from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


UpperCamelCase    				=		getLogger(__name__)


UpperCamelCase    				=		4
UpperCamelCase    				=		7

UpperCamelCase    				=		{
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}

def      lowercase_							(     _lowerCamelCase				:			int):
    if isinstance(_lowerCamelCase						,      torch.Tensor):
        return image
    elif isinstance(_lowerCamelCase						,      PIL.Image.Image):
        lowercase__					:  Any          =							[image]

    if isinstance(image[0]						,      PIL.Image.Image):
        lowercase__   ,  lowercase__					:  List[str]          =							image[0].size
        lowercase__   ,  lowercase__					:  int          =							(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

        lowercase__					:  Tuple          =							[np.array(i.resize((w, h)))[None, :] for i in image]
        lowercase__					:  Tuple          =							np.concatenate(_lowerCamelCase						,      axis=0)
        lowercase__					:  Optional[int]          =							np.array(_lowerCamelCase).astype(np.floataa) / 255.0
        lowercase__					:  Optional[int]          =							image.transpose(0						,      3						,      1						,      2)
        lowercase__					:  Tuple          =							2.0 * image - 1.0
        lowercase__					:  Dict          =							torch.from_numpy(_lowerCamelCase)
    elif isinstance(image[0]						,      torch.Tensor):
        lowercase__					:  List[str]          =							torch.cat(_lowerCamelCase						,      dim=0)

    return image



class     snake_case_			(						__A			):






   def __init__( self							:	Union[str, Any]      ,		lowercase_							:	OnnxRuntimeModel      ,		lowercase_							:	OnnxRuntimeModel      ,		lowercase_							:	Any      ,		lowercase_							:	OnnxRuntimeModel      ,		lowercase_							:	DDPMScheduler      ,		lowercase_							:	Any      ,		lowercase_							:	int = 3_50      ,		)   ->		Optional[Any]:
       super().__init__(
           vae=lowercase_      ,		text_encoder=lowercase_      ,		tokenizer=lowercase_      ,		unet=lowercase_      ,		low_res_scheduler=lowercase_      ,		scheduler=lowercase_      ,		safety_checker=lowercase_      ,		feature_extractor=lowercase_      ,		watermarker=lowercase_      ,		max_noise_level=lowercase_      ,		)






   def __call__( self							:	Tuple      ,		lowercase_							:	Union[str, List[str]]      ,		lowercase_							:	Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]      ,		lowercase_							:	int = 75      ,		lowercase_							:	float = 9.0      ,		lowercase_							:	int = 20      ,		lowercase_							:	Optional[Union[str, List[str]]] = None      ,		lowercase_							:	Optional[int] = 1      ,		lowercase_							:	float = 0.0      ,		lowercase_							:	Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,		lowercase_							:	Optional[torch.FloatTensor] = None      ,		lowercase_							:	Optional[np.ndarray] = None      ,		lowercase_							:	Optional[np.ndarray] = None      ,		lowercase_							:	Optional[str] = "pil"      ,		lowercase_							:	bool = True      ,		lowercase_							:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,		lowercase_							:	Optional[int] = 1      ,		)   ->		Optional[int]:
       self.check_inputs(lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_    )

       # 2. Define call parameters
       if prompt is not None and isinstance(lowercase_      ,		lowercase_    ):
           lowercase__					:  List[Any]          =							1
       elif prompt is not None and isinstance(lowercase_      ,		lowercase_    ):
           lowercase__					:  Tuple          =							len(lowercase_    )
       else:
           lowercase__					:  List[str]          =							prompt_embeds.shape[0]

       lowercase__					:  Optional[Any]          =							self._execution_device
       # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
       # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
       # corresponds to doing no classifier free guidance.
       lowercase__					:  int          =							guidance_scale > 1.0

       # 3. Encode input prompt
       lowercase__					:  Optional[int]          =							self._encode_prompt(
           lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		prompt_embeds=lowercase_      ,		negative_prompt_embeds=lowercase_      ,		)

       lowercase__					:  int          =							ORT_TO_PT_TYPE[str(text_embeddings.dtype    )]

       # 4. Preprocess image
       lowercase__					:  Any          =							preprocess(lowercase_    )
       lowercase__					:  int          =							image.cpu()

