

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowerCamelCase								=logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCamelCase								="\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_=8      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     SCREAMING_SNAKE_CASE										=width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_=512,   lowerCAmelCase_=512      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=pil_image.resize((w, h),   resample=Image.BICUBIC,   reducing_gap=1      )
     SCREAMING_SNAKE_CASE										=np.array(pil_image.convert('RGB'      )      )
     SCREAMING_SNAKE_CASE										=arr.astype(np.floataa      ) / 127.5 - 1
     SCREAMING_SNAKE_CASE										=np.transpose(lowerCAmelCase_,   [2, 0, 1]      )
     SCREAMING_SNAKE_CASE										=torch.from_numpy(lowerCAmelCase_      ).unsqueeze(0      )
     return image



class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      def __init__(				self					:							List[Any]      ,snake_case					:							UNetaDConditionModel      ,snake_case					:							DDPMScheduler      ,snake_case					:							VQModel      ,):
           super().__init__()

           self.register_modules(
               unet=snake_case      ,scheduler=snake_case      ,movq=snake_case      ,)
           SCREAMING_SNAKE_CASE										=2 ** (len(self.movq.config.block_out_channels    ) - 1)
      def 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							Dict      ,snake_case					:							Optional[int]      ,snake_case					:							Optional[int]    ):
           # get the original timestep using init_timestep
           SCREAMING_SNAKE_CASE										=min(int(num_inference_steps * strength    )      ,snake_case    )

           SCREAMING_SNAKE_CASE										=max(num_inference_steps - init_timestep      ,0    )
           SCREAMING_SNAKE_CASE										=self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start
      def 	_lowerCAmelCase					(				self					:							str      ,snake_case					:							Any      ,snake_case					:							Dict      ,snake_case					:							Any      ,snake_case					:							Optional[int]      ,snake_case					:							int      ,snake_case					:							List[str]      ,snake_case					:							Tuple=None    ):
           if not isinstance(snake_case      ,(torch.Tensor, PIL.Image.Image, list)    ):
                raise ValueError(
                    f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case    )}'    )

           SCREAMING_SNAKE_CASE										=image.to(device=snake_case      ,dtype=snake_case    )

           SCREAMING_SNAKE_CASE										=batch_size * num_images_per_prompt

           if image.shape[1] == 4:
                SCREAMING_SNAKE_CASE										=image

           else:
                if isinstance(snake_case      ,snake_case    ) and len(snake_case    ) != batch_size:
                     raise ValueError(
                         f'You have passed a list of generators of length {len(snake_case    )}, but requested an effective batch'
                         f' size of {batch_size}. Make sure the batch size matches the length of the generators.'    )

                elif isinstance(snake_case      ,snake_case    ):
                     SCREAMING_SNAKE_CASE										=[
                         self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(snake_case    )
                     ]
                     SCREAMING_SNAKE_CASE										=torch.cat(snake_case      ,dim=0    )
                else:
                     SCREAMING_SNAKE_CASE										=self.movq.encode(snake_case    ).latent_dist.sample(snake_case    )

                SCREAMING_SNAKE_CASE										=self.movq.config.scaling_factor * init_latents

           SCREAMING_SNAKE_CASE										=torch.cat([init_latents]      ,dim=0    )

           SCREAMING_SNAKE_CASE										=init_latents.shape
           SCREAMING_SNAKE_CASE										=randn_tensor(snake_case      ,generator=snake_case      ,device=snake_case      ,dtype=snake_case    )

           # get latents
           SCREAMING_SNAKE_CASE										=self.scheduler.add_noise(snake_case      ,snake_case      ,snake_case    )

           SCREAMING_SNAKE_CASE										=init_latents

           return latents
      def 	_lowerCAmelCase					(				self					:							Optional[int]      ,snake_case					:							Optional[int]=0    ):
           if is_accelerate_available():
                from accelerate import cpu_offload
           else:
                raise ImportError('Please install accelerate via `pip install accelerate`'    )

           SCREAMING_SNAKE_CASE										=torch.device(f'cuda:{gpu_id}'    )

           SCREAMING_SNAKE_CASE										=[
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                     cpu_offload(snake_case      ,snake_case    )
      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							int=0    ):
           if is_accelerate_available() and is_accelerate_version('>='      ,'0.17.0.dev0'    ):
                from accelerate import cpu_offload_with_hook
           else:
                raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'    )

