"""simple docstring"""




import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	torch.FloatTensor
					UpperCamelCase_ :	Optional[torch.FloatTensor]			=    None







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=0.9_99			,  lowerCamelCase__="cosine"			,  ):





		"""simple docstring"""




		if alpha_transform_type == "cosine":

				def alpha_bar_fn(lowerCamelCase__     ):
						return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2     ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(lowerCamelCase__     ):
						return math.exp(t * -12.0     )

		else:
				raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""     )

		lowerCAmelCase__        =   []
		for i in range(lowerCamelCase__     ):
				lowerCAmelCase__        =   i / num_diffusion_timesteps
				lowerCAmelCase__        =   (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(lowerCamelCase__     ) / alpha_bar_fn(lowerCamelCase__     )			,  lowerCamelCase__     )     )
		return torch.tensor(lowerCamelCase__			,  dtype=torch.floataa     )






class       a_      (     __UpperCamelCase ,		__UpperCamelCase			):




					@register_to_config
					def __init__(	self						:     List[Any]  ,						snake_case__						:     int = 1000  ,						snake_case__						:     str = "fixed_small_log"  ,						snake_case__						:     bool = True  ,						snake_case__						:     Optional[float] = 1.0  ,						snake_case__						:     str = "epsilon"  ,						snake_case__						:     str = "squaredcos_cap_v2"  ,						):
							if beta_schedule != "squaredcos_cap_v2":
									raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""     )

							lowerCAmelCase__        =   betas_for_alpha_bar(snake_case__     )

							lowerCAmelCase__        =   1.0 - self.betas
							lowerCAmelCase__        =   torch.cumprod(self.alphas  ,						dim=0     )
							lowerCAmelCase__        =   torch.tensor(1.0     )

							# standard deviation of the initial noise distribution
							lowerCAmelCase__        =   1.0

							# setable values
							lowerCAmelCase__        =   None
							lowerCAmelCase__        =   torch.from_numpy(np.arange(0  ,						snake_case__     )[::-1].copy()     )

							lowerCAmelCase__        =   variance_type




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     Optional[int] = None     ):
							return sample




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     int  ,						snake_case__						:     Union[str, torch.device] = None     ):
							lowerCAmelCase__        =   num_inference_steps
							lowerCAmelCase__        =   (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
							lowerCAmelCase__        =   (np.arange(0  ,						snake_case__     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
							lowerCAmelCase__        =   torch.from_numpy(snake_case__     ).to(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     List[str]=None  ,						snake_case__						:     Optional[int]=None  ,						snake_case__						:     Tuple=None     ):
							if prev_timestep is None:
									lowerCAmelCase__        =   t - 1

							lowerCAmelCase__        =   self.alphas_cumprod[t]
							lowerCAmelCase__        =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							lowerCAmelCase__        =   1 - alpha_prod_t
							lowerCAmelCase__        =   1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
									lowerCAmelCase__        =   self.betas[t]
							else:
									lowerCAmelCase__        =   1 - alpha_prod_t / alpha_prod_t_prev

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							lowerCAmelCase__        =   beta_prod_t_prev / beta_prod_t * beta

							if variance_type is None:
									lowerCAmelCase__        =   self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small_log":
									lowerCAmelCase__        =   torch.log(torch.clamp(snake_case__  ,						min=1E-20     )     )
									lowerCAmelCase__        =   torch.exp(0.5 * variance     )
							elif variance_type == "learned_range":
									# NOTE difference with DDPM scheduler
									lowerCAmelCase__        =   variance.log()
									lowerCAmelCase__        =   beta.log()

									lowerCAmelCase__        =   (predicted_variance + 1) / 2
									lowerCAmelCase__        =   frac * max_log + (1 - frac) * min_log

							return variance




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     int  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     Optional[int] = None  ,						snake_case__						:     Optional[int]=None  ,						snake_case__						:     bool = True  ,						):
							lowerCAmelCase__        =   timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
									lowerCAmelCase__   ,					lowerCAmelCase__        =   torch.split(snake_case__  ,						sample.shape[1]  ,						dim=1     )
							else:
									lowerCAmelCase__        =   None

							# 1. compute alphas, betas
							if prev_timestep is None:
									lowerCAmelCase__        =   t - 1

							lowerCAmelCase__        =   self.alphas_cumprod[t]
							lowerCAmelCase__        =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							lowerCAmelCase__        =   1 - alpha_prod_t
							lowerCAmelCase__        =   1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
									lowerCAmelCase__        =   self.betas[t]
									lowerCAmelCase__        =   self.alphas[t]
							else:
									lowerCAmelCase__        =   1 - alpha_prod_t / alpha_prod_t_prev
									lowerCAmelCase__        =   1 - beta

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
									lowerCAmelCase__        =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
									lowerCAmelCase__        =   model_output
							else:
									raise ValueError(
									    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
									    """ for the UnCLIPScheduler."""     )