       # 5. set timesteps
       self.scheduler.set_timesteps(lowercase_      ,		device=lowercase_    )
       lowercase__					:  int          =							self.scheduler.timesteps

       # 5. Add noise to image
       lowercase__					:  Optional[Any]          =							torch.tensor([noise_level]      ,		dtype=torch.long      ,		device=lowercase_    )
       lowercase__					:  int          =							torch.randn(image.shape      ,		generator=lowercase_      ,		device=lowercase_      ,		dtype=lowercase_    )
       lowercase__					:  List[Any]          =							self.low_res_scheduler.add_noise(lowercase_      ,		lowercase_      ,		lowercase_    )

       lowercase__					:  Optional[Any]          =							2 if do_classifier_free_guidance else 1
       lowercase__					:  Union[str, Any]          =							np.concatenate([image] * batch_multiplier * num_images_per_prompt    )
       lowercase__					:  Optional[Any]          =							np.concatenate([noise_level] * image.shape[0]    )

       # 6. Prepare latent variables
       lowercase__   ,  lowercase__					:  int          =							image.shape[2:]
       lowercase__					:  Union[str, Any]          =							self.prepare_latents(
           batch_size * num_images_per_prompt      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		)

       # 7. Check that sizes of image and latents match
       lowercase__					:  Any          =							image.shape[1]
       if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
           raise ValueError(
               "Incorrect configuration settings! The config of `pipeline.unet` expects"
               F''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
               F''' `num_channels_image`: {num_channels_image} '''
               F''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
               " `pipeline.unet` or your `image` input."    )

       # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
       lowercase__					:  Dict          =							self.prepare_extra_step_kwargs(lowercase_      ,		lowercase_    )

       lowercase__					:  Optional[Any]          =							next(
           (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")      ,		"tensor(float)"    )
       lowercase__					:  Any          =							ORT_TO_NP_TYPE[timestep_dtype]

       # 9. Denoising loop
       lowercase__					:  Union[str, Any]          =							len(lowercase_    ) - num_inference_steps * self.scheduler.order
       with self.progress_bar(total=lowercase_    ) as progress_bar:
           for i, t in enumerate(lowercase_    ):
               # expand the latents if we are doing classifier free guidance
               lowercase__					:  Any          =							np.concatenate([latents] * 2    ) if do_classifier_free_guidance else latents

               # concat latents, mask, masked_image_latents in the channel dimension
               lowercase__					:  List[Any]          =							self.scheduler.scale_model_input(lowercase_      ,		lowercase_    )
               lowercase__					:  Optional[Any]          =							np.concatenate([latent_model_input, image]      ,		axis=1    )

               # timestep to tensor
               lowercase__					:  Any          =							np.array([t]      ,		dtype=lowercase_    )

               # predict the noise residual
               lowercase__					:  int          =							self.unet(
                   sample=lowercase_      ,		timestep=lowercase_      ,		encoder_hidden_states=lowercase_      ,		class_labels=noise_level.astype(np.intaa    )      ,		)[0]

               # perform guidance
               if do_classifier_free_guidance:
                   lowercase__   ,  lowercase__					:  Any          =							np.split(lowercase_      ,		2    )
               lowercase__					:  Union[str, Any]          =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

               # compute the previous noisy sample x_t -> x_t-1
               lowercase__					:  Optional[int]          =							self.scheduler.step(
                   torch.from_numpy(lowercase_    )      ,		lowercase_      ,		lowercase_      ,		**lowercase_    ).prev_sample

               # call the callback, if provided
               if i == len(lowercase_    ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                   progress_bar.update()
                   if callback is not None and i % callback_steps == 0:
                       callback(lowercase_      ,		lowercase_      ,		lowercase_    )

        # 10. Post-processing
       lowercase__					:  List[str]          =							self.decode_latents(latents.float()    )

       # 11. Convert to PIL
       if output_type == "pil":
           lowercase__					:  List[Any]          =							self.numpy_to_pil(lowercase_    )

