import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
     import torch
     from torch import nn

     from transformers import ViTMAEForPreTraining, ViTMAEModel
     from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor



class  UpperCamelCase				:

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_=13				,				A_=30				,				A_=2				,				A_=3				,				A_=True				,				A_=True				,				A_=32				,				A_=5				,				A_=4				,				A_=37				,				A_="gelu"				,				A_=0.1				,				A_=0.1				,				A_=10				,				A_=0.02				,				A_=3				,				A_=0.6				,				A_=None				,				) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				parent
           _lowerCamelCase			    =				batch_size
           _lowerCamelCase			    =				image_size
           _lowerCamelCase			    =				patch_size
           _lowerCamelCase			    =				num_channels
           _lowerCamelCase			    =				is_training
           _lowerCamelCase			    =				use_labels
           _lowerCamelCase			    =				hidden_size
           _lowerCamelCase			    =				num_hidden_layers
           _lowerCamelCase			    =				num_attention_heads
           _lowerCamelCase			    =				intermediate_size
           _lowerCamelCase			    =				hidden_act
           _lowerCamelCase			    =				hidden_dropout_prob
           _lowerCamelCase			    =				attention_probs_dropout_prob
           _lowerCamelCase			    =				type_sequence_label_size
           _lowerCamelCase			    =				initializer_range
           _lowerCamelCase			    =				mask_ratio
           _lowerCamelCase			    =				scope

           # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
           # (we add 1 for the [CLS] token)
           _lowerCamelCase			    =				(image_size // patch_size) ** 2
           _lowerCamelCase			    =				int(math.ceil((1 - mask_ratio) * (num_patches + 1)						)						)



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

           _lowerCamelCase			    =				None
           if self.use_labels:
                 _lowerCamelCase			    =				ids_tensor([self.batch_size]				,				self.type_sequence_label_size						)

           _lowerCamelCase			    =				self.get_config()

           return config, pixel_values, labels



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           return ViTMAEConfig(
               image_size=self.image_size				,				patch_size=self.patch_size				,				num_channels=self.num_channels				,				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				,				is_decoder=A_				,				initializer_range=self.initializer_range				,				mask_ratio=self.mask_ratio				,				)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				ViTMAEModel(config=A_						)
           model.to(A_						)
           model.eval()
           _lowerCamelCase			    =				model(A_						)
           self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				ViTMAEForPreTraining(A_						)
           model.to(A_						)
           model.eval()
           _lowerCamelCase			    =				model(A_						)
           _lowerCamelCase			    =				(self.image_size // self.patch_size) ** 2
           _lowerCamelCase			    =				self.patch_size**2 * self.num_channels
           self.parent.assertEqual(result.logits.shape				,				(self.batch_size, num_patches, expected_num_channels)						)

           # test greyscale images
           _lowerCamelCase			    =				1
           _lowerCamelCase			    =				ViTMAEForPreTraining(A_						)
           model.to(A_						)
           model.eval()
           _lowerCamelCase			    =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
           _lowerCamelCase			    =				model(A_						)
           _lowerCamelCase			    =				self.patch_size**2
           self.parent.assertEqual(result.logits.shape				,				(self.batch_size, num_patches, expected_num_channels)						)





     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.prepare_config_and_inputs()
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				config_and_inputs
           _lowerCamelCase			    =				{'''pixel_values''': pixel_values}
           return config, inputs_dict





@require_torch
class  UpperCamelCase				( __lowercase   ,  __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
     A_										=  {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

     A_										=  False
     A_										=  False
     A_										=  False
     A_										=  False



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				ViTMAEModelTester(self						)
           _lowerCamelCase			    =				ConfigTester(self				,				config_class=A_				,				has_text_modality=A_				,				hidden_size=37						)



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           self.config_tester.run_common_tests()



     @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''						)
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           pass



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 _lowerCamelCase			    =				model_class(A_						)
                 self.assertIsInstance(model.get_input_embeddings()				,				(nn.Module)						)
                 _lowerCamelCase			    =				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(A_				,				nn.Linear						)						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 _lowerCamelCase			    =				model_class(A_						)
                 _lowerCamelCase			    =				inspect.signature(model.forward						)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 _lowerCamelCase			    =				[*signature.parameters.keys()]

                 _lowerCamelCase			    =				['''pixel_values''']
                 self.assertListEqual(arg_names[:1]				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A_						)



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				str:






           """simple docstring"""
           # make masks reproducible
           np.random.seed(2						)

           _lowerCamelCase			    =				int((pt_model.config.image_size // pt_model.config.patch_size) ** 2						)
           _lowerCamelCase			    =				np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
           _lowerCamelCase			    =				torch.from_numpy(A_						)