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
									lowerCAmelCase__        =   torch.clamp(
									    snake_case__  ,						-self.config.clip_sample_range  ,						self.config.clip_sample_range     )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							lowerCAmelCase__        =   (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
							lowerCAmelCase__        =   alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							lowerCAmelCase__        =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							lowerCAmelCase__        =   0
							if t > 0:
									lowerCAmelCase__        =   randn_tensor(
									    model_output.shape  ,						dtype=model_output.dtype  ,						generator=snake_case__  ,						device=model_output.device     )

									lowerCAmelCase__        =   self._get_variance(
									    snake_case__  ,						predicted_variance=snake_case__  ,						prev_timestep=snake_case__  ,						)

									if self.variance_type == "fixed_small_log":
											lowerCAmelCase__        =   variance
									elif self.variance_type == "learned_range":
											lowerCAmelCase__        =   (0.5 * variance).exp()
									else:
											raise ValueError(
											    F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
											    """ for the UnCLIPScheduler."""     )

									lowerCAmelCase__        =   variance * variance_noise

							lowerCAmelCase__        =   pred_prev_sample + variance

							if not return_dict:
									return (pred_prev_sample,)

							return UnCLIPSchedulerOutput(prev_sample=snake_case__  ,						pred_original_sample=snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     torch.IntTensor  ,						):
							# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
							lowerCAmelCase__        =   self.alphas_cumprod.to(device=original_samples.device  ,						dtype=original_samples.dtype     )
							lowerCAmelCase__        =   timesteps.to(original_samples.device     )

							lowerCAmelCase__        =   alphas_cumprod[timesteps] ** 0.5
							lowerCAmelCase__        =   sqrt_alpha_prod.flatten()
							while len(sqrt_alpha_prod.shape     ) < len(original_samples.shape     ):
									lowerCAmelCase__        =   sqrt_alpha_prod.unsqueeze(-1     )

							lowerCAmelCase__        =   (1 - alphas_cumprod[timesteps]) ** 0.5
							lowerCAmelCase__        =   sqrt_one_minus_alpha_prod.flatten()
							while len(sqrt_one_minus_alpha_prod.shape     ) < len(original_samples.shape     ):
									lowerCAmelCase__        =   sqrt_one_minus_alpha_prod.unsqueeze(-1     )

							lowerCAmelCase__        =   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples





"""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       a_      :




					def __init__(	self						:     Optional[int]  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Any=13  ,						snake_case__						:     int=30  ,						snake_case__						:     int=2  ,						snake_case__						:     Union[str, Any]=3  ,						snake_case__						:     Dict=True  ,						snake_case__						:     Optional[int]=True  ,						snake_case__						:     List[Any]=32  ,						snake_case__						:     List[str]=2  ,						snake_case__						:     Optional[Any]=4  ,						snake_case__						:     Optional[int]=37  ,						snake_case__						:     Tuple="gelu"  ,						snake_case__						:     str=0.1  ,						snake_case__						:     Any=0.1  ,						snake_case__						:     int=10  ,						snake_case__						:     Dict=0.02  ,						snake_case__						:     Union[str, Any]=3  ,						snake_case__						:     str=None  ,						snake_case__						:     List[Any]=2  ,						):
							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 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							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 						_SCREAMING_SNAKE_CASE						(	self						:     List[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=snake_case__  ,						initializer_range=self.initializer_range  ,						encoder_stride=self.encoder_stride  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     int  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     List[str]     ):
							lowerCAmelCase__        =   TFDeiTModel(config=snake_case__     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Dict  ,						snake_case__						:     Dict  ,						snake_case__						:     Dict     ):
							lowerCAmelCase__        =   TFDeiTForMaskedImageModeling(config=snake_case__     )
							lowerCAmelCase__        =   model(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(snake_case__     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   self.type_sequence_label_size
							lowerCAmelCase__        =   TFDeiTForImageClassification(snake_case__     )
							lowerCAmelCase__        =   model(snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)     )

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

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   config_and_inputs
							lowerCAmelCase__        =   {"""pixel_values""": pixel_values}
							return config, inputs_dict

@require_tf
class       a_      (     __UpperCamelCase ,		__UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Optional[Any]			=    (
					    (
					        TFDeiTModel,
					        TFDeiTForImageClassification,
					        TFDeiTForImageClassificationWithTeacher,
					        TFDeiTForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCamelCase_ :	Any			=    (
					    {
					        "feature-extraction": TFDeiTModel,
					        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
					    }
					    if is_tf_available()
					    else {}
					)

					UpperCamelCase_ :	Optional[Any]			=    False
					UpperCamelCase_ :	Optional[Any]			=    False
					UpperCamelCase_ :	Optional[int]			=    False
					UpperCamelCase_ :	int			=    False




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   TFDeiTModelTester(self     )
							lowerCAmelCase__        =   ConfigTester(self  ,						config_class=snake_case__  ,						has_text_modality=snake_case__  ,						hidden_size=37     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							self.config_tester.run_common_tests()




					@unittest.skip(reason="""DeiT does not use inputs_embeds"""     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							pass




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowerCAmelCase__        =   model_class(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]  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     int  ,						snake_case__						:     Union[str, Any]=False     ):
							lowerCAmelCase__        =   super()._prepare_for_class(snake_case__  ,						snake_case__  ,						return_labels=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 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase__        =   TFDeiTModel.from_pretrained(snake_case__     )
									self.assertIsNotNone(snake_case__     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




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






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




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




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

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

							# forward pass
							lowerCAmelCase__        =   model(**snake_case__     )

							# verify the logits
							lowerCAmelCase__        =   tf.TensorShape((1, 1000)     )
							self.assertEqual(outputs.logits.shape  ,						snake_case__     )

							lowerCAmelCase__        =   tf.constant([-1.0266, 0.1912, -1.2861]     )

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