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=lowercase_    )






   def  __UpperCamelCase	( self							:	List[Any]      ,		lowercase_							:	Tuple    )   ->		Optional[int]:
       lowercase__					:  Any          =							1 / 0.0_83_33 * latents
       lowercase__					:  str          =							self.vae(latent_sample=lowercase_    )[0]
       lowercase__					:  Any          =							np.clip(image / 2 + 0.5      ,		0      ,		1    )
       lowercase__					:  int          =							image.transpose((0, 2, 3, 1)    )
       return image







   def  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	Union[str, List[str]]      ,		lowercase_							:	Union[str, Any]      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	bool      ,		lowercase_							:	Optional[str]      ,		lowercase_							:	Optional[torch.FloatTensor] = None      ,		lowercase_							:	Optional[torch.FloatTensor] = None      ,		)   ->		str:
       if prompt is not None and isinstance(lowercase_      ,		lowercase_    ):
           lowercase__					:  List[Any]          =							1
       elif prompt is not None and isinstance(lowercase_      ,		lowercase_    ):
           lowercase__					:  int          =							len(lowercase_    )
       else:
           lowercase__					:  Any          =							prompt_embeds.shape[0]

       if prompt_embeds is None:
           lowercase__					:  int          =							self.tokenizer(
               lowercase_      ,		padding="max_length"      ,		max_length=self.tokenizer.model_max_length      ,		truncation=lowercase_      ,		return_tensors="pt"      ,		)
           lowercase__					:  Union[str, Any]          =							text_inputs.input_ids
           lowercase__					:  List[str]          =							self.tokenizer(lowercase_      ,		padding="longest"      ,		return_tensors="pt"    ).input_ids

           if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
               lowercase_      ,		lowercase_    ):
               lowercase__					:  List[str]          =							self.tokenizer.batch_decode(
                   untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]    )
               logger.warning(
                   "The following part of your input was truncated because CLIP can only handle sequences up to"
                   F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''    )

           # no positional arguments to text_encoder
           lowercase__					:  int          =							self.text_encoder(
               input_ids=text_input_ids.int().to(lowercase_    )      ,		)
           lowercase__					:  Dict          =							prompt_embeds[0]

       lowercase__   ,  lowercase__   ,  lowercase__					:  List[Any]          =							prompt_embeds.shape
       # duplicate text embeddings for each generation per prompt, using mps friendly method
       lowercase__					:  Tuple          =							prompt_embeds.repeat(1      ,		lowercase_    )
       lowercase__					:  List[str]          =							prompt_embeds.reshape(bs_embed * num_images_per_prompt      ,		lowercase_      ,		-1    )

       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance and negative_prompt_embeds is None:
           lowercase__					:  List[str]
           if negative_prompt is None:
               lowercase__					:  Dict          =							[""] * batch_size
           elif type(lowercase_    ) is not type(lowercase_    ):
               raise TypeError(
                   F'''`negative_prompt` should be the same type to `prompt`, but got {type(lowercase_    )} !='''
                   F''' {type(lowercase_    )}.'''    )
           elif isinstance(lowercase_      ,		lowercase_    ):
               lowercase__					:  Tuple          =							[negative_prompt]
           elif batch_size != len(lowercase_    ):
               raise ValueError(
                   F'''`negative_prompt`: {negative_prompt} has batch size {len(lowercase_    )}, but `prompt`:'''
                   F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                   " the batch size of `prompt`."    )
           else:
               lowercase__					:  Optional[Any]          =							negative_prompt

           lowercase__					:  Optional[int]          =							text_input_ids.shape[-1]
           lowercase__					:  int          =							self.tokenizer(
               lowercase_      ,		padding="max_length"      ,		max_length=lowercase_      ,		truncation=lowercase_      ,		return_tensors="pt"      ,		)

           # if hasattr(uncond_input, "attention_mask"):
           #     attention_mask = uncond_input.attention_mask.to(device)
           # else:
           #     attention_mask = None

           lowercase__					:  Any          =							self.text_encoder(
               input_ids=uncond_input.input_ids.int().to(lowercase_    )      ,		)
           lowercase__					:  Optional[int]          =							uncond_embeddings[0]

       if do_classifier_free_guidance:
           lowercase__					:  Optional[int]          =							uncond_embeddings.shape[1]
           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           lowercase__					:  Dict          =							uncond_embeddings.repeat(1      ,		lowercase_    )
           lowercase__					:  List[Any]          =							uncond_embeddings.reshape(batch_size * num_images_per_prompt      ,		lowercase_      ,		-1    )

