






"""simple docstring"""



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 					_UpperCAmelCase       :




					def __init__(				self     :						Tuple    ,						A     :						Any    ,						A     :						List[str]=13    ,						A     :						int=30    ,						A     :						Any=2    ,						A     :						int=3    ,						A     :						Optional[Any]=True    ,						A     :						Union[str, Any]=True    ,						A     :						List[str]=32    ,						A     :						Union[str, Any]=2    ,						A     :						List[Any]=4    ,						A     :						str=37    ,						A     :						str="gelu"    ,						A     :						Any=0.1    ,						A     :						Dict=0.1    ,						A     :						Union[str, Any]=10    ,						A     :						Union[str, Any]=0.02    ,						A     :						Optional[Any]=3    ,						A     :						List[str]=None    ,						A     :						Tuple=2    ,						)  ->  str:
							lowercase_ : int							       =   parent
							lowercase_ : Dict							       =   batch_size
							lowercase_ : str							       =   image_size
							lowercase_ : Any							       =   patch_size
							lowercase_ : int							       =   num_channels
							lowercase_ : List[str]							       =   is_training
							lowercase_ : str							       =   use_labels
							lowercase_ : Optional[Any]							       =   hidden_size
							lowercase_ : List[Any]							       =   num_hidden_layers
							lowercase_ : Dict							       =   num_attention_heads
							lowercase_ : Any							       =   intermediate_size
							lowercase_ : int							       =   hidden_act
							lowercase_ : Tuple							       =   hidden_dropout_prob
							lowercase_ : List[Any]							       =   attention_probs_dropout_prob
							lowercase_ : List[Any]							       =   type_sequence_label_size
							lowercase_ : Optional[Any]							       =   initializer_range
							lowercase_ : Any							       =   scope
							lowercase_ : Any							       =   encoder_stride

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




					def   A  (				self     :						Optional[Any]   )  ->  Optional[int]:
							lowercase_ : Union[str, Any]							       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

							lowercase_ : Optional[int]							       =   None
							if self.use_labels:
									lowercase_ : int							       =   ids_tensor([self.batch_size]    ,						self.type_sequence_label_size   )

							lowercase_ : Any							       =   self.get_config()

							return config, pixel_values, labels




					def   A  (				self     :						List[Any]   )  ->  Optional[Any]:
							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=lowerCamelCase_    ,						initializer_range=self.initializer_range    ,						encoder_stride=self.encoder_stride    ,						)




					def   A  (				self     :						Optional[Any]    ,						A     :						int    ,						A     :						Dict    ,						A     :						List[str]   )  ->  Any:
							lowercase_ : List[Any]							       =   TFDeiTModel(config=lowerCamelCase_   )
							lowercase_ : Union[str, Any]							       =   model(lowerCamelCase_   )
							self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)   )




					def   A  (				self     :						Optional[Any]    ,						A     :						str    ,						A     :						Optional[Any]    ,						A     :						List[str]   )  ->  Dict:
							lowercase_ : List[Any]							       =   TFDeiTForMaskedImageModeling(config=lowerCamelCase_   )
							lowercase_ : str							       =   model(lowerCamelCase_   )
							self.parent.assertEqual(
							    result.reconstruction.shape    ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)   )

							# test greyscale images
							lowercase_ : Optional[int]							       =   1
							lowercase_ : Dict							       =   TFDeiTForMaskedImageModeling(lowerCamelCase_   )

							lowercase_ : str							       =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
							lowercase_ : int							       =   model(lowerCamelCase_   )
							self.parent.assertEqual(result.reconstruction.shape    ,						(self.batch_size, 1, self.image_size, self.image_size)   )




					def   A  (				self     :						int    ,						A     :						Tuple    ,						A     :						Optional[int]    ,						A     :						int   )  ->  str:
							lowercase_ : Tuple							       =   self.type_sequence_label_size
							lowercase_ : List[str]							       =   TFDeiTForImageClassification(lowerCamelCase_   )
							lowercase_ : Union[str, Any]							       =   model(lowerCamelCase_    ,						labels=lowerCamelCase_   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)   )

							# test greyscale images
							lowercase_ : List[Any]							       =   1
							lowercase_ : Optional[Any]							       =   TFDeiTForImageClassification(lowerCamelCase_   )

							lowercase_ : Dict							       =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
							lowercase_ : Dict							       =   model(lowerCamelCase_    ,						labels=lowerCamelCase_   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)   )






					def   A  (				self     :						Tuple   )  ->  Union[str, Any]:
							lowercase_ : List[Any]							       =   self.prepare_config_and_inputs()
							lowercase_       ,					lowercase_       ,					lowercase_ : List[str]							       =   config_and_inputs
							lowercase_ : int							       =   {'''pixel_values''': pixel_values}
							return config, inputs_dict





@require_tf
class 					_UpperCAmelCase       ( _A   ,						_A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   (
					    (
					        TFDeiTModel,
					        TFDeiTForImageClassification,
					        TFDeiTForImageClassificationWithTeacher,
					        TFDeiTForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   (
					    {
					        "feature-extraction": TFDeiTModel,
					        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
					    }
					    if is_tf_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   False
					SCREAMING_SNAKE_CASE_		:				Dict							    =   False
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   False
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   False




					def   A  (				self     :						Optional[Any]   )  ->  int:
							lowercase_ : Tuple							       =   TFDeiTModelTester(self   )
							lowercase_ : Optional[Any]							       =   ConfigTester(self    ,						config_class=lowerCamelCase_    ,						has_text_modality=lowerCamelCase_    ,						hidden_size=37   )




					def   A  (				self     :						Dict   )  ->  Tuple:
							self.config_tester.run_common_tests()




