


'''simple docstring'''



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




def 			_a			(		_lowercase      :			Any						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[Any]													=    image.size
     __UpperCAmelCase							,     __UpperCAmelCase			:				List[str]													=    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     __UpperCAmelCase			:				int													=    image.resize((w, h)    ,      resample=PIL_INTERPOLATION['''lanczos''']						)
     __UpperCAmelCase			:				Union[str, Any]													=    np.array(_lowercase						).astype(np.floataa						) / 255.0
     __UpperCAmelCase			:				List[Any]													=    image[None].transpose(0    ,      3    ,      1    ,      2						)
     __UpperCAmelCase			:				Any													=    torch.from_numpy(_lowercase						)
     return 2.0 * image - 1.0



class 			a       (	_a ):


     """simple docstring"""






     def __init__(       self  :	Any							,				snake_case  :	VQModel							,				snake_case  :	UNetaDModel							,				snake_case  :	Union[
             DDIMScheduler,
             PNDMScheduler,
             LMSDiscreteScheduler,
             EulerDiscreteScheduler,
             EulerAncestralDiscreteScheduler,
             DPMSolverMultistepScheduler,
         ]							,				)		->							Optional[Any]:
          super().__init__()
          self.register_modules(vqvae=snake_case							,				unet=snake_case							,				scheduler=snake_case    )






     @torch.no_grad()
     def __call__(       self  :	str							,				snake_case  :	Union[torch.Tensor, PIL.Image.Image] = None							,				snake_case  :	Optional[int] = 1							,				snake_case  :	Optional[int] = 100							,				snake_case  :	Optional[float] = 0.0							,				snake_case  :	Optional[Union[torch.Generator, List[torch.Generator]]] = None							,				snake_case  :	Optional[str] = "pil"							,				snake_case  :	bool = True							,				)		->							Union[Tuple, ImagePipelineOutput]:
          if isinstance(snake_case							,				PIL.Image.Image    ):
               __UpperCAmelCase			:				int													=    1
          elif isinstance(snake_case							,				torch.Tensor    ):
               __UpperCAmelCase			:				Tuple													=    image.shape[0]
          else:
               raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case    )}'    )

          if isinstance(snake_case							,				PIL.Image.Image    ):
               __UpperCAmelCase			:				str													=    preprocess(snake_case    )

          __UpperCAmelCase							,     __UpperCAmelCase			:				List[str]													=    image.shape[-2:]

          # in_channels should be 6: 3 for latents, 3 for low resolution image
          __UpperCAmelCase			:				int													=    (batch_size, self.unet.config.in_channels // 2, height, width)
          __UpperCAmelCase			:				Union[str, Any]													=    next(self.unet.parameters()    ).dtype

          __UpperCAmelCase			:				List[str]													=    randn_tensor(snake_case							,				generator=snake_case							,				device=self.device							,				dtype=snake_case    )

          __UpperCAmelCase			:				Optional[Any]													=    image.to(device=self.device							,				dtype=snake_case    )

          # set timesteps and move to the correct device
          self.scheduler.set_timesteps(snake_case							,				device=self.device    )
          __UpperCAmelCase			:				str													=    self.scheduler.timesteps

          # scale the initial noise by the standard deviation required by the scheduler
          __UpperCAmelCase			:				Dict													=    latents * self.scheduler.init_noise_sigma

          # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
          # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
          # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
          # and should be between [0, 1]
          __UpperCAmelCase			:				Tuple													=    '''eta''' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
          __UpperCAmelCase			:				Tuple													=    {}
          if accepts_eta:
               __UpperCAmelCase			:				Optional[Any]													=    eta

          for t in self.progress_bar(snake_case    ):
               # concat latents and low resolution image in the channel dimension.
               __UpperCAmelCase			:				Union[str, Any]													=    torch.cat([latents, image]							,				dim=1    )
               __UpperCAmelCase			:				int													=    self.scheduler.scale_model_input(snake_case							,				snake_case    )
               # predict the noise residual
               __UpperCAmelCase			:				Tuple													=    self.unet(snake_case							,				snake_case    ).sample
               # compute the previous noisy sample x_t -> x_t-1
               __UpperCAmelCase			:				str													=    self.scheduler.step(snake_case							,				snake_case							,				snake_case							,				**snake_case    ).prev_sample

