




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
         TFDeiTModel,
     )
     from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import DeiTImageProcessor





class 					__lowerCamelCase			:




    """simple docstring"""

    def __init__(			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=13  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=30  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=2  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=3  ,      SCREAMING_SNAKE_CASE__	:	str=True  ,      SCREAMING_SNAKE_CASE__	:	int=True  ,      SCREAMING_SNAKE_CASE__	:	Tuple=32  ,      SCREAMING_SNAKE_CASE__	:	Tuple=2  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=4  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=37  ,      SCREAMING_SNAKE_CASE__	:	Tuple="gelu"  ,      SCREAMING_SNAKE_CASE__	:	int=0.1  ,      SCREAMING_SNAKE_CASE__	:	int=0.1  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=10  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=0.02  ,      SCREAMING_SNAKE_CASE__	:	Tuple=3  ,      SCREAMING_SNAKE_CASE__	:	Any=None  ,      SCREAMING_SNAKE_CASE__	:	List[str]=2  ,      ) ->			Optional[int]:
         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__            =					scope
         lowerCAmelCase__            =					encoder_stride

         # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
         lowerCAmelCase__            =					(image_size // patch_size) ** 2
         lowerCAmelCase__            =					num_patches + 2


    def   a (			self	:	Any  ) ->			Dict:
         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   a (			self	:	Optional[int]  ) ->			List[str]:
         return DeiTConfig(
             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=SCREAMING_SNAKE_CASE__  ,      initializer_range=self.initializer_range  ,      encoder_stride=self.encoder_stride  ,      )


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	str  ) ->			Any:
         lowerCAmelCase__            =					TFDeiTModel(config=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )
         self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)  )


    def   a (			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	Any  ) ->			int:
         lowerCAmelCase__            =					TFDeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )
         self.parent.assertEqual(
             result.reconstruction.shape  ,      (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

         # test greyscale images
         lowerCAmelCase__            =					1
         lowerCAmelCase__            =					TFDeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )
         self.parent.assertEqual(result.reconstruction.shape  ,      (self.batch_size, 1, self.image_size, self.image_size)  )


    def   a (			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	str  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	int  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					self.type_sequence_label_size
         lowerCAmelCase__            =					TFDeiTForImageClassification(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  ,      labels=SCREAMING_SNAKE_CASE__  )
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)  )

         # test greyscale images
         lowerCAmelCase__            =					1
         lowerCAmelCase__            =					TFDeiTForImageClassification(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  ,      labels=SCREAMING_SNAKE_CASE__  )
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)  )


    def   a (			self	:	Optional[Any]  ) ->			Any:
         lowerCAmelCase__            =					self.prepare_config_and_inputs()
         lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					config_and_inputs
         lowerCAmelCase__            =					{"pixel_values": pixel_values}
         return config, inputs_dict






@require_tf
class 					__lowerCamelCase			(       UpperCamelCase__      ,							UpperCamelCase__      ,							unittest.TestCase       ):




    """simple docstring"""

    snake_case__   = (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    snake_case__   = (
        {
            "feature-extraction": TFDeiTModel,
            "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    snake_case__   = False
    snake_case__   = False
    snake_case__   = False
    snake_case__   = False


    def   a (			self	:	Tuple  ) ->			Optional[int]:
         lowerCAmelCase__            =					TFDeiTModelTester(self  )
         lowerCAmelCase__            =					ConfigTester(self  ,      config_class=SCREAMING_SNAKE_CASE__  ,      has_text_modality=SCREAMING_SNAKE_CASE__  ,      hidden_size=37  )


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


    @unittest.skip(reason="DeiT does not use inputs_embeds"  )
    def   a (			self	:	Dict  ) ->			Optional[Any]:
         pass


    def   a (			self	:	Optional[int]  ) ->			Dict:
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__            =					model_class(SCREAMING_SNAKE_CASE__  )
              self.assertIsInstance(model.get_input_embeddings()  ,      (tf.keras.layers.Layer)  )
              lowerCAmelCase__            =					model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__  ,      tf.keras.layers.Dense  )  )


    def   a (			self	:	str  ) ->			List[str]:
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()

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

              lowerCAmelCase__            =					["pixel_values"]
              self.assertListEqual(arg_names[:1]  ,      SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Dict  ) ->			Optional[Any]:
         lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Any  ) ->			List[Any]:
         lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	str  ) ->			int:
         lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	int=False  ) ->			Optional[int]:
         lowerCAmelCase__            =					super()._prepare_for_class(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      return_labels=SCREAMING_SNAKE_CASE__  )

         if return_labels:
              if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call  ).parameters:
                   del inputs_dict["labels"]

         return inputs_dict


    @slow
    def   a (			self	:	List[Any]  ) ->			Optional[Any]:
         for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__            =					TFDeiTModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )






def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
     return image






@require_tf
@require_vision
class 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

    @cached_property
    def   a (			self	:	str  ) ->			str:
         return (
             DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"  )
             if is_vision_available()
             else None
         )


    @slow
    def   a (			self	:	Optional[Any]  ) ->			List[str]:
         lowerCAmelCase__            =					TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"  )

         lowerCAmelCase__            =					self.default_image_processor
         lowerCAmelCase__            =					prepare_img()
         lowerCAmelCase__            =					image_processor(images=SCREAMING_SNAKE_CASE__  ,      return_tensors="tf"  )

         # forward pass
         lowerCAmelCase__            =					model(**SCREAMING_SNAKE_CASE__  )

         # verify the logits
         lowerCAmelCase__            =					tf.TensorShape((1, 1_000)  )
         self.assertEqual(outputs.logits.shape  ,      SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					tf.constant([-1.0_266, 0.1_912, -1.2_861]  )

         self.assertTrue(np.allclose(outputs.logits[0, :3]  ,      SCREAMING_SNAKE_CASE__  ,      atol=1e-4  )  )



'''simple docstring'''

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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

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

											self.scheduler.set_timesteps(_lowercase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