           SCREAMING_SNAKE_CASE										=torch.device(f'cuda:{gpu_id}'    )

           if self.device.type != "cpu":
                self.to('cpu'      ,silence_dtype_warnings=snake_case    )
                torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

           SCREAMING_SNAKE_CASE										=None
           for cpu_offloaded_model in [self.unet, self.movq]:
                SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=cpu_offload_with_hook(snake_case      ,snake_case      ,prev_module_hook=snake_case    )

           # We'll offload the last model manually.
           SCREAMING_SNAKE_CASE										=hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 	_lowerCAmelCase					(				self					:							str    ):
           if not hasattr(self.unet      ,'_hf_hook'    ):
                return self.device
           for module in self.unet.modules():
                if (
                    hasattr(snake_case      ,'_hf_hook'    )
                    and hasattr(module._hf_hook      ,'execution_device'    )
                    and module._hf_hook.execution_device is not None
                ):
                     return torch.device(module._hf_hook.execution_device    )
           return self.device


      @torch.no_grad()
      @replace_example_docstring(snake_case    )
      def __call__(				self					:							Tuple      ,snake_case					:							Union[torch.FloatTensor, List[torch.FloatTensor]]      ,snake_case					:							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]      ,snake_case					:							Union[torch.FloatTensor, List[torch.FloatTensor]]      ,snake_case					:							torch.FloatTensor      ,snake_case					:							int = 512      ,snake_case					:							int = 512      ,snake_case					:							int = 100      ,snake_case					:							float = 4.0      ,snake_case					:							float = 0.3      ,snake_case					:							int = 1      ,snake_case					:							Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,snake_case					:							Optional[str] = "pil"      ,snake_case					:							bool = True      ,):
           SCREAMING_SNAKE_CASE										=self._execution_device

           SCREAMING_SNAKE_CASE										=guidance_scale > 1.0

           if isinstance(snake_case      ,snake_case    ):
                SCREAMING_SNAKE_CASE										=torch.cat(snake_case      ,dim=0    )
           if isinstance(snake_case      ,snake_case    ):
                SCREAMING_SNAKE_CASE										=torch.cat(snake_case      ,dim=0    )
           if isinstance(snake_case      ,snake_case    ):
                SCREAMING_SNAKE_CASE										=torch.cat(snake_case      ,dim=0    )

           SCREAMING_SNAKE_CASE										=image_embeds.shape[0]

           if do_classifier_free_guidance:
                SCREAMING_SNAKE_CASE										=image_embeds.repeat_interleave(snake_case      ,dim=0    )
                SCREAMING_SNAKE_CASE										=negative_image_embeds.repeat_interleave(snake_case      ,dim=0    )
                SCREAMING_SNAKE_CASE										=hint.repeat_interleave(snake_case      ,dim=0    )

           SCREAMING_SNAKE_CASE										=torch.cat([negative_image_embeds, image_embeds]      ,dim=0    ).to(dtype=self.unet.dtype      ,device=snake_case    )
           SCREAMING_SNAKE_CASE										=torch.cat([hint, hint]      ,dim=0    ).to(dtype=self.unet.dtype      ,device=snake_case    )

           if not isinstance(snake_case      ,snake_case    ):
                SCREAMING_SNAKE_CASE										=[image]
           if not all(isinstance(snake_case      ,(PIL.Image.Image, torch.Tensor)    ) for i in image    ):
                raise ValueError(
                    f'Input is in incorrect format: {[type(snake_case    ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'    )

           SCREAMING_SNAKE_CASE										=torch.cat([prepare_image(snake_case      ,snake_case      ,snake_case    ) for i in image]      ,dim=0    )
           SCREAMING_SNAKE_CASE										=image.to(dtype=image_embeds.dtype      ,device=snake_case    )

           SCREAMING_SNAKE_CASE										=self.movq.encode(snake_case    )['latents']
           SCREAMING_SNAKE_CASE										=latents.repeat_interleave(snake_case      ,dim=0    )
           self.scheduler.set_timesteps(snake_case      ,device=snake_case    )
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.get_timesteps(snake_case      ,snake_case      ,snake_case    )
           SCREAMING_SNAKE_CASE										=timesteps[:1].repeat(batch_size * num_images_per_prompt    )
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=downscale_height_and_width(snake_case      ,snake_case      ,self.movq_scale_factor    )
           SCREAMING_SNAKE_CASE										=self.prepare_latents(
               snake_case      ,snake_case      ,snake_case      ,snake_case      ,image_embeds.dtype      ,snake_case      ,snake_case    )
           for i, t in enumerate(self.progress_bar(snake_case    )    ):
                # expand the latents if we are doing classifier free guidance
                SCREAMING_SNAKE_CASE										=torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

