


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

import numpy as np
import torch

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


UpperCAmelCase      :						Tuple    =		logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 					__lowercase					(       a_     ):



		"""simple docstring"""

		UpperCamelCase    :				torch.FloatTensor







class 					__lowercase					(       a_ ,       a_     ):



		"""simple docstring"""

		UpperCamelCase    :				Any 					=		1







		@register_to_config
		def __init__(   self		,			A = 40		,			A = 0.002		,			A = 80.0		,			A = 0.5		,			A = 1.0		,			A = 7.0		,			A = True		,			)     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     sigma_max

					lowerCamelCase								=     np.linspace(0		,			1		,			A  )
					lowerCamelCase								=     self._convert_to_karras(A  )
					lowerCamelCase								=     self.sigma_to_t(A  )

					# setable values
					lowerCamelCase								=     None
					lowerCamelCase								=     torch.from_numpy(A  )
					lowerCamelCase								=     torch.from_numpy(A  )
					lowerCamelCase								=     False
					lowerCamelCase								=     False







		def 				__A      (   self		,			A		,			A=None  )     ->   List[str]:







					'''simple docstring'''







					if schedule_timesteps is None:
								lowerCamelCase								=     self.timesteps

					lowerCamelCase								=     (schedule_timesteps == timestep).nonzero()
					return indices.item()







		def 				__A      (   self		,			A		,			A  )     ->   torch.FloatTensor:







					'''simple docstring'''







					if isinstance(A		,			torch.Tensor  ):
								lowerCamelCase								=     timestep.to(self.timesteps.device  )
					lowerCamelCase								=     self.index_for_timestep(A  )
					lowerCamelCase								=     self.sigmas[step_idx]

					lowerCamelCase								=     sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

					lowerCamelCase								=     True
					return sample







		def 				__A      (   self		,			A  )     ->   Dict:







					'''simple docstring'''







					if not isinstance(A		,			np.ndarray  ):
								lowerCamelCase								=     np.array(A		,			dtype=np.floataa  )

					lowerCamelCase								=     10_00 * 0.25 * np.log(sigmas + 1e-4_4  )

					return timesteps







		def 				__A      (   self		,			A = None		,			A = None		,			A = None		,			)     ->   Union[str, Any]:







					'''simple docstring'''







					if num_inference_steps is None and timesteps is None:
								raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""  )

					if num_inference_steps is not None and timesteps is not None:
								raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""  )

					# Follow DDPMScheduler custom timesteps logic
					if timesteps is not None:
								for i in range(1		,			len(A  )  ):
											if timesteps[i] >= timesteps[i - 1]:
														raise ValueError("""`timesteps` must be in descending order."""  )

								if timesteps[0] >= self.config.num_train_timesteps:
											raise ValueError(
											    F'`timesteps` must start before `self.config.train_timesteps`:'
											    F' {self.config.num_train_timesteps}.'  )

								lowerCamelCase								=     np.array(A		,			dtype=np.intaa  )
								lowerCamelCase								=     True
					else:
								if num_inference_steps > self.config.num_train_timesteps:
											raise ValueError(
											    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
											    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
											    F' maximal {self.config.num_train_timesteps} timesteps.'  )

								lowerCamelCase								=     num_inference_steps

								lowerCamelCase								=     self.config.num_train_timesteps // self.num_inference_steps
								lowerCamelCase								=     (np.arange(0		,			A  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
								lowerCamelCase								=     False

