


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:     Any   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     List[str]=13   ,						UpperCamelCase_:     List[str]=30   ,						UpperCamelCase_:     Any=2   ,						UpperCamelCase_:     Dict=3   ,						UpperCamelCase_:     int=True   ,						UpperCamelCase_:     Union[str, Any]=True   ,						UpperCamelCase_:     List[str]=32   ,						UpperCamelCase_:     int=2   ,						UpperCamelCase_:     Optional[Any]=4   ,						UpperCamelCase_:     str=37   ,						UpperCamelCase_:     Optional[Any]="gelu"   ,						UpperCamelCase_:     Optional[int]=0.1   ,						UpperCamelCase_:     Optional[Any]=0.1   ,						UpperCamelCase_:     List[str]=10   ,						UpperCamelCase_:     Tuple=0.02   ,						UpperCamelCase_:     Optional[int]=3   ,						UpperCamelCase_:     Dict=None   ,						UpperCamelCase_:     Any=2   ,						)      ->       int:





									"""simple docstring"""







									lowercase__						=				parent
									lowercase__						=				batch_size
									lowercase__						=				image_size
									lowercase__						=				patch_size
									lowercase__						=				num_channels
									lowercase__						=				is_training
									lowercase__						=				use_labels
									lowercase__						=				hidden_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_act
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				type_sequence_label_size
									lowercase__						=				initializer_range
									lowercase__						=				scope
									lowercase__						=				encoder_stride

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

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

									lowercase__						=				self.get_config()

									return config, pixel_values, labels

							def 				lowerCamelCase_			(							self:     int							)      ->       Optional[Any]:





									"""simple docstring"""







									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=__A   ,						initializer_range=self.initializer_range   ,						encoder_stride=self.encoder_stride   ,						)

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Dict							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				TFDeiTModel(config=__A							)
									lowercase__						=				model(__A							)
									self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)							)

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





									"""simple docstring"""







									lowercase__						=				TFDeiTForMaskedImageModeling(config=__A							)
									lowercase__						=				model(__A							)
									self.parent.assertEqual(
									    result.reconstruction.shape   ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

									# test greyscale images
									lowercase__						=				1
									lowercase__						=				TFDeiTForMaskedImageModeling(__A							)

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

							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     Optional[int]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				self.type_sequence_label_size
									lowercase__						=				TFDeiTForImageClassification(__A							)
									lowercase__						=				model(__A   ,						labels=__A							)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.type_sequence_label_size)							)

									# test greyscale images
									lowercase__						=				1
									lowercase__						=				TFDeiTForImageClassification(__A							)

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


							def 				lowerCamelCase_			(							self:     Tuple							)      ->       Optional[int]:





									"""simple docstring"""







									lowercase__						=				self.prepare_config_and_inputs()
									lowercase__						=				config_and_inputs
									lowercase__						=				{"""pixel_values""": pixel_values}
									return config, inputs_dict


@require_tf
class 	_a							(      A__					,     A__					,     unittest.TestCase      ):
							_lowercase     :      Dict  				=			(
							    (
							        TFDeiTModel,
							        TFDeiTForImageClassification,
							        TFDeiTForImageClassificationWithTeacher,
							        TFDeiTForMaskedImageModeling,
							    )
							    if is_tf_available()
							    else ()
							)
							_lowercase     :      int  				=			(
							    {
							        "feature-extraction": TFDeiTModel,
							        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
							    }
							    if is_tf_available()
							    else {}
							)

							_lowercase     :      Union[str, Any]  				=			False
							_lowercase     :      str  				=			False
							_lowercase     :      Union[str, Any]  				=			False
							_lowercase     :      List[str]  				=			False

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				TFDeiTModelTester(self							)
									lowercase__						=				ConfigTester(self   ,						config_class=__A   ,						has_text_modality=__A   ,						hidden_size=37							)

							def 				lowerCamelCase_			(							self:     Any							)      ->       Optional[Any]:





									"""simple docstring"""







									self.config_tester.run_common_tests()

