels)  )
						def 					__A					( self				:					str	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					List[str]  )							->					Tuple:
										__magic_name__	  = TFDebertaForQuestionAnswering(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										__magic_name__	  = model(_lowerCamelCase  )
										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				:					Dict  )							->					Dict:
										__magic_name__	  = self.prepare_config_and_inputs()
										(
										    (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      
										)	  = config_and_inputs
										__magic_name__	  = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict






@require_tf
class 		UpperCamelCase_		(					A   ,		A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Tuple         =     (
						    (
						        TFDebertaModel,
						        TFDebertaForMaskedLM,
						        TFDebertaForQuestionAnswering,
						        TFDebertaForSequenceClassification,
						        TFDebertaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCAmelCase__	:       Optional[Any]         =     (
						    {
						        '''feature-extraction''': TFDebertaModel,
						        '''fill-mask''': TFDebertaForMaskedLM,
						        '''question-answering''': TFDebertaForQuestionAnswering,
						        '''text-classification''': TFDebertaForSequenceClassification,
						        '''token-classification''': TFDebertaForTokenClassification,
						        '''zero-shot''': TFDebertaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						UpperCAmelCase__	:       List[str]         =     False
						UpperCAmelCase__	:       List[str]         =     False
						def 					__A					( self				:					Optional[Any]  )							->					str:
										__magic_name__	  = TFDebertaModelTester(self  )
										__magic_name__	  = ConfigTester(self	,				config_class=_lowerCamelCase	,				hidden_size=37  )
						def 					__A					( self				:					int  )							->					Optional[Any]:
										self.config_tester.run_common_tests()
						def 					__A					( self				:					List[str]  )							->					Optional[Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase  )
						def 					__A					( self				:					List[str]  )							->					Dict:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase  )
						def 					__A					( self				:					Tuple  )							->					Union[str, Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase  )
						def 					__A					( self				:					List[Any]  )							->					Union[str, Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase  )
						def 					__A					( self				:					Union[str, Any]  )							->					str:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase  )

						@slow
						def 					__A					( self				:					Optional[int]  )							->					Any:
										__magic_name__	  = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
										self.assertIsNotNone(_lowerCamelCase  )






@require_tf
class 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						@unittest.skip(reason="Model not available yet"  )
						def 					__A					( self				:					str  )							->					str:
										pass

						@slow
						def 					__A					( self				:					str  )							->					Dict:
										__magic_name__	  = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
										__magic_name__	  = tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
										__magic_name__	  = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
										__magic_name__	  = model(_lowerCamelCase	,				attention_mask=_lowerCamelCase  )[0]

										__magic_name__	  = tf.constant(
										    [
										        [
										            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
										            [1.4_484_025, -0.93_483_794, -0.80_593_085],
										            [0.3_122_741, 0.00_316_059, -1.4_131_377],
										        ]
										    ]  )
										tf.debugging.assert_near(output[:, 1:4, 1:4]	,				_lowerCamelCase	,				atol=1e-4  )



