

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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_				:


    '''simple docstring'''






    a__       							=    42
    a__       							=    42

    # setable values
    a__       							=    42
    a__       							=    42
    a__       							=    None


    @classmethod
    def        lowerCAmelCase_  (cls       , lowercase__       , lowercase__       , lowercase__       , lowercase__       , )       -> Dict:
        return cls(
            common=lowercase__       , final_alpha_cumprod=lowercase__       , init_noise_sigma=lowercase__       , timesteps=lowercase__       , )






@dataclass
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    42






class    A_				( _a	,       _a  ):


    '''simple docstring'''






    a__       							=    [e.name for e in FlaxKarrasDiffusionSchedulers]

    a__       							=    42


    @property
    def        lowerCAmelCase_  (self					)       -> Dict:
        return True


    @register_to_config
    def __init__(self       , lowercase__ = 1_000       , lowercase__ = 0.0001       , lowercase__ = 0.02       , lowercase__ = "linear"       , lowercase__ = None       , lowercase__ = True       , lowercase__ = 0       , lowercase__ = "epsilon"       , lowercase__ = jnp.floataa       , )       -> List[Any]:
        __UpperCAmelCase			   =     dtype


    def        lowerCAmelCase_  (self       , lowercase__ = None					)       -> DDIMSchedulerState:
        if common is None:
            __UpperCAmelCase			   =     CommonSchedulerState.create(self					)

        # At every step in ddim, we are looking into the previous alphas_cumprod
        # For the final step, there is no previous alphas_cumprod because we are already at 0
        # `set_alpha_to_one` decides whether we set this parameter simply to one or
        # whether we use the final alpha of the "non-previous" one.
        __UpperCAmelCase			   =     (
            jnp.array(1.0       , dtype=self.dtype					) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
        )

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

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

        return DDIMSchedulerState.create(
            common=lowercase__       , final_alpha_cumprod=lowercase__       , init_noise_sigma=lowercase__       , timesteps=lowercase__       , )


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__ = None					)       -> jnp.ndarray:
        return sample


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__ = ()					)       -> DDIMSchedulerState:
        __UpperCAmelCase			   =     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
        __UpperCAmelCase			   =     (jnp.arange(0       , lowercase__					) * step_ratio).round()[::-1] + self.config.steps_offset

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


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> Tuple:
        __UpperCAmelCase			   =     state.common.alphas_cumprod[timestep]
        __UpperCAmelCase			   =     jnp.where(
            prev_timestep >= 0       , state.common.alphas_cumprod[prev_timestep]       , state.final_alpha_cumprod					)
        __UpperCAmelCase			   =     1 - alpha_prod_t
        __UpperCAmelCase			   =     1 - alpha_prod_t_prev

        __UpperCAmelCase			   =     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__       , lowercase__       , lowercase__ = 0.0       , lowercase__ = True       , )       -> Union[FlaxDDIMSchedulerOutput, Tuple]:
        if state.num_inference_steps is None:
            raise ValueError(
                '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''					)

        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
        # Ideally, read DDIM paper in-detail understanding

        # Notation (<variable name> -> <name in paper>
        # - pred_noise_t -> e_theta(x_t, t)
        # - pred_original_sample -> f_theta(x_t, t) or x_0
        # - std_dev_t -> sigma_t
        # - eta -> η
        # - pred_sample_direction -> "direction pointing to x_t"
        # - pred_prev_sample -> "x_t-1"

        # 1. get previous step value (=t-1)
        __UpperCAmelCase			   =     timestep - self.config.num_train_timesteps // state.num_inference_steps

        __UpperCAmelCase			   =     state.common.alphas_cumprod
        __UpperCAmelCase			   =     state.final_alpha_cumprod

        # 2. compute alphas, betas
        __UpperCAmelCase			   =     alphas_cumprod[timestep]
        __UpperCAmelCase			   =     jnp.where(prev_timestep >= 0       , alphas_cumprod[prev_timestep]       , lowercase__					)