                SCREAMING_SNAKE_CASE										={'image_embeds': image_embeds, 'hint': hint}
                SCREAMING_SNAKE_CASE										=self.unet(
                    sample=snake_case      ,timestep=snake_case      ,encoder_hidden_states=snake_case      ,added_cond_kwargs=snake_case      ,return_dict=snake_case      ,)[0]

                if do_classifier_free_guidance:
                     SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=noise_pred.split(latents.shape[1]      ,dim=1    )
                     SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=noise_pred.chunk(2    )
                     SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=variance_pred.chunk(2    )
                     SCREAMING_SNAKE_CASE										=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                     SCREAMING_SNAKE_CASE										=torch.cat([noise_pred, variance_pred_text]      ,dim=1    )

                if not (
                    hasattr(self.scheduler.config      ,'variance_type'    )
                    and self.scheduler.config.variance_type in ["learned", "learned_range"]
                ):
                     SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=noise_pred.split(latents.shape[1]      ,dim=1    )

                # compute the previous noisy sample x_t -> x_t-1

                SCREAMING_SNAKE_CASE										=self.scheduler.step(
                    snake_case      ,snake_case      ,snake_case      ,generator=snake_case      ,)[0]

           # post-processing
           SCREAMING_SNAKE_CASE										=self.movq.decode(snake_case      ,force_not_quantize=snake_case    )['sample']

           if output_type not in ["pt", "np", "pil"]:
                raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'    )

           if output_type in ["np", "pil"]:
                SCREAMING_SNAKE_CASE										=image * 0.5 + 0.5
                SCREAMING_SNAKE_CASE										=image.clamp(0      ,1    )
                SCREAMING_SNAKE_CASE										=image.cpu().permute(0      ,2      ,3      ,1    ).float().numpy()

           if output_type == "pil":
                SCREAMING_SNAKE_CASE										=self.numpy_to_pil(snake_case    )

           if not return_dict:
                return (image,)

           return ImagePipelineOutput(images=snake_case    )




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 		a_	:



      """simple docstring"""







      def __init__(				self					:							Any      ,snake_case					:							List[Any]      ,snake_case					:							Optional[Any]=13      ,snake_case					:							str=7      ,snake_case					:							List[Any]=True      ,snake_case					:							Optional[Any]=True      ,snake_case					:							int=False      ,snake_case					:							List[str]=True      ,snake_case					:							Optional[int]=99      ,snake_case					:							Any=64      ,snake_case					:							int=2      ,snake_case					:							str=4      ,snake_case					:							str=64      ,snake_case					:							List[Any]="gelu"      ,snake_case					:							Dict=0.1      ,snake_case					:							int=0.1      ,snake_case					:							List[Any]=512      ,snake_case					:							int=16      ,snake_case					:							List[Any]=2      ,snake_case					:							Union[str, Any]=0.02      ,snake_case					:							List[str]=3      ,snake_case					:							Any=4      ,snake_case					:							Tuple=None      ,):
           SCREAMING_SNAKE_CASE										=parent
           SCREAMING_SNAKE_CASE										=batch_size
           SCREAMING_SNAKE_CASE										=seq_length
           SCREAMING_SNAKE_CASE										=is_training
           SCREAMING_SNAKE_CASE										=use_input_mask
           SCREAMING_SNAKE_CASE										=use_token_type_ids
           SCREAMING_SNAKE_CASE										=use_labels
           SCREAMING_SNAKE_CASE										=vocab_size
           SCREAMING_SNAKE_CASE										=hidden_size
           SCREAMING_SNAKE_CASE										=num_hidden_layers
           SCREAMING_SNAKE_CASE										=num_attention_heads
           SCREAMING_SNAKE_CASE										=intermediate_size
           SCREAMING_SNAKE_CASE										=hidden_act
           SCREAMING_SNAKE_CASE										=hidden_dropout_prob
           SCREAMING_SNAKE_CASE										=attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE										=max_position_embeddings
           SCREAMING_SNAKE_CASE										=type_vocab_size
           SCREAMING_SNAKE_CASE										=type_sequence_label_size
           SCREAMING_SNAKE_CASE										=initializer_range
           SCREAMING_SNAKE_CASE										=num_labels
           SCREAMING_SNAKE_CASE										=num_choices
           SCREAMING_SNAKE_CASE										=scope
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