          # decode the image latents with the VQVAE
          __UpperCAmelCase			:				int													=    self.vqvae.decode(snake_case    ).sample
          __UpperCAmelCase			:				Optional[int]													=    torch.clamp(snake_case							,				-1.0							,				1.0    )
          __UpperCAmelCase			:				List[Any]													=    image / 2 + 0.5
          __UpperCAmelCase			:				List[str]													=    image.cpu().permute(0							,				2							,				3							,				1    ).numpy()

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

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=snake_case    )



'''simple docstring'''



import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
      import torch

      from transformers import (
          NystromformerForMaskedLM,
          NystromformerForMultipleChoice,
          NystromformerForQuestionAnswering,
          NystromformerForSequenceClassification,
          NystromformerForTokenClassification,
          NystromformerModel,
      )
      from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Union[str, Any]							,				snake_case  :	str							,				snake_case  :	Union[str, Any]=13							,				snake_case  :	int=7							,				snake_case  :	Union[str, Any]=True							,				snake_case  :	Tuple=True							,				snake_case  :	Tuple=True							,				snake_case  :	Union[str, Any]=True							,				snake_case  :	Tuple=99							,				snake_case  :	str=32							,				snake_case  :	Tuple=5							,				snake_case  :	int=4							,				snake_case  :	Union[str, Any]=37							,				snake_case  :	Union[str, Any]="gelu"							,				snake_case  :	List[Any]=0.1							,				snake_case  :	List[Any]=0.1							,				snake_case  :	Any=512							,				snake_case  :	Dict=16							,				snake_case  :	Dict=2							,				snake_case  :	int=0.02							,				snake_case  :	Dict=3							,				snake_case  :	Tuple=4							,				snake_case  :	Any=None							,				)		->							Tuple:
          __UpperCAmelCase			:				Optional[Any]													=    parent
          __UpperCAmelCase			:				int													=    batch_size
          __UpperCAmelCase			:				List[Any]													=    seq_length
          __UpperCAmelCase			:				Dict													=    is_training
          __UpperCAmelCase			:				Union[str, Any]													=    use_input_mask
          __UpperCAmelCase			:				Union[str, Any]													=    use_token_type_ids
          __UpperCAmelCase			:				Optional[Any]													=    use_labels
          __UpperCAmelCase			:				str													=    vocab_size
          __UpperCAmelCase			:				List[str]													=    hidden_size
          __UpperCAmelCase			:				Any													=    num_hidden_layers
          __UpperCAmelCase			:				Optional[int]													=    num_attention_heads
          __UpperCAmelCase			:				str													=    intermediate_size
          __UpperCAmelCase			:				Tuple													=    hidden_act
          __UpperCAmelCase			:				Tuple													=    hidden_dropout_prob
          __UpperCAmelCase			:				Optional[Any]													=    attention_probs_dropout_prob
          __UpperCAmelCase			:				List[str]													=    max_position_embeddings
          __UpperCAmelCase			:				Optional[Any]													=    type_vocab_size
          __UpperCAmelCase			:				Dict													=    type_sequence_label_size
          __UpperCAmelCase			:				Tuple													=    initializer_range
          __UpperCAmelCase			:				Union[str, Any]													=    num_labels
          __UpperCAmelCase			:				List[str]													=    num_choices
          __UpperCAmelCase			:				List[str]													=    scope

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Any:
          __UpperCAmelCase			:				int													=    ids_tensor([self.batch_size, self.seq_length]							,				self.vocab_size    )

          __UpperCAmelCase			:				Optional[int]													=    None
          if self.use_input_mask:
               __UpperCAmelCase			:				Tuple													=    random_attention_mask([self.batch_size, self.seq_length]    )