           # For classifier free guidance, we need to do two forward passes.
           # Here we concatenate the unconditional and text embeddings into a single batch
           # to avoid doing two forward passes
           lowercase__					:  Dict          =							np.concatenate([uncond_embeddings, prompt_embeds]    )

       return prompt_embeds




from __future__ import annotations

import unittest

from transformers import MPNetConfig, 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.models.mpnet.modeling_tf_mpnet import (
          TFMPNetForMaskedLM,
          TFMPNetForMultipleChoice,
          TFMPNetForQuestionAnswering,
          TFMPNetForSequenceClassification,
          TFMPNetForTokenClassification,
          TFMPNetModel,
      )



class     snake_case_			:






   def __init__( self							:	Optional[Any]      ,		lowercase_							:	Tuple      ,		lowercase_							:	List[str]=13      ,		lowercase_							:	str=7      ,		lowercase_							:	str=True      ,		lowercase_							:	Dict=True      ,		lowercase_							:	List[str]=False      ,		lowercase_							:	Optional[Any]=True      ,		lowercase_							:	List[str]=99      ,		lowercase_							:	Optional[int]=64      ,		lowercase_							:	Optional[int]=2      ,		lowercase_							:	List[Any]=4      ,		lowercase_							:	Union[str, Any]=64      ,		lowercase_							:	Optional[Any]="gelu"      ,		lowercase_							:	str=0.1      ,		lowercase_							:	Union[str, Any]=0.1      ,		lowercase_							:	Optional[Any]=5_12      ,		lowercase_							:	Optional[Any]=16      ,		lowercase_							:	Optional[Any]=2      ,		lowercase_							:	Any=0.02      ,		lowercase_							:	List[Any]=3      ,		lowercase_							:	List[str]=4      ,		lowercase_							:	Union[str, Any]=None      ,		)   ->		Optional[int]:
       lowercase__					:  str          =							parent
       lowercase__					:  List[str]          =							batch_size
       lowercase__					:  Optional[int]          =							seq_length
       lowercase__					:  List[str]          =							is_training
       lowercase__					:  Union[str, Any]          =							use_input_mask
       lowercase__					:  Tuple          =							use_token_type_ids
       lowercase__					:  str          =							use_labels
       lowercase__					:  int          =							vocab_size
       lowercase__					:  Optional[Any]          =							hidden_size
       lowercase__					:  Dict          =							num_hidden_layers
       lowercase__					:  Any          =							num_attention_heads
       lowercase__					:  Dict          =							intermediate_size
       lowercase__					:  List[Any]          =							hidden_act
       lowercase__					:  int          =							hidden_dropout_prob
       lowercase__					:  Dict          =							attention_probs_dropout_prob
       lowercase__					:  Union[str, Any]          =							max_position_embeddings
       lowercase__					:  List[Any]          =							type_vocab_size
       lowercase__					:  str          =							type_sequence_label_size
       lowercase__					:  Union[str, Any]          =							initializer_range
       lowercase__					:  str          =							num_labels
       lowercase__					:  List[Any]          =							num_choices
       lowercase__					:  List[str]          =							scope






   def  __UpperCamelCase	( self							:	Dict    )   ->		Tuple:
       lowercase__					:  int          =							ids_tensor([self.batch_size, self.seq_length]      ,		self.vocab_size    )

       lowercase__					:  List[str]          =							None
       if self.use_input_mask:
           lowercase__					:  str          =							random_attention_mask([self.batch_size, self.seq_length]    )

       lowercase__					:  Any          =							None
       lowercase__					:  Optional[Any]          =							None
       lowercase__					:  Optional[int]          =							None
       if self.use_labels:
           lowercase__					:  str          =							ids_tensor([self.batch_size]      ,		self.type_sequence_label_size    )
           lowercase__					:  Optional[Any]          =							ids_tensor([self.batch_size, self.seq_length]      ,		self.num_labels    )
           lowercase__					:  Any          =							ids_tensor([self.batch_size]      ,		self.num_choices    )