							@unittest.skip(reason='''DeiT does not use inputs_embeds'''							)
							def 				lowerCamelCase_			(							self:     List[str]							)      ->       List[Any]:





									"""simple docstring"""







									pass

							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs_for_common()

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

							def 				lowerCamelCase_			(							self:     int							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs_for_common()

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

											lowercase__						=				["""pixel_values"""]
											self.assertListEqual(arg_names[:1]   ,						__A							)

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





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__A							)

							def 				lowerCamelCase_			(							self:     Any							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*__A							)

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__A							)

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     Optional[Any]=False							)      ->       int:





									"""simple docstring"""







									lowercase__						=				super()._prepare_for_class(__A   ,						__A   ,						return_labels=__A							)

									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 				lowerCamelCase_			(							self:     Optional[int]							)      ->       List[str]:





									"""simple docstring"""







									for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowercase__						=				TFDeiTModel.from_pretrained(__A							)
											self.assertIsNotNone(__A							)
def       _a			(				):


		"""simple docstring"""




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


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

							@cached_property
							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Tuple:





									"""simple docstring"""







									return (
									    DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''							)
									    if is_vision_available()
									    else None
									)


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





									"""simple docstring"""







									lowercase__						=				TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''							)

									lowercase__						=				self.default_image_processor
									lowercase__						=				prepare_img()
									lowercase__						=				image_processor(images=__A   ,						return_tensors='''tf'''							)

									# forward pass
									lowercase__						=				model(**__A							)

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

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

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





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)


@flax.struct.dataclass
class 	_a							:
							_lowercase     :      CommonSchedulerState

							# setable values
							_lowercase     :      jnp.ndarray
							_lowercase     :      jnp.ndarray
							_lowercase     :      Optional[int]  				=			None

							@classmethod
							def 				lowerCamelCase_			(							cls:     Dict   ,						UpperCamelCase_:     CommonSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray							)      ->       List[str]:





									"""simple docstring"""







									return cls(common=UpperCamelCase_   ,						init_noise_sigma=UpperCamelCase_   ,						timesteps=UpperCamelCase_							)


@dataclass
class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      DDPMSchedulerState


class 	_a							(      UpperCamelCase__					,     UpperCamelCase__      ):
							_lowercase     :      Tuple  				=			[e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowercase     :      jnp.dtype

							@property
							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Union[str, Any]:





									"""simple docstring"""







									return True

							@register_to_config
							def __init__(							self:     Any   ,						UpperCamelCase_:     int = 1_000   ,						UpperCamelCase_:     float = 0.0001   ,						UpperCamelCase_:     float = 0.02   ,						UpperCamelCase_:     str = "linear"   ,						UpperCamelCase_:     Optional[jnp.ndarray] = None   ,						UpperCamelCase_:     str = "fixed_small"   ,						UpperCamelCase_:     bool = True   ,						UpperCamelCase_:     str = "epsilon"   ,						UpperCamelCase_:     jnp.dtype = jnp.floataa   ,						)      ->       int:





									"""simple docstring"""







									lowercase__						=				dtype

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     Optional[CommonSchedulerState] = None							)      ->       DDPMSchedulerState:





									"""simple docstring"""







									if common is None:
											lowercase__						=				CommonSchedulerState.create(self							)

									# standard deviation of the initial noise distribution
									lowercase__						=				jnp.array(1.0   ,						dtype=self.dtype							)

									lowercase__						=				jnp.arange(0   ,						self.config.num_train_timesteps							).round()[::-1]

									return DDPMSchedulerState.create(
									    common=UpperCamelCase_   ,						init_noise_sigma=UpperCamelCase_   ,						timesteps=UpperCamelCase_   ,						)

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     Optional[int] = None							)      ->       jnp.ndarray:





									"""simple docstring"""







									return sample

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Tuple = ()							)      ->       DDPMSchedulerState:





									"""simple docstring"""







									lowercase__						=				self.config.num_train_timesteps // num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# rounding to avoid issues when num_inference_step is power of 3
									lowercase__						=				(jnp.arange(0   ,						UpperCamelCase_							) * step_ratio).round()[::-1]

									return state.replace(
									    num_inference_steps=UpperCamelCase_   ,						timesteps=UpperCamelCase_   ,						)