           # Add `noise` argument.
           # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
           _lowerCamelCase			    =				pt_noise

           super().check_pt_tf_models(A_				,				A_				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 _lowerCamelCase			    =				model_class(A_						)
                 model.to(A_						)
                 model.eval()
                 # make random mask reproducible
                 torch.manual_seed(2						)
                 with torch.no_grad():
                       _lowerCamelCase			    =				model(**self._prepare_for_class(A_				,				A_						)						)

                 _lowerCamelCase			    =				outputs[0].cpu().numpy()
                 _lowerCamelCase			    =				0

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       model.save_pretrained(A_						)
                       _lowerCamelCase			    =				model_class.from_pretrained(A_						)
                       model.to(A_						)
                       # make random mask reproducible
                       torch.manual_seed(2						)
                       with torch.no_grad():
                             _lowerCamelCase			    =				model(**self._prepare_for_class(A_				,				A_						)						)

                       # Make sure we don't have nans
                       _lowerCamelCase			    =				after_outputs[0].cpu().numpy()
                       _lowerCamelCase			    =				0
                       _lowerCamelCase			    =				np.amax(np.abs(out_a - out_a						)						)
                       self.assertLessEqual(A_				,				1E-5						)



     @unittest.skip(
         reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''						)
     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           pass



     @unittest.skip(
         reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''						)
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           pass



     @unittest.skip(
         reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''						)
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           pass



     @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''						)
     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           pass



     @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''						)
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           pass





     @slow
     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _lowerCamelCase			    =				ViTMAEModel.from_pretrained(A_						)
                 self.assertIsNotNone(A_						)





def       __magic_name__(  )						->    Tuple:



      '''simple docstring'''

      _lowerCamelCase			    =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
      return image




@require_torch
@require_vision
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     @cached_property
     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''						) if is_vision_available() else None





     @slow
     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           # make random mask reproducible across the PT and TF model
           np.random.seed(2						)

           _lowerCamelCase			    =				ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''						).to(A_						)

           _lowerCamelCase			    =				self.default_image_processor
           _lowerCamelCase			    =				prepare_img()
           _lowerCamelCase			    =				image_processor(images=A_				,				return_tensors='''pt'''						).to(A_						)

           # prepare a noise vector that will be also used for testing the TF model
           # (this way we can ensure that the PT and TF models operate on the same inputs)
           _lowerCamelCase			    =				ViTMAEConfig()
           _lowerCamelCase			    =				int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
           _lowerCamelCase			    =				np.random.uniform(size=(1, num_patches)						)

           # forward pass
           with torch.no_grad():
                 _lowerCamelCase			    =				model(**A_				,				noise=torch.from_numpy(A_						).to(device=A_						)						)

           # verify the logits
           _lowerCamelCase			    =				torch.Size((1, 1_96, 7_68)						)
           self.assertEqual(outputs.logits.shape				,				A_						)

           _lowerCamelCase			    =				torch.tensor(
               [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]						)

           self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]				,				expected_slice.to(A_						)				,				atol=1E-4						)						)
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  ['vqvae']



     def __init__(					self				,				A_				,				A_				,				A_				,				A_				,				) ->				Tuple:






           """simple docstring"""
           super().__init__()
           self.register_modules(unet=A_				,				scheduler=A_				,				mel=A_				,				vqvae=A_						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return 50 if isinstance(self.scheduler				,				A_						) else 10_00



     @torch.no_grad()
     def __call__(					self				,				A_ = 1				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_=True				,				) ->				Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:






           """simple docstring"""
           _lowerCamelCase			    =				steps or self.get_default_steps()
           self.scheduler.set_timesteps(A_						)
           _lowerCamelCase			    =				step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size						) == int:
                 _lowerCamelCase			    =				(self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
                 _lowerCamelCase			    =				randn_tensor(
                     (
                         batch_size,
                         self.unet.config.in_channels,
                         self.unet.config.sample_size[0],
                         self.unet.config.sample_size[1],
                     )				,				generator=A_				,				device=self.device				,				)
           _lowerCamelCase			    =				noise
           _lowerCamelCase			    =				None

           if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(A_				,				A_						)
                 _lowerCamelCase			    =				self.mel.audio_slice_to_image(A_						)
                 _lowerCamelCase			    =				np.frombuffer(input_image.tobytes()				,				dtype='''uint8'''						).reshape(
                     (input_image.height, input_image.width)						)
                 _lowerCamelCase			    =				(input_image / 2_55) * 2 - 1
                 _lowerCamelCase			    =				torch.tensor(input_image[np.newaxis, :, :]				,				dtype=torch.float						).to(self.device						)

                 if self.vqvae is not None:
                       _lowerCamelCase			    =				self.vqvae.encode(torch.unsqueeze(A_				,				0						)						).latent_dist.sample(
                           generator=A_						)[0]
                       _lowerCamelCase			    =				self.vqvae.config.scaling_factor * input_images

                 if start_step > 0:
                       _lowerCamelCase			    =				self.scheduler.add_noise(A_				,				A_				,				self.scheduler.timesteps[start_step - 1]						)