       lowercase__					:  Any          =							MPNetConfig(
           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      ,		initializer_range=self.initializer_range      ,		)
       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






   def  __UpperCamelCase	( self							:	int      ,		lowercase_							:	Optional[Any]      ,		lowercase_							:	Union[str, Any]      ,		lowercase_							:	Any      ,		lowercase_							:	Optional[Any]      ,		lowercase_							:	Any      ,		lowercase_							:	List[Any]    )   ->		List[str]:
       lowercase__					:  int          =							TFMPNetModel(config=lowercase_    )
       lowercase__					:  Dict          =							{"input_ids": input_ids, "attention_mask": input_mask}
       lowercase__					:  Dict          =							model(lowercase_    )
       lowercase__					:  Tuple          =							[input_ids, input_mask]
       lowercase__					:  Any          =							model(lowercase_    )
       self.parent.assertEqual(result.last_hidden_state.shape      ,		(self.batch_size, self.seq_length, self.hidden_size)    )






   def  __UpperCamelCase	( self							:	List[Any]      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	Tuple      ,		lowercase_							:	str      ,		lowercase_							:	Union[str, Any]      ,		lowercase_							:	Union[str, Any]      ,		lowercase_							:	List[Any]    )   ->		Union[str, Any]:
       lowercase__					:  List[str]          =							TFMPNetForMaskedLM(config=lowercase_    )
       lowercase__					:  Dict          =							{"input_ids": input_ids, "attention_mask": input_mask}
       lowercase__					:  Optional[int]          =							model(lowercase_    )
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.vocab_size)    )






   def  __UpperCamelCase	( self							:	Optional[Any]      ,		lowercase_							:	List[Any]      ,		lowercase_							:	Union[str, Any]      ,		lowercase_							:	Dict      ,		lowercase_							:	Tuple      ,		lowercase_							:	Tuple      ,		lowercase_							:	Optional[Any]    )   ->		List[Any]:
       lowercase__					:  List[Any]          =							TFMPNetForQuestionAnswering(config=lowercase_    )
       lowercase__					:  Optional[int]          =							{
           "input_ids": input_ids,
           "attention_mask": input_mask,
       }
       lowercase__					:  int          =							model(lowercase_    )
       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  __UpperCamelCase	( self							:	Union[str, Any]      ,		lowercase_							:	List[str]      ,		lowercase_							:	List[Any]      ,		lowercase_							:	Tuple      ,		lowercase_							:	Optional[Any]      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	Any    )   ->		int:
       lowercase__					:  Optional[Any]          =							self.num_labels
       lowercase__					:  int          =							TFMPNetForSequenceClassification(lowercase_    )
       lowercase__					:  Any          =							{"input_ids": input_ids, "attention_mask": input_mask}
       lowercase__					:  Optional[Any]          =							model(lowercase_    )
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.num_labels)    )






   def  __UpperCamelCase	( self							:	List[str]      ,		lowercase_							:	int      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	Union[str, Any]      ,		lowercase_							:	Optional[Any]      ,		lowercase_							:	List[str]      ,		lowercase_							:	List[str]    )   ->		Optional[int]:
       lowercase__					:  List[Any]          =							self.num_choices
       lowercase__					:  List[str]          =							TFMPNetForMultipleChoice(lowercase_    )
       lowercase__					:  Tuple          =							tf.tile(tf.expand_dims(lowercase_      ,		1    )      ,		(1, self.num_choices, 1)    )
       lowercase__					:  List[str]          =							tf.tile(tf.expand_dims(lowercase_      ,		1    )      ,		(1, self.num_choices, 1)    )
       lowercase__					:  Tuple          =							{
           "input_ids": multiple_choice_inputs_ids,
           "attention_mask": multiple_choice_input_mask,
       }
       lowercase__					:  List[str]          =							model(lowercase_    )
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.num_choices)    )






   def  __UpperCamelCase	( self							:	Tuple      ,		lowercase_							:	Tuple      ,		lowercase_							:	str      ,		lowercase_							:	List[Any]      ,		lowercase_							:	str      ,		lowercase_							:	str      ,		lowercase_							:	Tuple    )   ->		int:
       lowercase__					:  Dict          =							self.num_labels
       lowercase__					:  int          =							TFMPNetForTokenClassification(lowercase_    )
       lowercase__					:  List[str]          =							{"input_ids": input_ids, "attention_mask": input_mask}
       lowercase__					:  Dict          =							model(lowercase_    )
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.num_labels)    )