        __UpperCAmelCase			   =     1 - alpha_prod_t

        # 3. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        if self.config.prediction_type == "epsilon":
            __UpperCAmelCase			   =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            __UpperCAmelCase			   =     model_output
        elif self.config.prediction_type == "sample":
            __UpperCAmelCase			   =     model_output
            __UpperCAmelCase			   =     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
            __UpperCAmelCase			   =     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
            __UpperCAmelCase			   =     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
            raise ValueError(
                F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
                ''' `v_prediction`'''					)

        # 4. compute variance: "sigma_t(η)" -> see formula (16)
        # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
        __UpperCAmelCase			   =     self._get_variance(lowercase__       , lowercase__       , lowercase__					)
        __UpperCAmelCase			   =     eta * variance ** 0.5

        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        __UpperCAmelCase			   =     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

        # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        __UpperCAmelCase			   =     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

        if not return_dict:
            return (prev_sample, state)

        return FlaxDDIMSchedulerOutput(prev_sample=lowercase__       , state=lowercase__					)


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__       , lowercase__       , )       -> jnp.ndarray:
        return add_noise_common(state.common       , lowercase__       , lowercase__       , lowercase__					)


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__       , lowercase__       , )       -> jnp.ndarray:
        return get_velocity_common(state.common       , lowercase__       , lowercase__       , lowercase__					)






    def __len__(self					)       -> Dict:
        return self.config.num_train_timesteps







import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
       import torch
       from torch import nn

       from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
       from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import DPTImageProcessor
class    A_				:


    '''simple docstring'''






    def __init__(self       , lowercase__       , lowercase__=2       , lowercase__=32       , lowercase__=16       , lowercase__=3       , lowercase__=True       , lowercase__=True       , lowercase__=32       , lowercase__=4       , lowercase__=[0, 1, 2, 3]       , lowercase__=4       , lowercase__=37       , lowercase__="gelu"       , lowercase__=0.1       , lowercase__=0.1       , lowercase__=0.02       , lowercase__=3       , lowercase__=False       , lowercase__=None       , )       -> Any:
        __UpperCAmelCase			   =     parent
        __UpperCAmelCase			   =     batch_size
        __UpperCAmelCase			   =     image_size
        __UpperCAmelCase			   =     patch_size
        __UpperCAmelCase			   =     num_channels
        __UpperCAmelCase			   =     is_training
        __UpperCAmelCase			   =     use_labels
        __UpperCAmelCase			   =     hidden_size
        __UpperCAmelCase			   =     num_hidden_layers
        __UpperCAmelCase			   =     backbone_out_indices
        __UpperCAmelCase			   =     num_attention_heads
        __UpperCAmelCase			   =     intermediate_size
        __UpperCAmelCase			   =     hidden_act
        __UpperCAmelCase			   =     hidden_dropout_prob
        __UpperCAmelCase			   =     attention_probs_dropout_prob
        __UpperCAmelCase			   =     initializer_range
        __UpperCAmelCase			   =     num_labels
        __UpperCAmelCase			   =     scope
        __UpperCAmelCase			   =     is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        __UpperCAmelCase			   =     (image_size // patch_size) ** 2
        __UpperCAmelCase			   =     num_patches + 1


    def        lowerCAmelCase_  (self					)       -> List[str]:
        __UpperCAmelCase			   =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

        __UpperCAmelCase			   =     None
        if self.use_labels:
            __UpperCAmelCase			   =     ids_tensor([self.batch_size, self.image_size, self.image_size]       , self.num_labels					)

        __UpperCAmelCase			   =     self.get_config()

        return config, pixel_values, labels


    def        lowerCAmelCase_  (self					)       -> str:
        return DPTConfig(
            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       , backbone_out_indices=self.backbone_out_indices       , 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=lowercase__       , initializer_range=self.initializer_range       , is_hybrid=self.is_hybrid       , )