					@unittest.skip(reason='''DeiT does not use inputs_embeds'''   )
					def   A  (				self     :						Tuple   )  ->  int:
							pass




					def   A  (				self     :						Tuple   )  ->  Dict:
							lowercase_       ,					lowercase_ : Dict							       =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase_ : Optional[Any]							       =   model_class(lowerCamelCase_   )
									self.assertIsInstance(model.get_input_embeddings()    ,						(tf.keras.layers.Layer)   )
									lowercase_ : Dict							       =   model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(lowerCamelCase_    ,						tf.keras.layers.Dense   )   )




					def   A  (				self     :						Tuple   )  ->  List[str]:
							lowercase_       ,					lowercase_ : List[Any]							       =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase_ : Optional[Any]							       =   model_class(lowerCamelCase_   )
									lowercase_ : str							       =   inspect.signature(model.call   )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									lowercase_ : Dict							       =   [*signature.parameters.keys()]

									lowercase_ : List[str]							       =   ['''pixel_values''']
									self.assertListEqual(arg_names[:1]    ,						lowerCamelCase_   )




					def   A  (				self     :						Optional[int]   )  ->  str:
							lowercase_ : str							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase_   )




					def   A  (				self     :						Optional[Any]   )  ->  Any:
							lowercase_ : str							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase_   )




					def   A  (				self     :						str   )  ->  Tuple:
							lowercase_ : List[Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_   )




					def   A  (				self     :						Dict    ,						A     :						Tuple    ,						A     :						Dict    ,						A     :						Dict=False   )  ->  List[str]:
							lowercase_ : str							       =   super()._prepare_for_class(lowerCamelCase_    ,						lowerCamelCase_    ,						return_labels=lowerCamelCase_   )

							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     :						Optional[int]   )  ->  List[Any]:

							for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_ : Dict							       =   TFDeiTModel.from_pretrained(lowerCamelCase_   )
									self.assertIsNotNone(lowerCamelCase_   )



def 						lowercase    (				):
		lowercase_ : List[str]							       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image






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




					@cached_property
					def   A  (				self     :						Union[str, Any]   )  ->  Optional[Any]:
							return (
							    DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''   )
							    if is_vision_available()
							    else None
							)






					@slow
					def   A  (				self     :						List[Any]   )  ->  int:
							lowercase_ : str							       =   TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''   )

							lowercase_ : str							       =   self.default_image_processor
							lowercase_ : Optional[int]							       =   prepare_img()
							lowercase_ : Optional[Any]							       =   image_processor(images=lowerCamelCase_    ,						return_tensors='''tf'''   )

							# forward pass
							lowercase_ : int							       =   model(**lowerCamelCase_   )

							# verify the logits
							lowercase_ : Union[str, Any]							       =   tf.TensorShape((1, 10_00)   )
							self.assertEqual(outputs.logits.shape    ,						lowerCamelCase_   )

							lowercase_ : Optional[int]							       =   tf.constant([-1.0266, 0.1912, -1.2861]   )

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








import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							(DDPMScheduler,)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "variance_type": "fixed_small",
						    "clip_sample": True,
						}

						config.update(**lowerCamelCase_		)
						return config




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,       [0.002, 0.02, 0.2, 2]		):
											self.check_over_configs(beta_start=lowerCamelCase_		,       beta_end=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.check_over_configs(thresholding=lowerCamelCase_		)
						for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(
																    thresholding=lowerCamelCase_		,       prediction_type=lowerCamelCase_		,       sample_max_value=lowerCamelCase_		,       )




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for t in [0, 500, 999]:
											self.check_over_forward(time_step=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_0979		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

						a					     =   self.dummy_model()
						a					     =   self.dummy_sample_deter
						a					     =   torch.manual_seed(0		)

						for t in reversed(range(lowerCamelCase_		)		):
											# 1. predict noise residual
											a					     =   model(lowerCamelCase_		,       lowerCamelCase_		)

											# 2. predict previous mean of sample x_t-1
											a					     =   scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       generator=lowerCamelCase_		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											a					     =   pred_prev_sample

						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

						assert abs(result_sum.item() - 258.9606		) < 1E-2
						assert abs(result_mean.item() - 0.3372		) < 1E-3




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config(prediction_type="v_prediction"		)
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

						a					     =   self.dummy_model()
						a					     =   self.dummy_sample_deter
						a					     =   torch.manual_seed(0		)

						for t in reversed(range(lowerCamelCase_		)		):
											# 1. predict noise residual
											a					     =   model(lowerCamelCase_		,       lowerCamelCase_		)

											# 2. predict previous mean of sample x_t-1
											a					     =   scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       generator=lowerCamelCase_		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											a					     =   pred_prev_sample

						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

						assert abs(result_sum.item() - 202.0296		) < 1E-2
						assert abs(result_mean.item() - 0.2631		) < 1E-3




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 1, 0]

						scheduler.set_timesteps(timesteps=lowerCamelCase_		)

						a					     =   scheduler.timesteps

						for i, timestep in enumerate(lowerCamelCase_		):
											if i == len(lowerCamelCase_		) - 1:
																a					     =   -1
											else:
																a					     =   timesteps[i + 1]

											a					     =   scheduler.previous_timestep(lowerCamelCase_		)
											a					     =   prev_t.item()

											self.assertEqual(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 51, 0]

						with self.assertRaises(lowerCamelCase_		,       msg="`custom_timesteps` must be in descending order."		):
											scheduler.set_timesteps(timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 1, 0]
						a					     =   len(lowerCamelCase_		)

						with self.assertRaises(lowerCamelCase_		,       msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."		):
											scheduler.set_timesteps(num_inference_steps=lowerCamelCase_		,       timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    lowerCamelCase_		,       msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"		,       ):
											scheduler.set_timesteps(timesteps=lowerCamelCase_		)