   def  __UpperCamelCase	( self							:	List[Any]    )   ->		List[str]:
       lowercase__					:  int          =							self.prepare_config_and_inputs()
       ((lowercase__)   ,  (lowercase__)   ,  (lowercase__)   ,  (lowercase__)   ,  (lowercase__)   ,  (lowercase__))					:  str          =							config_and_inputs
       lowercase__					:  Union[str, Any]          =							{"input_ids": input_ids, "attention_mask": input_mask}
       return config, inputs_dict



@require_tf
class     snake_case_			(						__A    ,__A    ,unittest.TestCase			):
   __A      :						List[str]   =							(
       (
           TFMPNetForMaskedLM,
           TFMPNetForMultipleChoice,
           TFMPNetForQuestionAnswering,
           TFMPNetForSequenceClassification,
           TFMPNetForTokenClassification,
           TFMPNetModel,
       )
       if is_tf_available()
       else ()
   )
   __A      :						int   =							(
       {
           "feature-extraction": TFMPNetModel,
           "fill-mask": TFMPNetForMaskedLM,
           "question-answering": TFMPNetForQuestionAnswering,
           "text-classification": TFMPNetForSequenceClassification,
           "token-classification": TFMPNetForTokenClassification,
           "zero-shot": TFMPNetForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __A      :						str   =							False
   __A      :						Union[str, Any]   =							False






   def  __UpperCamelCase	( self							:	List[Any]    )   ->		List[str]:
       lowercase__					:  Optional[int]          =							TFMPNetModelTester(self    )
       lowercase__					:  List[Any]          =							ConfigTester(self      ,		config_class=lowercase_      ,		hidden_size=37    )






   def  __UpperCamelCase	( self							:	int    )   ->		Dict:
       self.config_tester.run_common_tests()






   def  __UpperCamelCase	( self							:	Any    )   ->		Any:
       lowercase__					:  Tuple          =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_model(*lowercase_    )






   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		Dict:
       lowercase__					:  str          =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_masked_lm(*lowercase_    )






   def  __UpperCamelCase	( self							:	Optional[int]    )   ->		Any:
       lowercase__					:  Optional[Any]          =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_    )






   def  __UpperCamelCase	( self							:	int    )   ->		List[Any]:
       lowercase__					:  Dict          =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_    )






   def  __UpperCamelCase	( self							:	Dict    )   ->		Any:
       lowercase__					:  List[Any]          =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_    )






   def  __UpperCamelCase	( self							:	List[Any]    )   ->		Dict:
       lowercase__					:  List[Any]          =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_    )







   @slow
   def  __UpperCamelCase	( self							:	Dict    )   ->		int:
       for model_name in ["microsoft/mpnet-base"]:
           lowercase__					:  List[str]          =							TFMPNetModel.from_pretrained(lowercase_    )
           self.assertIsNotNone(lowercase_    )



@require_tf
class     snake_case_			(						unittest.TestCase			):






   @slow
   def  __UpperCamelCase	( self							:	int    )   ->		List[Any]:
       lowercase__					:  Optional[Any]          =							TFMPNetModel.from_pretrained("microsoft/mpnet-base"    )
       lowercase__					:  Tuple          =							tf.constant([[0, 1, 2, 3, 4, 5]]    )
       lowercase__					:  List[str]          =							model(lowercase_    )[0]

       lowercase__					:  List[Any]          =							[1, 6, 7_68]
       self.assertEqual(output.shape      ,		lowercase_    )

       lowercase__					:  Optional[int]          =							tf.constant(
           [
               [
                   [-0.1_06_71_72, 0.08_21_64_73, 0.0_02_45_43],
                   [-0.03_46_58_79, 0.8_35_41_18, -0.03_25_22_88],
                   [-0.06_56_94_76, -0.12_42_41_11, -0.0_49_44_36],
               ]
           ]    )
       tf.debugging.assert_near(output[:, :3, :3]      ,		lowercase_      ,		atol=1E-4    )