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> Any:
        __UpperCAmelCase			   =     DPTModel(config=lowercase__					)
        model.to(lowercase__					)
        model.eval()
        __UpperCAmelCase			   =     model(lowercase__					)
        self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)					)


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> List[Any]:
        __UpperCAmelCase			   =     self.num_labels
        __UpperCAmelCase			   =     DPTForDepthEstimation(lowercase__					)
        model.to(lowercase__					)
        model.eval()
        __UpperCAmelCase			   =     model(lowercase__					)
        self.parent.assertEqual(result.predicted_depth.shape       , (self.batch_size, self.image_size, self.image_size)					)


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__       , lowercase__					)       -> str:
        __UpperCAmelCase			   =     self.num_labels
        __UpperCAmelCase			   =     DPTForSemanticSegmentation(lowercase__					)
        model.to(lowercase__					)
        model.eval()
        __UpperCAmelCase			   =     model(lowercase__       , labels=lowercase__					)
        self.parent.assertEqual(
            result.logits.shape       , (self.batch_size, self.num_labels, self.image_size, self.image_size)					)






    def        lowerCAmelCase_  (self					)       -> Union[str, Any]:
        __UpperCAmelCase			   =     self.prepare_config_and_inputs()
        __UpperCAmelCase   ,       __UpperCAmelCase   ,       __UpperCAmelCase			   =     config_and_inputs
        __UpperCAmelCase			   =     {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_torch
class    A_				( _a	,       _a	,       unittest.TestCase  ):


    '''simple docstring'''






    a__       							=    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    a__       							=    (
        {
            "depth-estimation": DPTForDepthEstimation,
            "feature-extraction": DPTModel,
            "image-segmentation": DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    a__       							=    False
    a__       							=    False
    a__       							=    False


    def        lowerCAmelCase_  (self					)       -> Tuple:
        __UpperCAmelCase			   =     DPTModelTester(self					)
        __UpperCAmelCase			   =     ConfigTester(self       , config_class=lowercase__       , has_text_modality=lowercase__       , hidden_size=37					)


    def        lowerCAmelCase_  (self					)       -> Dict:
        self.config_tester.run_common_tests()


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


    def        lowerCAmelCase_  (self					)       -> Any:
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()

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


    def        lowerCAmelCase_  (self					)       -> int:
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()

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

            __UpperCAmelCase			   =     ['''pixel_values''']
            self.assertListEqual(arg_names[:1]       , lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Optional[int]:
        __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Optional[Any]:
        __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*lowercase__					)


    def        lowerCAmelCase_  (self					)       -> int:
        __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Any:
        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

            __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()
            __UpperCAmelCase			   =     True

            if model_class in get_values(lowercase__					):
                continue

            __UpperCAmelCase			   =     model_class(lowercase__					)
            model.to(lowercase__					)
            model.train()
            __UpperCAmelCase			   =     self._prepare_for_class(lowercase__       , lowercase__       , return_labels=lowercase__					)
            __UpperCAmelCase			   =     model(**lowercase__					).loss
            loss.backward()


    def        lowerCAmelCase_  (self					)       -> Tuple:
        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

            __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()
            __UpperCAmelCase			   =     False
            __UpperCAmelCase			   =     True

            if model_class in get_values(lowercase__					) or not model_class.supports_gradient_checkpointing:
                continue
            __UpperCAmelCase			   =     model_class(lowercase__					)
            model.to(lowercase__					)
            model.gradient_checkpointing_enable()
            model.train()
            __UpperCAmelCase			   =     self._prepare_for_class(lowercase__       , lowercase__       , return_labels=lowercase__					)
            __UpperCAmelCase			   =     model(**lowercase__					).loss
            loss.backward()


    def        lowerCAmelCase_  (self					)       -> Union[str, Any]:
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCAmelCase			   =     _config_zero_init(lowercase__					)
        for model_class in self.all_model_classes:
            __UpperCAmelCase			   =     model_class(config=lowercase__					)
            # Skip the check for the backbone
            __UpperCAmelCase			   =     []
            for name, module in model.named_modules():
                if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                    __UpperCAmelCase			   =     [F'''{name}.{key}''' for key in module.state_dict().keys()]
                    break

            for name, param in model.named_parameters():
                if param.requires_grad:
                    if name in backbone_params:
                        continue
                    self.assertIn(
                        ((param.data.mean() * 1E9).round() / 1E9).item()       , [0.0, 1.0]       , msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''       , )