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Optional[int]=None   ,						UpperCamelCase_:     str=None							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				state.common.alphas_cumprod[t]
									lowercase__						=				jnp.where(t > 0   ,						state.common.alphas_cumprod[t - 1]   ,						jnp.array(1.0   ,						dtype=self.dtype							)							)

									# 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
									lowercase__						=				(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

									# hacks - were probably added for training stability
									if variance_type == "fixed_small":
											lowercase__						=				jnp.clip(UpperCamelCase_   ,						a_min=1E-2_0							)
									# for rl-diffuser https://arxiv.org/abs/2205.09991
									elif variance_type == "fixed_small_log":
											lowercase__						=				jnp.log(jnp.clip(UpperCamelCase_   ,						a_min=1E-2_0							)							)
									elif variance_type == "fixed_large":
											lowercase__						=				state.common.betas[t]
									elif variance_type == "fixed_large_log":
											# Glide max_log
											lowercase__						=				jnp.log(state.common.betas[t]							)
									elif variance_type == "learned":
											return predicted_variance
									elif variance_type == "learned_range":
											lowercase__						=				variance
											lowercase__						=				state.common.betas[t]
											lowercase__						=				(predicted_variance + 1) / 2
											lowercase__						=				frac * max_log + (1 - frac) * min_log

									return variance

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     Optional[jax.random.KeyArray] = None   ,						UpperCamelCase_:     bool = True   ,						)      ->       Union[FlaxDDPMSchedulerOutput, Tuple]:





									"""simple docstring"""







									lowercase__						=				timestep

									if key is None:
											lowercase__						=				jax.random.PRNGKey(0							)

									if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
											lowercase__					,		lowercase__						=				jnp.split(UpperCamelCase_   ,						sample.shape[1]   ,						axis=1							)
									else:
											lowercase__						=				None

									# 1. compute alphas, betas
									lowercase__						=				state.common.alphas_cumprod[t]
									lowercase__						=				jnp.where(t > 0   ,						state.common.alphas_cumprod[t - 1]   ,						jnp.array(1.0   ,						dtype=self.dtype							)							)
									lowercase__						=				1 - alpha_prod_t
									lowercase__						=				1 - alpha_prod_t_prev

									# 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":
											lowercase__						=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
											lowercase__						=				model_output
									elif self.config.prediction_type == "v_prediction":
											lowercase__						=				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									else:
											raise ValueError(
											    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
											    ''' for the FlaxDDPMScheduler.'''							)

									# 3. Clip "predicted x_0"
									if self.config.clip_sample:
											lowercase__						=				jnp.clip(UpperCamelCase_   ,						-1   ,						1							)

									# 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
									lowercase__						=				(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
									lowercase__						=				state.common.alphas[t] ** 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
									lowercase__						=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

									# 6. Add noise
									def random_variance():
											lowercase__						=				jax.random.split(UpperCamelCase_   ,						num=1							)
											lowercase__						=				jax.random.normal(UpperCamelCase_   ,						shape=model_output.shape   ,						dtype=self.dtype							)
											return (self._get_variance(UpperCamelCase_   ,						UpperCamelCase_   ,						predicted_variance=UpperCamelCase_							) ** 0.5) * noise

									lowercase__						=				jnp.where(t > 0   ,						random_variance()   ,						jnp.zeros(model_output.shape   ,						dtype=self.dtype							)							)

									lowercase__						=				pred_prev_sample + variance

									if not return_dict:
											return (pred_prev_sample, state)

									return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase_   ,						state=UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						)      ->       jnp.ndarray:





									"""simple docstring"""







									return add_noise_common(state.common   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						)      ->       jnp.ndarray:





									"""simple docstring"""







									return get_velocity_common(state.common   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)


							def __len__(							self:     str							)      ->       List[Any]:





									"""simple docstring"""







									return self.config.num_train_timesteps