					# Map timesteps to Karras sigmas directly for multistep sampling
					# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
					lowerCamelCase								=     self.config.num_train_timesteps
					lowerCamelCase								=     timesteps[::-1].copy()
					lowerCamelCase								=     ramp / (num_train_timesteps - 1)
					lowerCamelCase								=     self._convert_to_karras(A  )
					lowerCamelCase								=     self.sigma_to_t(A  )

					lowerCamelCase								=     np.concatenate([sigmas, [self.sigma_min]]  ).astype(np.floataa  )
					lowerCamelCase								=     torch.from_numpy(A  ).to(device=A  )

					if str(A  ).startswith("""mps"""  ):
								# mps does not support float64
								lowerCamelCase								=     torch.from_numpy(A  ).to(A		,			dtype=torch.floataa  )
					else:
								lowerCamelCase								=     torch.from_numpy(A  ).to(device=A  )







		def 				__A      (   self		,			A  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     self.config.sigma_min
					lowerCamelCase								=     self.config.sigma_max

					lowerCamelCase								=     self.config.rho
					lowerCamelCase								=     sigma_min ** (1 / rho)
					lowerCamelCase								=     sigma_max ** (1 / rho)
					lowerCamelCase								=     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
					return sigmas







		def 				__A      (   self		,			A  )     ->   str:







					'''simple docstring'''







					lowerCamelCase								=     self.config.sigma_data

					lowerCamelCase								=     sigma_data**2 / (sigma**2 + sigma_data**2)
					lowerCamelCase								=     sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
					return c_skip, c_out







		def 				__A      (   self		,			A  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     self.config.sigma_min
					lowerCamelCase								=     self.config.sigma_data

					lowerCamelCase								=     sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
					lowerCamelCase								=     (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
					return c_skip, c_out







		def 				__A      (   self		,			A		,			A		,			A		,			A = None		,			A = True		,			)     ->   Union[CMStochasticIterativeSchedulerOutput, Tuple]:







					'''simple docstring'''







					if (
					    isinstance(A		,			A  )
					    or isinstance(A		,			torch.IntTensor  )
					    or isinstance(A		,			torch.LongTensor  )
					):
								raise ValueError(
								    (
								        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
								        F' `{self.__class__}.step()` is not supported. Make sure to pass'
								        """ one of the `scheduler.timesteps` as a timestep."""
								    )		,			)

					if not self.is_scale_input_called:
								logger.warning(
								    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
								    """See `StableDiffusionPipeline` for a usage example."""  )

					if isinstance(A		,			torch.Tensor  ):
								lowerCamelCase								=     timestep.to(self.timesteps.device  )

					lowerCamelCase								=     self.config.sigma_min
					lowerCamelCase								=     self.config.sigma_max

					lowerCamelCase								=     self.index_for_timestep(A  )

					# sigma_next corresponds to next_t in original implementation
					lowerCamelCase								=     self.sigmas[step_index]
					if step_index + 1 < self.config.num_train_timesteps:
								lowerCamelCase								=     self.sigmas[step_index + 1]
					else:
								# Set sigma_next to sigma_min
								lowerCamelCase								=     self.sigmas[-1]

					# Get scalings for boundary conditions
					lowerCamelCase		,		lowerCamelCase								=     self.get_scalings_for_boundary_condition(A  )

					# 1. Denoise model output using boundary conditions
					lowerCamelCase								=     c_out * model_output + c_skip * sample
					if self.config.clip_denoised:
								lowerCamelCase								=     denoised.clamp(-1		,			1  )

					# 2. Sample z ~ N(0, s_noise^2 * I)
					# Noise is not used for onestep sampling.
					if len(self.timesteps  ) > 1:
								lowerCamelCase								=     randn_tensor(
								    model_output.shape		,			dtype=model_output.dtype		,			device=model_output.device		,			generator=A  )
					else:
								lowerCamelCase								=     torch.zeros_like(A  )
					lowerCamelCase								=     noise * self.config.s_noise

					lowerCamelCase								=     sigma_next.clamp(min=A		,			max=A  )

					# 3. Return noisy sample
					# tau = sigma_hat, eps = sigma_min
					lowerCamelCase								=     denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

					if not return_dict:
								return (prev_sample,)

					return CMStochasticIterativeSchedulerOutput(prev_sample=A  )







		def 				__A      (   self		,			A		,			A		,			A		,			)     ->   torch.FloatTensor:







					'''simple docstring'''







					lowerCamelCase								=     self.sigmas.to(device=original_samples.device		,			dtype=original_samples.dtype  )
					if original_samples.device.type == "mps" and torch.is_floating_point(A  ):
								# mps does not support float64
								lowerCamelCase								=     self.timesteps.to(original_samples.device		,			dtype=torch.floataa  )
								lowerCamelCase								=     timesteps.to(original_samples.device		,			dtype=torch.floataa  )
					else:
								lowerCamelCase								=     self.timesteps.to(original_samples.device  )
								lowerCamelCase								=     timesteps.to(original_samples.device  )

					lowerCamelCase								=     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

					lowerCamelCase								=     sigmas[step_indices].flatten()
					while len(sigma.shape  ) < len(original_samples.shape  ):
								lowerCamelCase								=     sigma.unsqueeze(-1  )

					lowerCamelCase								=     original_samples + noise * sigma
					return noisy_samples







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







					'''simple docstring'''







					return self.config.num_train_timesteps





import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
		import torch

		from transformers import EfficientNetForImageClassification, EfficientNetModel
		from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor







class 					__lowercase					:



		"""simple docstring"""

		def __init__(   self		,			A		,			A=13		,			A=32		,			A=3		,			A=[3, 3, 5]		,			A=[32, 16, 24]		,			A=[16, 24, 40]		,			A=[1, 1, 2]		,			A=[1, 1, 2]		,			A=[1, 6, 6]		,			A=True		,			A=True		,			A=37		,			A="gelu"		,			A=10		,			)     ->   Dict:







					'''simple docstring'''







					lowerCamelCase								=     parent
					lowerCamelCase								=     batch_size
					lowerCamelCase								=     image_size
					lowerCamelCase								=     num_channels
					lowerCamelCase								=     kernel_sizes
					lowerCamelCase								=     in_channels
					lowerCamelCase								=     out_channels
					lowerCamelCase								=     strides
					lowerCamelCase								=     num_block_repeats
					lowerCamelCase								=     expand_ratios
					lowerCamelCase								=     is_training
					lowerCamelCase								=     hidden_act
					lowerCamelCase								=     num_labels
					lowerCamelCase								=     use_labels







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







					'''simple docstring'''







					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.num_labels  )

					lowerCamelCase								=     self.get_config()
					return config, pixel_values, labels







		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					return EfficientNetConfig(
					    num_channels=self.num_channels		,			kernel_sizes=self.kernel_sizes		,			in_channels=self.in_channels		,			out_channels=self.out_channels		,			strides=self.strides		,			num_block_repeats=self.num_block_repeats		,			expand_ratios=self.expand_ratios		,			hidden_act=self.hidden_act		,			num_labels=self.num_labels		,			)







		def 				__A      (   self		,			A		,			A		,			A  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     EfficientNetModel(config=A  )
					model.to(A  )
					model.eval()
					lowerCamelCase								=     model(A  )
					# expected last hidden states: B, C, H // 4, W // 4
					self.parent.assertEqual(
					    result.last_hidden_state.shape		,			(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)		,			)







		def 				__A      (   self		,			A		,			A		,			A  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     EfficientNetForImageClassification(A  )
					model.to(A  )
					model.eval()
					lowerCamelCase								=     model(A		,			labels=A  )
					self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)  )







		def 				__A      (   self  )     ->   Any:







					'''simple docstring'''







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







@require_torch
class 					__lowercase					(       a_ ,       a_ ,       unittest.TestCase     ):



		"""simple docstring"""

		UpperCamelCase    :				str 					=		(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
		UpperCamelCase    :				List[Any] 					=		(
		    {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
		    if is_torch_available()
		    else {}
		)

		UpperCamelCase    :				Any 					=		False
		UpperCamelCase    :				Dict 					=		False
		UpperCamelCase    :				Optional[int] 					=		False
		UpperCamelCase    :				str 					=		False
		UpperCamelCase    :				Optional[Any] 					=		False







		def 				__A      (   self  )     ->   str:







					'''simple docstring'''







					lowerCamelCase								=     EfficientNetModelTester(self  )
					lowerCamelCase								=     ConfigTester(
					    self		,			config_class=A		,			has_text_modality=A		,			hidden_size=37  )







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







					'''simple docstring'''







					self.create_and_test_config_common_properties()
					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()







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







					'''simple docstring'''







					return







		@unittest.skip(reason="""EfficientNet does not use inputs_embeds"""  )
		def 				__A      (   self  )     ->   Optional[Any]:







					'''simple docstring'''







					pass







		@unittest.skip(reason="""EfficientNet does not support input and output embeddings"""  )
		def 				__A      (   self  )     ->   Any:







					'''simple docstring'''







					pass







		@unittest.skip(reason="""EfficientNet does not use feedforward chunking"""  )
		def 				__A      (   self  )     ->   List[Any]:







					'''simple docstring'''







					pass







		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase		,		lowerCamelCase								=     self.model_tester.prepare_config_and_inputs_for_common()

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

								lowerCamelCase								=     ["""pixel_values"""]
								self.assertListEqual(arg_names[:1]		,			A  )







		def 				__A      (   self  )     ->   Dict:







					'''simple docstring'''







					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A  )







		def 				__A      (   self  )     ->   Tuple:







					'''simple docstring'''







					def check_hidden_states_output(A		,			A		,			A  ):
								lowerCamelCase								=     model_class(A  )
								model.to(A  )
								model.eval()

								with torch.no_grad():
											lowerCamelCase								=     model(**self._prepare_for_class(A		,			A  )  )

								lowerCamelCase								=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
								lowerCamelCase								=     sum(config.num_block_repeats  ) * 4
								self.assertEqual(len(A  )		,			A  )

								# EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]  )		,			[self.model_tester.image_size // 2, self.model_tester.image_size // 2]		,			)

					lowerCamelCase		,		lowerCamelCase								=     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								lowerCamelCase								=     True
								check_hidden_states_output(A		,			A		,			A  )

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								lowerCamelCase								=     True

								check_hidden_states_output(A		,			A		,			A  )







		def 				__A      (   self  )     ->   int:







					'''simple docstring'''







					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A  )







		@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
		def 				__A      (   self  )     ->   Union[str, Any]:







					'''simple docstring'''







					pass







		@slow
		def 				__A      (   self  )     ->   Any:







					'''simple docstring'''







					for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCamelCase								=     EfficientNetModel.from_pretrained(A  )
								self.assertIsNotNone(A  )







		@is_pipeline_test
		@require_vision
		@slow
		def 				__A      (   self  )     ->   Optional[Any]:







					'''simple docstring'''







					super().test_pipeline_image_classification()






def   __lowerCamelCase	(       ):





			'''simple docstring'''




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







@require_torch
@require_vision
class 					__lowercase					(       unittest.TestCase     ):



		"""simple docstring"""

		@cached_property
		def 				__A      (   self  )     ->   Dict:







					'''simple docstring'''







					return AutoImageProcessor.from_pretrained("""google/efficientnet-b7"""  ) if is_vision_available() else None







		@slow
		def 				__A      (   self  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     EfficientNetForImageClassification.from_pretrained("""google/efficientnet-b7"""  ).to(A  )

					lowerCamelCase								=     self.default_image_processor
					lowerCamelCase								=     prepare_img()
					lowerCamelCase								=     image_processor(images=A		,			return_tensors="""pt"""  ).to(A  )

					# forward pass
					with torch.no_grad():
								lowerCamelCase								=     model(**A  )

					# verify the logits
					lowerCamelCase								=     torch.Size((1, 10_00)  )
					self.assertEqual(outputs.logits.shape		,			A  )

					lowerCamelCase								=     torch.tensor([0.0001, 0.0002, 0.0002]  ).to(A  )
					self.assertTrue(torch.allclose(outputs.logits[0, :3]		,			A		,			atol=1e-4  )  )