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

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

          __UpperCAmelCase			:				int													=    self.get_config()

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

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							int:
          return NystromformerConfig(
              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							,				is_decoder=snake_case							,				initializer_range=self.initializer_range							,				)

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	Optional[int]							,				snake_case  :	str							,				snake_case  :	Optional[Any]							,				snake_case  :	List[str]							,				snake_case  :	str							,				snake_case  :	Union[str, Any]							,				snake_case  :	Tuple    )		->							Dict:
          __UpperCAmelCase			:				Any													=    NystromformerModel(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				str													=    model(snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case    )
          __UpperCAmelCase			:				List[str]													=    model(snake_case							,				token_type_ids=snake_case    )
          __UpperCAmelCase			:				Dict													=    model(snake_case    )
          self.parent.assertEqual(result.last_hidden_state.shape							,				(self.batch_size, self.seq_length, self.hidden_size)    )

     def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	int							,				snake_case  :	Optional[int]							,				snake_case  :	int							,				snake_case  :	int							,				snake_case  :	List[str]							,				snake_case  :	Any							,				snake_case  :	Tuple    )		->							List[Any]:
          __UpperCAmelCase			:				List[Any]													=    NystromformerForMaskedLM(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				List[str]													=    model(snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case							,				labels=snake_case    )
          self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.vocab_size)    )

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	Optional[Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[str]							,				snake_case  :	Optional[Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[Any]							,				snake_case  :	Tuple    )		->							Union[str, Any]:
          __UpperCAmelCase			:				List[str]													=    NystromformerForQuestionAnswering(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				List[str]													=    model(
              snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case							,				start_positions=snake_case							,				end_positions=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  :	Dict							,				snake_case  :	Any							,				snake_case  :	int							,				snake_case  :	Dict							,				snake_case  :	Any							,				snake_case  :	Optional[int]							,				snake_case  :	Tuple							,				snake_case  :	Any    )		->							List[str]:
          __UpperCAmelCase			:				int													=    self.num_labels
          __UpperCAmelCase			:				str													=    NystromformerForSequenceClassification(snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				List[str]													=    model(snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case							,				labels=snake_case    )
          self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)    )

     def 				lowerCamelCase__				(       self  :	Tuple							,				snake_case  :	List[Any]							,				snake_case  :	Optional[int]							,				snake_case  :	int							,				snake_case  :	List[str]							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[str]							,				snake_case  :	List[str]    )		->							Optional[Any]:
          __UpperCAmelCase			:				str													=    self.num_labels
          __UpperCAmelCase			:				int													=    NystromformerForTokenClassification(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				str													=    model(snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case							,				labels=snake_case    )
          self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.num_labels)    )

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	Tuple							,				snake_case  :	Optional[int]							,				snake_case  :	Union[str, Any]							,				snake_case  :	Dict							,				snake_case  :	List[Any]							,				snake_case  :	Optional[int]							,				snake_case  :	Union[str, Any]    )		->							Any:
          __UpperCAmelCase			:				List[str]													=    self.num_choices
          __UpperCAmelCase			:				List[str]													=    NystromformerForMultipleChoice(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				Optional[int]													=    input_ids.unsqueeze(1    ).expand(-1							,				self.num_choices							,				-1    ).contiguous()
          __UpperCAmelCase			:				Dict													=    token_type_ids.unsqueeze(1    ).expand(-1							,				self.num_choices							,				-1    ).contiguous()
          __UpperCAmelCase			:				Optional[Any]													=    input_mask.unsqueeze(1    ).expand(-1							,				self.num_choices							,				-1    ).contiguous()
          __UpperCAmelCase			:				Union[str, Any]													=    model(
              snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case							,				labels=snake_case							,				)
          self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_choices)    )






     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Optional[int]:
          __UpperCAmelCase			:				Union[str, Any]													=    self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     
          )			:				int													=    config_and_inputs
          __UpperCAmelCase			:				Tuple													=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict



@require_torch
class 			a       (	_a			,						_a			,						unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				List[str]							     =     (
         (
             NystromformerModel,
             NystromformerForMaskedLM,
             NystromformerForMultipleChoice,
             NystromformerForQuestionAnswering,
             NystromformerForSequenceClassification,
             NystromformerForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     (
         {
             "feature-extraction": NystromformerModel,
             "fill-mask": NystromformerForMaskedLM,
             "question-answering": NystromformerForQuestionAnswering,
             "text-classification": NystromformerForSequenceClassification,
             "token-classification": NystromformerForTokenClassification,
             "zero-shot": NystromformerForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE :				Tuple							     =     False
     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     False

     def 				lowerCamelCase__				(       self  :	Dict    )		->							List[str]:
          __UpperCAmelCase			:				str													=    NystromformerModelTester(self    )
          __UpperCAmelCase			:				List[str]													=    ConfigTester(self							,				config_class=snake_case							,				hidden_size=37    )

     def 				lowerCamelCase__				(       self  :	int    )		->							Any:
          self.config_tester.run_common_tests()

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Optional[int]:
          __UpperCAmelCase			:				List[str]													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case    )

     def 				lowerCamelCase__				(       self  :	Dict    )		->							Tuple:
          __UpperCAmelCase			:				Dict													=    self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               __UpperCAmelCase			:				List[str]													=    type
               self.model_tester.create_and_check_model(*snake_case    )

     def 				lowerCamelCase__				(       self  :	Any    )		->							List[Any]:
          __UpperCAmelCase			:				List[Any]													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Optional[Any]:
          __UpperCAmelCase			:				Optional[Any]													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*snake_case    )

     def 				lowerCamelCase__				(       self  :	str    )		->							Optional[Any]:
          __UpperCAmelCase			:				int													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*snake_case    )

     def 				lowerCamelCase__				(       self  :	str    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Any													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*snake_case    )

     def 				lowerCamelCase__				(       self  :	str    )		->							int:
          __UpperCAmelCase			:				Dict													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*snake_case    )






     @slow
     def 				lowerCamelCase__				(       self  :	Any    )		->							Union[str, Any]:
          for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase			:				List[Any]													=    NystromformerModel.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )



@require_torch
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     @slow
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							List[str]:
          __UpperCAmelCase			:				Union[str, Any]													=    NystromformerModel.from_pretrained('''uw-madison/nystromformer-512'''    )
          __UpperCAmelCase			:				Any													=    torch.tensor([[0, 1, 2, 3, 4, 5]]    )

          with torch.no_grad():
               __UpperCAmelCase			:				Union[str, Any]													=    model(snake_case    )[0]

          __UpperCAmelCase			:				Tuple													=    torch.Size((1, 6, 768)    )
          self.assertEqual(output.shape							,				snake_case    )

          __UpperCAmelCase			:				Tuple													=    torch.tensor(
              [[[-0.4_532, -0.0_936, 0.5_137], [-0.2_676, 0.0_628, 0.6_186], [-0.3_629, -0.1_726, 0.4_716]]]    )

          self.assertTrue(torch.allclose(output[:, :3, :3]							,				snake_case							,				atol=1E-4    )    )






     @slow
     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							str:
          __UpperCAmelCase			:				str													=    '''the [MASK] of Belgium is Brussels'''

          __UpperCAmelCase			:				Dict													=    AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512'''    )
          __UpperCAmelCase			:				Tuple													=    NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512'''    )

          __UpperCAmelCase			:				Optional[Any]													=    tokenizer(snake_case							,				return_tensors='''pt'''    )

          with torch.no_grad():
               __UpperCAmelCase			:				Union[str, Any]													=    model(encoding.input_ids    ).logits

          __UpperCAmelCase			:				str													=    token_logits[:, 2, :].argmax(-1    )[0]

          self.assertEqual(tokenizer.decode(snake_case    )							,				'''capital'''    )