    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
    def        lowerCAmelCase_  (self					)       -> int:
        pass






    @slow
    def        lowerCAmelCase_  (self					)       -> List[str]:
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCAmelCase			   =     DPTModel.from_pretrained(lowercase__					)
            self.assertIsNotNone(lowercase__					)






def  __a    (				) ->       Any:






    '''simple docstring'''
    __UpperCAmelCase			   =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
    return image
@require_torch
@require_vision
@slow
class    A_				( unittest.TestCase  ):


    '''simple docstring'''






    def        lowerCAmelCase_  (self					)       -> Any:
        __UpperCAmelCase			   =     DPTImageProcessor.from_pretrained('''Intel/dpt-large'''					)
        __UpperCAmelCase			   =     DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''					).to(lowercase__					)

        __UpperCAmelCase			   =     prepare_img()
        __UpperCAmelCase			   =     image_processor(images=lowercase__       , return_tensors='''pt'''					).to(lowercase__					)

        # forward pass
        with torch.no_grad():
            __UpperCAmelCase			   =     model(**lowercase__					)
            __UpperCAmelCase			   =     outputs.predicted_depth

        # verify the predicted depth
        __UpperCAmelCase			   =     torch.Size((1, 384, 384)					)
        self.assertEqual(predicted_depth.shape       , lowercase__					)

        __UpperCAmelCase			   =     torch.tensor(
            [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]					).to(lowercase__					)

        self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]       , lowercase__       , atol=1E-4					)					)


    def        lowerCAmelCase_  (self					)       -> Tuple:
        __UpperCAmelCase			   =     DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''					)
        __UpperCAmelCase			   =     DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''					).to(lowercase__					)

        __UpperCAmelCase			   =     prepare_img()
        __UpperCAmelCase			   =     image_processor(images=lowercase__       , return_tensors='''pt'''					).to(lowercase__					)

        # forward pass
        with torch.no_grad():
            __UpperCAmelCase			   =     model(**lowercase__					)

        # verify the logits
        __UpperCAmelCase			   =     torch.Size((1, 150, 480, 480)					)
        self.assertEqual(outputs.logits.shape       , lowercase__					)

        __UpperCAmelCase			   =     torch.tensor(
            [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]					).to(lowercase__					)

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






    def        lowerCAmelCase_  (self					)       -> str:
        __UpperCAmelCase			   =     DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''					)
        __UpperCAmelCase			   =     DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''					).to(lowercase__					)

        __UpperCAmelCase			   =     prepare_img()
        __UpperCAmelCase			   =     image_processor(images=lowercase__       , return_tensors='''pt'''					).to(lowercase__					)

        # forward pass
        with torch.no_grad():
            __UpperCAmelCase			   =     model(**lowercase__					)

        __UpperCAmelCase			   =     outputs.logits.detach().cpu()

        __UpperCAmelCase			   =     image_processor.post_process_semantic_segmentation(outputs=lowercase__       , target_sizes=[(500, 300)]					)
        __UpperCAmelCase			   =     torch.Size((500, 300)					)
        self.assertEqual(segmentation[0].shape       , lowercase__					)

        __UpperCAmelCase			   =     image_processor.post_process_semantic_segmentation(outputs=lowercase__					)
        __UpperCAmelCase			   =     torch.Size((480, 480)					)
        self.assertEqual(segmentation[0].shape       , lowercase__					)