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

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

           SCREAMING_SNAKE_CASE										=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 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							Any      ,snake_case					:							Optional[int]      ,snake_case					:							str      ,snake_case					:							Union[str, Any]      ,snake_case					:							Optional[Any]      ,snake_case					:							Any    ):
           SCREAMING_SNAKE_CASE										=TFMPNetModel(config=snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           SCREAMING_SNAKE_CASE										=[input_ids, input_mask]
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)    )
      def 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							List[str]      ,snake_case					:							Tuple      ,snake_case					:							Dict      ,snake_case					:							Optional[Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Dict    ):
           SCREAMING_SNAKE_CASE										=TFMPNetForMaskedLM(config=snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							List[str]      ,snake_case					:							Dict      ,snake_case					:							List[Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Optional[int]      ,snake_case					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=TFMPNetForQuestionAnswering(config=snake_case    )
           SCREAMING_SNAKE_CASE										={
               'input_ids': input_ids,
               'attention_mask': input_mask,
           }
           SCREAMING_SNAKE_CASE										=model(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 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							Union[str, Any]      ,snake_case					:							str      ,snake_case					:							str      ,snake_case					:							Any      ,snake_case					:							Tuple      ,snake_case					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=self.num_labels
           SCREAMING_SNAKE_CASE										=TFMPNetForSequenceClassification(snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							Any      ,snake_case					:							Union[str, Any]      ,snake_case					:							int      ,snake_case					:							List[str]      ,snake_case					:							Tuple      ,snake_case					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=self.num_choices
           SCREAMING_SNAKE_CASE										=TFMPNetForMultipleChoice(snake_case    )
           SCREAMING_SNAKE_CASE										=tf.tile(tf.expand_dims(snake_case      ,1    )      ,(1, self.num_choices, 1)    )
           SCREAMING_SNAKE_CASE										=tf.tile(tf.expand_dims(snake_case      ,1    )      ,(1, self.num_choices, 1)    )
           SCREAMING_SNAKE_CASE										={
               'input_ids': multiple_choice_inputs_ids,
               'attention_mask': multiple_choice_input_mask,
           }
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)    )
      def 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							Tuple      ,snake_case					:							Dict      ,snake_case					:							Tuple      ,snake_case					:							Union[str, Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.num_labels
           SCREAMING_SNAKE_CASE										=TFMPNetForTokenClassification(snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)    )


      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=self.prepare_config_and_inputs()
           ((SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE))										=config_and_inputs
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           return config, inputs_dict







@require_tf
class 		a_	(							lowerCamelCase_			,      lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				(
          (
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetModel,
          )
          if is_tf_available()
          else ()
      )
      __UpperCAmelCase      		=				(
          {
              'feature-extraction': TFMPNetModel,
              'fill-mask': TFMPNetForMaskedLM,
              'question-answering': TFMPNetForQuestionAnswering,
              'text-classification': TFMPNetForSequenceClassification,
              'token-classification': TFMPNetForTokenClassification,
              'zero-shot': TFMPNetForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=TFMPNetModelTester(self    )
           SCREAMING_SNAKE_CASE										=ConfigTester(self      ,config_class=snake_case      ,hidden_size=37    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           self.config_tester.run_common_tests()
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_model(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_masked_lm(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case    )


      @slow
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           for model_name in ["microsoft/mpnet-base"]:
                SCREAMING_SNAKE_CASE										=TFMPNetModel.from_pretrained(snake_case    )
                self.assertIsNotNone(snake_case    )







@require_tf
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      @slow
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=TFMPNetModel.from_pretrained('microsoft/mpnet-base'    )
           SCREAMING_SNAKE_CASE										=tf.constant([[0, 1, 2, 3, 4, 5]]    )
           SCREAMING_SNAKE_CASE										=model(snake_case    )[0]

           SCREAMING_SNAKE_CASE										=[1, 6, 768]
           self.assertEqual(output.shape      ,snake_case    )

           SCREAMING_SNAKE_CASE										=tf.constant(
               [
                   [
                       [-0.1_067_172, 0.08_216_473, 0.0_024_543],
                       [-0.03_465_879, 0.8_354_118, -0.03_252_288],
                       [-0.06_569_476, -0.12_424_111, -0.0_494_436],
                   ]
               ]    )
           tf.debugging.assert_near(output[:, :3, :3]      ,snake_case      ,atol=1e-4    )