                 _lowerCamelCase			    =				(
                     self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                 )
                 _lowerCamelCase			    =				int(mask_start_secs * pixels_per_second						)
                 _lowerCamelCase			    =				int(mask_end_secs * pixels_per_second						)
                 _lowerCamelCase			    =				self.scheduler.add_noise(A_				,				A_				,				torch.tensor(self.scheduler.timesteps[start_step:]						)						)

           for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
                 if isinstance(self.unet				,				A_						):
                       _lowerCamelCase			    =				self.unet(A_				,				A_				,				A_						)['''sample''']
                 else:
                       _lowerCamelCase			    =				self.unet(A_				,				A_						)['''sample''']

                 if isinstance(self.scheduler				,				A_						):
                       _lowerCamelCase			    =				self.scheduler.step(
                           model_output=A_				,				timestep=A_				,				sample=A_				,				eta=A_				,				generator=A_				,				)['''prev_sample''']
                 else:
                       _lowerCamelCase			    =				self.scheduler.step(
                           model_output=A_				,				timestep=A_				,				sample=A_				,				generator=A_				,				)['''prev_sample''']

                 if mask is not None:
                       if mask_start > 0:
                             _lowerCamelCase			    =				mask[:, step, :, :mask_start]
                       if mask_end > 0:
                             _lowerCamelCase			    =				mask[:, step, :, -mask_end:]

           if self.vqvae is not None:
                 # 0.18215 was scaling factor used in training to ensure unit variance
                 _lowerCamelCase			    =				1 / self.vqvae.config.scaling_factor * images
                 _lowerCamelCase			    =				self.vqvae.decode(A_						)['''sample''']

           _lowerCamelCase			    =				(images / 2 + 0.5).clamp(0				,				1						)
           _lowerCamelCase			    =				images.cpu().permute(0				,				2				,				3				,				1						).numpy()
           _lowerCamelCase			    =				(images * 2_55).round().astype('''uint8'''						)
           _lowerCamelCase			    =				list(
               (Image.fromarray(_[:, :, 0]						) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(A_				,				mode='''RGB'''						).convert('''L'''						) for _ in images)						)

           _lowerCamelCase			    =				[self.mel.image_to_audio(A_						) for _ in images]
           if not return_dict:
                 return images, (self.mel.get_sample_rate(), audios)

           return BaseOutput(**AudioPipelineOutput(np.array(A_						)[:, np.newaxis, :]						)				,				**ImagePipelineOutput(A_						)						)



     @torch.no_grad()
     def 		UpperCamelCase_						(					self				,				A_				,				A_ = 50						) ->				np.ndarray:






           """simple docstring"""
           assert isinstance(self.scheduler				,				A_						)
           self.scheduler.set_timesteps(A_						)
           _lowerCamelCase			    =				np.array(
               [np.frombuffer(image.tobytes()				,				dtype='''uint8'''						).reshape((1, image.height, image.width)						) for image in images]						)
           _lowerCamelCase			    =				(sample / 2_55) * 2 - 1
           _lowerCamelCase			    =				torch.Tensor(A_						).to(self.device						)

           for t in self.progress_bar(torch.flip(self.scheduler.timesteps				,				(0,)						)						):
                 _lowerCamelCase			    =				t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                 _lowerCamelCase			    =				self.scheduler.alphas_cumprod[t]
                 _lowerCamelCase			    =				(
                     self.scheduler.alphas_cumprod[prev_timestep]
                     if prev_timestep >= 0
                     else self.scheduler.final_alpha_cumprod
                 )
                 _lowerCamelCase			    =				1 - alpha_prod_t
                 _lowerCamelCase			    =				self.unet(A_				,				A_						)['''sample''']
                 _lowerCamelCase			    =				(1 - alpha_prod_t_prev) ** 0.5 * model_output
                 _lowerCamelCase			    =				(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                 _lowerCamelCase			    =				sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

           return sample





     @staticmethod
     def 		UpperCamelCase_						(					A_				,				A_				,				A_						) ->				torch.Tensor:






           """simple docstring"""
           _lowerCamelCase			    =				acos(torch.dot(torch.flatten(A_						)				,				torch.flatten(A_						)						) / torch.norm(A_						) / torch.norm(A_						)						)
           return sin((1 - alpha) * theta						) * xa / sin(A_						) + sin(alpha * theta						) * xa / sin(A_						)